Analysis and Prediction of Accident Frequency During Peak Hours at Urban Intersections Using the Random-Effects Negative Binomial (RENB) Model

Document Type : Original Article

Authors
Shahin Shabani 1
َAmir Bahador َAzimi 2
1 Department of Civil Engineering, Payame Noor University (PNU), Tehran, Iran.
2 Ph.D., Student, Department of Civil Engineering, Payame Noor University (PNU), Tehran, Iran.
10.22034/road.2026.591863.2525
Abstract
This study aimed to predict crash frequency during daily peak hours at urban intersections using a Random-Effects Negative Binomial (RENB) model. The dataset comprised crash records from 34 urban intersections in Borujerd, Iran, covering a five-year period from 2021 to 2025 (corresponding to the Iranian calendar years 1400–1404). After aggregating the data on an hourly basis, the final dataset consisted of 204 observations and 698 reported crashes. The explanatory variables included hourly traffic volumes on major and minor roads, the number of intersection approaches, the number of traffic signals, and dummy variables representing peak-hour periods. Statistical analysis confirmed the presence of significant overdispersion in the crash data. Moreover, the relatively low proportion of zero-count observations indicated that the Negative Binomial model was more appropriate than zero-inflated alternatives. A comparison between the conventional Negative Binomial (NB) model and the Random-Effects Negative Binomial (RENB) model, based on the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Likelihood Ratio (LR) test, demonstrated that incorporating random effects significantly improved model performance. The results revealed that traffic volume on minor roads had a greater impact on crash frequency than traffic volume on major roads. Specifically, an increase of 1,000 vehicles per hour on the minor road was associated with an approximately 146% increase in the expected crash frequency. Furthermore, increasing the number of intersection approaches from three to four resulted in a 76% increase in the expected crash frequency.
Keywords
Accident modeling
Peak hours
Random-Effects Negative Binomial model
Urban intersections
-کوهی، محمد و شعبانی، شاهین (1399). مدلسازی فراوانی تصادفات در تقاطع های سه شاخه شهری. فصلنامه جاده، دوره 28، شماره 103. 50-37.
-Abdel-Aty, M. A., & Radwan, A. E. (2000). Modeling traffic accident occurrence and involvement. Accident Analysis & Prevention, 32(5), 633–642.
-Almutairi, O., (2024), A nested grouped random parameter negative binomial model for modeling segment-level crash counts, Heliyon, 10, e28900.
-Alqatawna, A., Rivas Álvarez, A. M., & García-Moreno, S. S.-C. (2021). Comparison of Multivariate Regression Models and Artificial Neural Networks for Prediction Highway Traffic Accidents in Spain: A Case Study. Transportation Research Procedia, 58, 277–284.
-Anastasopoulos, P.C., Mannering, F.L., (2009). A note on modeling vehicle accident frequencies with random-parameters count models. Accid. Anal. Prev. 41 (1), 153–159.
-Anastasopoulos, P.Ch., Mannering, F.L., (2009). A note on modeling vehicle accident frequencies with random-parameters count models. Accid. Anal. Prev. 41, 153–159.
-Azmeri Khan, Sh., Afghari, A. P., Yasmin, Sh., -Haque, M., (2023). Effects of design consistency on run-off-road crashes: An application of a Random Parameters Negative Binomial Lindley model, Accident Analysis and Prevention, 186, 107042.
-Basso, F., Basso, L. J., Bravo, F., & Pezoa, R. (2018). Real-time crash prediction in an urban expressway using disaggregated data. Transportation Research Part C: Emerging Technologies, 86, 202–219.
-Cai, Q., Abdel-Aty, M., Yuan, J., Lee, J., & Wu, Y. (2020). Real-time crash prediction on expressways using deep generative models. Transportation Research Part C: Emerging Technologies, 117, 102697.
-Champahom, T., Jomnonkwao, S., Karoonsoontawong, A., & Ratanavaraha, V. (2022). Spatial zero-inflated negative binomial regression models: Application for estimating frequencies of rear-end crashes on Thai highways. Journal of Transportation Safety & Security, 14(3), 523–540.
-Chen, E., Tarko, A.P., (2014). Modeling safety of highway work zones with random parameters and random effects models. Anal. Methods Accid. Res. 1, 86–95.
-Chin, H.C., Quddus, M.A., (2003). Applying the random effect negative binomial model to examine traffic accident occurrence at signalized intersections. Accid. Anal. Prev. 35, 253–259.
-Fu, X., Liu, J., Jones, S., Barnett, T., & Khattak, A. J. (2022). From the past to the future: Modeling the temporal instability of safety performance functions. Accident Analysis & Prevention, 167, 106592.
