Introduction Red-light running by food delivery riders contributes to crashes at signalized intersections. Although several studies have examined its influencing factors, few have investigated the differential effects at various times of the day. Given that the travel characteristics of riders vary considerably throughout the day, overlooking such variations may lead to biased model estimates and ineffective interventions. Thus, the study aimed to explore the time-of-day variations of factors affecting food delivery riders’ red-light violations. Methods A field survey was carried out at six signalized intersections in Jinhua, China. Four statistical models were employed to identify the factors affecting the red-light running behavior, including the binary logit model, random parameters logit model, random parameters logit model with heterogeneity in means, and random parameters logit model with heterogeneity in means and variances. Meanwhile, likelihood ratio tests were conducted to check the time-varying differences of the factors among midday rush hours, non-peak hours, and evening rush hours. Results 1) The red-light running behavior of food delivery riders is affected by factors such as the behaviors of riding the wrong way and stopping beyond the stop line, pedestrians' red-light running, presence of non-motorized vehicle lanes, long red-light duration, and location near schools or residential areas. 2) The effects of these factors are not consistent across the midday rush, non-peak, and evening rush hours. Conclusions The study confirms the significant time-varying characteristics of factors influencing food delivery riders’ red-light running behavior, which can be valuable in developing differentiated countermeasures to reduce traffic violations.

Pedestrians usually sustain more severe injuries in traffic crashes due to the lack of protection. Although previous studies have analyzed crash-influencing factors, the causal relationships between injury severity and factors remain limited. Thus, the study aims to investigate the causal effects of factors, particularly the hazardous driving behaviors, on pedestrian injury severity. It employs causal machine learning and the Shapley Additive exPlanations to analyze the marginal contribution of the feature variables on crash injury severity. Furthermore, the causal tree model is used to reveal the heterogeneous causal effects. The results indicate that 1) the pedestrian injury severity is influenced by various factors, such as pedestrian age, speed limit, and vehicle type, 2) hazardous driving behaviors can deteriorate pedestrian injury severity, with 'failing to yield right of way' showing the highest mean conditional average treatment effect (CATE) (0.768), followed by 'careless driving' (0.626) and 'violating traffic signs or signals' (0.590), and 3) hazardous driving behaviors, road speed limits, and pedestrian age jointly contribute to the pedestrian injury severity. The study reveals heterogeneity in the causal relationship between hazardous driving behaviors and the injury severity of pedestrian-vehicle crashes, which serves to develop differential intervention strategies to mitigate pedestrian injury.

Signalized intersections are the areas where traffic crashes with severe injuries frequently happen. Although existing studies have explored the factors affecting crash injury severity at signalized intersections, intricate causal relationships between factors often fail to be captured. Thus, usage of Bayesian network reveals factors contributing to injury severity and the causal relationships between them, with the use of crash data extracted from the Crash Report Sampling System in 2021. The K2 algorithm and Expectation-Maximization algorithms are adopted for structure learning and parameter learning in Bayesian networks, respectively. The results indicate that 1) factors such as speeding, drunk driving, and use of airbags can significantly affect the injury severity, 2) causal relationships exist between distraction, running the red signal, collision type, and crash injury severity, and 3) compared to the random parameter logit model and random forest, Bayesian network has better accuracy in predicting the crash injury severity. The findings can serve to propose effective traffic safety intervention measures to reduce the injury severity of crashes at signalized intersections.

OBJECTIVE:Due to inherent vulnerability, motorcyclists sustain more severe injuries in motorcycle-vehicle crashes. Although previous studies have analyzed diverse factors affecting injury severity of crashes involving a motorcyclist, few have been available to uncover how the factors and inherent interactions affect the motorcyclists' injury severities in motorcycle-vehicle crashes comprehensively. Thus, the study aims to investigate determinants of injury severity from multiple dimensions. METHODS:The study utilizes the machine learning models and the Random Parameters Logit model with Heterogeneity in Means (RPLM-HM) to explore the key factors contributing to injury severity sustained by motorcyclists. As an optimal machine learning model, CatBoost is used with the Shapley Additive exPlanations (SHAP) method to reveal the potential interaction between the crucial multidimensional factors. RESULTS:Results indicate that 1) factors such as angle crash, elderly motorcyclists, high-speed limit, and risky behavior, are linked to a higher probability of severe injuries, 2) conversely, some features, including young motorcyclists, elderly drivers, wet road, and morning, are associated with reduced probability of severe injuries, and 3) there are interaction effects between the factors regarding the characteristics of motorcyclists, drivers and accident circumstances. CONCLUSIONS:The findings could help put forward targeted countermeasures to better manage motorcyclists, drivers, and road facilities for improving the traffic safety of motorcyclists.

