To examine the difference in contributing factors of rear-end crashes of different injury severity involving different types of vehicles, this paper proposed random-parameters multinomial logit models with heterogeneity in means and variances. A three-year (2017–2019) rear-end crash data collected from Beijing-Shanghai Highways in China was used to calibrate the models. The rear-end crashes were classified as five types (Car-Car, Car-Truck, Truck-Truck, Truck-Car, Others). With two possible injury severity outcomes of medium/severe injury and light injury, a wide range of possible variables including crash, traffic, speed, geometric, and sight characteristics were considered in this study. Likelihood ratio tests revealed the rationality of adopting merged models using the data across three-year periods. Remarkably significant differences were shown in crashes involving different types of vehicles. The results accounting for the possible heterogeneity could be of value to roadway designers and traffic management departments seeking to promote highway safety and raise accurate safety countermeasures.

Single and multi-vehicle (SMV) crashes remain a significant issue, causing serious safety and economic concerns, and therefore deserve more attention. Using crash data in the Beijing-Shanghai and Changchun-Shenzhen freeways over the five years (2015–2019), this paper explored the transferability and heterogeneity for crash type (single-vehicle versus multi-vehicle crashes) and spatiotemporal stability of determinants affecting the injury severity. The random parameters logit approach with heterogeneity in means and variances was used to model three possible crash injury severity outcomes (measured by the most severely injured individual in the crash) of no injury, minor injury, and severe injury and identify the determinants in terms of driver, vehicle, roadway, environment, temporal, spatial, traffic, and crash characteristics. Remarkable differences were observed in the SMV crashes, and the contributing factors also reported considerable temporal and (or) spatial instabilities. The insights of this study should be valuable to help freeway designers and decision-makers understand the contributing mechanism of the factors and develop the proper management strategies and enforcement countermeasures.

Highways provide the basis for safe and efficient driving. Road geometry plays a critical role in dynamic driving systems. Contributing factors such as plane, longitudinal alignment, and traffic volume, as well as drivers sight characteristics, determine the safe operating speed of cars and trucks. In turn, the operating speed influences the frequency and type of crashes on the highways. Methods. Independent negative binomial and Poisson models are considered as the base approaches to modeling in this study. However, random-parameter models reduce unobserved heterogeneity and obtain higher dimensions. Therefore, we propose the random-parameter multivariate negative binomial (RPMNB) model to analyze the influence of the traffic, speed, road geometry, and sight characteristics on the rear-end, bumping-guardrail, other, noncasualty, and casualty crashes. Subsequently, we compute the goodness-of-fit and predictive measures to confirm the superiority of the proposed model. Finally, we also calculate the elasticity effects to augment the comparison. Results. Among the significant variables, black spots, average annual daily traffic volume (AADT), operating speed of cars, speed difference of cars, and length of the present plane curve positively influence the crash risk, whereas the speed difference of trucks, length of the longitudinal slope corresponding to the minimum grade, and stopping sight distance negatively influence the crash risk. Based on the results, several practical and efficient measures can be taken to promote safety during the road design and operating processes. Moreover, the goodness-of-fit and predictive measures clearly highlight the greater performance of the RPMNB model compared to standard models. The elasticity effects across all the models show comparable performance with the RPMNB model. Thus, the RPMNB model reduces the unobserved heterogeneity and yields better performance in terms of precision, with more consistent explanatory power compared to the traditional models.

Distraction and overspeed behaviors are acknowledged as two significant contributors to single-vehicle motorcycle crashes, injuries and fatalities resulting from which are severe and critical issues in Pakistan. To explore the temporal instability and differences in the factors determining the injury severities between single-vehicle motorcycle crashes caused by distraction and overspeed behaviors, this study estimated two groups of random parameter logit models with heterogeneity in means and variances. Single-vehicle motorcycle crash data in Rawalpindi city between 2017 and 2019 was used for model estimation, and a wide variety of explanatory variables relating to the rider, roadways, environments, and temporal attributes was simulated in the models. The current study considered three possible crash injury severity outcomes: minor injury, severe injury and fatal injury. Likelihood ratio tests were conducted to explore the temporal instability and non-transferability. Marginal effects were also calculated to further reveal temporal instability of the variables. Except for several variables, the most significant factors reported temporal instability and non-transferability, manifested as the effects varied from year to year and across different crashes. Moreover, out-of-sample prediction was also implemented to capture temporal instability and non-transferability between distraction and overspeed crash observations. The non-transferability between motorcycle crashes caused by distraction and overspeed behaviors provides insights into developing differentiated countermeasures and policies targeted at preventing and mitigating single-vehicle motorcycle crashes caused by the two risk-taking behaviors.

This study explored the effects of contributing factors on crash frequency, by injury severity of all, daytime, and nighttime crashes that occurred on freeways. With three injury severity outcomes classified as light injury, minor injury, and severe injury, the effects of the explanatory variables affecting the crash frequency were examined in terms of the crash, traffic, speed, geometric, and sight characteristics. Regarding the model estimations, the lowest AIC and BIC values (2263.87 and 2379.22, respectively) showed the superiority of the random-parameter multivariate negative binomial (RPMNB) model in terms of the goodness-of-fit measure. Additionally, the RPMNB model indicated the highest R² (0.25) and predictive accuracy, along with a significantly positive $\alpha$ parameter. Moreover, transferability tests were conducted to confirm the rationality of separating the daytime and nighttime crashes. Based on the RPMNB models, several explanatory variables were observed to exhibit relatively stable effects whereas other variables presented obvious variations. This study can be of certain value in guiding highway design and policies and developing effective safety countermeasures.