www.semargroup.org, www.ijsetr.com ISSN 2319-8885 Vol.03,Issue.10 May-2014, Pages:2058-2063 Analysis and Design of Urban Transportation Network for Pyi Gyi Ta Gon Township PHOO PWINT ZAN 1, DR. NILAR AYE 2 1 Dept of Civil Engineering, Mandalay Technological University, Mandalay, Myanmar, Email: phooopwintzan@gmail.com. 2 Dept of Civil Engineering, Mandalay Technological University, Mandalay, Myanmar, Email: dnilaraye@gmail.com. Abstract: This paper presents Analysis and Design of Urban Transportation Network for Pyi GYi Ta Gon Township. Urban transportation modeling system consists of four major stages which are often referred to as the four-step model: trip generation, trip distribution, modal split and trip assignment. Pyi Gyi Ta Gon Township is selected as the study area. This is divided by five zones. Each zone is classified according to their socioeconomic characteristics and location. Home-interview method is used for trips production and road side-interview method is used for trips attraction by using regression equation. The second step is the trip distribution. Second step can be produced zone to zone trip distribution with Gravity Model. Modal split model predict the percentages of flow, which will use each of the modes that are available for travel between each origin-destination pair. The method used in this stage is regression equation to fine utility of each mode and multinomial logit model. Final step assigns the best route for interzonal trips. User equilibrium Method is used for traffic assignment. Keywords: Trip Generation, Trip Distribution, Modal Split, Utility Function, Trip Assignment. I. INTRODUCTION Urban transportation System is a system responding to social and economical factors. These factors are again influenced by transportation system. The land use development with environment influence the transportation system. The change in transportation system may also cause changes in land use development. Transportation modeling (also known as Travel Demand Forecasting) estimates travel on the transportation system and gives a preview of travel on proposed facilities. Travel patterns are based on relationships developed from survey data between employment sites, housing and, transportation facilities. The demand for personal transportation is derived from the need to participate in activities of various types (e.g., work, shop, visit friends) that occur in places dispersed throughout the metropolitan area. Trip Generation estimate how many trips are made by each household for each of the trip purpose(work, shopping, etc) and how many trips are attracted to each location (work places, shopping center, etc).trip Distribution estimate how many trips go from one location to all other locations. Mode Choice given that someone will travel from one location to another, compare a mode options and choose which mode the traveler would likely use. Utility function measure the degree of satisfaction that people derive from their choice. Assignment deals with the supply side of transport modeling and the equilibrium between demand and supply. Supply is made up of network (links) and the costs of travelling on those links. Demand is indicated by the number of origindestination pairs and mode for a given level of service. II. LOCATION OF STUDY AREA The location of the study area is Pyi Gyi Ta Gon Township in Mandalay city. The area is 9.99 square miles. The study area has an estimated population of 145306. The study area includes 16 wards. It is situated between the Mandalay- Yangon railway and 44 th street, the Mandalay Pyi Oo Lwin railway and Pan Te Min Gyi street. Location map of Pyi Gyi Ta Gon Township is shown in Figure 1. Figure 1. Location Map of Pyi Gyi Ta Gon Township. III. FOUR STEP MODEL A. Trip generation Trip generation models are used to predict the trip ends generated by a individual household or a traffic analysis zone, usually on a daily or a peak-period basis. There are two Copyright @ 2014 SEMAR GROUPS TECHNICAL SOCIETY. All rights reserved.
