Modeling of Accidents Using Safety Performance Functions

Transcription

1 Modeling of Accidents Using Safety Performance Functions Khair S. Jadaan, Lamya Y. Foudeh, Mohammad N. Al-Marafi, and Majed Msallam Abstract Extensive research has been carried out in the field of road safety with the aim of understanding why accidents occur which help authorities to identify safety improvement projects more efficiently and effectively. Accidents are inherent as vehicles travel on roads. Safety Performance Functions (SPF) model accident frequencies for various design variables. They can provide a statistical relationship between the expected number of accidents and roadway characteristics. This paper explains the procedure used for the development of SPF for the road segments in Amman, the capital of Jordan. The adopted methodology was extracted from the published literature including studies on identifying variables that have to be considered while developing the SPF. In view of the lack of necessary data, the developed SPF predicts accident frequency was based solely on traffic volumes but produced satisfactory results when used to determine which of the studied sites in Amman have the highest potential for safety improvement. Keywords Safety Performance function, Continuous Risk Profile, High Collision Location, Safety Improvement. Road segmentation, Crash Frequency, Jordan. I. INTRODUCTION Transportation safety has become an intensively researched topic with the goal of better understanding why crashes occur. If they know why crashes occur, agencies will be able to identify safety improvement projects more efficiently and effectively. The main causes of accidents can be summarized in four main categories (1): 1. Human Behavior, which includes pedestrians, passengers and drivers who together form the most complex elements of the traffic system. 2. Environment, which includes weather conditions, and any temporary conditions like a construction and detours. 3.Vehicle characteristics, which include such characteristics as vehicle length, width, number of axles and engine power. Dr.Khair Jadaan Prof., Faculty of Engineering / University of Jordan, Jordan. id: kjadaan@gmail.com Engineer Lamya Y. Foudeh, Faculty of Engineering / University of Jordan, Jordan. id: Foudeh-lamya@hotmail.com Engineer Mohammad N. Al-Marafi, Faculty of Engineering / Tafila Technical University, Jordan. id: mohammadnour1@hotmail.com Majed Msallam, Faculty of Engineering / Al-Israa University, Jordan id: lasam_um@yahoo.com 4. Road characteristics, which include constant factors like capacity, intersection control, land use and the geometric aspects of the road and variable factors, that depends on other factors like traffic flow characteristics and surface condition. According to the Jordanian Public Security Department (PSD) road accidents over the past decade have killed 7,869 people and injured 171,143 persons (2). Financial and physical losses due to traffic accidents are huge with the total cost of 2009 accidents estimated to be over $500 million (3). The main objective of this study is to develop a SPF model that incorporates accident contributory factors and can be used for safety screening of Amman road network The network is analyzed using the predicted number of accidents obtained from the SPF and the observed number of accidents to identify the sites with the highest potential for safety improvement. II. LITERATURE REVIEW Observational studies have been adjusted to explore the relationships between crashes and their causes since the experimental studies which based on changing one variable to study the relation are not available in the safety analysis (4). This approach was reflected into the development of SPFs. Safety Performance Functions were estimated using Poisson and Negative Binomial regression on data of similar facilities.this way of estimation is suitable for fatalities which are isolated in space, such as intersections. But it is not appropriate for continuous fatalities such as highway segments because the approach assumes that (5, 6): -The rate of traffic collisions along a highway is spatially uncorrelated. -The rate at which collisions occur within the segment remains constant. -The factors causing high collision rates reside within the segment. Different statistical methods were developed in order to estimate the accident rates and frequencies at a specific location over an interval of time; the first step of such models is to target hot spots on roadway. Black spots were ranked according to their reported number of accidents which exceeds a chosen threshold value. These methods are too sensitive to random variation in accident counts, so the number of accidents estimated from Model was used instead (7). Recent approaches have been introduced for the purpose of identifying high accident locations with potential safety improvements. Kwon et al. (6) have investigated the 73

