Development of models to study traffic accidents on the final sections of access roads to the cities: a case study of three major Iranian cities
DOI:
https://doi.org/10.5604/01.3001.0015.2646Keywords:
traffic accidents, city access roads, entrance roads, traffic safety, accident prediction model, neural networksAbstract
The final sections of main access roads to the cities require especial attention as the frequency of accidents in these road sections are considerably higher than other parts of interurban roads. These road sections operate as an interface between the rural roads and urban streets. The previous researches available on this subject are limited and they have also mainly focused on a narrow range of factors contributing to the accidents in these areas. The main contribution of this research is to consider a relatively comprehensive range of potential factors , and to examine their impacts through the development and comparison of both conventional probabilistic models and Artificial Neural Network (ANN) models. For this purpose, information related to the main access roads of three major Iranian cities were collected. This information consisted of accident frequency data together with the field observations of traffic characteristics, roadway conditions and roadside features of these roads. Various ANN and probabilistic models were developed. The frequency of accidents, i.e. fatal, injured, or damaged accidents, was considered as the output of the developed models. The results indicated that a hybrid of ANN models, each comprised of 10 input variables representing traffic, roadway and roadside conditions, outperformed several probabilistic models, i.e. Poisson, Negative binomial, Zero-truncated Poisson, and Zero-truncated Negative Binomial models, also developed under similar conditions in this study. Moreover, effective roadway width, roadway lighting condition, the standard deviation of vehicles speed, percentage of drivers violating the speed limit, average annual daily traffic, percentage of heavy goods vehicles, the density of roadside commercial and industrial land uses, the density of median U-turns, the density of local access roads, and the effective width of the left-side shoulder were identified as the most effective factors contributing to the accidents in these areas. The developed ANN model can be used as a tool to predict accident rates in these road sections, and to estimate a potential reduction in the accident rates, following any improvements in the major factors contributing to the traffic accidents in these areas.
References
ABDEL-ATY, M., PANDE, A., 2005. Identifying crash propensity using specific traffic speed conditions. Journal of Safety Research, 36(1): 97-108.
ABDELWAHAB, H.T., ABDEL-ATY, M., 2001. Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections. Transportation Research Record: Journal of the Transportation Research Board, 1746, 6-13. DOI: http://dx.doi.org/ 10.3141/1746-02.
ABOUNOAS, Z., RAPHAEL, W., BADR, Y., FADDOUL, R., GUILLAUME, A., 2020. Crash data reporting systems in fourteen Arab countries: challenges and improvement. Archives of Transport, 56(4), 73-88. DOI: https://doi.org/10.5604/01.3001.0014.5628.
AFANDIZADEH, S., GOLSHAN KHAVAS, R., 2006. Design of a Safety Entrance Model for City Entrance Roads. Journal of Transportation Research. 3(4), 233-241. (in Persian).
AKAIKE, H., 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723, DOI:10.1109/ TAC.1974.1100705.
AKGUNGOR, A.P., DOGAN, E.,2008. Estimating road accidents of Turkey based on regression analysis and artificial neural network approach. Advances in Transportation Studies, 16, 11–22. DOI: https://trid.trb.org/view.aspx?idD873702.
ALARIFI, A.S., ABDEL-ATY, M., LEE, J., 2018. A Bayesian multivariate hierarchical spatial joint model for predicting crash counts by crash type at intersections and segments along corridors. Accident Analysis and Prevention, 119:263-273.
AMBROS, J., ALTMANN, J., JUREWICZ, C., CHEVALIER, C., 2019. Proactive assessment of road curve safety using floating car data: An exploratory study. Archives of Transport, 50(2), 7-15. DOI: https://doi.org/10.5604/01.3001.0013.5570.
AMBROS, J., ELGNER, J., TUREK, R., VALENTOVÁ, V., 2020. Where and when do drivers speed? A feasibility study of using probe vehicle data for speeding analysis. Archives of Transport, 53(1), 103-113. DOI: https://doi.org/10.5604/01.3001.0014.1747.
