MICRO-SIMULATION OF THE ROUNDABOUT AT IDROTTSPARKEN USING AIMSUN

Transcription

1 LiU-ITN-TEK-A--12/051--SE MICRO-SIMULATION OF THE ROUNDABOUT AT IDROTTSPARKEN USING AIMSUN A Case Study of Idrottsparken Roundabout in Norrköping, Sweden SEPTARINA Linköping University Department of Science and Technology Intelligent Transport System 2012

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3 LiU-ITN-TEK-A--12/051--SE MICRO-SIMULATION OF THE ROUNDABOUT AT IDROTTSPARKEN USING AIMSUN A Case Study of Idrottsparken Roundabout in Norrköping, Sweden Submitted in partial fulfillment of the requirements for the degree of Master of Science in the Department of Science and Technology Programme of Intelligent Transport System Linköping University SEPTARINA EXAMINATOR CLAS RYDERGREN Norrköping 6/23/2012 iii

4 COPYRIGHT The publishers will keep this document online on the Internet or its possible replacement from the date of publication barring exceptional circumstances. The online availability of the document implies permanent permission for anyone to read, to download, or to print out single copies for his/hers own use and to use it unchanged for non-commercial research and educational purpose. Subsequent transfers of copyright cannot revoke this permission. All other uses of the document are conditional upon the consent of the copyright owner. The publisher has taken technical and administrative measures to assure authenticity, security and accessibility. According to intellectual property law the author has the right to be mentioned when his/her work is accessed as described above and to be protected against infringement. For additional information about the Linköping University Electronic Press and its procedures for publication and for assurance of document integrity, please refer to its www home page: Septarina iv

5 ABSTRACT Microscopic traffic simulation is a useful tool in analysing traffic and estimating the capacity and level of service of road networks. In this thesis, the four legged Idrottsparken roundabout in the city of Norrkoping in Sweden is analysed by using the microscopic traffic simulation package AIMSUN. For this purpose, data regarding traffic flow counts, travel times and queue lengths were collected for three consecutive weekdays during both the morning and afternoon peak periods. The data were then used in model building for simulation of traffic of the roundabout. The Root Mean Square Error (RMSE) method is used to get the optimal parameter value between queue length and travel time data and validation of travel time data are carried out to obtain the basic model which represents the existing condition of the system. Afterward, the results of the new models were evaluated and compared to the results of a SUMO model for the same scenario model. Based on calibrated and validated model, three alternative scenarios were simulated and analyzed to improve efficiency of traffic network in the roundabout. The three scenarios includes: (1) add one free right turn in the north and east sections; (2) add one free right turn in the east and south sections; and (3) addition of one lane in roundabout. The analysis of these scenarios shows that the first and second scenario are only able to reduce the queue length and travel time in two or three legs, while the third scenario is not able to improve the performance of the roundabout. In this research, it can be concluded that the first scenario is considered as the best scenario compared to the second scenario and the third scenario. The comparison between AIMSUN and SUMO for the same scenario shows that the results have no significance differences. In calibration process, to get the optimal parameter values between the model measurements and the field measurements, both of AIMSUN and SUMO uses two significantly influencing parameters for queue and travel time. AIMSUN package uses parameters of driver reaction time and the maximum acceleration, while SUMO package uses parameter of the driver imperfection and also the driver reaction time. Keywords: AIMSUN, SUMO, Roundabout, Micro-simulation, Root Mean Square Error v

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7 ACKNOWLEDGMENTS Praise and thanks to Allah the Al-Mighty, the Most Gracious, the Most Merciful for the grace, blessing, guidance, gift of love and talent in my life. This thesis is written in order to fulfill the requisite of Master Programme in Department of Science and Technology, Programme of Intelligent Transport System, Linkoping University and Master Programme in Transportation System and Engineering and Environment Department, Faculty of Engineering, Gadjah Mada University. The author would like to express her gratitude to those who have contributed to this thesis. Without their support and encouragement, the completion of this thesis would not have happened. Many thanks to my examiner Clas Rydergren, Ph.D and Prof. Jan Lundgren from Linkoping University, and also Prof. Dr. Ir. Siti Malkhamah, M.Sc. and Ir. Wardhani Sartono, M.Sc. from Gadjah Mada University for their guidance and invaluable feedback in this research. Thanks to the lecturers and staffs in postgraduate programme in Transport System and Engineering, Gadjah Mada University and Intelligent Transport System, Linkoping University, Sweden. Not to forget my classmates in batch XXII MSTT UGM and colleagues in Linkoping University, Sweden. I would like to express my sincere thanks to my parents and to my family. Thank you for support, prayer, love and encouragement. Thanks to all my friends; Catur, Aji, Taufiq and Tina for their support in collection data process. This thesis still needs to be improved. Any suggestion and critic from the reader is appreciated as an input to refine for further study. Norrkoping, 23 June 2012 SEPTARINA vii

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9 LIST OF ABBREVIATIONS AIMSUN : Advanced Interactive Microscopic Simulator for Urban and Non-urban Network CORSIM : CORridor-microscopic SIMulation program DLR : Deutsches Zentrum für Luft- und Raumfahrt e.v (German for German Aerospace Center) FHWA : The Federal Highway Administration KYTC : Kentucky Transportation Cabinet MKJI : Manual Kapasitas Jalan Indonesia (Indonesia for the Indonesia Road Capacity Manual) MSE : Mean Square Error NAASRA : National Association of Australian State Road Authorities O-D : Origin - Destination PARAMICS : Parallel Microscopic Simulation pcu : Passenger Car Unit SUMO : Simulation for Urban Mobility RMSE : Root Mean Square Error TRRL : Transport and Road Research Laboratory TSS : Transport Simulation System VISSIM : Verkehr In Städten - SIMulationsmodell (German for Traffic in cities - simulation model) XML : Extensible Markup Language 2-D : 2 Dimensional 3-D : 3 Dimensional ix

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11 TABLE OF CONTENTS Page COPYRIGHT... iv ABSTRACT... v ACKNOWLEDGMENTS... vii LIST OF ABBREVIATIONS... ix LIST OF TABLES... xiii LIST OF FIGURES... xv I. INTRODUCTION... 1 I.1. Background... 1 I.2. Project Aim and Purpose... 2 I.3. Limitation... 2 I.4. Previous Researches... 2 II. LITERATURE REVIEW AND THEORETICAL BASE... 5 II.1. Simulation Model Using AIMSUN... 5 II.2. Roundabouts... 6 II.3. Calibration... 7 II.3.1. Select calibration parameters... 7 II.3.2. Optimal parameter value... 7 II.4. Validation... 8 III. RESEARCH METHODOLOGY III.1. Research Location III.2. Micro-simulation Model Process III.3. Data Collection III.3.1. Road inventory III.3.2. Traffic flow count III.3.3. survey III.3.4. time measurements IV. MODELING AND SIMULATION IV.1. Base Model Development IV.2. Error Checking IV.3. Calibration Process xi

12 IV.3.1. Model parameter setting IV.3.2. Optimal Parameter Value IV.4. Validation Process V. ALTERNATIVE SCENARIOS V.1. Alternative ario 1: Free right turn implementation (on North and East sections) V.2. Alternative ario 2: Free right turn implementation (on East and South sections) V.3. Alternative ario 3: Addition of lane in roundabout VI. RESULT AND ANALYSIS VI.1. ario VI.2. ario VI.3. ario VI.4. Comparison of Performance Results VI.5. Comparison Between AIMSUN and SUMO VI.5.1. System properties VI.5.2. Simulation result VII. CONCLUSION REFERENCES APPENDIX 1. PRELIMINARY SURVEY... A-1 APPENDIX 2. TRAFFIC FLOW COUNT... A-3 APPENDIX 3. MEAN OF MAXIMUM QUEUE LENGTH... A-7 APPENDIX 4. MEAN OF TRAVEL TIME... A-8 APPENDIX 5. OUTPUT SIMULATION... A-9 APPENDIX 6. DOCUMENTATION... A-13 xii

13 LIST OF TABLES page Table 3.1. Schedule of survey Table 3.2. Traffic flow count per section in morning Table 3.3. Traffic flow count per section in afternoon Table 3.4. Turning proportion per type of vehicle in morning Table 3.5. Turning proportion per type of vehicle in afternoon Table 3.6. length in morning Table 3.7. length in afternoon Table 3.8. time in morning Table 3.9. time in afternoon Table 4.1. Input coding error checking Table 4.2. Table 4.3. The RMSE combination between the maximum queue length and travel time in morning The RMSE combination between the maximum queue length and travel time in afternoon Table 4.4. Calibrated model parameter Table 4.5. Table 4.6. Result for validation model in morning (driver reaction time = 0.65 and max acceleration: max = 3, mean = 2.5, min = 2) Result for validation model in morning (driver reaction time = 0.65 and max acceleration: max = 1, mean = 1, min = 1) Table 4.7. Final result for validation model in afternoon (driver reaction time = 0.55 and max acceleration: max = 3, mean = 2.5, min = 2) Table 6.1. Table 6.2. Table 6.3. Table 6.4. Table 6.5. Table 6.6. Table 6.7. Comparison of the mean of maximum queue length and travel time between scenario 1 and the validated model in morning The mean of maximum queue length and travel time t-test between scenario 1 and the validated model in morning Comparison of the mean of maximum queue length and travel time between scenario 1 and the validated model in afternoon The mean of maximum queue length and travel time t-test between scenario 1 and the validated model in afternoon Comparison of the mean of maximum queue length and travel time between scenario 2 and the validated model in morning The Mean of maximum queue length and travel time t- test between scenario 2 and the validated model in morning Comparison of the mean of maximum queue length and travel time between scenario 2 and the validated model in afternoon xiii

