Modeling crash frequencies by transportation mode using micro/macro level variables

Document Type : Research Article

Authors

1 PhD in Civil Engineering, Barzin Eskan Paya (BEP) Consulting Engineering, Yazd, Iran

2 Department of Civil & Environmental Engineering, Tarbiat Modares University, Tehran, Iran.

3 Islamic Azad University, Science and Research Branch, Tehran

4 Department of Civil Engineering, Faculty of Engineering, Ardakan University, Ardakan, Iran

Abstract

In this study, traffic and geometric factors affecting accidents occurring in road segments are investigated across different transportation modes (vehicle, motorcycle, and pedestrian) using micro and macro levels variables simultaneously while accounting for the effect of intra-zone correlation due to the same independent variables for accidents occurring within a zone. The data relating to 14903 accidents that had occurred in 96 Traffic Analysis Zones (TAZ) in Tehran were collected and imported into Geographic Information System (GIS) application. Negative Binomial models and multilevel models were adopted to predict the number of traffic accidents. Due to considering the multilevel structure of the data in multilevel models, it showed a better performance in explaining the factors affecting accidents. Moreover, based on the results obtained from analyzing the sensitivity analysis of variables for final models, the effect size of one variable in accidents varies across different modes of transport. This discloses the necessity of investigating accidents across modes of transport. According to the results, variables like the high intensity of intersection in one TAZ and the length of the road segment increase the number of traffic accidents in all three modes of transport. Variable of the ratio of the principal arterial length to total roads available in one zone has almost 3.2 times stronger effect on motorcycle accident than vehicle and pedestrian accidents. So that adding 1 unit to this variable increases the number of vehicle and pedestrian accidents by a factor of 1.7, whereas, this variable increases motorcycle accidents by a weight of 5.4.