types of trip ends associated with each trip, productions and attractions, and separate models typically are used to predict each of these types.. For household production models, all trips are initially generated at the home location, and NHB trips must be re-allocated to be produced in the actual origin zone of the trip. Such production models can reflect a variety of explanatory and policy-sensitive variables (e.g., car ownership, household size, number of workers). Trip attraction models serve primarily to scale the subsequent destination choice (trip distribution) problem. Essentially, these models provide a measure of relative attractiveness for various trip purposes as a function of socio-economic and demographic and sometimes land use) variables. B. Trip distribution Trip distribution is the second step of the four-step urban transportation model system. Trip distribution is a model of the number of trips that occur between each origin zone and each destination zone. It uses the predicted number of trips originating in each origin zone (trip production model) and the predicted number of trips ending in each destination zone (trip attraction model). There are a number of methods to distribute trips among destinations; and two such methods are growth factor model and gravity model. Widely use trip distribution technique is gravity model. Trip distribution produces a matrix of origins and destinations between all zones for each trip purpose. This is done according to the attractiveness of a zone, based on its proximity to other zones and on the total number of trips generated in that zone. C. Modal split Transportation planners use a series of mathematical models to estimate demand for planned transportation routes (highways or transit). Model spilt represents the ratio of different transport modes in the total journey from the origin (O) to the destination (D). The modal split analyses are basically of two types, which are binary logit model and multinomial logit model. Binomial logit models deal with two modes, whereas multinomial logit models can deal with more than two modes. In this paper, multinomial logit model is used to determine mode choice. Utility equations are estimated using a travel survey (a home interview survey), travel time and cost data for the transportation alternative. D. Traffic Assignment Assignment is the last stage of a traditional four step model. The fundamental aim of the traffic assignment process is to reproduce on the transportation system, the pattern of vehicular movements which would be observed when the travel demand represented by the trip matrix, or matrices to be assigned is satisfied. Traffic assignment determines what route, or path, trips will take in going from zone to zone. Each individual path is determined through factors such as minimum travel time (determined by the speed, capacity and intersection delays of the utilized links), and congestion that would arise from too many vehicles using a particular link or route. The end result produces traffic volumes for all roads in the network. The frequently use model are user equilibrium assignment. PHOO PWINT ZAN, DR. NILAR AYE IV. METHOD USE TO COLLECT DATA Origin-Destination (OD) Studies are an important tool for transportation professionals. There are a number of methods for collecting the O & D data. Home-interview method and Roadside-interview method are used in this paper. A. Home-interview method Household travel surveys provide: (i) household and person-level socio-economic data (typically including income and the number of household members, workers, and cars); (ii) activity-travel data (typically including for each activity performed over a 24-hr period activity type, location, start time, duration, and, if travel was involved, mode, departure time, and arrival time; and (iii) household vehicle data. This survey data is utilized to validate the representativeness of the sample, to develop, and estimate trip generation, trip distribution, and mode choice models, and to conduct timein-motion studies. B. Roadside interview survey Travel data were collected for two basic reasons: (1) to determine the origin and destination of highway (2) to estimate the total number of vehicles using urban highways. Automatic or manual traffic counter were used to determine vehicle volume on congested highways. Roadside interview is one of the vehicle intercept technique. For studies in which a sample of drivers is interviewed, a proportional share of vehicle type should be sampled in order to prevent bias. Information is collected real-time directly from the person who is driving the vehicle. Roadside interview involves stopping cars and commercial vehicles at interview stations ( e.g., cordon and screen line points) and asking the driver questions on origin, destination, trip purpose, route used, and intermediate stops made. Because it is impractical to stop all traffic at interview stations, a sample selection procedure is employed. Although the response rate can be quite high, traffic backups could be caused by drivers waiting to be interviewed. V. METHOD USED TO ESTIMATE MODEL A. Regression model Regression is the technique for describing the relationship between the dependent variable and independent variables. The dependent variable is the number of trips (Y). The independent variables employed by a disaggregate model are household characteristics. Therefore the equation would be: Y= β o + β 1 X 1 + β 2 X 2 +..