14 Table 6.8. Table The Mean of maximum queue length and travel time t-test between scenario 2 and the validated model in afternoon Comparison of the mean of maximum queue length and travel time between scenario 3 and the validated model in morning Table The mean of maximum queue length and travel time t-test between scenario 3 and the validated model in morning Table Comparison of mean of queue length and travel time between scenario 3 and the validated model in afternoon Table The mean of maximum queue length and travel time t-test between scenario 3 and the validated model in afternoon Table Comparison of system properties between AIMSUN and SUMO xiv

15 LIST OF FIGURES Page Figure 2.1. Basic geometric elements of a roundabout (FHWA, 2000)... 7 Figure 3.1. Street View of the Idrottsparken Roundabout (Bing Maps, 2010) Figure 3.2. Micro-simulation model development and application process Figure 3.3. Surveyor position Figure 3.4. The dimension of the Idrottsparken roundabout Figure 4.1. Project model simulation Figure 4.2. Minimization of RMSE between the model output and the field measurements by using maximum queue length data in morning Figure 4.3. Minimization of RMSE between the model output and the field measurements by using travel time data in morning Figure 4.4. Minimization of RMSE between the model output and the field measurements by using maximum queue length data in afternoon Figure 4.5. Minimization of RMSE between the model output and the field measurements by using travel time data in afternoon Figure 4.6. Measured travel time versus validated travel time on North section in morning Figure 4.7. Measured travel time versus validated travel time on East section in morning Figure 4.8. Measured travel time versus validated travel time on South section in morning Figure 4.9. Measured travel time versus validated travel time on West section in morning Figure Measured travel time versus validated travel time on North section in afternoon Figure Measured travel time versus validated travel time on East section in afternoon Figure Measured travel time versus validated travel time on South section in afternoon Figure Measured travel time versus validated travel time on West section in afternoon Figure Free right turn implementation on North and East sections Figure 5.2. Free right turn implementation on East and South sections Figure 5.3. Addition of lane in roundabout xv

16 Figure 6.1. Mean of maximum queue length between scenario 1 and the validated model in morning Figure 6.2. Mean of travel time between scenario 1 and the validated model in morning Figure 6.3. Mean of maximum queue length between scenario 1 and the validated model in afternoon Figure 6.4. Mean of travel time between scenario 1 and the validated model in afternoon Figure 6.5. Mean of maximum queue length between scenario 2 and the validated model in morning Figure 6.6. Mean of travel time between scenario 2 and the validated model in morning Figure 6.7. Mean of maximum queue length between scenario 2 and the validated model in afternoon Figure 6.8. Mean of travel time between scenario 2 and the validated model in afternoon Figure 6.9. Mean of maximum queue length between scenario 3 and the validated model in morning Figure Mean of travel time between scenario 3 and the validated model in morning Figure Mean of maximum queue length between scenario 3 and the validated model in afternoon Figure Mean of travel time between scenario 3 and the validated model in afternoon Figure The percentage of the maximum queue length between ario 1, 2 and 3 in morning Figure The percentage of travel time between ario 1, 2 and 3 in morning Figure The percentage of the maximum queue length between ario 1, 2 and 3 in afternoon Figure The percentage of the maximum queue length between ario 1, 2 and 3 in afternoon Figure The percentage of the maximum queue length between AIMSUN model and SUMO model in morning Figure The percentage of travel time between AIMSUN model and SUMO model in morning Figure The percentage of the maximum queue length between AIMSUN model and SUMO model in afternoon Figure The percentage of travel time between AIMSUN model and SUMO model in afternoon xvi

17 I. INTRODUCTION With rapid urbanisation and the consequent growth of vehicle population, the urban roads are becoming more and more congested day by day. This necessitates the application of advanced technologies in the planning, design and operation of road traffic networks in order to ensure smooth flow of traffic. With the advent of computing technologies in the last few decades, a broad horizon has opened regarding the application of innovative tools like computer simulation for the planning, design and operation of road networks. Microscopic traffic simulation, which is one of the approaches of simulating road traffic, is defined as the one where individual vehicles are modelled as separate entities in order to assess the performance of the overall system. By using this type of simulation models, practitioners and transportation engineers will be able to create analysis with reliable data on the situation and the problem of the existing transportation system and focus on improving traffic performance by generating and evaluating the relevant measures to find the best alternative. In many European countries, roundabouts have become more popular over the last decades in improving traffic performance and also reduce the number of accidents acting as a traffic calming device. Roundabouts have proved to be a safer alternative at intersection for both motor vehicles and pedestrians (FHWA, 2000). In recent years, traffic simulation method has been used to study the performance of not only signalised junctions, but also unsignalised junctions like roundabouts. Simulation is widely accepted as a useful technique to provide an experimental test in terms of comparing the alternate system design, replacing the experiments on the physical system by experiments on its formal depiction in a computer. The result can be the basis for a quantitive support to decision-makers. Based on this concept, the simulation model is used to carry out experiments on a model of the system to draw valid conclusion for the real system (TSS, 2010). There are a number of commercially available microscopic traffic simulation packages such as CORSIM (USA), PARAMICS (UK), AIMSUN (Spain), SUMO and VISSIM (Germany). Among these packages, this research uses AIMSUN to build, calibrate and validate the model. This project presents an overview of the model building process of an AIMSUN model for an urban area. I.1. Background The roundabout at Idrottsparken connects three main streets at Norrkoping city, namely Kungsgatan (North section), Albrektsvägen (South section) and Södra Promenaden (West and East section). An observation of the performance of the streets shows that traffic congestions occur during the morning and evening peak hours. The streets are mainly affected by the vehicular and pedestrian trips generated through the activities from campus, sport stadium, business areas, shopping centre and industrial areas. The preliminary survey shows that significant queue builds up at each section of the roundabout during peak hours. 1

18 I.2. Project Aim and Purpose The aim of the project is to build, calibrate, and validate a traffic model of the roundabout at Idrottsparken by using the microscopic traffic simulation software AIMSUN in order to experiment with a small reality traffic network. In addition, different sets of data, such as traffic flow, queue length and travel time will be used in calibration and validation processes in order to get more accurate results. Finally, alternative modifications were applied to improve efficiency of traffic network on the roundabout. The results of the new models were evaluated and compared to the results of a SUMO model for the same scenario model. The purpose of the project is to combine the traffic simulation models with the statistical methods so it can be used to analyze the traffic problem. I.3. Limitation Some limitations encountered to make the project more focused and comprehensive are described as follows: a. This project is using only the micro-simulation procedures of AIMSUN to analyse the traffic problem at the roundabout. Hence, the validity of the model depends on the validity of methodology associated with AIMSUN. b. Traffic data collection is limited during the morning and afternoon peak hours, at AM and PM, while possibility remains that the peak period extends this period. c. Problem assessment is solely focused on the roundabout at Idrottsparken without considering the minor junctions and side frictions along the street. d. In this research, the subject is vehicles occupying the roundabout. The types of vehicles consist of passenger cars, buses and heavy vehicles (trucks). In field survey, the pedestrian and cyclist are not included believed not to significantly affect the traffic. I.4. Previous Researches Some previous researches related to micro simulation using AIMSUN package and its comparison with other softwares have carried out and published. The author made some review studies to compare the previous to the present research so that the author may combine and afford new hypothesis to analyze and solve the problem of this research. However, those researches differ in the focus of the discussions, objectives and case study. G. Kotusevski and K.A. Hawick, 2009 have reviewed some of the traffic simulation software applications, the features and characteristics as well as the issues these applications performance, such as AIMSUN, PARAMICS, CORSIM and SUMO software. In this report, the authors analyzed that as traffic simulation software, AIMSUN were more user friendly than SUMO. In AIMSUN, to create traffic networks and associated vehicle patterns, the user 2

19 just simply using the available graphical network editor. While SUMO package, the user have to create manually (by hand) and write it in an XML file. The authors also found that pedestrian feature is available in AIMSUN, while SUMO package did not include this feature in its applications at all (Kotusevski & Hawick, 2009). Jones et al., 2004 have compared some of the traffic simulation softwares. AIMSUN package was found to operate acceptably well compared to both SimTraffic and CORSIM packages and it possesses features that would be useful for creating large urban and regional networks. The dynamic traffic assignment capability is unmatched by either SimTraffic or CORSIM, but AIMSUN requires cumbersome coding (Jones et al., 2004). Marjan Mosslemi, 2008 have applied a micro-simulation by using AIMSUN for roundabout with unbalanced flow pattern. This research is concluded that the capabilities of AIMSUN regarding traffic actuated control are enormously suitable and helpful for studying a variety of signalization and metering scenarios. This micro simulation package is strongly recommended as a powerful tool for later case studies of roundabout metering (Mosslemi, 2008). In 2009, Mohsin Shahzad Chaudhry and Prakash Ranjitkar have conducted a microsimulation model using AIMSUN to analyze the capacity and queue discharge flow rate at a signalized intersection. The research is concluded that AIMSUN is a viable tool for the evaluation and capacity analysis of a signalized intersection. However few limitations of AIMSUN were also observed during this research. These limitations are generally associated with editing and coding of the model. Among them the most important one is absence of the lane utilization factor (Chaudhry & Ranjitkar, 2009). Manstetten et al., 1998 stated that the car following model in AIMSUN has been tested and calibrated in various research studies including research group from Robert Bosch Gmb H. The Bosch car-following test was conducted on different micro-simulation models commonly used in Europe and North America. The results in the test show that the AIMSUN car-following model was able to present fairly good replication of observed values (Manstetten et al., 1997). In 2001, Malkhamah et al. have analyzed a roundabout in Bulaksumur, Yogyakarta by calculating the capacity and delay using three difference methods: Australia method (NAASRA 1986), UK method (TRRL) and Indonesia method (MKJI 1997). This study also analyzed the effect of driver behavior are not given priority when entering the roundabout will affect the capacity of the roundabout (Malkhamah et al., 2001). Irvan Nurpandi, 2012 have evaluated a roundabout in Cianjur city in Indonesia using simulation of AIMSUN to know the expected performance. The research is concluded that the first alternative is not effective by changing the movement of the vehicle in the roundabout. While the second alternative gives a better and produces significant result by redesign of the roundabout (Nurpandi, 2012). 3