2 performance of three different methods for segmenting freeway sites for the purpose of identifying high collision concentration locations. These are: Sliding Moving Window (SMW), Peak Searching (PS) and Continuous Risk Profile (CRP). Continuous Risk Profile (CRP) has a lower false positive rate (i.e., identifying sites for safety improvements that should not have been selected) than the conventional sliding moving window approach, CRP approach can proactively identify high collision concentration locations and capture the benefit of safety improvements observed in the project location and in neighboring sites. III. SAFETY PERFORMANCE FUNCTION Safety Performance Functions (SPF) is an observed mathematical relationship between explanatory variables and the collision frequency among the same type of roadway group (i.e., section of roadway that shares similar features). The SPF is used to classify the safety Level of Service (LOS) for a facility into one of the following ratings: A, B, C or D, with LOS A being the best. The SPF can be used to detect the sites that have LOS D. However, if the function defining the LOS is not properly estimated, it can result in both false positives (i.e., identifying sites for safety improvements that should not have been selected) and false negatives (i.e., not identifying sites that should have been selected). There are two types of Safety Performance Functions which differ in the variables they take into consideration to represent frequencies of fatalities for a specific highway (8). Descriptive Analysis Model (Level l) This model is used to predict the expected fatalities frequencies based mainly on the volume of traffic which has the largest impact on the fatalities frequencies. Developing of SPF using only Average Annual Daily Traffic (AADT) volume as an explanatory variable does not mean that AADT is the only explanatory variable which should be included in developing SPFs. Therefore the second type of SPFs considers further variables to be included in the model. The formula for this type of analysis is (1) Where; a and b are regression parameters SL is the segment length in miles AADT is Annual Average Daily Traffic Multivariate Analysis Model (Level ll) or causality models. This model used to determine the fatalities frequencies based on a variety of variables such as weather, road geometry, traffic volume and human factors. The formula for this type of analysis is Where; a and b are regression parameters (2) SL is the segment length in miles AADT is Annual Average Daily Traffic X1* X2*.* Xn are the variables of interest SPFs help determine what the expected number of crashes should be for various settings and design variables. The two parts of the network i.e. segments and intersections have distinctly different characteristics thus requiring different sets of SPF to model the crash frequencies. A roadway segment depends on characteristics such as AADT, lane width, median type, and shoulders, whereas an intersection depends on characteristics such as control type, turning movement, and AADT for both the major and minor approaches (8). The SPFs are used to calculate a given site s Potential for Safety Improvement (PSI) and thus help in identifying the locations that have the highest potential for improvement. There are at least two different ways in which SPFs can be used by jurisdictions to make better safety decisions: 1. Use SPFs to determine the safety impacts of design changes at the project level. 2. Use SPFs as part of network screening to identify sections that may have the best potential for improvements (6, 9). IV. ROAD SEGMENTATION As segments make up the majority of the roadway network, this study deals with the development of segment of SPFs. The first step in developing SPFs is related to road segmentation and the selection of segment length. There are various ways to select the segment length; the following subtitles represent how to analyze the road segments. Single Segment -The roadway can be treated as a single segment for its entire length. The problem with treating roadways as a large entity is the inability to spot locations where safety is a concern in addition to the fact that roadway lengths vary tremendously. This could mean that during site selection, shorter roadways will have a higher representation than longer roadways even though the safety concerns could be the same. Fixed Segments-Breaking a roadway into fixed segments allows comparable units to be used on roadways that have different lengths. With small enough subdivisions, typically 0.1 miles, identifying locations of safety concerns can be easier because they will not be averaged over a larger distance. The main concern in using a fixed scale is that it is difficult to locate a peak safety concern unless the peak is located completely within the analysis segment. If the peak is on the edge of two segments, the peaks will be distributed over two windows, and therefore the segments could be determined safer for the segments than what actually exists (9). Sliding Window Approach-A sliding window takes a set length of roadway and continuously moves the window for the length of the roadway. The purpose is that areas of peak safety concern can be accurately identified since all points along the roadway can be compared to their surroundings more effectively. The problem that arises with the sliding window is that it may require the use of several SPF for a given segment. For 74