+ β n X n (1) Y is the dependent variable β are the value obtained by the regression analysis X are the independent variables B. Gravity model The gravity model is much like Newton s theory of gravity. The gravity model assumes that the trips produced at an origin and attracted to a destination are directly proportional to the total trip productions at the origin and the
Analysis and Design of Urban Transportation Network for Pyi Gyi Ta Gon Township total attractions at the destination. Standard form of gravity model can be expressed as follow. (2) T o =base travel time at zero volume = travel time at practical capacity x 0.87 V= assigned volume C p =practical capacity Where, T ij = trips produced at i and attracted at j P i = total trip production at i A j = total trip attraction at j F ij = a calibration term for interchange ij, (friction factor) or travel time factor ( F ij = C/t ij n ) C = calibration factor for the friction factor K ij = a socioeconomic adjustment factor for interchange ij I = origin zone N = number of zones C. Multinomial logit model Binomial logit model deal with two modes, whereas multinomial logit model can deal with more than two modes. An example of the mathematical formulation of multinomial logit model is given below: Where, p(k) U k U x n P(k) n e x 1 uk e ux = probability of using mode k = utility of using mode k = utility of using any particular mode x = number of modes to choose from Utility equations are estimated using a travel survey (a home interview survey) and travel time and cost data for the transportation alternative. A linear form usually used: (3) U= a o + a 1 x 1 + a 2 x 2 +..+ a n x n (4) Where, U is the utility function. a is the model parameters. That can be calculated by using regression method. D. User equilibrium assignment model The user equilibrium assignment is based on first principle of Wardrop, which states that no driver can unilaterally reduce his travel cost by shifting to another route. The assumptions should be made in this assignment. They are 1. The user has perfect knowledge of the path cost. 2. Travel time on a given link is a function of the flow on that link. 3. Travel time functions are positive and increasing. At the end of each assignment, however, the assigned volume on each link is compared with the respective capacity and the travel time is adjusted according to a given formula. Relationship between Travel time and volume VI. CALCULATION OF TRIP GENERATION MODEL Regression analysis is used to calculate the trip generation step. Average household size, average income and average workers are considered as three independent variables in regression model of trip production. Data used in trip production are shown in Table I. TABLE I: DATA USED IN TRIP PRODUCTION Zone avg:household size avg: income avg: workers House hold 1 4.5979 426.2134 2.5244 4618 2 6.0667 414.5623 3.1583 7728 3 3.8276 468.4779 2.5517 2258 4 6.9121 433.3444 3.0549 7703 5 6.4167 403.4392 3.6429 7339 By using survey data and Linest formula from excel, regression coefficients are obtained. A typical equation for trip production is Y =1.4871+0.655 (avg: household)+0.0032 (avg: income)+0.3354 (avg: worker) (6) Production trips are evaluated using trip production equation and are shown in Table II. TABLE II: PRODUCTION TRIPS FOR EACH ZONE Zone Trip rate per household household Production trip 1 6.7099 4618 30986 2 7.8475 7728 60646 3 6.3500 2258 14338 4 8.4267 7703 64911 5 8.2037 7339 60207 Retail area and non-retail area are considered as two independent variables in regression model of trip attraction. Data used in trip attraction are shown in Table III. TABLE III: DATA USED IN TRIP ATTRACTION Zone Retail area (ft 2 ) Non retail area(ft 2 ) 1 66421 110603 2 0 103237 3 69696000 717038 4 0 1063909 5 0 232929 T=T o {1+0.15(V/C p ) 4 } (5) Where, T= travel time at which assigned volume can travel on the subject link By using survey data and Linest formula from excel, regression coefficients are obtained. A typical equation for trip attraction is