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21 II. LITERATURE REVIEW AND THEORETICAL BASE II.1. Simulation Model Using AIMSUN AIMSUN is one of the microscopic traffic simulation software commonly used at present by transport professionals. The highly detailed model of traffic network and the specific difference of categorisation between the driver and vehicle type can be provided in this software. AIMSUN is able to simulate vehicles and pedestrians at the same time. Traffic equipment and control devices such as traffic light, traffic detector, variable message sign and ramp metering devices can also be modelled. Furthermore, the simulation model can be used to carry out experiments on a model of the system to draw valid conclusion for the real system (TSS, 2010). There are three principle parts for a simulation model in AIMSUN: the error checking, the calibration, and the validation processes. The error checking step is essential in developing a working model. The succeeding steps of the calibration process rely on the elimination of all major errors in demand and network coding before calibration. In AIMSUN, there are three basic stages in the error checking procedure: (1) software error checking; (2) input coding error checking; and (3) animation review to spot less obvious input errors. The calibration process is the adjustment of model parameters to improve the model s ability to reproduce observed local driver behaviour and traffic performance characteristics. Without the calibration process, the analyst has no assurance that the model will accurately predict traffic performance for future scenarios. Validation is the process checking to determine whether the calibration result is valid to be used on field data to ensure the results are reasonable (FHWA, 2004). In terms of calibration purposes, the AIMSUN vehicle modeling parameters can be classified into three groups according to their influence in the simulation output. The classifications are global parameters, local section parameters, and particular vehicle type parameters (TSS, 2010). Global parameters give impacts on all vehicles while driving in the network without considering the vehicle type. Some examples of global parameters are reaction time, reaction time at stop, queue up and leaving speed, etc. Section parameters give impacts on all vehicles when driving in a certain section of the network without considering the type of vehicles. Some examples of local section parameters are speed limit, turning speed, visibility distance, etc. Vehicle parameters give impacts on all vehicles of particular type when driving anywhere in the network. Some examples of particular type parameters are maximum desired speed, maximum acceleration, normal and maximum deceleration, speed acceptance, minimum distance between vehicles, give-way time, and guidance acceptance. 5

22 II.2. Roundabouts A roundabout is defined as a form of intersection design and control which accommodates traffic flow in one direction around a central island, operates with yield control at the entry points, and gives priority to vehicles within the roundabout (circulating flow). The main objective of roundabout design is to secure the safe interchange of traffic between crossing traffic streams with the minimum delay. Roundabouts have been categorized into six basic categories based on environment, number of lanes, and size. They are mini-roundabouts, urban compact roundabouts, urban single-lane roundabouts, urban double-lane roundabouts, rural single-lane roundabouts, and rural double-lane roundabouts (FHWA, 2000). Roundabouts have considerable advantages in respect of their safety record because they can improve vehicle safety by eliminating or altering conflict points, reducing speed differentials at intersections, and forcing drivers to decrease speeds when enter to roundabout (FHWA, 2000). Roundabouts are particularly successful to increase traffic safety when the traffic flows are in balance on all approach legs, but they are a less effective form of intersection when the number of entry legs exceeds four. It mainly because of the size of the junction and the higher circulating speeds that can be achieved (FHWA, 2001). There are three types of roundabout in Sweden: (1) mini, with inner radii R < 2 meters, requiring the center island to be fully traversable; small, with inner radii 2 < R < 10 meters, requiring the center island to be partly traversable; and (3) Normal, with inner radii R > l0, allowing for maneuver buses and trucks (Bergh, n.d.). According to this classification, the Idrottsparken roundabout is included into the classification of normal roundabout. The optimal roundabout size, the optimal position, and the optimal alignment and arrangement of approach legs are three fundamental elements which must be determined in preliminary design of a roundabout. Design of double-lane roundabout is significantly different from design of single-lane roundabout. The techniques used in single-lane roundabout design do not directly transfer to double-lane design because designing the geometry of double-lane roundabouts is more complicated. Speeds through the roundabout, design vehicle, geometric elements, and the natural vehicle path have to be considered (FHWA, 2000). 6

23 Figure 2.1. Basic geometric elements of a roundabout (FHWA, 2000) II.3. Calibration II.3.1. Select calibration parameters The purpose of calibration is to adjust the key parameters related to the simulation model, in order to ensure that the computer simulation closely replicates the true scenario in the field as closely as possible. The actual calibration of the model is carried out by changing the numerous parameters of the model, which influence the vehicle behavior in three different ways. The global parameters such as driver reaction time influence the general behavior of all vehicles regardless of type. The section parameters such as speed limit and turning speeds are defined for each section of the network and influence the behavior of all vehicles on a given section. Vehicle parameters such as maximum desired speed and maximum acceleration influence a certain vehicle type throughout the network. II.3.2. Optimal parameter value The calibration problem is a nonlinear least-squares optimization problem. To obtain optimal parameters, the calibration process should have been progressed further by minimizing the Mean Square Error (MSE) between the model measurements and the field measurements of capacity. The MSE is the sum of the squared errors averaged over several model run repetitions (FHWA, 2004). The formula of Mean Square Error value (MSE) can be seen in an equation below. MSE = 1 (M F) R (1) Where: MSE = Mean Square Error M = model output 7

24 F R = field measurement = number of repetitive model run In this research, The Root Mean Square Error (RMSE) method is used to get the optimal parameter value between queue length and travel time data. The RMSE (also called the root mean square deviation, RMSD) is a frequently used measure of the difference between values predicted by a model and the values actually observed from the environment that is being modeled (Wikipedia, 2012). The RMSE of a model prediction with respect to the estimated variable y is defined as the square root of the mean squared error. RMSE = MSE = E (x y ) = 1 N (x y ) (2) where: x = the mean value of measured data y = the mean value of model output N = number of observations II.4. Validation Validation describes a process where the analyst checks the overall model-predicted traffic performance for the road network system against field measurements of traffic performance (FHWA, 2004). In other words, validation is the process of determining whether the calibration result is valid to be used on the other field data. In validation process, a statistic-test is used to compare the validated model data with the observed data. H : μ = μ (3) H : μ μ (4) where: μ μ = mean of input model data = mean of output model data T > t (α 2) : Reject H o (5) S = 1 n 1 (x x ) (6) 8

25 S = 1 n 1 (y y ) (7) s = (n 1)s + n 1 s n + n 2 (8) T = x y n n (9) s n + n Where the t (α 2 ) value is obtained from T-table, α is confidence interval, here set to 95% (α = 0.05), n is the total number of measured or modeled data, and S is standard deviation of measured or modeled data. In the hypothesis test, the significance of the improvement of certain parameter in the new scenario (H ) will be tested. The H will be rejected if T < T or T > T. It indicates that the data of the simulation is invalid or there is a significance differences between the simulation data and measured data. 9

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27 III. RESEARCH METHODOLOGY III.1. Research Location The research area is around the roundabout which has been mentioned before at Idrottsparken in Norrkoping city. It connects 3 roads. They are Kungsgatan (North section), Albrektsvägen (South section) and Södra Promenaden (West and East section). The problem within this area is that the roundabout has heavy congestion during peak hours due to the large traffic flows in almost all directions. The street view of the location can be seen in Figure 3.1. Figure 3.1. Street View of the Idrottsparken Roundabout (Bing Maps, 2010) III.2. Micro-simulation Model Process In order to carry out this project, there are six major steps in developing and implementing a micro-simulation model i.e. input data, base model development, error checking, calibration, validation, and to propose improvement scenarios. The process of micro simulation model development can be seen in Figure 3.2. Input data consists of road geometry data (lengths, lanes, and curvatures), signs identification, existing demands (turning volumes, traffic flows) and calibration data (travel times, queues). The current and accurate data required for the micro simulation analysis. A basic model of micro simulation in AIMSUN is built to represent the real traffic conditions. The development of base model consists of building the link-node diagram, coding links and nodes, filling in the link geometries, coding travel demand data, adding 11

28 traveller behaviour data and finally selecting the model parameters. The purpose of this step is to build a model that is verifiable, reproducible, and accurate (FHWA, 2004). Before the calibration and validation process, the base model must be checked to identify input coding errors. The error-checking step is necessary to identify and correct model coding errors so it would not interfere with the calibration process. In the calibration and validation process, the accuracy of model must be checked whether it represents the reality precisely with the current condition. The basic model is calibrated by using queue length and travel data. In the calibration process, the Root Mean Square Error (RMSE) method is used to find optimal parameters which can minimize the RMSE value between the model measurements and the field measurements. For the validation process, the t-test method is used to check whether the conceptual simulation model is an accurate representation of the system under study. This research validates vehicles travel time of the simulation model. Analysis of all scenarios will be carried out using t-test method whether the proposed scenarios significantly or insignificantly improve the performance of the roundabout. 12

29 INPUT DATA Traffic Flow Geometric Design BASE MODEL DEVELOPMENT ERROR CHECKING Software error checking Input coding error checking Animation review CALIBRATION Minimize of RMSE ( and ) YES VALIDATION BASE MODEL ( data) NO ADJUST MODEL PARAMETER - Modify Global parameter - Modify Local Parameter - Modify Vehicle Parameter ario 1 ario 2 ario 3 ANALYSIS OF SCENARIO MODELS time The best scenario Figure 3.2. Micro-simulation model development and application process 13