Distracted driving is a threat to traffic safety that can result in more traffic crashes. Although previous studies have been conducted to explore the relationship between driver distraction and hazardous driving actions, few studies are available to identify the causation between them. Thus, the study intended to evaluate the causal effects of distraction on hazardous driving actions at intersections based on the crash data extracted from the Crash Report Sampling System (2021-2022). The multinomial logit model was employed to reveal the factors contributing to driver distraction. Then, propensity score weighting was adopted to balance the factor distributions between distraction and non-distraction cases to identify the causal effects on hazardous actions. Results indicated that 1) the propensity of distraction is relevant to factors such as the driver's age, gender, vehicle type, speed limit, area, weather, and light condition, 2) driver distraction can significantly increase the probability of risky actions including speeding, running red lights, failing to obey stop signs, failing to yield, following too closely, and 3) the causal effects show great diversity for different distraction types. The findings serve to understand the influence mechanism of distraction on specific crash risks and develop countermeasures to reduce distraction and hazardous driving actions.

ObjectiveHit-and-run behavior is believed to exacerbate the injury severity of traffic crashes due to the delayed emergency response for the victims. However, several previous studies indicated the opposite finding that hit-and-run crashes were associated with less severe injuries. The relevant studies mainly identified the statistical associations between hit-and-run behavior and injury severity without revealing causation between them. To this end, the study aims to explore the reciprocal causation between the two variables.MethodThe two-stage probit model with endogenous regressors is employed to identify the reciprocal causation between hit-and-run behavior and crash injury severity for single- and two-vehicle crashes, respectively, with the use of crash data extracted from the Crash Report Sampling System and Fatality Analysis Reporting System (2016-2019).ResultsThe results indicate that 1) for both single- and two-vehicle crashes, the fleeing behavior can significantly increase the injury severity of the victims in the crashes while the severe injury of the victims has a negative impact on the propensity of such behavior, 2) the propensity of hit-and-run behavior is influenced by various instrumental variables such as driver age, gender, alcohol involvement, weekday, area type, and light condition, and 3) crash injury severity is significantly related to the victim age, gender, and vehicle damage.ConclusionsThere is a reciprocal causation between hit-and-run behavior and injury severity in traffic crashes. The analytical results can provide a reasonable explanation for the counterintuitive finding on hit-an-run crashes and help mitigate the injury severity.

Introduction: Speeding behavior is a major threat to road traffic safety, which can increase crash risks and result in severe injury outcomes. Although several studies have been conducted to analyze speeding crashes and relevant influential factors, the heterogeneity of variables has not been fully explored. Based on the traffic crash data extracted from the Crash Report Sampling System, the study aims to identify the factors that influence speeding driving with the consideration of variable heterogeneity. Method: Quasi-induced exposure technique is adopted to identify the disparities in the propensities of speeding for various driving cohorts. The random parameter logit model with heterogeneity in means is employed to examine the factors impacting speeding behavior. Results: Results indicate that: (a) driving cohorts such as young drivers, male drivers, passenger cars, and pickups appear to have higher propensities of engaging in speeding driving; (b) the propensity of speeding is higher when the driver is drinking, distracted, changing lanes, negotiating a curve, driving in lighted condition, and on curved roads; and (c) the random parameter logit model with heterogeneity in means has better performance as opposed to that without heterogeneity in means. Conclusions: Speeding behavior can be influenced by various factors in terms of driver-vehicle characteristics, physical condition, driving actions, and environmental conditions. Practical Applications: The findings could serve to develop effective countermeasures to reduce speeding behavior and improve traffic safety.

