Proposing a Geospatial Approach for Segmentation and Analysis of Rural Two-Lane Two-Way Roads Crash Frequency Using the Poisson Regression Algorithm

Document Type : Scientific - Research

Authors
meysam effati 1
AmirMohammad Ramezanpoor 2
1 Associate Professor, Department of Civil Engineering (Road and Transportation), Faculty of Engineering, University of Guilan, Iran
2 M.Sc., Department of Civil Engineering (Road and Transportation), Faculty of Engineering, University of Guilan, Iran
10.22119/jte.2024.401352.2670
Abstract
Researchers have used various types of fixed and dynamic segmentation in crash frequency models for safety studies on rural roads. The conducted research indicates that dynamic segmentation provides better results in rural roads safety and especially in the analysis of factors affecting the frequency of high crash potential road segments. Generally, the purpose of analyzing the safety of rural road segments is to facilitate the design of effective and efficient strategies to improve safety by determining the effective variables related to crashes for the users of these type of roads. Therefore, the aim of this research is to present a spatial approach based on GIS for dynamic segmentation of rural two-lane two-way roads and determine the factors affecting the frequency of crashes in these segments, emphasizing on road geometry and weather variables. For this purpose, the Poisson model with random parameter was applied to the Lushan-Qazvin two-lane two-way crash data from 2010-2015 based on the dynamic parts of the proposed segmentation phase. The results, while proving the effectiveness of the proposed segmentation method in the investigated segments, showed that two-way divided median, location of the crash, roadway conditions, clear weather, driver's haste and unnecessary acceleration, and vehicle crash with a fixed object, respectively, with 8.304. 4.763, 3.825, 1.909 and 1.205 marginal effects have the most effect on the frequency of crashes.

Keywords

Subjects

  • آمار ایران. (1394) آمار تصادفات برون‌شهری.

 

  • حاجی حسینلو، م. و قیاسی، ا. (1391). مکانیابی و اولویت بندی نقاط حادثه خیز تصادفات عابرین پیاده در شبکه های درون شهری با استفاده از GIS (مطالعه موردی شهر تهران)، یازدهمین کنفرانس مهندسی حمل و نقل و ترافیک ایران، تهران.

 

  • عفتی، م.، رجبی، م. و شعبانی، ش. (1391). توسعه یک سیستم دانش مبنای مکانمند جهت پیش بینی تصادفات در مسیرهای برون شهری.

 

  • عفتی، م.، رجبی، م.، حکیم پور، ف. و شعبانی، ش. (1393). تحلیل عوامل مکانی موثر بر تمرکز تصادفات در راه های برون شهری با استفاده از GIS و داده کاوی. علوم و فنون نقشه برداری.

 

  • Abdel-Aty, M. A., & Radwan, A. E. (2000). Modeling traffic accident occurrence and involvement. Accident Analysis & Prevention, 32(5), 633-642.

 

  • Agbelie, B. R. (2014). An empirical analysis of three econometric frameworks for evaluating economic impacts of transportation infrastructure expenditures across countries. Transport Policy, 35, 304-310.

 

  • Akbari, M., Shafabakhsh, G. and Ahadi, M.R., 2020. The impact of segmentation method on the aggregate goodness-of-fit measurements of non-linear crash prediction models. SN applied sciences, 2, pp.1-13.
  • American Association of State Highway and Transportation Officials (AASHTO), 2010. Highway Safety Manual 1st Edition Volume.

 

  • Anastasopoulos, P.C. and Mannering, F.L., 2011. An empirical assessment of fixed and random parameter logit models using crash-and non-crash-specific injury data. Accident Analysis & Prevention, 43(3), pp.1140-1147.

 

  • Atumo, E. A., Zhang, B., Li, H., Jiang, X., Yu, Q., Ambo, T. B., & Hu, Z. (2023). Segment-Level Spatial Spillover Effects of Exogenous Characteristics of Arterials on Crash Frequency. Transportation Research Record, 03611981231152471.

 

  • Bektas, B. A., Gkritza, K., & Smadi, O. (2016). Pavement marking retroreflectivity and crash frequency: segmentation, line type, and imputation effects. Journal of Transportation Engineering, 142(8), 04016030.

 

  • Bhat, C. R. (2003). Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences. Transportation Research Part B: Methodological, 37(9), 837-855.

 

  • Borsos, A., Ivan, J. N., & Orosz, G. (2016). Development of safety performance functions for two‐lane rural first‐class main roads in Hungary. Traffic Safety, 4, 87-100.

