فادي عبد الله موسى الحميدات

Rapid urbanization and increasing traffic density have increased the crash risk of vulnerable road users (VRUs), particularly pedestrians and cyclists. Meanwhile, the conventional statistical models have difficulties in capturing the nonlinear and complex nature of crash data, limiting their safety analysis effectiveness. This study utilized advanced Gradient Boosting machine learning and integrated it with SHapley Additive exPlanations (SHAP) using five years of crash data from Michigan, USA, employing a two-tiered modeling design consisting of a 4-class joint structure and binary subset frameworks. Rigorously evaluated using 10-fold stratified cross-validation to predict crash severity for VRUs, the CatBoost model had better predictive performance (AUC = 0.917) than LightGBM, Random Forest and the traditional Logistic Regression models. The analysis further indicated that prior crash actions, particularly risky crossing behaviors, are the most significant determinants of injury severity for both user groups. However, the pedestrian crash severity is strongly associated with lighting conditions and speed limits, while cyclist crash severity is more heavily influenced by intersection involvement and roadway geometry. Moreover, SHAP interaction analysis showed that the speed effect on severity significantly increases when it interacts with hazardous actions or poor visibility. The findings provide a critical insight into the implementation of effective measures and infrastructure improvements to increase the safety of VRUs.

Dynamic Message Signs (DMSs) play a critical role in conveying real-time traffic information to drivers; however, their effectiveness heavily relies on how messages are structured and displayed, particularly regarding phasing duration and content length. This study examines the influence of these two factors on driver readability, comprehension, and gaze behavior using an advanced virtual reality (VR) driving simulator. Controlled experiments simulated four DMS scenarios, combining two phasing intervals (2.5 and 4 s) with short and long message formats, adhering to Michigan Department of Transportation (MDOT) guidelines. The experiment integrated eye-tracking technology to measure fixation duration and frequency, while statistical methods, including survival analysis and LASSO regression, were employed to identify significant predictors of message readability. Results revealed that shorter messages with shorter phasing intervals led to the highest comprehension rates and reduced cognitive strain. Furthermore, individual characteristics such as gender, driving speed, and highway driving experience significantly affected how drivers engaged with DMS messages. These findings contribute to the development of more effective DMS deployment strategies and provide practical design recommendations to enhance traffic safety and information delivery on high-speed roadways.

This paper presents a descriptive analysis of travel behavior over 10 years using household survey data collected by the Southeast Michigan Council of Governments (SEMCOG) in 2005 and 2015, respectively. The data used in this work were 12,000 and 6,500 sample sizes for 2005 and 2015, respectively. Generally, results indicated that a 12% reduction in all trip rates occurred during the study period. On the other hand, trip rates for multiple age ranges, including the elderly, increased from 2005 to 2015. Also, the average travel distance for all modes increased during the study period, and transit was mainly used for long travel distances. Also, several spatiotemporal changes in human travel behavior were analyzed using different travel indicators, namely, travel time, trip purpose, travel mode, and travel distance. The analysis results show the change in travel behavior across different counties in the SEMCOG area during the study period. The study indicates that the travel time changes across different travel modes, as well as trip purposes, were influenced by the economic impact changes during the study period. It was found that travel time distributions for most purposes are concentrated on trips of travel time shorter than 40 min.

Abstract Jordan Vision prioritizes the utilization of domestic resources, particularly renewable energy. The transportation sector, responsible for 49% of national energy consumption, remains central to this transition and accounts for around 28% of total greenhouse gas emissions. Electric vehicles (EVs) offer a promising solution to reduce waste and pollution, but they also pose challenges for grid stability and charging infrastructure development. This study addresses a critical gap in the planning of renewable-powered EV charging stations along Jordanian highways, where EV infrastructure is still limited and underdeveloped, by optimizing the design of a hybrid energy charging station using HOMER Grid (v1.9.2) Software. Region-specific constraints and multiple operational scenarios, including rooftop PV integration, are assessed to balance cost, performance, and reliability. This study also investigates suitable locations for charging stations along the Sahrawi Highway in Jordan. The proposed station, powered by a hybrid system of 53% wind and 29% solar energy, is projected to generate 1.466 million kWh annually at USD 0.0375/kWh, reducing CO2 emissions by approximately 446 tonnes annually. The findings highlight the potential of hybrid systems to increase renewable energy penetration, support national sustainability targets, and offer viable investment opportunities for policymakers and the private sector in Jordan.

Cycling safety at intersections is a growing concern as both cycling activity and motor vehicle traffic continue to rise. Intersections pose heightened risks for cyclists due to complex traffic patterns, ambiguous right-of-way rules, and insufficient signaling, often leading to collisions. This study investigates how intersection control types and operational characteristics influence driver failure-to-yield behavior toward cyclists. Using ten years of Michigan crash data involving single motor vehicle–cyclist collisions, we apply a Mixed Logit Model to account for unobserved heterogeneity in driver behavior. The analysis focuses on three types of intersection control: traffic signals, stop/yield signs, and uncontrolled intersections, examining their impact on various driver-cyclist interaction scenarios. Key findings indicate that driver age, day of the week, vehicle type, and speed limit consistently affect yielding behavior across all control types. Impairment due to alcohol or drugs significantly increases the likelihood of hazardous driver actions. Drivers are more prone to fail to yield in straight-ahead scenarios, though they are less likely to be deemed at fault in non-yield crashes. Intersection control effectiveness also varies by maneuver type; signalized intersections reduce failure rates in straight-travel scenarios, while stop/yield signs are more effective during left turns. This research addresses a critical gap by linking infrastructure features with driver yielding performance, offering evidence-based insights for improving intersection safety. The findings support targeted interventions in roadway design, driver education, and the integration of advanced technologies such as cyclist detection systems and vehicle-to-vehicle communication to enhance cyclist protection

Dynamic Message Signs (DMSs) are an integral feature of any Intelligent Transportation System (ITS), providing drivers with real-time information such as travel time, incidents, routes, and weather conditions. This study aims to estimate the economic impacts associated with DMS use for route choice, weather advisories, and work zone management on freeways. Several freeway locations with varying levels of traffic congestion were selected to ensure a comprehensive evaluation under diverse conditions. Travel time savings and speed reductions were used as performance metrics to assess Benefit-Cost Ratios (BCRs) for each application. The findings show that DMSs yield substantial economic advantages across all use cases. For route guidance messages with a 35% diversion rate, the BCR was 1.032, indicating a cost-effective investment. Weather advisory messages recommending speed reduction achieved a notably higher BCR of 6.0, reflecting strong safety and financial benefits. Work zone applications using Portable Changeable Message Signs (PCMS) projected a BCR of 1.22. This study offers a data-driven justification for DMS deployment and contributes to the literature by focusing on financial performance, supporting strategic investment decisions beyond qualitative or operational assessments.

Travel time is one of the most important pieces of information that the Dynamic Message Sign (DMS) provides to drivers. However, several variables, including traffic-related factors and DMS message characteristics, might impact the accuracy of the travel time when the DMS is used to display the travel time for alternate routes. Therefore, this study aims to look at the variables that affect the route choice’s displayed travel time accuracy as it relates to the individual driver. The accuracy of the travel time displayed on a DMS on I-75 in Saginaw, Michigan, was examined using logistic regression analysis. The results suggest that for effective traffic management for high traffic demand times (peak hour and day of the week), avoiding using travel time information displayed with other types of messages at the same time (phasing) and adapting a message update time between 2 to 3 min can improve the DMS travel time information accuracy. Practitioners and planners can use the findings to improve driver compliance with the DMS message that is being displayed.