30 III.3. Data Collection Data collection is an important step in this project since the accurate and reliable data influences the quality of the AIMSUN model. This section discusses the types of data that needs to be used to build, calibrate and validate an AIMSUN model. It also describes the methods used in collecting these data. In this project, it was decided to collect following data: road inventory, traffic flow and turning movement count, queue length, and travel time. Road inventory survey is conducted to collect some data related to speed limit and the geometry of the roundabout. Traffic flow is measured as the input of an OD-matrix for this project. It describes the total traffic flow entered to the roundabout from each direction. Moreover, it also gives the information of turning proportion for each direction. length is a quite useful and intuitive parameter to describe the current situation of traffic congestion. This data is used for calibration process. For the type of data used for calibration and validation, travel time data is chosen. A preliminary survey was conducted before subsequent surveys to get the data for modeling the traffic condition. This preliminary survey was done in order to find the traffic condition and to identify the peak hour. Based on the observation during the preliminary survey, the peak hour takes place at AM in the morning and PM in the afternoon. The result of the preliminary survey can be seen in Appendix 1. The period of survey was chosen on that time by considering that the survey should be started before the onset of congestion and after the dissipation of all congestion, so that we can get the highest value of peak hours. Surveyor positions while doing the survey can be seen in Figure 3.2. A B C D : Surveyor in North section : Surveyor in East section : Surveyor in South section : Surveyor in West section Figure 3.3. Surveyor position In this project, the surveys to collect the data were conducted at both of peak hours within one week. Each data set is observed in three weekdays from Tuesday until Thursday. The schedule of the survey can be seen in Table 3.1 below. 14

31 Table 3.1. Schedule of survey Date Tuesday March, 20 th, 2012 Wednesday March, 21 st, 2012 Thursday March, 22 nd, 2012 Tuesday April, 24 th, 2012 Wednesday April, 25 th, 2012 Thursday April, 26 th, 2012 Traffic Flow Turning Movement AM PM AM PM AM PM Survey Types AM PM AM PM AM PM Methods and results of each data collection will be introduced in the following sections. III.3.1. Road inventory This survey was conducted to collect data about the speed limit and the geometry of the roundabout. The geometry data is needed to model the roundabout using AIMSUN software. The geometric dimension data was obtained from field measuring and using the map digitisation from Google Earth software. However, the geometry data is needed to make sure that the created model represents the real condition by doing road inventory survey. To make sure that the drawing is appropriate with the field condition, the map is compared with the field measurement. The field measurement consists of the width and length of road at each section. According to the signs, at the roundabout of Idrottsparken, the speed limit is 30 km/h. Figure 3.3 shows the dimensions of the roundabout. 15

32 Figure 3.4. The dimension of the Idrottsparken roundabout III.3.2. Traffic flow count Traffic flow is the most important data which is used to construct model in AIMSUN. It includes the type of vehicle, the turning proportion and the origin-destination at a certain time of period. In this survey, the types of vehicles have been divided into three categories: car, bus and truck. For the turning proportions, it has been divided into three directions which are: turn left, straight through and turn right. The traffic flow data was recorded at 5 minute for an hour of every survey in order to make it possible to observe changes over time. To observe the O-D flow correctly and accurately, each of surveyors stood at the corner in each section of the roundabout and keeps counting during the observation period. The surveyors could only focus on the flow coming from their directions to avoid or mitigate count error due to the heavy flow in a certain peak hour. The results of traffic flow and turning movement counting from each direction are shown in Table 3.2 to 3.5. The results of these OD-flow observations will be used as an input of AIMSUN model. More details can be found in Appendix 2. Table 3.2. Traffic flow count per section in morning Total NORTH EAST SOUTH WEST Car Bus Truck Car Bus Truck Car Bus Truck Car Bus Truck (%)

33 Table 3.3. Traffic flow count per section in afternoon Total NORTH EAST SOUTH WEST Car Bus Truck Car Bus Truck Car Bus Truck Car Bus Truck (%) Table 3.4. Turning proportion per type of vehicle in morning NORTH APPROACH Car Bus Truck Turn Turn Turn Turn Turn Turn Through Through Through Left Right Left Right Left Right Total (%) EAST APPROACH Car Bus Truck Turn Turn Turn Turn Turn Turn Through Through Through Left Right Left Right Left Right Total (%) SOUTH APPROACH Car Bus Truck Turn Turn Turn Turn Turn Turn Through Through Through Left Right Left Right Left Right Total (%) WEST APPROACH Car Bus Truck Turn Turn Turn Turn Turn Turn Through Through Through Left Right Left Right Left Right Total (%)

34 Table 3.5. Turning proportion per type of vehicle in afternoon NORTH APPROACH Car Bus Truck Turn Turn Turn Turn Turn Turn Through Through Through Left Right Left Right Left Right Total (%) EAST APPROACH Car Bus Truck Turn Turn Turn Turn Turn Turn Through Through Through Left Right Left Right Left Right Total (%) SOUTH APPROACH Car Bus Truck Turn Turn Turn Turn Turn Turn Through Through Through Left Right Left Right Left Right Total (%) WEST APPROACH Car Bus Truck Turn Turn Turn Turn Turn Turn Through Through Through Left Right Left Right Left Right Total (%) III.3.3. survey This data will be used for the calibration process. For the queue length data collection, the vehicles which were totally stopped in the queue waiting to enter the roundabout were counted by surveyors. The queue length data were recorded every 5 minutes for an hour of each survey. The observation time was in the morning peak hour between AM and in the afternoon peak hour between PM which is same as the travel time observation time. The maximum queue length is determined based on the longest queue in one hour period of observation. Table 3.6 and Table 3.7 show the results of queue length observation from each direction. Detailed information of each single queue length measurement result can be found in Appendix 3. Table 3.6. length in morning QUEUE LENGTH North Approach East Approach South Approach West Approach Max Min Average

35 Table 3.7. length in afternoon QUEUE LENGTH North Approach East Approach South Approach West Approach Max Min Average From tables above, it can be seen that the queue length in the morning peak hour is longer than that in the afternoon peak hour. The queue observation results show that East and South sections have the biggest maximum queue length in the morning; North and East sections have the biggest maximum queue length in the afternoon. III.3.4. time measurements time measurements are performed by fixing certain reference points in each section: 80 m in north section, 122 m in east section, 117 m in south section, and 127 m in west section. The travel time of each vehicle is measured when the vehicle enters one certain point and exits another certain point. The surveyor selects the vehicles randomly, then measures the travel time during one hour for each survey ( AM and PM) by using stopwatch. The surveyors recorded the travel time in five minutes time intervals. The travel time samples will be taken as many as possible during one hour. The mean value of travel time obtained from the sum of measurement in one hour divided by the number of measurement in one hour. The Table 3.8 and Table 3.9 below show the mean value of travel time for each approach. More details can be found in Appendix 4. Table 3.8. time in morning Day TRAVEL TIME North Approach East Approach South Approach West Approach 1 st nd rd Table 3.9. time in afternoon Day TRAVEL TIME North Approach East Approach South Approach West Approach 1 st nd rd As shown in tables above, it can be seen that the travel time in the North and West sections during the morning peak hour is shorter than in the afternoon peak hour. In contrary, 19

36 the travel time in the East and South sections during the morning peak hour is longer than in the afternoon peak hour. These results are consistent with the maximum queue observations.. 20

37 IV. MODELING AND SIMULATION IV.1. Base Model Development In this research, the microscopic simulation package AIMSUN has been used for microscopic modelling of the roundabout at Idrottsparken. Several components and parameters are considered to be able to represent the existing condition such as network layout, traffic demand data, traffic control, driver behavior and public transport. In this roundabout, there is no traffic light signal and bus stop allocated within the observation area. So it was not needed to add a traffic control plan and public transport plan in the scenario. In order to ensure that the developed model copes well with its real life counterpart, it is of utmost importance that the road network must be reproduced as accurately as possible with due consideration to number of lanes, curves and intersection connecting pattern. For this purpose, satellite imagery of the site with appropriate scale was superimposed on the road network for altering the road geometry of the crude network according to the real geometric configuration of the network. Figure 4.1. Project model simulation Based on the measured values of approach widths on the four approaches and exits of the roundabout, it was assumed that all four approaches and exits consist of one lane each. Although this assumption is reasonable enough, it would have been better to use the actual lane widths. As far as the traffic demand is concerned, this research makes use of O-D matrices on the basis of the traffic counts from the field survey with due consideration to the direction of turn. 21

38 IV.2. Error Checking Error checking proceeds divided into three basic stages: (1) software error checking; (2) input coding error checking; and (3) animation review. Input coding error checking consist of a checklist for verifying the accuracy of the coded input data such as link and code network, demand, and traveler behavior and vehicle characteristics (FHWA, 2004). This research was conducted with AIMSUN version 6.1 standard. Software errors are tested by review of the model documentation and other material for input/output data in the software. The result indicated that there were no known problems or bugs related to the network under study and the scenarios to be simulated. Table 4.1. Input coding error checking Coding Error Checking Link and code network: North to East to South to West to W S E N W S E N W S E N a. Basic network connectivity b. Link geometry (size of roundabout, number of lanes, free-flow speed) Demand: c. Check the identified O- D for traffic. d. Vehicles turning movement ler behaviour and vehicle characteristics: e. The default vehicle types f. vehicle mix proportions After the coding error checking have done and already correct and appropriate as described in Table 4.1, in order to visually check the errors associated with the simulation model, the model was run in the form of an animated simulation. If a rigorous scrutiny of the animation regarding vehicle types, vehicle manoeuvres and route choice shows animated results look fine based on judgment or field inspection, it can be concluded that the model is free from errors and therefore, it is acceptable to proceed with it in the subsequent steps, the calibration and validation process. 22