Left-turn waiting area (LWA) is an innovative traffic design that is popularly applied to improve the traffic capacity of signalized intersections in China. The traffic safety impacts of the LWA, however, have not been fully discussed in previous studies. Thus, the study aims to evaluate the safety performance of the LWA by means of the traffic conflict technique. A field investigation was conducted to collect the post-encroachment time (PET) of conflicts and relevant variables at the signalized intersections in Jinhua, China. The Chi-square and two sample t-tests were adopted to examine the difference in conflict distribution between the intersections with and without LWA. The random parameter ordered logit model was employed to identify the factors contributing to the risks of vehicular collisions. Results indicate that (1) intersections with LWA are generally associated with more merging conflicts; (2) there are no significant discrepancies in the PET values between intersections with and without LWA; and (3) factors such as the number of left-turn lanes, number of receiving lanes, conflict type, vehicle type, driving direction, stopping outside LWA and overtaking behavior are identified to significantly impact the traffic conflicts. The findings serve to develop the countermeasures to ensure the safe operation of LWA.

In quasi-induced exposure (QIE) theory, the presence of hazardous driving action is the typical determinant of the driver's responsibility for a crash. However, there is a lack of effort available to analyze the impacts of hazardous actions on the QIE estimate, which may result in estimation bias. Thus, the study aims to explore the difference in QIE to crashes involving various hazardous driving actions. Chi-square test is conducted to examine the consistency of non-responsible party distributions among the crashes involving various hazardous actions. Multinomial logit model and nested logit model are employed to identify the disparities of contributing factors to the actions. Results indicate that: 1) the estimated exposures appear to be inconsistent among the crashes with different hazardous actions, 2) driving cohorts have differential propensities of performing various hazardous actions, and 3) factors such as driver-vehicle characteristics, time, area, and environmental condition significantly affect the occurrence of hazardous actions while the directions and magnitude of the effects show great disparities for various actions. It can be concluded that the QIE estimates are significantly different for crashes involving various hazardous actions, which serves to highlight the importance of clarifying the specific hazardous actions for responsibility assignment in QIE theory.

Quasi-induced exposure theory requires the clear-cut assignment of crash responsibility for individual crash-involved drivers. The assignment method based on the citation by police officers poses a concern that the citation would be issued due to the nonmoving violations rather than the driving actions that directly contribute to the crash. Thus, the objective of the study is to improve the accuracy of citation-based responsibility assignments. Binary logistic regression is employed to identify the factors affecting the citation decision of the police officers. An ensemble machine learning method that combines random forest, neural network, and extreme gradient boosting classifiers is established to allocate the crash responsibility. The findings include that (1) the police citation is closely related to the presence of hazardous driving behavior, but it can also be influenced by several factors such as driver age, drinking status, and the collision impact point of the vehicle; and (2) compared to the conventional models, the ensemble machine learning methods have better performance for crash responsibility assignment in terms of accuracy, Kappa coefficient, and area under the curve. The study serves to provide a reliable crash responsibility assignment approach to improve the accuracy of exposure estimation.

Analyzing speed mean and variance is vital to safety management in urban roadway networks. However, modeling speed mean and variance on structured roads could be influenced by the spatial effects, which are rarely addressed in the existing studies. The inadequacy may lead to biased conclusions when considering vehicle speed as a surrogate safety measure. The current study focuses on developing a Bayesian modeling approach with three types of spatial effects, i.e., spatial correlation, spatial heterogeneity, and spillover effect. To capture the spatial correlation, the study employs the intrinsic conditional autoregressive (ICAR) models, spatial lag models (SLM), and spatial error models (SEM). Spatial heterogeneity and spillover effect are considered by the random parameters approach and spatially lagged covariates (SLCs). Speed data are collected from the float cars running on 134 urban arterials in Chengdu, China. The results indicate that the random parameters ICAR model with SLCs (RPICAR-SLC) outperforms others in terms of goodness-of-fit, accuracy, and efficiency for modeling speed mean, while the random parameters ICAR model (RPICAR) is the best for modeling speed variance. Moreover, RPICAR-SLC and RPICAR models are beneficial to address spatial correlation of residuals, explaining the unobserved influence among the observations, and are less likely to cause biased or overestimated parameters. The study also discusses how traffic conditions, road characteristics, traffic management strategies, and facilities on roadway networks influence speed mean and variance. The findings highlight the importance of multi-type spatial effects on modeling speed mean and variance along the structured roadways.