BAYATA, H.F., HATTATOGLU, F., KARSLI, N., 2011. Modeling of monthly traffic accidents with the artificial neural network method. International Journal of the Physical Sciences, 6(2), 244-254.
BEN-BASSAT, T., SHINAR, D., 2011. Effect of shoulder width, guardrail and road-way geometry on driver perception and behavior. Accident Analysis and Prevention, 43(6): 2142-2152.
CALIENDO, C., GUIDA, M., PARISI, A., 2007. A crash-prediction model for multilane roads. Accident Analysis and Prevention, 39(4):657-670.
CAMERON, A.C., TRIVEDI, P., 1998. Regression analysis of count data”. Cambridge: Cambridge University Press.
CAREY, E. M., ANDERSON, D. E., MANSOUR, R., SLOAN, J., CURRY, E. A., 2020. Missed opportunities to advance knowledge on traffic safety: Accessibility of driver licensing and crash data for scientific research. Accident Analysis and Prevention, 139: 105500.
CHANG, L. Y., 2005. Analysis of freeway accident frequencies: Negative binomial regression versus artificial neural network. Safety Science, 43(8), 541–557. DOI: http://dx.doi.org/10.1016/j.ssci.2005.04.004.
DADASHOVA, B., RAMÍREZ, B.A., MC WILLIAMS, J.M., IZQUIERDO, F.A., 2016. The identification of patterns of interurban road accident frequency and severity using road geometry and traffic indicators. Transportation Research Procedia, 14:4122–4129.
DAS, D., BURGER, E., 2015. Evaluation of the role of the urban planning related factors on the occurrence of traffic accident in urban areas in South Africa - A case study in Bloemfontein city. Proceedings of the 34th Southern African Transport Conference (SATC 2015), 553–567.
DAVOODI, S.R., AHMADI, P., 2015. Prioritizing suggested strategies to reduce road accidents at the cities entrance using analytic hierarhy process. International Journal of Asian Social Science, 5: 706–714.
DOUGHERTY, M., 1995. A review of neural networks applied to transport. Transportation Research Part C: Emerging Technologies, 3(4), 247–260. DOI: http://dx.doi.org/10.1016/0968-090X(95)00009-8.
EDQUIST, J. RUDIN-BROWN, C., LENNÉ, M., 2012. The effects of on-street parking and road environment visual complexity on travel speed and reaction time. Accident Analysis and Prevention, 45: 759–765.
ELAMRANI ABOU ELASSAD, Z., MOUSANNIF, H., AL MOATASSIME, H., 2020. A proactive decision support system for predicting traffic crash events: A critical analysis of imbalanced class distribution. Knowledge-Based Systems, DOI: https://doi.org/10.1016/j.knosys.2020.106314.
ELVIK, R., 2017. A synthesis of studies of access point density as a risk factor for road accidents. Accident Analysis and Prevention, 107:1–10.
ELYASI, M., SAFFARZADEH, M., BOROUJERDIAN, A. M., SEMNARSHAD, M., MAZAEHRI, M., 2016. Prioritization of suburban accident factors based on analytical network process. International Journal of Transportation Engineering, 5:197-209.
FITZPATRICK, K., CARLSON, P., BREWER, M., WOOLDRIDGE, M., 2001. Design factors that affect driver speed on suburban streets. Transportation Research Record: Journal of the Transportation Research Board 1751, p. 18-25.
GACA, S., POGODZIŃSKA, S., 2017. Speed management as a measure to improve road safety on Polish regional roads. Archives of Transport, 43(3).
GOMES, S.V., GEEDIPALLY, S.R., LORD, D., 2012. Estimating the safety performance of urban intersections in Lisbon, Portugal. Safety Science, 50(9):1732–1739.
HEATON, J.T., 2010). Introduction to neural networks with Java (2nd Ed.). Washington: Heaton Research.
HILBE, J., 2011. Negative Binomial Regression. Cambridge University Press, New York. United States of America, 2rd ed.
HOLDRIDGE, J., SHANKAR, V., ULFARSSON, G., 2005. The crash severity impacts of fixed roadside objects. Journal of Safety Research, 36(2):139–147.