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Main Subjects


[1] Road Maintenance and Transportation Organization (RMTO), RMTO-Statistical yearbook, Ministry of Road and Urban Development, Iran, (2016).
[2] European Commission, Road safety in the European Union: Trends, statistics and main challenges, European Commission, November (2016).
[3] H. Huang, H. Zhou, J. Wang, F. Chang, M. Ma, A multivariate spatial model of crash frequency by transportation modes for urban intersections, Analytic methods in accident research, 14 (2017) 10-21.
[4] N. Soltani, M. Saffarzadeh, A. Naderan, Multi-Level Crash Prediction Models Considering Influence of Adjacent Zonal Attributes, Civil Engineering Journal, 5(3) (2019) 649-661.
[5] G.R. Lovegrove, T. Sayed, Macro-level collision prediction models for evaluating neighbourhood traffic safety, Canadian Journal of Civil Engineering, 33(5) (2006) 609-621.
[6] N. Soltani, M. Saffarzadeh, A. Naderan, M. Abolhasani, Development of Safety Improvement Method in City Zones Based on Road Network Characteristics, Archives of Trauma Research, 9(1) (2020) 16-23.
[7] M. Abdel-Aty, J. Lee, C. Siddiqui, K. Choi, Geographical unit based analysis in the context of transportation safety planning, Transportation Research Part A: Policy and Practice, 49 (2013) 62-75.
[8] S. Mitra, S. Washington, On the significance of omitted variables in intersection crash modeling, Accident Analysis & Prevention, 49 (2012) 439-448.
[9] J. Lee, M. Abdel-Aty, Q. Cai, Intersection crash prediction modeling with macro-level data from various geographic units, Accident Analysis & Prevention, 102 (2017) 213-226.
[10] A. Gelman, J. Hill, Data analysis using regression and multilevelhierarchical models, Cambridge University Press New York, NY, USA, 2007.
[11] N. Soltani, A.R. Mamdoohi, A Model of Driver Behavior in Response to Road Roughness: A Case Study of Yazd Arterials, Journal of Geotechnical and Transportation Engineering, 2(2) (2016) 46-50.
[12] A. Edrisi, M. Askari, Comparing the lane-changing behaviour of Iran and the United States, in:  Proceedings of the Institution of Civil Engineers-Municipal Engineer, Thomas Telford Ltd, 172(1) (2019) 46-52.
[13] M. Haghani, R. Jalalkamali, M. Berangi, Assigning crashes to road segments in developing countries, in: Proceedings of the Institution of Civil Engineers-Transport, Thomas Telford Ltd, 172(5) (2019) 299-307.
[14] A.M. Asad, S. Seyedabrishami, M. Saffarzadeh, M. Askari, Poisson Regression Model of Frequency and Severity of Road Accidents in Rural Roads, journal of civil engineering, 31(3) (2018) 33-46.
[15] H. Huang, H.C. Chin, Modeling road traffic crashes with zero-inflation and site-specific random effects, Statistical Methods & Applications, 19(3) (2010) 445-462.
[16] M. Schlögl, A multivariate analysis of environmental effects on road accident occurrence using a balanced bagging approach, Accident Analysis & Prevention, 136 (2020) 105398.
[17] A. Kabli, T. Bhowmik, N. Eluru, A Multivariate Approach For Modeling Driver Injury Severity By Body Region, Analytic methods in accident research, 28 (2020) 100129.
[18] O.O. Awe, M.I. Adarabioyo, Multivariate regression techniques for analyzing auto-crash variables in Nigeria, Journal of Natural Sciences Research, 1 (2011) 19-33.
[19] B.-J. Park, D. Lord, J.D. Hart, Bias properties of Bayesian statistics in finite mixture of negative binomial regression models in crash data analysis, Accident Analysis & Prevention, 42(2) (2010) 741-749.
[20] S.A. Alarifi, M.A. Abdel-Aty, J. Lee, J. Park, Crash modeling for intersections and segments along corridors: a Bayesian multilevel joint model with random parameters, Analytic methods in accident research, 16 (2017) 48-59.
[21] Y. Yao, O. Carsten, D. Hibberd, P. Li, Exploring the relationship between risk perception, speed limit credibility and speed limit compliance, Transportation research part F: traffic psychology and behaviour, 62 (2019) 575-586.
[22] A. Ghasemzadeh, M.M. Ahmed, Quantifying regional heterogeneity effect on drivers’ speeding behavior using SHRP2 naturalistic driving data: A multilevel modeling approach, Transportation Research Part C: Emerging Technologies, 106 (2019) 29-40.
[23] D. Lord, F. Mannering, The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives, Transportation Research Part A: Policy and Practice, 44(5) (2010) 291-305.
[24] F.L. Mannering, C.R. Bhat, Analytic methods in accident research: Methodological frontier and future directions, Analytic methods in accident research, 1 (2014) 1-22.
[25] M.M. Yaghoubi, A.A. Rassafi, N. Emrani, Investigating the effect of road characteristics on fatal crash count and crash severity; Case study: Birjand-Qayen route, AUT Journal of Civil Engineering, (2019) Article in press.
[26] L. Najib, L. Abdullah, I. Abdullah, Z. Salleh, Weights of road accident causes using analytic hierarchy process, ARPN Journal of Science and Technology, 2(2) (2012) 39-44.
[27] J.J. Rolison, S. Moutari, Combinations of factors contribute to young driver crashes, Journal of safety research, 73 (2020) 171-177.
[28] J. Aguero-Valverde, P.P. Jovanis, Analysis of road crash frequency with spatial models, Transportation Research Record, 2061(1) (2008) 55-63.
[29] M. Abdel-Aty, C. Siddiqui, H. Huang, X. Wang, Integrating trip and roadway characteristics to manage safety in traffic analysis zones, Transportation Research Record: Journal of the Transportation Research Board, (2213) (2011) 20-28.
[30] K.K. Mukoko, S.S. Pulugurtha, Examining the influence of network, land use, and demographic characteristics to estimate the number of bicycle-vehicle crashes on urban roads, IATSS research, 44(1) (2020) 8-16.
[31] C. Wang, C. Xu, P. Fan, Effects of traffic enforcement cameras on macro-level traffic safety: a spatial modeling analysis considering interactions with roadway and land use characteristics, Accident Analysis & Prevention, 144 (2020) 105659.
[32] H. Huang, F. Chang, H. Zhou, J. Lee, Modeling unobserved heterogeneity for zonal crash frequencies: A Bayesian multivariate random-parameters model with mixture components for spatially correlated data, Analytic methods in accident research, 24 (2019) 100105.
[33] Q. Cai, J. Lee, N. Eluru, M. Abdel-Aty, Macro-level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models, Accident Analysis & Prevention, 93 (2016) 14-22.
[34] F. Guo, X. Wang, M.A. Abdel-Aty, Modeling signalized intersection safety with corridor-level spatial correlations, Accident Analysis & Prevention, 42(1) (2010) 84-92.
[35] H. Huang, B. Song, P. Xu, Q. Zeng, J. Lee, M. Abdel-Aty, Macro and micro models for zonal crash prediction with application in hot zones identification, Journal of Transport Geography, 54 (2016) 248-256.
[36] E. Dupont, E. Papadimitriou, H. Martensen, G. Yannis, Multilevel analysis in road safety research, Accident Analysis & Prevention, 60 (2013) 402-411.
[37] H. Huang, M. Abdel-Aty, Multilevel data and Bayesian analysis in traffic safety, Accident Analysis & Prevention, 42(6) (2010) 1556-1565.
[38] Q. Shi, M. Abdel-Aty, R. Yu, Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway, Accident Analysis & Prevention, 88 (2016) 68-76.
[39] K.A. Abbas, Traffic safety assessment and development of predictive models for accidents on rural roads in Egypt, Accident Analysis & Prevention, 36(2) (2004) 149-163.
[40] D.N. Gujarati, Basic econometrics, Tata McGraw-Hill Education, (2009).
[41] K. Jones, Using multilevel models for survey analysis, JOURNAL-MARKET RESEARCH SOCIETY, 35 (1993) 249-249.
[42] P. Xu, H. Huang, N. Dong, M. Abdel-Aty, Sensitivity analysis in the context of regional safety modeling: Identifying and assessing the modifiable areal unit problem, Accident Analysis & Prevention, 70 (2014) 110-120.