3 segments of fixed length, the lengths and segments can be selected so that the roadway has homogenous features in the area of study. However, the sliding window does not allow for this since it is a continuous procedure, so combining SPFs is necessary to represent the expected number of crashes (9). V. CASE DESCRIPTION AND DATA GATHERING Three urban roads, namely, Queen Rania Al-Abdullah (A), Wasfi Al-Tal Street (B) and Mecca Al Mukaramah (C) were included in the study (Table 1). Data were collected on these roads for the purpose of developing Safety Performance Functions (SPFs) for Amman road network. The routes are located in Greater Amman Municipality (GAM) urban area and were selected based on their characteristics to represent Urban major and minor roads. Queen Rania Al-Abdullah Street 6,153 m long is located west of Amman starting at Sport City Circle and ending at Sweileh Circle. The studied segment is 2500m long. Wasfi Al-Tal Street is located west of Amman with a length of 7,297 m, starting at Safeway intersection and ending at Khilda Circle. The length of the studied section is 3,000 m Mecca Al Mukaramah Street located west of Amman, starts at Al-Kindi Signals and ends at Mecca Al Mukarama Circle with a length of 4,640 m. The study section is 1,500 m long. For the purpose of study, the selected routes were divided into fixed length segments. Each segment connecting two successive nodes. Queen Rania Al-Abdullah Street was divided into five segments each 500 m long. Routes (B) and (C) are divided into six and three similar segments respectively The collected data also included: -Directional AADT on each segment of the selected routes for the years 2007 to 2012 obtained from GAM records. -The number of fatalities that occurred on each segment for the period from 2007 to 2012 obtained from the Central Traffic Department of the Public Security Directorate. The model used traffic volume for the year 2008on both directions of the selected routes. Traffic volume for the rest of years are determined using growth factor values. Growth factor values which are recorded at GAM model take into account the variables which affect the traffic volume such as population growth, wars, migrations, the number of cars that are licensed and many other variables. These growth factors are used to estimate the traffic volumes for the years 2007, 2009, 2010, 2011 and For Queen Rania Al-Abdullah Street the AADT in year 2008 for one direction (A) was and on the other direction (B) on the first segment. The total traffic volume of is used in analysis. Using the growth factor value as recorded at Greater Amman Municipality (GAM) traffic volume for the rest of years (2007, 2009, 2010, 2011 and 2012) are estimated. The growth factor on Queen Rania Al-Abdullah Street on Direction (A) was 6.4% and for direction (B) was 4%, then traffic volume on direction (A) and (B) for year 2009 are and 67655,respectively with a total AADT on that street for that year being The values of AADT for years 2007, 2009, 2010, 2011 and 2012 on route A are determined in the same manner ans the AADT on routes (B) and (C) TABLE I THE STUDY SITES Street ID Street Name Street Classification A Queen Rania Al-Abdullah Urban Major Arterial B Wasfi Al-Tal Urban Minor Arterial C Mecca Al-Mukaramah Urban Minor Arterial VI. DATA ANALYSIS The first type of Safety Performance Function (Level l) is used to determine the crash frequencies based on the volume of traffic (AADT).The developed function was of the following form: C / year = a*(sl) *(AADT) ^b (3) For each segment, the above equation transferred into linear form as follows: (4) Where; a0,a1 and a2 : are regression parameters x1 : ln (SL) x2: ln (AADT) Six models for each route are developed based on the data for years 2007 until Each model is transferred into linear form then the regression parameters are determined using Excel. Based on the analysis shown in the previous section the coefficients of regression model (a and b) of route A based on number of fatalities and AADT of year 2007 are equal and respectively. Then the fatalities frequency is C= *(AADT^0.588). The other models which are developed based on the data of the rest of years for Queen Rania Al Abdullah Street are shown in Table (2). Crash frequency SPF models for routes B and C are shown in tables (3) and (4), respectively. TABLE II CRASH FREQUENCY MODELS (QUEEN RANIA AL-ABDULLAH ST.)