Distracted driving is a critical factor affecting traffic safety. Although previous studies have been devoted to exploring the effects of driver distraction on crash risks, few of them are available to quantitatively analyze the impacts on crash injury outcomes. Thus, the study aims to examine the effects of distracted driving on injury severities for both at-fault and not-at-fault drivers. Propensity score matching method is adopted to balance the covariates between the data sets of distracting and normal driving, so as to avoid the potential effects caused by confounding factors. Average treatment effect is calculated to quantify the impacts of distraction on specific injury severity levels. The results indicate that 1) drivers are inclined to be distracted in the scenarios of roadways with higher speed limits, non-peak hours, intersection area, sunlight, cloudy weather, young and female drivers, and heavy vehicles; 2) for the at-fault drivers, the distraction appears to increase the occurrence probabilities of both fatal and injury crashes; and 3) as for the not-at-fault drivers, the distraction can lead to the increase of possible injury crashes. The study confirms the significant causal effects of driver distraction on crash injury severities and serves to propose the countermeasures to reduce the distracted driving.

Distracted driving can pose great risks to road traffic safety. Although there is a rich body of literature devoted to identifying the statistical association between distracted driving and crash risks, few are available to examine the causal effect mechanism of distracted driving. Thus, the study attempts to conduct the causal mediation analysis to reveal the impact mechanism of distracted driving on crash injury risks, in which various hazardous driving actions are used as the mediators between driver distraction and crash injuries. Sensitivity analysis is also carried out to validate the underlying assumption of causal mediation analysis. The analytic results indicate that 1) distracted driving can lead to a higher likelihood of hazardous driving actions such as failing to yield, disobeying traffic control devices, driving left of lane center, and failing to stop in assured clear distance, 2) both the driver distraction and hazardous actions are the contributory factors to the severe crash injuries, and 3) distracted driving is identified to have significant mediation effects on crash injury risks. The study confirms the causal mediation effects of distracted driving on crash injury risks, which can serve to propose specific safety countermeasures to mitigate the crash injury risks.

Introduction: Hit-and-run crashes occur when the driver at fault leaves the scene without reporting, which could delay emergency response for the victims who are left. For this reason, it is assumed that hit-and-run crashes lead to more serious injuries; however, the research in this area is limited. The objectives of the study are to examine the differences in hit-and-run and non-hitand-run victim injury severities and to identify the factors that may influence any differences. Methods: Quasi-induced exposure technique, an indirect method, is employed to measure the relative crash exposures between hit-and-run and non-hit-and-run crashes using Michigan twovehicle injury crashes 2012-2014. Random parameter ordered logit model is used to reveal the discrepancy of the factors contributing to victim injury severity. Results: We found that the injuries sustained by the drivers left at the scene (victims) of hit-andrun crashes were generally less severe compared to non-hit-and-run driver victims, which may be attributed to the differential crash factors in terms of driver age and vehicle type. The injuryseverity contributing factors of hit-and-run crashes differed considerably from the non-hit-andrun crashes. Characteristics such as occurring in rural areas, at nighttime, at intersections, crash type, and alcohol involvement significantly increased the injury severities of the driver victims. Conclusion: We inform the hit-and-run literature to suggest a contradiction to the assumption that leaving the crash scene may lead to more serious injuries. This example emphasizes the importance of distinguishing different types of crashes and their contributing factors. We offer an indirect approach that can help to identify underlying factors and reduce bias, which can inform traffic safety methods and serve to propose effective safety countermeasures.