-Geedipally, S.R., Lord, D., Dhavala, S.S., (2012). The negative binomial-Lindley generalized linear model: Characteristics and application using crash data. Accid. Anal. Prev. 45, 258–265.
-Gong, H., Wang, F., Zhou, B., Dent, Sh., (2020). Application of random effects negative binomial model with clustered dataset for vehicle crash frequency analysis, International Journal of Transportation Science and Technology, 9,183–194.
-Gu, X., Yan, X., Ma, L., & Liu, X. (2020). Modeling the service-route-based crash frequency by a spatiotemporal-random-effect zero-inflated negative binomial model:
An empirical analysis for bus-involved crashes. Accident Analysis & Prevention, 144, 105674.
-Guo, M., Zhao, X., Yao, Y., Yan, P., Su, Y., Bi, C., & Wu, D. (2021). A study of freeway crash risk prediction and interpretation based on risky driving behavior and traffic flow data. Accident Analysis & Prevention, 160, 106328.
-Islam, A. S. M. M., Shirazi, M., & Lord, D. (2022). Finite mixture Negative Binomial Lindley for modeling heterogeneous crash data with many zero observations. Accident Analysis & Prevention, 175, 106765.
-Joshua, S. C., & Garber, N. J. (1990). Estimating truck accident rate and involvements using linear and Poisson regression models. Transportation Planning and Technology, 15(1), 41–58.
-Jovanis, P. P., & Chang, H. L. (1986). Modeling relationship of accidents to miles traveled. Transportation Research Record Journal of the Transportation Research Board 1068.
-Lord, D., Manar, A., Vizioli, A., (2005). Modeling crash-flow-density and crash-flow-V/C ratio relationships for rural and urban freeway segments. Accid. Anal. Prev. 37 (1), 185–199.
Lord, D., Mannering, F., (2010). The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transp. Res. A Policy Pract. 44 (5), 291–305.
-Mannering, F.L., Shankar, V., Bhat, C.R., (2016). Unobserved heterogeneity and the statistical analysis of highway accident data. Anal. Methods Accid. Res. 11, 1–16.
-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.
-Miaou, S.-P. (1994). The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accident Analysis & Prevention, 26(4), 471–482.
-Mitra, S., Washington, S., 2007. On the nature of over-dispersion in motor vehicle crash prediction models. Accid. Anal. Prev. 39 (3), 459–468.
-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.
-Oviedo-Trespalacios, O., Afghari, A.P., Haque, M.M., (2020). A hierarchical Bayesian multivariate ordered model of distracted drivers’ decision to initiate risk- compensating behaviour. Anal. Methods Accid. Res. 26, 100121.
-Poch, M., Mannering, F., (1996). Negative binomial analysis of intersection-accident frequences. J. Transp. Eng. 122 (2), 105–113.
Retallack, A. E., & Ostendorf, B. (2020). Relationship between Traffic Volume and Accident Frequency at Intersections. IJERPH, 17(4), 1393.
-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.
 -Tavakoli Kashani, A., Mirhashemi, A., Amirifar, S., (2022), A review on Intersection Safety Studies with Bibliometeric Methods, Amirkabir Journal of Civil Engineering, 54(5), 363-366.
-Washington, S., Karlaftis, M.G., Mannering, F., Anastasopoulos, P., (2020). Statistical and econometric methods for transportation data analysis. CRC Press.
-Xu, P., & Huang, H. (2015). Modeling crash spatial heterogeneity: Random parameter versus geographically weighting. Accident Analysis & Prevention, 75, 16–25.
-Young, J., & Park, P. Y. (2013). Benefits of small municipalities using jurisdiction-specific safety performance functions rather than the Highway Safety Manual’s calibrated or uncalibrated safety performance functions. Can. J. Civ. Eng., 40(6), 517–527.
-Yuan, J., Abdel-Aty, M., Fu, J., Wu, Y., Yue, L., & Eluru, N. (2021). Developing safety performance functions for freeways at different aggregation levels using multi-state microscopic traffic detector data. Accident Analysis & Prevention, 151, 105984.
-Yuan, J., Abdel-Aty, M., Fu, J., Wu, Y., Yue, L., & Eluru, N. (2021). Developing safety performance functions for freeways at different aggregation levels using multi-state microscopic traffic detector data. Accident Analysis & Prevention, 151, 105984.
-Zegeer, C., Stewart, R., Reinfurt, D., Neuman, T., Hamilton, E., Miller, T., & Hunter, W. (1991). Cost-effective geometric improvements for safety upgrading of horizontal curves.
TRID - TRB, 237.