Bus–pedestrian crashes typically result in more severe injuries and deaths than any other type of bus crash. Thus, it is important to screen and improve the risk factors that affect bus–pedestrian crashes. However, bus–pedestrian crashes that are affected by a company’s and regional characteristics have a cross-classified hierarchical structure, which is difficult to address properly using a single-level model or even a two-level multi-level model. In this study, we used a cross-classified, multi-level model to consider simultaneously the unobserved heterogeneities at these two distinct levels. Using bus–pedestrian crash data in South Korea from 2011 through to 2015, in this study, we investigated the factors related to the injury severity of the crashes, including crash level, regional and company level factors. The results indicate that the company and regional effects are 16.8% and 5.1%, respectively, which justified the use of a multi-level model. We confirm that type I errors may arise when the effects of upper-level groups are ignored. We also identified the factors that are statistically significant, including three regional-level factors, i.e., the elderly ratio, the ratio of the transportation infrastructure budget, and the number of doctors, and 13 crash-level factors. This study provides useful insights concerning bus–pedestrian crashes, and a safety policy is suggested to enhance bus–pedestrian safety.

Ride-pooling has significantly enhanced the system efficiency of current on-demand ride-sharing services. However, as the numbers of on-board passengers increase, more detours inevitably occur since ride-pooling provides door-to-door service for everyone. To solve this problem, we focus on rider-participating dispatch by searching walking points, equivalent to alternative pick-up points from origins and alternative drop-off points from destinations. Based on the existing framework for large-scale ride-pooling, we develop our walking point search algorithm, which finds cost-minimizing alternatives. In addition, our approach enables the model to reflect the sensitivity of riders to given walking points by introducing the probability of riders’ acceptance. We conduct a simulation with the Yellow Cap Taxi data set in New York City to validate and compare with the base model, which does not include walking. The results show an increase from 69.56% to 77.84% in the service rate, an improvement of 18.2% in delay time, and 8.6% in in-vehicle time. With the increased service rate, the average travel times of vehicles are reduced by 1.5%, allowing drivers to spend more time rebalancing. Furthermore, we show that the effect of walking is maximized in high-demand areas during peak hours. This study demonstrates that walking can substantially enhance operational efficiency, mitigating the supply-demand imbalance with limited fleets. The proposed model can also be used in optimizing the meeting points for various high-capacity vehicles, such as on-demand shuttles.

Existing intersection crash analysis studies primarily consider macro infrastructure and traffic conditions. However, drivers’ micro-level visual perception of the surrounding environment also affects their driving behaviors and, thus, safety, and this has not been fully investigated yet for intersection crash modeling. Leveraging recent image semantic segmentation techniques, this study extracts six types of visual object from Google Street View (GSV) images: sky, road, buildings, vegetation, vehicles, and walk area. Their pixel proportions were then aggregated as the drivers’ visual environment features. These features, along with geometric design, traffic, and socioeconomic features, were combined into a random parameter negative binomial-Lindley (RPNB-L) model to analyze their impact on intersection crashes. Data from 501 Florida, U.S., signalized intersections were used for the empirical study. Results show that: 1) Compared with conventional models, the RPNB-L model achieves a superior fit by employing a mixed distribution to capture the heterogeneous effects of features; 2) Incorporating drivers’ visual environment features enhances model fit as evidenced by lower deviance information criterion, mean absolute error, mean squared error, and a higher McFadden’s pseudo R ² ; 3) For total crashes, intersections near more underserved communities suffer more crashes. Two visual environment features (i.e., buildings and vegetation) are significantly negatively correlated with crash frequency; and 4) For different crash types, the contributing factors differ. A high proportion of buildings and vegetation at intersections are correlated with a reduction in rear-end, sideswipe, and severe crashes. However, the proportions of road and vehicle increase visual complexity, thus being significantly positively correlated with the frequency of rear-end crashes and crashes involving vulnerable road users.

Signalized intersections are a critical component of road safety, yet rear-end crashes due to the yellow and red clearance phases remain a persistent problem. Traditional methods for setting these phases rely on kinematic models and video-based trajectory analysis, which suffer from high costs, weather/lighting limitations, and an inability to isolate signal timing impacts from confounding factors. This study introduces a novel framework for optimizing yellow and red clearance durations using Automated Traffic Signal Performance Measures (ATSPM). Analyzing over 446,000 traffic signal cycles across multiple intersections, the study examines rear-end conflicts during these phases and estimates the impact of signal timing adjustments using a causal forest. The results show that even small changes in yellow or red clearance durations can significantly affect rear-end conflict rates, with variations depending on intersection characteristics, traffic direction, and time of day. Unlike current standards, the proposed framework identifies context-specific optima, demonstrating how adaptive signal timing strategies can enhance safety without compromising traffic flow. This research expands the application of ATSPM beyond mobility optimization, offering a scalable and data-driven framework to improve intersection safety.