4 TABLE III CRASH FREQUENCY MODELS (WASFI AL-TAL ST.) TABLE IV CRASH FREQUENCY MODELS (MECCA AL-MUKARAMAH ST.) C = C = For Queen Rania Al-Abdullah Street ( Route A ), the model which will be used for forecasting crash frequencies is that of year 2007 model, which has the highest R-squared value of , The best-fit model to be used for crash frequency forecasting uses between the seven models which are developed for Wasfi Al-Tal Street (Route B) is that of the year 2011 with R-square value equals Year 2007 model with R-square value is the model which will be used for crash frequency forecasting for Mecca Al-Mukaramah Street (Route C). The low R-square values indicate that the independent variables incorporated in the model are insufficient to explain the variation in the values of the predicted model. As shown before the term of Segment Length was cancelled and the coefficient of this term was zero in the analysis that shows that the segment length which is fixed in this case to be 500 m length for each segment was not an important factor in developing the crash frequencies. The chosen models for the studied routes were used to predict the crash frequencies along the segments for 10 years, starts from year 2013 and ends at year In order to produce reliable predictions, it is recommended that the forecasting period be less than or equal to half of the number of years used for model development, but due to lack of more adequate and reliable data for model development, the developed model were used in this study to predict crash frequencies for 10 years. Table 5 shows the predicted crash frequencies for Queen Rania Al-Abdullah Street. Prediction of crash frequencies for Wasfi Al-Tal Street and Mecca Al- Mukaramah Street are estimated for the years 2013 until Based on these results, the sites can be ranked in the order of their priority for safety improvements as follows: - Mecca Al-Mukaramah Street with 187 fatality during Queen Rania Al-Abdullah Street with 107 fatality during Wasfi Al-Tal Street with 54 fatality during TABLE V CRASH FREQUENCY PREDICTION (QUEEN RANIA AL-ABDULLAH ST.) Year Segment NO VII. CONCLUSIONS AND RECOMMENDATIONS Safety Performance Functions has been used and proved to be an effective safety measurement in many countries but no yet implemented in Jordan. This study aimed primarily at development of SPFs for selected urban roads in Amman; the capital of Jordan. Based on the Jordanian data on the available number of fatalities and traffic volumes SPFs were developed for three main roads; Queen Rania Al-Abdullah, Wasfi Al-Tal and Mecca Al-Mukaramah. The developed SPFs are based solely on traffic volumes because of the lack of necessary data on other possible variables. The most statistically acceptable developed models were found to have the following forms: -Queen Rania Al-Abdullah road (5) -Wasfi Al-Tal road (6) -Mecca Al-Mukaramah road (7) The developed functions are considered appropriates for predicting the crash frequencies and were used as such to identify the sites that have the highest potential for safety improvement. Accordingly, the studied sites were ranked as follows: 1. Mecca Al-Mukaramah Street with predicted 187 fatalities between 2013 and Queen Rania Al-Abdullah Street with 107 predicted fatalities between 2013 and Wasfi Al-Tal Street with 54 predicted fatalities between 2013 and 2022 The models developed in this study were based only on 76