Y= 35673.5921+0.00093834 (retail area)-0.00634 (nonretail area) (7) The total attraction trips of each traffic analysis zone are collected from the traffic signals at southern part of Mandalay city research program of conducted simultaneously at Mandalay Technology University. Total attraction trips are shown in Table IV. PHOO PWINT ZAN, DR. NILAR AYE The utility of each mode is shown in Table IX, Table X and Table XI. TABLE VI: RESULT OF F-TEST AND R 2 FOR PRODUCTION TABLE IV: TOTAL ATTRACTION TRIPS Attraction Trips 61000 18600 96500 30200 23400 VII. BALANCING OF PRODUCTIONS AND ATTRACTIONS Trip production and attraction model are applied using estimates of independent variables to determine the zonal productions and attractions. A statistical adjustment is done in order to ensure that the total number origins equal the total number of destinations. This is called balancing of production and attraction. trip productions Adjustment factor for attractions (8) trip attractions TABLE VII: RESULT OF F-TEST AND R 2 FOR ATTRACTION TABLE VIII: TRIP INTERCHNAGE MATRIX Which lies between 0.9 and 1.1. Therefore the results are satisfactory. Table V shows the trip productions and attractions. TABLE V: TRIP PRODUCTIONS AND TRIP ATTRACTIONS zone Attraction, Production, Adjustment Adjusted A j P i factor A j 1 61000 30986 1.006 61368 2 18600 60646 1.006 18714 3 96500 14338 1.006 97081 4 30200 64911 1.006 30383 5 23400 60207 1.006 23542 Total 229700 231088 231088 F test is applied to check the model. F-test is carried out to show the linear relationship between dependent variables and independent variables. The coefficient of determination, R 2 is calculated to express the quality of fit between the regression model and sample data. Table VI and Table VII show the result of F-test for production and attraction. R 2 >0.3 is the fitting the regression model is good with significant level 0.05. In trip distribution, gravity method is used for computing the trip interchange in the study area. Table VIII shows the trip interchange matrix of Pyi Gyi Ta Gon Township. The utility equation is applied to obtain the utility of each mode. The utility equation is applied to obtain the utility of each mode. The utility of each mode is shown in Table IX, Table X and Table XI. U car = -0.12218 +7.0831 10-5 (avg: income) -0.00076(avg: time) +0.31119(avg: cost) (9) U motorbike = 1.153885-1.58475 10-5 (avg: income) 0.01957(avg: time) -0.132(avg: cost) (10) U bicycle = -0.01975-4.3518 10-5 (avg: income) +0.01856(avg: time) -0.1802(avg: cost) (11) TABLE IX: UTILITY OF CAR 1 0.1697 0.2281 0.1550 0.2756 0.3844 2 0.2281 0.1661 0.2853 0.2119 0.2107 3 0.1550 0.2853 0.1539 0.2158 0.3663 4 0.2756 0.2119 0.2158 0.2188 0.2740 5 0.3844 0.2107 0.3663 0.2740 0.1661
Analysis and Design of Urban Transportation Network for Pyi Gyi Ta Gon Township TABLE X: UTILITY OF MOTORBIKE 1 0.9501 0.8587 0.7280 0.6958 0.8065 2 0.8587 0.9511 0.8593 0.8076 0.7559 3 0.7280 0.8593 0.9054 0.8732 0.7035 4 0.6958 0.8076 0.8732 0.9059 0.8086 5 0.8065 0.7559 0.7035 0.8086 0.9512 TABLE XI: UTILITY OF BICYCLE 1 0.1902 0.3389 0.7098 0.5245 0.3387 2 0.3389 0.2459 0.5241 0.4314 0.7099 3 0.7098 0.5241 0.1920 0.3401 0.7121 4 0.5245 0.4314 0.3401 0.1942 0.4363 5 0.3387 0.7099 0.7121 0.4363 0.1942 and the travel behaviour of the individuals. In this paper, interzonal trips are only considered to evaluate link travel times on each link of the proposed network. The free flow travel time, number of trips on links and link capacity are considered as input data for Equilibrium Assignment equation. Traffic flows and travel times of zone to zone travel on each link are shown in Table XV. (12) By using probability equation, the results of each mode are shown in Table XII, Table XIII and Table XIV. TABLE XII: PROBABILITY OF CAR 1 0.2379 0.2503 0.2215 0.2628 0.2873 2 0.2503 0.2339 0.2472 0.2463 0.2287 3 0.2215 0.2472 0.2404 0.2462 0.2622 4 0.2628 0.2463 0.2432 0.2517 0.2575 5 0.2873 0.2287 0.2622 0.2575 0.2369 TABLE XIII: PROBABILITY OF MOTORBIKE 1 0.5192 0.4702 0.3928 0.4 0.4382 2 0.4702 0.5128 0.4389 0.4469 0.3945 3 0.3928 0.4389 0.5098 0.4751 0.3673 4 0.4 0.4469 0.4751 0.502 0.4396 5 0.4382 0.3945 0.3673 0.4396 0.5194 TABLE XIV: PROBABILITY OF BICYCLE 1 0.2428 0.2796 0.3857 0.3371 0.2745 2 0.2796 0.2533 0.3139 0.3068 0.3768 3 0.3857 0.3139 0.2498 0.2788 0.3705 4 0.3371 0.3068 0.2788 0.2464 0.3029 5 0.2745 0.3768 0.3705 0.3029 0.2437 VIII. ASSIGNMENT MODEL Traffic assignment is to assign origin-destination demand to individual paths and links of a network. Its main objective is to estimate the traffic volume and the corresponding travel time on each of the transportation system by the help of interzonal or intrazonal trip movements Figure 2. Network for Pyi Gyi Ta Gon Township. TABLE XV: TRAFFIC FLOWS AND TRAVEL TIMES OF ZONE TO ZONE TRAVEL ON EACH LINK link Free flow capacity t(x 0 ) x 1 t(x 1 ) travel time (min) 1-20 0.47 1367 0.49 893 0.49 20-2 3.22 15653 3.22 893 3.22 2-21 2.26 11368 2.26 2142 2.26 21-25 2.12 6837 2.12 975 2.12 21-22 0.3 1619 0.31 1166 0.31 22-23 1.76 5110 1.76 1166 1.76 23-3 0.41 936 0.55 1166 0.55 3-28 1.91 4894 1.96 3161 1.96 28-25 1.47 3598 1.48 1858 1.48 25-24 1.04 3455 1.04 737 1.04 28-4 1.3 1259 1.49 1103 1.4 4-26 1.3 1943 1.33 2084 1.56 4-29 0.83 828 0.93 1660 2.84 29-5 1.69 1871 1.7 1660 1.85 5-26 0.44 648 14.75 1587 2.84 26-25 1.01 1432 1.7 2096 1.7 26-27 1.12 1565 1.27 1538 1.27 27-24 1.19 1547 1.39 1574 1.39 24-1 2.42 5902 2.43 2311 2.43