HOU, Q., MENG, X., LENG, J., YU, L., 2018. Application of a random effects negative binomial model to examine crash frequency for freeways in China. Physica A 509: 937–944.
HUANG, B., ZHANG, Y., LU, L., LU, J.J., 2014. A new access density definition and its correlation with crash rates by microscopic traffic simulation method. Accident Analysis and Prevention, 64:111-122.
JIAWEI, H., MICHELINE, K., 2006. Data Mining: Concepts and Techniques. Morgan Kaufmann Publisher.
KHALILI, M., PAKGOHAR, A., 2013. Logistic regression approach in road defects impact on accident severity. Journal of emerging technologies in web intelligence, 5(2).
KHISHDARI, A., FALLAH TAFTI, M., 2017. Development of crash frequency models for safety promotion of urban collector streets, Journal of Injury Control and Safety Promotion, 24(4), 519-533, DOI: 10.1080/17457300.2016.1278237.
LAFERRIÈRE, R., 1999. The air network itinerary and trip aggregation (ANITA) model. In M. Gaudry R. Mayes (Eds.), Taking stock of air liberalization, 223–228. Boston, MA: Springer US.
LEE, C., ABDEL-ATY, M., PARK, J., WANG, J.H., 2015. Development of crash modification factors for changing lane width on roadway segments using generalized nonlinear models. Accident Analysis and Prevention, 76: 83-91.
LI, X., LORD, D., ZHANG, Y., XIE, Y., 2008. Predicting motor vehicle crashes using Support Vector Machine models. Accident Analysis Prevention, 40(4), 1611–1618. DOI: http://dx.doi.org/10.1016/j.aap.2008.04.010.
LIU, Y., ZOU, B., NI, A., GAO, L., ZHANG, C., 2020. Calibrating microscopic traffic simulators using machine learning and particle swarm optimization. Transportation Letters, DOI: 10.1080/19427867.2020.1728037.
MA, Q., YANG, H., WANG, Z., XIE, K., YANG, D., 2020. Modeling crash risk of horizontal curves using large-scale auto-extracted roadway geometry data. Accident Analysis and Prevention, 144: 105669.
MECHERI, S., ROSEY, F., LOBJOIS, R., 2017. The effects of lane width, shoulder width, and road cross-sectional reallocation on drivers’ behavioral adaptations. Accident Analysis and Prevention, 104: 65–73.
MOOMEN, M., REZAPOUR, M., RAJA, M., KSAIBATI, K., 2020. Predicting downgrade crash frequency with the random-parameters negative binomial model: Insights into the impacts of geometric variables on downgrade crashes in Wyoming. IATSS Research 44: 94-102.
MUSTAKIM, F., YUSOF, I., RAHMAN, I., SAMAD, A.A.A., SALLEH, N.E.B.M., 2008. Blackspot study and accident prediction model using multiple liner regression. First International Conference on Construction in Developing Countries (ICCIDC–I), (1): 121–136.
NARAYANAMOORTHY, S., PALETI, R., BHAT, C., 2013. On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level. Transportation Research Part B: Methodological, 55: 245–264.
OLKIN, I., GLESER, L.J., DERMAN, C., 1980. Probability models and applications. New York, NY: MacMillan.
POTTS, I., HARWOOD, D., RICHARD, K., 2007. Relationship of lane width to safety on urban and suburban arterials. Transportation Research Record: Journal of the Transportation Research Board, 2023: 63-82.
PU, Z., LI, Z., KE, R., HUA, X., WANG, Y., 2020. Evaluating the Nonlinear Correlation between Vertical Curve Features and Crash Frequency on Highways Using Random Forests. Journal of Transportation Engineering, Part A: Systems, 146(10): 04020115.
PULUGURTHA, S. SAMBHARA, V. (2011). Pedestrian crash estimation models for signalized intersections. Accident Analysis and Prevention, 43(1):439-446.
RŮŽIČKA, J., BĚLINOVÁ, Z., KOREC, V., CIKHARDTOVÁ, K., TICHÝ, T., 2019. Prediction of Traffic Accidents by Using Neural Network. Archives of Transport System Telematics, 12(4).
