Article
Transportation
Shanshan Sun, Yiik Diew Wong, Xueqin Wang, Andreas Rau
Summary: This study examines the causality of travel-based multitasking behavior using three theoretical frameworks. The results show that habit has the strongest impact on multitasking behavior, and norm significantly affects habit formation and smart device addiction. Policy-makers should consider the differences among intention, habit, and addiction in designing interventions.
TRAVEL BEHAVIOUR AND SOCIETY
(2024)
Article
Transportation
Munavar Fairooz Cheranchery
Summary: This study proposes a behavioral-based methodology to evaluate the viability of Inland Waterway Transportation (IWT) as a sustainable solution to passenger transportation challenges in emerging countries. The study identifies nine key intervention areas and demonstrates the potential for enhancing the service quality of IWT. It also provides both short-term and long-term strategies for improvement.
CASE STUDIES ON TRANSPORT POLICY
(2024)
Article
Transportation
Evangelia Pantelaki, Anna Claudia Caspani, Elena Maggi
Summary: Car use for commuting to school contributes to a significant amount of CO2 emissions, negatively impacting climate change. The study finds that high school students have lower emissions compared to primary and middle school teachers/students due to their higher use of public transport. Shifting from car use to walking and public transport can greatly reduce CO2 emissions.
CASE STUDIES ON TRANSPORT POLICY
(2024)
Article
Transportation
Tyler B. Spence, Steven M. Leib
Summary: Operation of the civilian global aviation industry is a complex process that relies on intergovernmental collaboration while balancing issues of sovereignty. Factors driving the development of international air operations involve economics, diplomatic relationships, democracy strength, and international organization participation.
JOURNAL OF AIR TRANSPORT MANAGEMENT
(2024)
Article
Transportation
Asa Thomas, Rachel Aldred
Summary: This study conducts a meta-analysis of traffic data from 46 Low Traffic Neighbourhood schemes in 11 London boroughs. The results show a significant decline in motor traffic on internal roads, while the changes in traffic volume on boundary roads are marginal. This research supports the position that Low Traffic Neighbourhoods can be an effective part of wider strategies to reduce motor traffic and its associated disbenefits.
CASE STUDIES ON TRANSPORT POLICY
(2024)
Article
Public, Environmental & Occupational Health
Tianzheng Wei, Tong Zhu, Miao Lin, Haoxue Liu
Summary: This study utilizes machine learning methods to model and analyze the severity of accident injuries in two-wheeled motorcyclists. The results show that the LightGBM algorithm has good prediction performance. The driver's annual kilometers traveled, the throwing distance of the motorcyclist, and the road speed limit are the three most important factors influencing the severity of accident injuries.
TRAFFIC INJURY PREVENTION
(2024)
Article
Public, Environmental & Occupational Health
John Mccombs, Haitham Al-Deek, Adrian Sandt
Summary: This study developed corridor-level network screening models to reduce fatal and injury crashes by identifying high-risk corridors for safety improvements. The corridor-level models were more accurate and statistically reliable than similar HSM models while requiring less data. Agencies can easily replicate the methods using readily available data to identify corridors in need of safety improvements.
TRAFFIC INJURY PREVENTION
(2024)
Article
Public, Environmental & Occupational Health
N. Mohamed Hasain, Mokaddes Ali Ahmed
Summary: The study aimed to evaluate the safety of heterogeneous traffic by identifying critical conflicts based on the speeds of the involved vehicles. The proposed Critical Following Speed method was validated using accident data and showed a correlation between critical conflicts and road accidents. The study highlighted the importance of considering vehicle speed in assessing traffic safety in mixed traffic conditions.
TRAFFIC INJURY PREVENTION
(2024)
Article
Public, Environmental & Occupational Health
Abdallah Kinero, Kabhabhela Bukuru, Enock E. Mwambeleko, Thobias Sando, Priyanka Alluri
Summary: This study examines the injury severity of golf cart (GC) crashes in a retirement community in Florida. The findings highlight the factors that influence GC crash severity and provide recommendations for improving GC safety.
TRAFFIC INJURY PREVENTION
(2024)
Article
Ergonomics
Min Deng, Aaron Gluck, Yijin Zhao, Da Li, Carol C. Menassa, Vineet R. Kamat, Julian Brinkley
Summary: This paper analyzes the effects of takeover behaviors on common physiological indicators of drivers, including brain signals, skin conductance level, and heart rate. The results show that performing secondary tasks prior to takeover activities can decrease drivers' engagement, while higher task difficulty and traffic density can increase drivers' mental workload and heart rate. Moreover, a fake takeover alert can also affect drivers' physiological indicators. The paper also discusses the correlation between physiological data, takeover scenarios, and vehicle data, emphasizing the importance of data standardization or normalization for estimating takeover readiness.
ACCIDENT ANALYSIS AND PREVENTION
(2024)
Article
Ergonomics
Song Wang, Zhixia Li, Yi Wang, Wenjing Zhao, Tangzhi Liu
Summary: This study quantitatively reveals the reasons behind changes in AV acceptance after experiencing automated driving and objectively validates that safety is the primary factor influencing AV acceptance.