39 IV.3. Calibration Process IV.3.1. Model parameter setting The first step in model parameter setting began with the adjustment of the more general global parameter, such as driver reaction time and reaction time at stop. Driver reaction time and reaction time at stop are two user specified driver behaviour parameter that strongly influence the performance and capacity of the network (FHWA, 2004). Once these parameters were adjusted to produce a good fit between observed and modelled data, and then began to have little further influence on the model outputs, local model parameter were adjusted. This was a speed limit. This parameter is defined at the section level and applied locally to vehicles while they are driving along a section. The last parameter to be adjusted is maximum acceleration in vehicles attribute parameter. IV.3.2. Optimal Parameter Value According to the literature review, the Root Mean Square Error (RMSE) method is used to get a minimum error value between the model measurements and the field measurements of capacity. In this research, two significantly influencing parameters for the queue length and travel time are simulation time step (driver reaction time) and maximum acceleration. To find the RMSE value, two parameters that significantly influencing the capacity of the network are used. For driver reaction time parameter, the value used is in the range 0.55 to 1.05, and for maximum acceleration parameter, the range of values used is 1 to 3. The results of RMSE minimization in the morning peak hour can be seen in Figure 4.2, Figure 4.3 and Table Root Mean Square Error Driver Reaction Max Acceleration 1 Max Acceleration 2 Max Acceleration 3 Max Acceleration 4 Note : Max Acceleration 1 : max = 3, mean = 3, min = 3 Max Acceleration 2 : max = 3, mean = 2.5, min = 2 Max Acceleration 3 : max = 2, mean = 1.5, min = 1 Max Acceleration 4 : max = 1, mean = 1, min = 1 Figure 4.2. Minimization of RMSE between the model output and the field measurements by using maximum queue length data in morning 23

40 From figure above, the result shows that by using parameters driver reaction time = 0.65 and maximum acceleration: max = 3, mean = 2.5, min = 2, the minimum RMSE value for queue length data in the morning peak hour is obtained. 8.5 Rooot Mean Square Error Driver Reaction (s) Max Acceleration 1 Max Acceleration 2 Max Acceleration 3 Max Acceleration 4 Note : Max Acceleration 1 : max = 3, mean = 3, min = 3 Max Acceleration 2 : max = 3, mean = 2.5, min = 2 Max Acceleration 3 : max = 2, mean = 1.5, min = 1 Max Acceleration 4 : max = 1, mean = 1, min = 1 Figure 4.3. Minimization of RMSE between the model output and the field measurements by using travel time data in morning From Figure 4.3, the result shows that by using parameters driver reaction time = 0.65 and maximum acceleration max = 1, mean = 1, min = 1, the minimum RMSE value for travel time data in the morning peak hour is obtained. Ideally, a combination of performance measures should be used for the calibration of a model. To get the optimal value of RMSE between queue length data and travel time data, the RMSE results is combined by adding the individual of results. Conversion factors will be used to equalize the unit between the queue length (in vehicle) and travel time (in second), so the RMSE values can be summed. To obtain the conversion factors, the total vehicles from field measurement is used with the assumption a single unit truck/bus is equivalent to 1.5 passenger car unit (pcu) and a truck with trailer is equivalent to 2 pcu (FHWA, 2000). According to the assumptions, the conversion factors in the morning peak hour is /3600 = veh/sec, while the conversion factors in the afternoon peak hour is /3600 = veh/sec. Then, the conversion value will be multiplied with the RMSE value of travel time. After that, the new of the RMSE of travel time can be added to the RMSE of queue length to obtain the RMSE combination between queue length and travel time. 24

41 Table 4.2. The RMSE combination between the maximum queue length and travel time in morning Driver Reaction Max Acceleration Max Min Mean RMSE of Max (Veh) RMSE of (s) RMSE Combination (Veh) The red number indicates the optimal RMSE value. Based on the results above, the parameters used to obtain the optimal RMSE value between the RMSE of queue length, the RMSE of travel time, and the RMSE combination are different. To find the best parameter will be used, a validation process have to be done so it can be concluded that the model represent the actual conditions. The results of RMSE minimization in the afternoon peak hour can be seen in Figure 4.4, Figure 4.5 and Table

42 5 Root Mean Square Error Max Acceleration 1 Max Acceleration 2 Max Acceleration 3 Max Acceleration Driver Reaction Note : Max Acceleration 1 : max = 3, mean = 3, min = 3 Max Acceleration 2 : max = 3, mean = 2.5, min = 2 Max Acceleration 3 : max = 2, mean = 1.5, min = 1 Max Acceleration 4 : max = 1, mean = 1, min = 1 Figure 4.4. Minimization of RMSE between the model output and the field measurements by using maximum queue length data in afternoon 8.5 Root Mean Square Error Driver Reaction (s) Max Acceleration 1 Max Acceleration 2 Max Acceleration 3 Max Acceleration 4 Note : Max Acceleration 1 : max = 3, mean = 3, min = 3 Max Acceleration 2 : max = 3, mean = 2.5, min = 2 Max Acceleration 3 : max = 2, mean = 1.5, min = 1 Max Acceleration 4 : max = 1, mean = 1, min = 1 Figure 4.5. Minimization of RMSE between the model output and the field measurements by using travel time data in afternoon From Figure 4.4 and 4.5, the result shows that by using parameters Driver Reaction = 0.55 and maximum acceleration Max = 3, Mean = 2.5, Min = 2, the RMSE minimization value for both of queue length data and travel time data in the afternoon peak hour is obtained. 26

43 Table 4.3. The RMSE combination between the maximum queue length and travel time in afternoon Driver Reaction Max Acceleration Max Min Mean RMSE of Max (Veh) RMSE of (s) RMSE Combination (Veh) Based on the results above, the parameters used to obtain the optimal RMSE value between the RMSE of queue length, the RMSE of travel time, and the RMSE combination are the same parameters. Before it can be concluded that the model represent the actual conditions, a validation process have to be done. The parameters that were calibrated are shown in Table 4.4 below. 27

44 Table 4.4. Calibrated model parameter No Parameter Adjustment Default Value 1. Global Parameter Simulation time step (driver reaction time) Morning Calibrated Value Afternoon Reaction time at stop Local Section Parameter Speed limit (arterial) Vehicle Parameter Maximum Acceleration - Max Mean Min Minimum Distance Vehicle - Max Mean Min IV.4. Validation Process In this process, we used the travel time data as comparison to the output from modeling. T-test method is used in the validation process to check whether the simulation model is good enough to satisfy any other measured traffic performance. For validation procedure, the equations used are equation (3) (9) as expressed in section II.4. The result of the validation of the mean travel time is shown in the Table 4.5 and 4.6 below. Table 4.5. Result for validation model in morning (driver reaction time = 0.65 and max acceleration: max = 3, mean = 2.5, min = 2) North Section East Section South Section West Section The Measured Data n x x S x The Calibrated Model n y y S y S p T-statistic t nx ny 2 ( 2 ) Validity* No Yes No Yes * Validity : Yes = similar, No = unsimilar 28

45 Table 4.6. Result for validation model in morning (driver reaction time = 0.65 and max acceleration: max = 1, mean = 1, min = 1) North Section East Section South Section West Section The Measured Data n x x S x The Calibrated Model n y y S y S p T-statistic t nx ny 2 ( 2 ) Validity* Yes Yes Yes Yes * Validity : Yes = similar, No = unsimilar According to the assumption: if T < t ( α 2), then it is considered as μ = μ, the hypothesis cannot be rejected and validation is successful. In this case, all the T- values are satisfied. It means that the output of simulation data is similar with the observed data. From Table 4.5 and 4.6, by using parameter of driver reaction time = 0.65 and max acceleration max = 1, mean = 1, min = 1, the model satisfies with all sections and it means the model represent the actual conditions in morning peak hour. Whereas when using parameter of driver reaction time = 0.65 and max acceleration: max = 3, mean = 2.5, min = 2, the model only satisfies on East and West sections. By using t-test method, the travel time data of the validation model have no significance differences compared to the measured data since the T value is in range T The travel time in the output of AIMSUN is checked to ensure that the model has the same travel time as the input does. 22 (s) Measured Model Validated Model (min) Figure 4.6. Measured travel time versus validated travel time on North section in morning 29

46 (s) Measured Model Validated Model (min) Figure 4.7. Measured travel time versus validated travel time on East section in morning 60 (s) Measured Model Validated Model (min) Figure 4.8. Measured travel time versus validated travel time on South section in morning 28 (s) Measured Model Validated Model (min) Figure 4.9. Measured travel time versus validated travel time on West section in morning 30

47 From Figures 4.6 to 4.9, it can be concluded that the travel time of the model is similar with the observed data. Although in AIMSUN output the deviation between the highest value and the lowest value is very small compared to observed data of travel time. Table 4.7. Final result for validation model in afternoon (driver reaction time = 0.55 and max acceleration: max = 3, mean = 2.5, min = 2) North Section East Section South Section West Section The Measured Data n x x S x The Calibrated Model n y y S y S p T-statistic t nx ny 2 ( 2 ) Validity* Yes Yes Yes Yes * Validity : Yes = similar, No = unsimilar From Table 4.7, by using parameter of driver reaction time = 0.55 and max acceleration: max = 3, mean = 2.5, min = 2, the model satisfies with all sections and it means the model represent the actual conditions in afternoon peak hour. To ensure that the model has the same travel time as the input does, the travel time in the output of AIMSUN is compared with the travel time in observed data. 35 (s) Measured Model Validated Model (min) Figure Measured travel time versus validated travel time on North section in afternoon 31

48 (s) Measured Model Validated Model (min) Figure Measured travel time versus validated travel time on East section in afternoon (s) Measured Model Validated Model (min) Figure Measured travel time versus validated travel time on South section in afternoon (s) Measured Model Validated Model (min) Figure Measured travel time versus validated travel time on West section in afternoon 32

49 Similar with output in morning peak hour, it can be concluded that the travel time of the model is similar with the travel time in the observed data. After the steps of calibration and validation which result the conclusion that the model is acceptable and appropriate with the field condition, then the model is ready to be analyzed for further steps. 33