Table XVI shows the total travel times between each origin and destination by using shortest paths in the study area. These travel time are calculated by adding the links between each origin and destination. Network for Pyi Gyi Ta gon Township is shown in Figure 2. TABLE XVI: SHORTEST PATHS BETWEEN EACH ORIGIN-DESTINATION Zone to Zone Shortest Paths Total Travel Times(min) 1 to 2 (1-20)+(20-2) 3.7 1 to 3 (1-24)+(24-25)+(25-28)+(28-3) 6.89 1 to 4 (1-24)+(24-27)+(27-26)+(26-4) 6.63 1 to 5 (1-24)+(24-27)+(27-26)+ (26-5) 7.91 2 to 1 (2-20)+(20-1) 3.7 2 to 3 (2-21)+(21-22)+(22-23)+(23-3) 4.88 2 to 4 (2-21)+(21-25)+(25-26)+(26-4) 7.64 2 to 5 (2-21)+(21-25)+(25-26)+(26-5) 8.92 3 to 1 (3-28)+(28-25)+(25-4)+(24-1) 6.89 3 to 2 (3-23)+(23-22)+(22-21)+(21-2) 4.88 3 to 4 (3-28)+(28-4) 3.34 3 to 5 (3-28)+(28-25)+(25-26)+(26-5) 7.97 4 to 1 (4-26)+(26-27)+(27-24)+(24-1) 6.63 4 to 2 (4-26)+(26-25)+(25-21)+(21-2) 7.64 4 to 3 (4-28)+(28-3) 3.34 4 to 5 (4-29)+(29-5) 4.7 5 to 1 (5-26)+(26-27)+(27-24)+(24-1) 7.91 5 to 2 (5-26)+(26-25)+(25-21)+(21-2) 8.92 5 to 3 (5-26)+(26-25)+(25-28)+(28-3) 7.97 5 to 4 (5-29)+(29-4) 4.7 IX. DISCUSSION AND CONCLUSION This study reveals the urban transportation network for Pyi Gyi Ta Gon Township in Mandalay. The study area is divided into five traffic analysis zone according to their socioeconomic characteristics and location. The results of the survey show that people with higher income and more automobile availability make more trips than people with low income and less automobile availability. Total daily trip productions of the study area are 231088 trips. Total daily trip attractions of the study area are 229700 trips. In trip distribution model, it is found that zone 3 is the most attractive zone in the study area because this zone has the many industries to attract people. Total trips attraction of zone 3 is 97081 trips. In the study area the number of trips production for zone 4 is 64911 trips. Multinomial logit model is used to calculate the probability of mode used for individual travel. Three types of vehicles such as car, motor bike and bicycle are considered in this model. In Table XII to Table XIV, it is found that the calculated probability of choice for motorbike alternative is the highest in all PHOO PWINT ZAN, DR. NILAR AYE individual choice cases. In the network of the study area, the congested link is (26-5). The travel time for this link is 14.75 minutes. The travel flows of that links are shifted to another link (4-26), (4-29) and (29-5) to reduce the travel time. After shifting the travel flows on that links, travel time is reduced to 2.84 minutes from 14.75 minutes. X. ACKNOELEDGEMENT Firstly, the author would like to express her respectful gratitude to her parents for her support and encouragement to attain the destination without any trouble. The author also acknowledge to Dr. Kyaw Moe Aung, Associate Professor and Head of Civil Engineering Department, for his helpful advice, management and encouragement. The author wishes to express specially grateful to her supervisor, Dr. NiLar Aye, Associate Professor, civil engineering Department of Mandalay Technology University, for her helpful and valuable advice, true-line guidance, and great supervision, excellent editing this thesis and continuous encouragement through the thesis period. The author would like to express special thanks to Dr. Kay Thwe Htun, Associate Professor, Department of civil engineering, Mandalay Technology University for her valuable advice, comments and special interest to be perfect this thesis. The author also takes to mention her thanks to all of her teachers of Civil Engineering Department, Mandalay Technology University. Finally, the author would like to express her gratefulness to the people who participated in writing the paper completely. XI. REFERENCE [1] Arun Chatterjee and Mohan M.Venigalla, Travel demand forecasting for urban transportation planning. [2] Michael G.Mcnally, the four step model. [3] MANSOUREH JEZHANI AND RICARDO A.CAMILO,2009, trip generation studies. [4] Anderson- 2011- The Gravity Model. [5] Bryan P.guy, 2005, Guidelines for Data Collection Techniques and Methods for Roadside Station Origin- Destination Studies. [6] Kadiyslli L.R.: Traffic Engineering and Transportation Planning, (2008).