ACCIDENT ANALYSIS AND PREVENTION
(2024)
Article
Ergonomics
Ziluo Xiong, Suren Chen
Summary: Road vehicles are prone to single-vehicle crashes (SVCs) under complex road geometry and bad weather conditions, posing a significant threat to traffic safety and mobility. Researchers have developed a novel multi-fidelity approach that balances simulation accuracy and efficiency for reliable risk assessment of SVCs. By using a high-fidelity transient dynamic vehicle model and a low-fidelity simplified physics-based vehicle model, the proposed approach provides accurate and efficient reliability evaluation of SVCs.
ACCIDENT ANALYSIS AND PREVENTION
(2024)
Article
Ergonomics
Tao Wang, Ying-En Ge, Yongjie Wang, Wenqiang Chen
Summary: This paper introduces a method to simulate the propagation patterns of conflict risk on freeways, which can help prevent traffic accidents and improve the deployment of advanced vehicle technologies. By introducing a conflict risk index and a spatio-temporal transformer network, it is possible to effectively simulate the propagation patterns of conflict risk. Experimental results show that the model based on proportion of stopping distance exhibits robust performance, while the model based on deceleration rate more distinctly delineates spatio-temporal conflict risk heterogeneity.
ACCIDENT ANALYSIS AND PREVENTION
(2024)
Article
Economics
Xiaolei Wang, Fangfang Yuan
Summary: The study models the mode choices of travelers in a simple urban transportation scenario with and without ride-hailing services, and examines the potential of ride-hailing in mitigating congestion. It also investigates the impacts of three traffic regulation policies and their ability to achieve system optimum in the presence of ride-hailing.
Article
Ergonomics
Ziqian Zhang, Haojie Li, Gang Ren
Summary: This study introduces a novel encoder-decoder framework that utilizes multi-source data to predict the severity of jaywalking violations. The experimental results show that the proposed model outperforms classical models and the incorporation of background information significantly enhances the model's performance.
ACCIDENT ANALYSIS AND PREVENTION
(2024)
Article
Ergonomics
Zihang Wei, Subasish Das, Yue Wu, Zihao Li, Yunlong Zhang
Summary: In traditional roadway crash studies, cross-sectional modeling methods have limitations when dealing with highly time-varying variables related to weather conditions and speed variation. This study employs the distributed lag model (DLM) and the distributed lag nonlinear model (DLNM) to investigate the lagged impacts of weather and speed variation factors on segment crash risk. The results demonstrate coherent and interpretable lagged impact patterns, emphasizing the need for considering time-series effects in future crash modeling research.
ACCIDENT ANALYSIS AND PREVENTION
(2024)
Article
Ergonomics
Narelle Haworth
Summary: Close passes by motor vehicles pose threats to the safety and comfort of bicycle riders. Governments in many countries have implemented laws to ensure a minimum distance between vehicles and cyclists during overtaking. This paper discusses the evaluation of a two-year trial in Queensland, Australia, which aimed to understand the circumstances and reasons behind close passes. The study used video observations and experimental studies to gather data and analyze crash causation.
ACCIDENT ANALYSIS AND PREVENTION
(2024)
Article
Ergonomics
Petya Ventsislavova, Thom Baguley, Josceline Antonio, Daniel Byrne
Summary: The use of e-scooters is increasing rapidly, but it comes with potential dangers such as collisions and illegal riding behavior. Research shows that e-scooter riders tend to be younger and more prone to engage in illegal riding behavior compared to non-users. Knowledge of current regulations related to e-scooters is limited, especially in areas like parking, speeding, and designated infrastructure. Targeted interventions and educational campaigns are necessary to improve riders' understanding of regulations and promote safer riding practices.
ACCIDENT ANALYSIS AND PREVENTION
(2024)
Article
Ergonomics
Ryan Miller, Timothy Brown, Rose Schmitt, Gary Gaffney, Gary Milavetz
Summary: This study investigated the changes in driving performance following cannabis use, and found that self-reported readiness to drive and previous cannabis use experience can predict some of these changes. However, readiness to drive does not fully explain the observed degradation in performance.
ACCIDENT ANALYSIS AND PREVENTION
(2024)
Article
Ergonomics
Qian Liu, Xuesong Wang, Shikun Liu, Chunjun Yu, Yi Glaser
Summary: Intersections are high-risk locations for autonomous vehicles (AVs). Analyzing the pre-crash scenarios and contributing factors of AV crashes at intersections using the association rule method revealed that rear-end and lane change crashes were the most frequently occurring scenarios for AVs. The main contributing factors of these scenarios were identified, such as the location outside the intersection, traffic signal control, autonomous engaged mode, mixed-use or public land, and weekdays. Inadequate stop and deceleration decisions by the AV's automated driving system (ADS) and insufficient collision avoidance decisions in lane change crashes were important causes of these AV crashes.
ACCIDENT ANALYSIS AND PREVENTION
(2024)