50 34

51 V. ALTERNATIVE SCENARIOS The proposed three scenarios have been developed in order to improve the performance of the roundabout. The first scenario is an addition of free right turn (exit side lane) on two sections (north and east section), the second scenario is an addition of free right turn on east and south section, and the third scenario is addition of one lane on the roundabout. The effectiveness of these approaches would be observed in terms of queue length and travel time on these approaches. The success indicator is obtained from the reduce queue length and travel time. If the queue length and the travel time in sections are reduced, it means the proposed scenarios successfully improve the performance of the roundabout and thus, the proposed scenario is acceptable. V.1. Alternative ario 1: Free right turn implementation (on North and East sections) Based on the visual observation of the performance of the roundabout, it was observed that there is build-up of queue on the North and East approaches. According to the collected data, the percentage of cars turning right in the morning peak hour are 29.05%, 21.14%, 19.05%, and 10.58% for North, East, South, and West sections respectively. In afternoon peak hour, the percentages of cars turning right are 21.60%, 14%, 24.32%, and 18.99% for North, East, South, and West sections. In order to reduce the queue entering the roundabout, it was decided to add one free right turn to prevent the cars taking the right turn of entering the roundabout. According to the right turn percentage mention above, the North, East and South sections have the highest turn proportions. In terms of the possibility of implementation, since there is a building in the North and South sections, making free right turning in these sections is not easy. By considering the nearest signalized intersection on the North section with the distance from the roundabout less than 100 m, which it gives a direct effect to the length queue in the North section, it was decided to add one free right turn in this section. Therefore, the first alternative scenario in this project is making free right turning in the North and East sections. This alternative is motivated by reducing the queue length in the North and East sections. 35

52 Figure Free right turn implementation on North and East sections In this scenario, the sections which modified are North and East section. In the North section, the existing condition is one lane with a width of 4.5 meters. The change is by adding a lane to turn right as far as 20 meters from the end of section and width of 4.5 meters. In the East section, the existing condition of this section is one lane with 3.5 meters of width. The change made is by adding a lane to turn right as far as 20 meters from the end of section and width of 3.5 meters. The changing is also made on the lane in roundabout from the North to West section and from the East to North section. This scenario has a disadvantage in the high cost of investment in the new infrastructure development. V.2. Alternative ario 2: Free right turn implementation (on East and South sections) According to the queue observation, the North and South sections have the biggest maximum queue length in the morning peak hour. Similar with the first scenario, by adding a free right turn on these sections, the queue length on the East and South sections can be reduced. In the East section, the existing condition is one lane with a width of 3.5 meters. The change is by adding a lane to turn right as far as 20 meters from the end of section and width of 3.5 meters. In the South section, the existing condition of this section is one lane with 4.5 meters of width. The change made is by adding a lane to turn right as far as 20 meters from the end of section and width of 4.5 meters. The changing is also made on the lane in roundabout from the East to North section and from the South to East section. Similar with the first scenario, this scenario has a disadvantage in the high cost of investment. 36

53 Figure 5.2. Free right turn implementation on East and South sections V.3. Alternative ario 3: Addition of lane in roundabout The third alternative in this project is an addition of one lane in the Idrottsparken roundabout. This alternative is motivated to increase the capacity of roundabout and to reduce the queue entering the roundabout. For double lane roundabout the minimum inscribed circle diameter based upon design vehicle and turning movements is 110 ft (33.5 m) for through direction and 125 ft (38 m) for left turn or U-turn (KYTC, 2009). The existing condition of this roundabout is one lane with a width of 8 m. The change made is by adding one lane into two lanes with 10 meters of width for each lane. The land use taken from the outside roundabout area. The total of inscribed diameter will be 38 m. Similar to the first scenario, the disadvantages in conducting this scenario is the high cost investment. The new lane will need to cut the physical building surrounding the roundabout. The change of geometric layout is illustrated in Figure

54 Figure 5.3. Addition of lane in roundabout 38

55 VI. RESULT AND ANALYSIS VI.1. ario 1 By implementing the first scenario and comparing it to the validated model, the result of the queue length and travel time in the morning peak hour as expressed in the Figure 6.1, Figure 6.2 and Table North East South West Validated Model ario Figure 6.1. Mean of maximum queue length between scenario 1 and the validated model in morning North East South West Validated Model ario Figure 6.2. Mean of travel time between scenario 1 and the validated model in morning 39

56 Table 6.1. Comparison of the mean of maximum queue length and travel time between scenario 1 and the validated model in morning Result Validated Model North Section East Section South Section West Section ario Comparison (%) As shown in Figure 6.1, Figure 6.2 and Table 6.1, the maximum queue length and travel time in the morning peak hour is decreased in the North and East sections compared to the validated model. In contrary, the South and West sections show that the queue length have increase of 14.57% and 5.56%, while the travel time have increased of 8.75% and 7.96% compared to the validated model. In the morning peak hour, the high percentage of decrease in queue length occurs at East section and for travel time occurs at North section which decreases by 35.93% and 29.66%. To know the significance of the reduction, the result of the scenario 1 is tested by using the t-test method. In the hypothesis test, the H will be rejected if T < T or T > T. It indicates that the data of the simulation is invalid or there is a significance differences between the simulation data and measured data. The results of the t- test can be seen in the Table 6.2 below. Table 6.2. The mean of maximum queue length and travel time t-test between scenario 1 and the validated model in morning North Section East Section South Section West Section Validated Model n x x S x ario 1 n y y S y S p T-statistic t nx ny 2 ( 2 ) Analysis Result Significant Decrease Significant Decrease Significant/Insignificant Increase Insignificant Increase 40

57 As shown in Table 6.2, the significance of the reduction in the morning peak hour for the maximum queue length and the travel time occurs in the North and East section. In the South section, the queue length seems significantly increase since the T < - t-table. For the travel time in the South section, the result shows insignificantly increase since the T > - t- table. It means that the value of parameters, in this case is travel time, resulting from scenario 1 and the validated model are similar. It can be concludes that there is no changes of travel time in South section between the validated model and scenario 1, even though from the result of AIMSUN there were slight differences. The West section shows that the queue length and travel time seem insignificantly increase since the T > - t-table. For afternoon peak hour result, the result between alternative scenario 1 and the validated model can be seen in the Figure 6.3, Figure 6.4 and Table North East South West Validated Model ario Figure 6.3. Mean of maximum queue length between scenario 1 and the validated model in afternoon North East South West Validated Model ario Figure 6.4. Mean of travel time between scenario 1 and the validated model in afternoon 41

58 Table 6.3. Comparison of the mean of maximum queue length and travel time between scenario 1 and the validated model in afternoon Result Validated Model North Section East Section South Section West Section ario Comparison (%) Based on Figure 6.3, Figure 6.4 and Table 6.3, the maximum queue length and travel time in afternoon peak hour is decreased in all sections compared to the existing model. The high percentage of decrease in queue length and travel time occurs at North section which decreases by 32.02% and 36.09%. The results of the t-test can be seen in the Table 6.4 below. Table 6.4. The mean of maximum queue length and travel time t-test between scenario 1 and the validated model in afternoon North Section East Section South Section West Section Validated Model n x x S x ario 1 n y y S y S p T-statistic t nx ny 2 ( 2 ) Analysis Result Significant Decrease Significant Decrease Insignificant Decrease Insignificant Decrease As shown in Table 6.4, the significance of the reduction in the afternoon peak hour for the maximum queue length and the travel time occurs in North and East section. In the South and West sections, the queue length and travel time seem insignificantly reduce since the T < t-table. It means that the values of parameters, in this case are queue length and travel time, resulting from scenario 1 and validated model are similar. It can be concludes that in afternoon peak hour there is no changes of queue length and travel time in the South and West sections between the validated model and scenario 1. 42

59 VI.2. ario 2 The result between alternative scenario 1 and the validated model in the morning peak hour can be seen in the Figure 6.5, Figure 6.6 and Table North East South West Validated Model ario Figure 6.5. Mean of maximum queue length between scenario 2 and the validated model in morning North East South West Validated Model ario Figure 6.6. Mean of travel time between scenario 2 and the validated model in morning Table 6.5. Comparison of the mean of maximum queue length and travel time between scenario 2 and the validated model in morning Result Validated Model North Section East Section South Section West Section ario Comparison (%)

60 As shown in Figure 6.5, Figure 6.7 and Table 6.5, the maximum queue length and travel time in the morning peak hour is decreased in the East and South sections compared to the validated model. In contrary, the North and West sections show that the travel time have increased of 10.74% and 4.31% and in the North section the queue length have increased of 22.56% and 19.03%, while in the West section the queue length have decreased of 2.78% compared to the validated model. In the morning peak hour, the high percentage of decrease in queue length occurs at South section and for travel time occurs at East section which decreases by 27.99% and 40.23%. The results of the t-test can be seen in the Table 6.6 below. Table 6.6. The Mean of maximum queue length and travel time t- test between scenario 2 and the validated model in morning North Section East Section South Section West Section Validated Model n x x S x ario 2 n y y S y S p T-statistic t nx ny 2 ( 2 ) Analysis Result Significant/Insignifi cant Increase Significant Decrease Significant Decrease Insignificant Increase As shown in Table 6.6, the significance of the reduction for the maximum queue length and the travel time in the morning peak hour occurs in the East and South section. In the North section, the travel time seems insignificantly increase since the T > - t-table. It means that the values of parameters, in this case are travel time, resulting from scenario 2 and validated model are similar. It can be concludes that there is no changes of travel time in the North section between the validated model and scenario 2. For queue length, the result shows significantly increase since the T < - t-table. The West section shows that the queue length and travel time seem insignificantly increase since the T > - t-table. The result between alternative scenario 2 and validated model in the afternoon peak hour can be seen in the Figure 6.7, Figure 6.8 and Table

61 North East South West Validated Model ario Figure 6.7. Mean of maximum queue length between scenario 2 and the validated model in afternoon North East South West Validated Model ario Figure 6.8. Mean of travel time between scenario 2 and the validated model in afternoon Table 6.7. Comparison of the mean of maximum queue length and travel time between scenario 2 and the validated model in afternoon Result Validated Model North Section East Section South Section West Section ario Comparison (%)