 

  • Cafiso, S., D'Agostino, C., & Persaud, B. (2018). Investigating the influence of segmentation in estimating safety performance functions for roadway sections. Journal of traffic and transportation engineering (English edition), 5(2), 129-136.

 

  • Cafiso, S., Di Graziano, A., Di Silvestro, G., & La Cava, G. (2008). Safety performance indicators for local rural roads: comprehensive procedure from low-cost data survey to accident prediction model (No. 08-2542).
  • Cenek, P. D., Davies, R. B., McLarin, M. W., Griffith-Jones, G., & Locke, N. J. (1997). Road environment and traffic crashes. Transfund New Zealand Research Report, (79).

 

  • Cheng, G., Cheng, R., Pei, Y. and Xu, L., 2020. Probability of roadside accidents for curved sections on highways. Mathematical Problems in Engineering, 2020, pp.1-18.

 

  • Chiou, Y. C., & Fu, C. (2013). Modeling crash frequency and severity using multinomial-generalized Poisson model with error components. Accident Analysis & Prevention, 50, 73-82.

 

  • Cook, D. J., Souleyrette, R. R., & Jackson, J. (2011). Effect of road segmentation on highway safety analysis (No. 11-1995).

 

  • Dereli, M. A., & Erdogan, S. (2017). A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods. Transportation Research Part A: Policy and Practice, 103, 106-117.

 

  • Dong, C., Clarke, D.B., Yan, X., Khattak, A. and Huang, B., 2014. Multivariate random-parameters zero-inflated negative binomial regression model: An application to estimate crash frequencies at intersections. Accident Analysis & Prevention, 70, pp.320-329.

 

  • Effati, M., & Saheli, M. V. (2022). Examining the influence of rural land uses and accessibility-related factors to estimate pedestrian safety: The use of GIS and machine learning techniques. International journal of transportation science and technology, 11(1), 144-157.

 

  • Frost, J., 2017. Multicollinearity in regression analysis: problems, detection, and solutions. Statistics by Jim, 2.

 

  • Geyer, J., Lankina, E., Chan, C. Y., Ragland, D. R., Pham, T., & Sharafsaleh, A. (2008). Methods for identifying high collision concentration locations for potential safety improvements. California PATH Program, Institute of Transportation Studies, University of California at Berkeley.

 

  • Greene, W. (2007). Functional form and heterogeneity in models for count data. Foundations and Trends® in Econometrics, 1(2), 113-218.

 

  • Gutierrez-Osorio, C. and Pedraza, C., 2020. Modern data sources and techniques for analysis and forecast of road accidents: A review. Journal of traffic and transportation engineering (English edition), 7(4), pp.432-446.

 

  • Hasan, A. S., Kabir, M. A. B., Jalayer, M., & Das, S. (2022). Severity modeling of work zone crashes in New Jersey using machine learning models. Journal of Transportation Safety & Security, 1-32.

 

  • Hosseinpour, M., Sahebi, S., Zamzuri, Z. H., Yahaya, A. S., & Ismail, N. (2018). Predicting crash frequency for multi-vehicle collision types using multivariate Poisson-lognormal spatial model: A comparative analysis. Accident Analysis & Prevention, 118, 277-288.

 

  • Hourdos, J. N., Garg, V., Michalopoulos, P. G., & Davis, G. A. (2006). Real-time detection of crash-prone conditions at freeway high-crash locations. Transportation research record, 1968(1), 83-91.

 

  • Infante, P., Jacinto, G., Afonso, A., Rego, L., Nogueira, V., Quaresma, P., ... & Manuel, P. R. (2022). Comparison of statistical and machine-learning models on road traffic accident severity classification. Computers, 11(5), 80.

 

  • Iranmanesh, M., Seyedabrishami, S. and Moridpour, S., 2022. Identifying high crash risk segments in rural roads using ensemble decision tree-based models. Scientific reports, 12(1), p.20024.

 

  • Lian, Y., Zhang, G., Lee, J. and Huang, H., 2020. Review on big data applications in safety research of intelligent transportation systems and connected/automated vehicles. Accident Analysis & Prevention, 146, p.105711.

 

  • Lord, D. and Mannering, F., 2010. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transportation research part A: policy and practice, 44(5), pp.291-305.

 

  • Lu, J., Gan, A., Haleem, K., & Wu, W. (2013). Clustering-based roadway segment division for the identification of high-crash locations. Journal of Transportation Safety & Security, 5(3), 224-239.

