An empirical analysis of factors contributing to roadway infrastructure damage from expressway accidents: A Bayesian random parameters Tobit approach

Modeling temporal correlation and heterogeneity in real-time conflict rates using Bayesian Tobit models for signalized intersections

The use of real-time traffic conflicts for safety studies provide more insight into how important dynamic signal cycle-related characteristics can affect intersection safety. However, such short-time window for data collection raises a critical issue that the observed conflicts are temporally correlated. As well, there is likely unobserved heterogeneity across different sites that exist in conflict data. The objective of this study is to develop real-time traffic conflict rates models simultaneously accommodating temporal correlation and unobserved heterogeneity across observations. Signal cycle level traffic data, including traffic conflicts, traffic and shock wave characteristics, collected from six signalized intersections were used. Three types of Tobit models: conventional Tobit model, temporal Tobit (T-Tobit) model, and temporal grouped random parameters (TGRP-Tobit) model were developed under full Bayesian framework. The results show that significant temporal correlations are found in T-Tobit models and TGRP-Tobit models, and the inclusion of temporal correlation considerably improves the goodness-of-fit of these Tobit models. The TGRP-Tobit models perform best with the lowest Deviance Information Criteria (DIC), indicating that accounting for the unobserved heterogeneity can further improve the model fit. The parameter estimates show that real-time traffic conflict rates are significantly associated with traffic volume, shock wave area, shock wave speed, queue length, and platoon ratio.

Research on traffic accident prediction of expressway tunnel based on B-NB model

OBJECTIVE

The study investigates the relationship between traffic accidents in expressway tunnels and their influencing factors, with the aim of predicting traffic accidents within tunnels and presenting reasonable recommendations to improve tunnel safety.

METHODS

The study utilizes a dataset of 586 traffic accidents occurring exclusively within 8 tunnels along a Guangdong Province expressway from 2017 to 2021. It applies the geometric alignment consistency principle to segment road sections, defines tunnel boundaries based on driving behavior, and employs a Bayesian-modified negative binomial regression model (B-NB model) to identify 6 significant variables from a pool of 17 factors.

RESULTS

The predictive performance of the B-NB model demonstrated similarities to that of the fixed parametric model. This outcome might be attributed to the chosen prior distribution settings and the limited amount of data. Nonetheless, the model effectively captures relationships among variables, leading to improved accuracy in accident prediction and the predictive model achieves a 76.1% accuracy rate.

CONCLUSIONS

Drawing from the estimation results, practical measures are suggested across three dimensions: road geometric alignment design, tunnel traffic safety facilities, and traffic emergency management. These proposals aim to ameliorate the severe consequences of tunnel accidents. Future research will explore an in-depth comparison of estimation results, considering the impact of time and variable correlation on the prediction model by expanding the existing data. This will guide the direction of subsequent research endeavors.

Analysis of the injury severity of motor vehicle-pedestrian crashes at urban intersections using spatiotemporal logistic regression models

This paper conducted a comprehensive study on the injury severity of motor vehicle-pedestrian crashes at 489 urban intersections across a dense road network based on high-resolution accident data recorded by the police from 2010 to 2019 in Hong Kong. Given that accounting for the spatial and temporal correlations simultaneously among crash data can contribute to unbiased parameter estimations for exogenous variables and improved model performance, we developed spatiotemporal logistic regression models with various spatial formulations and temporal configurations. The results indicated that the model with the Leroux conditional autoregressive prior and random walk structure outperformed other alternatives in terms of goodness-of-fit and classification accuracy. According to the parameter estimates, pedestrian age, head injury, pedestrian location, pedestrian actions, driver maneuvers, vehicle type, first point of collision, and traffic congestion status significantly affected the severity of pedestrian injuries. On the basis of our analysis, a range of targeted countermeasures integrating safety education, traffic enforcement, road design, and intelligent traffic technologies were proposed to improve the safe mobility of pedestrians at urban intersections. The present study provides a rich and sound toolkit for safety analysts to deal with spatiotemporal correlations when modeling crashes aggregated at contiguous spatial units within multiple years.