62 From figures and table above, the maximum queue length and travel time in the afternoon peak hour is decreased in all sections compared to the validated model. In the afternoon peak hour, the high percentage of decrease in queue length occurs at South section and travel time occurs at East section which decreases by 23.42% and 30.48%. The results of the t-test can be seen in the Table 6.8 below. Table 6.8. The Mean of maximum queue length and travel time t-test between scenario 2 and the validated model in afternoon North Section East Section South Section West Section Validated Model n x x S x ario 2 n y y S y S p T-statistic t nx ny 2 ( 2 ) Analysis Result Insigficant/ Insignificant/ Significant Insignificant Significant Significant Decrease Decrease Decrease Decrease As shown in Table 6.8, the significance of the reduction for the maximum queue length and the travel time in the afternoon peak hour occurs in the South section. In the North and East sections, the queue length seems insignificantly reduce since the T < t-table, while for the travel time the result shows significantly reduce since the T > t-table. It means that the value of parameters, in this case is queue length, resulting from scenario 2 and validated model are similar. It can be concludes that there is no changes of queue length in the North and East sections between the validated model and scenario 2. The West section shows that the queue length and the travel time seem insignificantly decrease since the T < t-table. VI.3. ario 3 The result between alternative scenario 3 and the validated model in the morning peak hour can be seen in the Figure 6.9, Figure 6.10 and Table

63 North East South West Validated Model ario Figure 6.9. Mean of maximum queue length between scenario 3 and the validated model in morning North East South West Validated Model ario Figure Mean of travel time between scenario 3 and the validated model in morning Table Comparison of the mean of maximum queue length and travel time between scenario 3 and the validated model in morning Result Validated Model North Section East Section South Section West Section ario Comparison (%) As shown in Figure 6.9, Figure 6.10 and Table 6.9, the maximum queue length and travel time in the morning peak hour is increased in all sections compared to the validated 47

64 model. In the morning peak hour, the high percentage of increase in queue length occurs at West section and travel time occurs at South section which increases by 71.95% and 31.86%. The results of the t-test can be seen in the Table 6.10 below. Table The mean of maximum queue length and travel time t-test between scenario 3 and the validated model in morning North Section East Section South Section West Section Validated Model n x x S x ario 3 n y y S y S p T-statistic t nx ny 2 ( 2 ) Analysis Result Significant Increase Significant Increase Significant Increase Insignificant/ Significant Increase As shown in Table 6.10, there is no the significance of the reduction for the maximum queue length and the travel time during morning peak hour. The significance of the increase for the maximum queue length and the travel time occurs in the North, East and South sections. In the West section, the queue length seems significantly increase since the T < - t- table. In contrary, the travel time seems insignificantly increase since the T > - t-table. It means that the values of parameters, in this case is travel time, resulting from scenario 3 and validated model are similar. It can be concludes that there is no changes of travel time in the West section between the validated model and scenario 3. The result between alternative scenario 3 and the validated model in the afternoon peak hour can be seen in the Figure 6.11, Figure 6.12 and Table

65 North East South West Validated Model ario Figure Mean of maximum queue length between scenario 3 and the validated model in afternoon North East South West Validated Model ario Figure Mean of travel time between scenario 3 and the validated model in afternoon Table Comparison of mean of queue length and travel time between scenario 3 and the validated model in afternoon Result Validated Model North Section East Section South Section West Section ario Comparison (%)

66 From figures and table above, the maximum queue length and travel time in the afternoon peak hour is increased in all sections compared to the validated model. In the afternoon peak hour, the high percentage of increase in queue length and travel time occurs at South section which increases by 84.68% and 34.98%. The results of the t-test can be seen in the Table 6.12 below. Table The mean of maximum queue length and travel time t-test between scenario 3 and the validated model in afternoon North Section East Section South Section West Section Validated Model n x x S x ario 3 n y y S y S p T-statistic t nx ny 2 ( 2 ) < t-table < Analysis Result Significant Increase Significant Increase Significant Increase Insignificant Increase As shown in Table 6.12, the significance of the increase for the maximum queue length and the travel time occurs in the North, East and South sections. In the West section, the queue length and travel time seem insignificantly increase since the T > - t-table. It means that the value of parameters, in this case is queue length and travel time, resulting from scenario 3 and validated model are similar. It can be concludes that there is no changes of queue length and travel time in the West section between the validated model and scenario 3. VI.4. Comparison of Performance Results In the previous subchapter, the author has explained three alternatives to improve the performance of Idrottsparken roundabout. This part will provide the comparison between scenario1, 2 and 3 in terms of queue length and travel time as presented in Figure 6.13 to

67 % Queu North East South West ario ario ario Figure The percentage of the maximum queue length between ario 1, 2 and 3 in morning % time North East South West ario ario ario Figure The percentage of travel time between ario 1, 2 and 3 in morning 51

68 % Queu North East South West ario ario ario Figure The percentage of the maximum queue length between ario 1, 2 and 3 in afternoon % North East South West ario ario ario Figure The percentage of the maximum queue length between ario 1, 2 and 3 in afternoon As shown in figures above, in the morning peak hour, the first scenario shows the highest percentage of decrease in queue length occurs in the North section by 35.93%. For travel time, the second scenario shows the highest percentage of decrease occurs in the East section by 35.34%. In the afternoon peak hour, the highest percentage of decrease in queue length and travel time occurs in the North section by implementing the first scenario. More details can be seen in Appendix 5. According to the result mention above, the best alternative scenario in this project is scenario 1 because compared with scenario 2 this alternative is more significant in reducing the queue length and travel time in almost all sections. The third scenario shows that the result increases in all sections compared to the validated model. In this research, it can be concluded that the first scenario is considered as the best scenario compared to the second scenario and the third scenario. 52

69 VI.5. Comparison Between AIMSUN and SUMO This section will describe the comparison of the simulation result between AIMSUN and SUMO packages for the same scenario model. The comparison of micro-simulation systems was based on the simulation on roundabout of Idrottsparken. In order to improve the performance of Idrottsparken roundabout by reducing the queue length and travel time in all approaches, it was decided to build a scenario model by making free right turning in the North and East sections. This scenario models have been performed using two selected packages, AIMSUN and SUMO. The SUMO model was carried out by Leksono C.Y. & Andriyana T., Each package was evaluated according to criteria that included: system properties and scenario model outputs. VI.5.1. System properties AIMSUN, which is short of Advanced Interactive Microscopic Simulator for Urban and Non-urban Networks, was developed by the Department of Statistics and Operational Research, Universitat Poletecnica de Catalunya, Barcelona, Spain. It is used to improve road network infrastructure, reduce emissions and congestion, support system for public transport management and improve a design urban environments for vehicles and pedestrians (TSS, 2012). In AIMSUN 6.1, there are three types of transport models are present in this tool: macroscopic, mesoscopic and microscopic into one software application. The microscopic model is based in car-following Gipps (1981) model, lane changing and gap acceptance algorithms (TSS, 2010). SUMO (Simulation for Urban Mobility) is a free of the open source traffic simulation package developed by German Aerospace Center in 2001 (DLR, 2011). It is purely microscopic traffic simulation software. The model is based on safe distance car following Krauss model, an extension of the Gipps model, and the Krajzewicz model of lane change (Maciejewski, 2010). The project was conducted with SUMO version More detailed comparison of system properties between AIMSUN and SUMO can be seen in Table

70 Table Comparison of system properties between AIMSUN and SUMO Properties AIMSUN version 6.1 SUMO version ** Software category Commercial Free Visualization of simulation On-line, 2D and 3D, high details On-line, 2D, low details Network Representation Links and Nodes Links and Nodes Maximum scope area City/region City/region Car following model Gipps Model* Krauss Model Two-wheeled vehicles Yes No Trams Yes No Pedestrians Yes No Modeling of roundabouts and complex intersection Creating traffic network and associated vehicle pattern Traffic demand data * (Panwai & Dia, 2005) ** (Maciejewski, 2010) High level precision Manually using the available network editor 2 types : input traffic flows and turning percentages, the other is based on O/D matrices and routes or paths Limited precision Manual (by hand) write up a traffic network in an XML file manually by hand using OD matrix, turning flow, turn probabilities, and random vehicle generator VI.5.2. Simulation result In calibration process, to get the optimal parameter values between the model measurements and the field measurements, both of AIMSUN and SUMO uses two significantly influencing parameters for queue and travel time. AIMSUN package uses parameters of driver reaction time and the maximum acceleration, while SUMO package uses parameter of the driver imperfection and also the driver reaction time. The comparison of simulation result between AIMSUN model output and SUMO model output in the morning peak hour for the same scenario can be seen in the Figure 6.17 and Figure

71 % North East South West AIMSUN SUMO Figure The percentage of the maximum queue length between AIMSUN model and SUMO model in morning % North East South West AIMSUN SUMO Figure The percentage of travel time between AIMSUN model and SUMO model in morning As shown in Figure 6.17 and Figure 6.18, the AIMSUN output show that by making free right turning in the North and East sections, the maximum queue length in the morning peak hour have decreased by 35.93% and 27.02% and the travel time have decreased by 11.47% and 29.66%. In contrary, the South and West sections show that the maximum queue length have increased by 14.57% and 5.56% and the travel time have increased by 8.75% and 7.96%. The SUMO output shows that the maximum queue length in the morning peak hour is increased in all sections compared to the validated model. The high percentage of increase in queue length occurs at West section which increases by 45.45%. For travel time, the output 55