SAEED, U.T., HALL, T., BAROUD, H., VOLOVSKI, J.M., 2019. Analyzing road crash frequencies with uncorrelated and correlated random-parameters count models: An empirical assessment of multilane highways. Analytic Methods in Accident Research 23: 100101.
SAFFARI, M., SEYED ABRISHAMI, E., ADNAN, R., GREENE, W., 2011. Handling of over-dispersion of count data via truncation using Poisson regression model. Journal of Computer Science Computational Mathematics, 1(1), 1–4.
SAHA, D., ALLURI, P., GAN, A., 2015. Prioritizing Highway Safety Manual’s crash prediction variables using boosted regression trees. Accident Analysis and Prevention, 79: 133-144.
SPIEGELHALTER, D.J., BEST, N.G., CARLIN, B..P., VAN DER LINDE, A., 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 583-639, DOI: 10.1111/1467-9868.00353.
SUKIRTY J., SANYAM, S., RAJESH, W., 2018. Dynamic selection of Normalization Techniques using Data Complexity Measures. Expert Systems with Applications.
TERCAN, E. , BEŞDOK, E., TAPKIN , S., 2020. Heuristic Modelling of traffic accident characteristics”, Transportation Letters, DOI: 10.1080/19427867.2020.1734273.
THAKUR, S., BISWAS, S., 2019. Assessment of pedestrian-vehicle interaction on urban roads: a critical review. Archives of Transport, 51(3), 49-63. DOI: https://doi.org/10.5604/01.3001.0013.6162.
UKKUSURI, S., MIRANDA-MORENO, L., RAMADURAI, G., ISA-TAVAREZ, J., 2012. The role of built environment on pedestrian crash frequency. Safety Science, 50(4): 1141–1151.
VAYALAMKUZHI, P., AMIRTHALINGAM, V., 2016. Influence of geometric design characteristics on safety under heterogeneous traffic flow. Journal of Traffic and Transportation Engineering (English Edition), 3(6): 559-570.
WANG, J., KONG, Y., FU, T., 2019. Express-way crash risk prediction using back propagation neural network: A brief investigation on safety resilience. Accident Analysis and Prevention 124: 180–192.
WANG, X., SONG, Y., YU, R., SCHULTZ, G., 2014. Safety modeling of sub urban arterials in shanghai, China. Accident Analysis and Prevention, 70: 215-224.
WANG, X., YUAN, J., SCHULTZ, G., FANG, S., 2018. Investigating the safety impact of roadway network features of suburban arterials in Shanghai. Accident Analysis and Prevention, 113: 137-148.
WHO, 2018. WORLD HEALTH ORGANIZATION, 2018. Global status report on road safety. Geneva, Licence: CC BY NC-SA 3.0 IGO.
WU, P., MENG, X., SONG, L., 2019. A novel ensemble learning method for crash prediction using road geometric alignments and traffic data. Journal Of Transportation Safety Security. DOI: 10.1080/19439962.2019.1579288.
XIE, Y., LORD, D., ZHANG, Y., 2007. Predicting motor vehicle collisions using Bayesian neural network models: An empirical analysis. Accident Analysis Prevention,. 39(5), 922–933. DOI: http://dx.doi.org/10.1016/j.aap.2006.12.014.
YANG, B., LIU, P., CHAN, C., XU, C., GUO, Y., 2019. Identifying the crash characteristics on freeway segments based on different ramp influence areas. Traffic Injury Prevention, DOI: 10.1080/15389588.2019.1588965.
YUAN, J., ABDEL-ATY, M., GONG, Y., CAI, Q., 2019. Real-Time Crash Risk Prediction using Long Short-Term Memory Recurrent Neural Network.. Journal of the Transportation Research Board. 1-13. DOI:10.1177/0361198119840611.
ZOU, Y., TARKO, A., CHEN, E., ROMERO, M., 2014. Effectiveness of cable barriers, guardrails, and concrete barrier walls in reducing the risk of injury. Accident Analysis and Prevention, 72: 55-65.
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