 

  • Mannering, F. L., & Bhat, C. R. (2014). Analytic methods in accident research: Methodological frontier and future directions. Analytic methods in accident research, 1, 1-22.

 

  • Mathew, S., Pulugurtha, S. S., & Duvvuri, S. (2022). Exploring the effect of road network, demographic, and land use characteristics on teen crash frequency using geographically weighted negative binomial regression. Accident Analysis & Prevention, 168, 106615.

 

  • Milton, J. C., Shankar, V. N., & Mannering, F. L. (2008). Highway accident severities and the mixed logit model: an exploratory empirical analysis. Accident Analysis & Prevention, 40(1), 260-266.

 

  • Mittal, M., Gupta, S., Chauhan, S., & Saraswat, L. K. (2022). Analysis on road crash severity of drivers using machine learning techniques. International Journal of Engineering Systems Modelling and Simulation, 13(2), 154-163.

 

  • Neyens, D. M., & Boyle, L. N. (2007). The effect of distractions on the crash types of teenage drivers. Accident Analysis & Prevention, 39(1), 206-212.

 

  • Park, E. S., & Lord, D. (2007). Multivariate Poisson-lognormal models for jointly modeling crash frequency by severity. Transportation Research Record, 2019(1), 1-6.

 

  • Pervaz, S., Bhowmik, T., & Eluru, N. (2022). Integrating macro and micro level crash frequency models considering spatial heterogeneity and random effects. Analytic methods in accident research, 36, 100238.

 

  • Roshandeh, A.M., Agbelie, B.R. and Lee, Y., 2016. Statistical modeling of total crash frequency at highway intersections. Journal of traffic and transportation engineering (English edition), 3(2), pp.166-171.

 

  • Roy, U., Farid, A., & Ksaibati, K. (2022). Effects of Pavement Friction and Geometry on Traffic Crash Frequencies: A Case Study in Wyoming. International Journal of Pavement Research and Technology, 1-14.

 

  • Saha, D., Alluri, P., Dumbaugh, E., & Gan, A. (2020). Application of the Poisson-Tweedie distribution in analyzing crash frequency data. Accident Analysis & Prevention, 137, 105456.

 

  • Saheli, M. V., & Effati, M. (2019). Investigation of factors contributing to pedestrian crash severity in rural roads. Journal of Injury and Violence Research, 11(4 Suppl 2).

 

  • Sarker, A. A., Paleti, R., Mishra, S., Golias, M. M., & Freeze, P. B. (2017). Prediction of secondary crash frequency on highway networks. Accident Analysis & Prevention, 98, 108-117.

 

  • Sawtelle, A., Shirazi, M., Garder, P.E. and Rubin, J., 2023. Exploring the impact of seasonal weather factors on frequency of lane-departure crashes in Maine. Journal of Transportation Safety & Security, 15(5), pp.445-466.

 

  • Seraneeprakarn, P., Huang, S., Shankar, V., Mannering, F., Venkataraman, N. and Milton, J., 2017. Occupant injury severities in hybrid-vehicle involved crashes: A random parameters approach with heterogeneity in means and variances. Analytic Methods in Accident Research, 15, pp.41-55.

 

  • Shankar, V., Mannering, F., & Barfield, W. (1995). Effect of roadway geometrics and environmental factors on rural freeway accident frequencies. Accident Analysis & Prevention, 27(3), 371-389.

 

 

  • Sil, G., Nama, S., Maji, A. and Maurya, A.K., 2020. Modelling operating speeds for multilane divided highways. In Transportation Research: Proceedings of CTRG 2017 (pp. 367-375). Springer Singapore.

 

  • Srinivasan, A.V., 2019. Why exclude highly correlated features when building regression model?.

 

  • Train, K. E. (2009). Discrete choice methods with simulation. Cambridge university press.

 

  • Wang, C., Chen, F., Zhang, Y., Wang, S., Yu, B., & Cheng, J. (2022). Temporal stability of factors affecting injury severity in rear-end and non-rear-end crashes: A random parameter approach with heterogeneity in means and variances. Analytic methods in accident research, 35, 100219.

 

  • Washington, S. P., Karlaftis, M. G., & Mannering, F. L. (2011). Statistical and econometric methods for transportation data analysis. 2010. View Article.
Quarterly Journal of Transportation Engineering
Volume 16, Issue 1 - Serial Number 62
Autumn 2024
Pages 4301-4316

  • Receive Date 26 June 2023
  • Revise Date 16 September 2023
  • Accept Date 09 January 2024