5 traffic volume as it is the only variable for which reliable data are available. Therefore, further studies based on multivariate analysis are recommended. to determine the contribution of various variables such as roadway geometries, pavement condition, street lighting and weather conditions to accidents. This approach would produce a more proactive model that can be applied on the whole road network of Amman. REFERENCES [1] Al-Bana Y. (2011), Study of Travel Time at Main Highways In Greater Amman Municipality, M.Sc.Thesis, University Of Jordan. [2] Public Security Department, Annual Report,(2012),Amman,Jordan. [3] Al-Hyari, I, (May 2011), The Future Of Traffic Safety In Jordan And Its Impact On National Economy, M.Sc. Thesis, University Of Jordan. [4] Hauer, E. (1997). Observational Before-After Studies in Road Safety, Pergamon Press. [5] Kononov, J., and Allery, B.(2003). Level of Service of Safety. In Transportation Research Record: Journal of the Transportation Research Board, No. 1840, TRB, National Research Council, Washington, D.C., pp [6] Kwon O., Park M., Chung K. and Yeo H., The Continuous Risk Profile Approach for the Identification of High Collision Concentration Locations on Congested Highways. [7] Karl-Olov Hedman (2001).TRAFFIC SAFETY PROJECT, BLACK SPOT MANUAL. [8] Kwon O., Park M., Yeo H. and Chung H., (2012), Evaluating the performance of network screening methods for detecting high collision concentration locations on highways. [9] Tegge R.A., Jang-Hyeon Jo and Ouyang Y., (2010). Development and Application of Safety Performance Functions for Illinois Illinois Centre of Transportation Research Report ICI , March [10] American Association of State Highway and Transportation Officials (AASHTO), (2010).Highway Safety Manual, 1st Edition, Washington, DC. [11] Federal Highway Administration, Office of Safety, 1200 New Jersey Avenue SE, Washington, DC [12] In Kyu Q Lim Ph.D., P.E.,Young Jun Kweon Ph.D., P.E. Development of Safety Performance Functions for Multilane Highway in Virginia. [13] Kononov J., Identifying Locations with Potential for Accident Reductions: Use of Direct Diagnostics and Pattern Recognition Methodologies. [14] Kononov, J. (2002). Identifying Location with Potential for Accident Reductions: Use of Direct Diagnostics and Pattern Recognition Methodologies. Transportation Research Record, No. 1784, pp [15] Greater Amman Municipality (GAM). Directorate of Transportation and Traffic. [16] Central Traffic Department, Amman, Jordan. [17] HCM (2000). Highway Capacity Manual, Transportation Research Board, National Research council, Washington, D.C. [18] Chung K., Ragland D., (2007). A Method for Generating a Continuous Risk Profile for Highway Collisions. Prof. Dr. Khair Jadaan born in 1948 earned his Ph.D. degree in traffic engineering and planning in 1975 from the University of Bradford, U.K. Khair working experience covers a variety of positions in both private and public sectors in various developed and developing countries including New Zealand, Germany, U.K., U.S.A., Kuwait, Iraq and Jordan. He is currently a PROFESSOR of transportation engineering at the University of Jordan. He published over 100 papers in international journals and conferences. Prof. Jadaan is a Fellow of IHTE (U.K), member of IPENZ(New Zealand), member of ASCE (USA) and Jordan Engineers Association and been awarded a number of honoraries. Eng. Mohammad Nour Al-Marafi born in He received a B.SC, in civil engineering from Tafila Technical University in 2011 and his master's degree in Transportation Engineering from the University of Jordan in He is currently working as a lecturer at Tafila Technical University after working for 3 years with Al Hanaqth Contracting Company as a site engineer. Engineer Lamya Foudeh born in 1991.She received her B.SC, degree in civil engineering from the University of Jordan in 2014.Her research interest focuses mainly on the area of Traffic Safety. Lamya Foudeh is currently working with Khotowat Contracting Company as a Civil Engineer. Dr. Majed Musallam was born in 1967 earned his Ph.D degree in Transportation engineering in 1999 from Petersburg State University of Transport Communication, Russia. His research intrest is in traffic safety and, highway managemnt and maintenance. Dr. Majed working experience covers a variety of positions in both public and private sectors; He is currently an ASSISTANT PROFESSOR at Isra University, Amman, Jordan. He published several papers in international journals and conferences. He is a Fellow of Jordanian Engineers Association (JEA), Jordan Roads Society,and Roads Accident Prevention Society. 77