In the corresponding research available, the safety impact remains controversial in implementing signal coordination on arterials, which calls for an in-depth exploration with the appropriate statistical methods. Based on the traffic data from Ann Arbor City (Michigan, USA), the paper proposes a safety evaluation model considering the multiple heterogeneities. In terms of arterials with the coordinated signalization, modeling results imply that (1) the multivariate heterogeneity shows the strongest interaction on crash frequency, followed by the spatiotemporal and structural heterogeneities, and (2) the spatial variation is unrelated to the temporal change among crashes in the denoted traffic analysis zones (TAZs). In an attempt to alleviate the coupled crash risks along the coordinated arterials, the study emphasizes the necessity of dividing the subcontrol traffic areas in real time according to the correlative degree of crash distribution. Meanwhile, the modeling framework with multiple heterogeneities can be applied for the safety analysis of other urban roads.

Signal coordination has been popularly implemented at signalized intersections along the urban arterials. It can generate the green waves for vehicles to travel through the intersections with fewer or no stops to improve the operational efficiency of the arterials. Despite extensive safety studies on the topic of signal coordination, few are available to examine the factors affecting crash injury severity on coordinated arterials. Thus, the article aims to assess the impacts of signal coordination on injury severity and identify the influencing factors by employing the random parameter ordered logit and generalized additive models. In the case study (Ann Arbor, Michigan), results indicate that (1) the signal coordination may decrease the probability of minor injury crashes but increase the frequency of crashes with more severe injuries, (2) many factors (e.g., number of involved vehicles, female drivers, time of day, crash types, hazardous actions, and abnormal driving conditions) are found to contribute to injury severity, and (3) injury severities exhibit more spatial dependence on the coordinated arterials as opposed to those on the noncoordinated ones. The findings will help to develop effective countermeasures to mitigate injury severity and thus improve traffic safety of arterials with signal coordination.

INTRODUCTION:Quasi-induced exposure (QIE) technique has been popularly applied in the field of traffic safety research for decades. One of the basic assumptions of QIE theory is that the not-at-fault driving parties (D2s) involved in the crashes are the random selection of overall driving population at the event of crash occurrence. Very few literatures, however, can be identified to validate the assumption for crashes with specific injury severities that may not be satisfied in reality.METHOD:The study aims to check the validity of the assumption categorized by crash injury severity with the use of Michigan crash data. Latent class analysis is employed to generate several latent classes for the crashes with specific injury outcomes. Chi-square test is adopted to identify the significance of the similarity of D2 distributions among the latent classes.RESULTS:The results indicate that: (a) for fatal crashes the statistical tests do not identify the significant discrepancies for D2 distributions of driver gender, age, and vehicle type between latent classes; (b) for injury crashes, both D2 driver gender and age have the similar distributions between/among various classes, while the D2 vehicle types show the inconsistent distributions; and (c) with respect to property damage only crashes, the distributions of three vehicle-driver characteristics are significantly different among the latent classes. It implies that the underlying assumption may not entirely hold true for all the injury severities and driver-vehicle characteristics. Practical Applications: The findings pinpoint the applicability of the QIE technique under specific scenarios and highlight the importance of validating the underlying assumption of QIE prior to its application.

oordinated signalization has been applied on arterials since 1960s. However, long-term effects of signal coordination on traffic safety have not been fully discussed, especially in terms of the temporal and spatial variations among crashes. With Bayesian Poisson lognormal model, the paper compares three types of spatiotemporal correlations on the coordinated arterials, including the fixed spatial and linear temporal variation, the fixed spatial and linear-quadratic temporal variation, and the time-varying spatial interaction. With 9-year crash data from Michigan, the study demonstrates that (1) different models should be separately applied for the coordinated arterials, e.g. BPLM with the time-varying spatial interaction fits roadway segments, and BPLM with the fixed spatial and linear temporal variation excels at signalized intersections and (2) the temporal and spatial variations are correlated on roadway segments. The findings emphasize the importance of conducting the safety analysis regularly and dynamically monitoring driving behaviors along the roadway segments.