72 shows similar result with AIMSUN model. The travel time have decreased by 12.12% and 17.76% in the North and East sections, while in the South and West sections, the result shows the travel time have increased by 6.24% and 6.72%. The comparison of simulation between AIMSUN model output and SUMO model output in the afternoon peak hour can be seen in the Figure 6.19 and Figure % North East South West AIMSUN SUMO Figure The percentage of the maximum queue length between AIMSUN model and SUMO model in afternoon % North East South West AIMSUN SUMO Figure The percentage of travel time between AIMSUN model and SUMO model in afternoon As shown in Figure 6.19 and Figure 6.10, the AIMSUN and SUMO models show that the maximum queue length and the travel time in afternoon peak hour is decreased in almost all sections compared to the validated model. In contrary, the SUMO output shows the queue length have increased of 2.26% in the West section. 56

73 Based on the results above, it can be concluded that the AIMSUN output have no significance differences compared to the SUMO output, although the value of percentages are not similar. 57

74 58

75 VII. CONCLUSION In this research, AIMSUN modelling of the Idrottsparken roundabout was carried out in order to visualise and evaluate the operating conditions of the roundabout. For this purpose, traffic survey on all four approaches of the roundabout was carried out to collect data on traffic count (with due consideration to vehicle type and turning movement). The road network including geometric configuration was reproduced in the software and the time dependent traffic demand was incorporated into the system in terms of O-D matrices. The simulation model was checked for errors, followed by calibration of parameters and validation the model. The main goal of this project is to reduce congestion in Idrottsparken roundabout in the morning and the afternoon peak hour due to the most congested traffic. The reduction of congestion is indicated by the reduction of queue length and travel time. This project proposes three alternative scenarios for improving the traffic performance. The first scenario is adding one free right turns in North and East section. In the morning peak hour, although this scenario is able to reduce the queue length and the travel time in North and East sections, this scenario has not been able to reduce the queue length and the travel time in the South and West sections. In the afternoon peak hour, this scenario is able to improve the performance of the roundabout by decreasing the queue length and the travel time in all sections. The second scenario is adding one free right turns in East and South section. In the morning peak hour, this scenario is only able to reduce the queue length and the travel time in East and South sections but this scenario has not been able to reduce the queue length and the travel time in North and West section. Similar with the first scenario, in the afternoon peak hour this scenario is able to improve the performance of the roundabout by decreasing the queue length and the travel time in all sections. The third scenario is adding one lane on Idrottsparken roundabout. This scenario is not able to improve the performance of the roundabout. The best alternative scenario recommended in this project is scenario 1 because compared with scenario 2 this alternative is more significant in reducing the queue length and travel time in almost all sections which is 3 of 4 sections. All of scenarios have disadvantages in the high cost of investment. The comparison between AIMSUN and SUMO for the same scenario shows that the results have no significance differences. In morning peak hour, AIMSUN output shows the reduction for the queue length and travel time occur in North and East sections compared to the validated model. In contrary, the maximum queue length and travel time are increased in South and West sections. In SUMO model, the maximum queue length in the morning peak hour is increased in all sections compared to the validated model. For travel time, the output shows similar result with AIMSUN model. In afternoon peak hour, the AIMSUN and SUMO models show that the maximum queue length and the travel time are decreased in almost all sections compared to the validated model. In contrary, the SUMO output shows the queue length have increased in the West section. As generally, AIMSUN result gives more significant decrease compared to SUMO model. It can be occurred due to in modelling of 59

76 roundabouts and complex intersection, AIMSUN result provide high level precision compared to SUMO. This research is conducted in limited time, limited human resources and also focused on the roundabout at Idrottsparken without considering the minor junctions and side frictions along the street, so the proposed scenarios are also limited. In the next research, a broader scope area could be applied so other alternatives such as using signalized at Idrottsparken roundabout can be taken into consideration. 60

77 REFERENCES Bergh, T., n.d. Roundabouts-Current Swedish Practice and Research. Third International Symposium on Intersections Without Traffic Signal. Borlange, Sweden: Swedish National Road Administration. Bing Maps, Bing Maps. [Online] Available at: [Accessed 18 March 2012]. Chaudhry, M.S. & Ranjitkar, P., Capacity Analysis of Signalised Intersection using Microsimulation. Auckland, New Zealand: 32nd Australian Transport Research Forum. DLR, SUMO. [Online] Available at: [Accessed 3 June 2012]. FHWA, ROUNDABOUTS: An Informational Guide. Oregon, USA: FHWA-RD FHWA, Geometric Design Practices for European Roads. International Technology Exchange Program. Washington DC. FHWA, Traffic Analysis Toolbox Volume III: Guidelines for applying Traffic Microsimulation Software. Washington D.C, USA: FHWA-HRT Jones, S.L. et al., Traffic Simulation Software Comparison Study. UTCA Report Alabama, USA: University Transportation Center for Alabama. Kotusevski, G. & Hawick, K.A., A Review of Traffic Simulation Software. Auckland, New Zealand. Krajzewicz, D., The "Simulation of Urban MObility" package: An open source traffic simulation. Berlin, Germany: German Aerospace Centre, Institute for Transportation Research. KYTC, Roundabout Policy and Development. In KYTC Partnering Conference., Leksono, C.Y. & Andriyana, T., Roundabout Microsimulation Using SUMO. Master Thesis. Norrkoping: Linkoping University. Maciejewski, M., A Comparison of Microscopic Traffic Flow Simulation Systems for an Urban Area. Posnan, Poland: Poznan University of Technology. Malkhamah, S. et al., Analisis Kinerja Bundaran dan Perilaku Pengemudi di Bundaran (Studi kasus Bundaran Bulaksumur Yogyakarta). Research. Yogyakarta: Gadjah Mada University. 61

78 Manstetten, D., Krautter, W. & Schwab, T., Traffic Simulation Supporting Urban Control System Development. Proceedings of the 4th World conference on ITS, Berlin. Brussel, Belgium. Mosslemi, M., Using Metering Signals at Roundabouts with Unbalanced Flow Patterns to Improve the Traffic Condition. Norrkoping, Sweden: Linkoping University. Nurpandi, I., Evaluation of Roundabout Using Aimsun Simulation (Case Study Tugu Roundabout In Cianjur, West Java). Master Thesis. Yogyakarta: Gadjah Mada University. Panwai, S. & Dia, H., Comparative Evaluation of Microscopic Car-Following Behavior. Queensland, Australia: IEEE Transactions on Intelligent Transportation Systems, Vol. 6, No. 3 The University of Queensland. TSS, Microsimulator and Mesosimulator Aimsun 6.1 User's Manual. Barcelona: TSS-Transport Simulation Systems, S.L. TSS, AIMSUN. [Online] Available at: [Accessed 3 June 2012]. Wikipedia, Root Mean Square Deviation. [Online] Available at: [Accessed 22 June 2012]. 62

79 APPENDIX 1. PRELIMINARY SURVEY Section North East Turn Left Flows Through Preliminary Survey Results Turn Right Max A-1

80 APPENDIX 1. (cont d) Section South West Turn Left Flows Through Preliminary Survey Results Turn Right Max A-2

81 APPENDIX 2. TRAFFIC FLOW COUNT The recapitulation of traffic flow counting in the morning observation: Number of vehicle movement from North Section Turn Left Through Turn Right Car Bus Truck Car Bus Truck Car Bus Truck Total Number of vehicle movement from East Section Turn Left Through Turn Right Car Bus Truck Car Bus Truck Car Bus Truck Total A-3

82 APPENDIX 2. (cont d) Number of vehicle movement from South Section Turn Left Through Turn Right Car Bus Truck Car Bus Truck Car Bus Truck Total Number of vehicle movement from West Section Turn Left Through Turn Right Car Bus Truck Car Bus Truck Car Bus Truck Total A-4

83 APPENDIX 2. (cont d) The recapitulation of traffic flow counting in the afternoon observation: Number of vehicle movement from North Section Turn Left Through Turn Right Car Bus Truck Car Bus Truck Car Bus Truck Total Number of vehicle movement from East Section Turn Left Through Turn Right Car Bus Truck Car Bus Truck Car Bus Truck Total A-5

84 APPENDIX 2. (cont d) Number of vehicle movement from South Section Turn Left Through Turn Right Car Bus Truck Car Bus Truck Car Bus Truck Total Number of vehicle movement from West Section Turn Left Through Turn Right Car Bus Truck Car Bus Truck Car Bus Truck Total A-6

85 APPENDIX 3. MEAN OF MAXIMUM QUEUE LENGTH Mean of Maximum Morning Afternoon North East South West North East South West 1' - 5' ' - 10' ' - 15' ' - 20' ' - 25' ' - 30' ' - 35' ' - 40' ' - 45' ' - 50' ' - 55' ' - 60' Average A-7

86 APPENDIX 4. MEAN OF TRAVEL TIME Mean of Morning Afternoon North East South West North East South West 1' - 5' ' - 10' ' - 15' ' - 20' ' - 25' ' - 30' ' - 35' ' - 40' ' - 45' ' - 50' ' - 55' ' - 60' Average A-8

87 APPENDIX 5. OUTPUT SIMULATION The recapitulation of output queue length in the morning : No. Validated Model North Section East Section South Section West Section Validated Model Validated Model Validated Model Mean % change A-9

88 APPENDIX 5. (cont d) The recapitulation of output queue length in the afternoon : North Section East Section South Section West Section No. Validated Model Validated Model Validated Model Validated Model Mean % change A-10

89 APPENDIX 5. (cont d) The recapitulation of output travel time in the morning : North Section East Section South Section West Section No Validated Model Validated Model Validated Model Validated Model Mean % change A-11

90 APPENDIX 5. (cont d) The recapitulation of output travel time in the afternoon : North Section East Section South Section West Section No Validated Model Validated Model Validated Model Validated Model Mean % change , A-12

91 APPENDIX 6. DOCUMENTATION Picture 1. Street view of the Idrottsparken roundabout Picture 2. in the North section A-13

92 APPENDIX 6. (cont d) Picture 3. in the East section Picture 4. in the South section A-14

93 APPENDIX 6. (cont d) Picture 5. in the West section A-15