IATSS Research最新文献

Lane markings are considered an essential component of a road system. As highlighted in previous studies, they are directly linked to efficiency and safety. The rearrangement or reallocation of lane markings can be an economical way to improve efficiency. However, such changes could influence driver behavior. Thus, there is a tradeoff between efficiency and safety. Through a case study in Kochi City, Japan, this study evaluated the change in crash risk caused by a lane marking reallocation. Video data were collected before and after the implementation of a new road layout (achieved by reallocating lane markings) that was intended to mitigate traffic congestion at a signalized intersection. Based on the video data, PICUD (Possibility Index for Collision with Urgent Deceleration), a surrogate safety index used to estimate collision risk, was estimated for lane changes and conflicts between leading and following vehicles in the through lane. In particular, it was confirmed that the collision risk between a lane-changing vehicle and a leading vehicle in the through lane was reduced due to the reduction in traffic density caused by the new road layout. In addition, the results indicate that the PICUD value tends to decrease (i.e., the crash risk tends to increase) with increasing speed of the following vehicle relative to the leading vehicle. Overall, the improvement in safety after the implementation of the new road layout was marginal and statistically insignificant. Therefore, this study highlights the necessity of incorporating speed control measures, such as speed limits, along with congestion alleviation measures in order to enhance safety.

The paradigm shift in mobility and travel behaviour caused by the successive waves of the COVID-19 pandemic has been unreal. The long-term effects of the pandemic resulting from the fear of the spread of the virus against the belief in the remedial measures are to be understood from a behavioural perspective to strengthen the current transportation system against such impediments. The current study adds to the literature on COVID-19 pandemic by unravelling the long-term impacts of the pandemic on the trip frequency of commute trips. A dataset of 467 individuals from all over India is analysed to understand the factors impacting the willingness of the respondents to reduce their commute trips in a post-vaccinated scenario. An integrated choice and latent variable structure, with a generalized ordered logit kernel, was considered to incorporate the influence of psycho-attitudinal variables and socio-demographics on the willingness to reduce trip frequency among individuals. The results indicate a significant impact for variables including fear of the virus spread, age of individuals, and job satisfaction of working from home on the stated willingness towards trip reduction. The study concludes by presenting policy measures that target to overcome the effects of the pandemic and restore normalcy.

In India, the crossing conflicts between right-turn vehicles of minor road traffic and conflicting vehicles on major roads have become more severe at unsignalized T-intersections in recent times. Due to the rapid growth of vehicular traffic, including motorized two-wheelers, auto-rickshaws, etc., results in an increase in right-turn crossing conflicts (RTCC) at unsignalized T-intersections. The severity of right-turn vehicles is related to the characteristics of both right-turning and conflicting through vehicles. Therefore, this study examines the effect of vehicle types on RTCC. The RTCC are observed using time-to-collision (TTC), at five unsignalized T-intersections in three different cities in India. The RTCC are categorized into critical RTCC and non-critical RTCC based on TTC threshold values obtained by the k-means clustering algorithm. A Generalized Poisson Regression model was developed using Python software. The study results revealed that the presence of higher composition of two-wheelers, auto-rickshaws, and cars in right-turn and conflicting through vehicles significantly influences the severity of RTCC. Also, the model results concluded that the speeds of right-turn and conflicting through vehicles, conflicting through traffic, right-turn traffic, vehicle gap, waiting time, and abnormal driving paths significantly affect the RTCC at unsignalized T-intersections. The findings of this study help traffic engineers and safety experts identify the critical unsignalized T-intersections using the number of right-turn crossing conflicts.

The present research investigates a range of factors affecting autonomous vehicle (AV) acceptance of Greek citizens through a questionnaire distributed to 563 respondents. Following the extraction of descriptive statistics, self-organizing maps (SOMs) were employed to meaningfully categorize and aggregate questions pertaining to four main pillars of the questionnaire, which are conceptually relevant namely: (i) how several factors affect general car choices of respondents, (ii) what the respondents perceived that AVs would offer, (iii) how much they agreed with stated expected technology and efficiency-oriented AV traits and (iv) how they believe several factors affect driving behavior overall. A Random Forest (RF) algorithm was applied to classify the AV acceptance decisions of a training subset of the respondents, and was subsequently assessed on a test subset. SOM results indicate that participants can be meaningfully separated into two SOM cluster groups for pillars (i), (ii) and (iv), while pillar (iii) yielded separations into three SOM cluster groups. RF feature importance calculation indicated a number of affecting variables; the five most contributing ones are: distance covering capabilities of AVs was a major factor affecting acceptance decisions, followed (by a wide margin) by responder opinions on whether the principles and conscience of drivers can be replaced by an AI navigator without reducing safety levels, while the algorithm itself conducted successful classification to about 80% of test cases. Present results can be used to anticipate AV penetration levels based on sample characteristics and to improve AV traits in cases where higher AV penetration is sought.

Distracted driving is one of the most significant factors leading to fatal car crashes. Using a cell phone while driving is one of the riskiest behaviors while driving and is the cause of death for hundreds of drivers in the United States. Distraction prevention technologies, such as cell phone blocking apps that limit the functioning of cell phones while the car is moving, are one strategy for combating distracted driving. The main goal of this study is to investigate the effect of cell phone blocking apps on driving behaviors and crashes caused by distracted driving using a machine learning algorithm. Some 158 participants were recruited from the state of Maryland to investigate their driving behavior using a state-specific survey. The results of the survey revealed that most people have cell phone blocking apps (62.6%); however, they do not use them on a daily basis (86.7%). A Bayesian network model was then deployed, and the results showed that if all drivers use cell phone blocking apps, crashes occurring due to distraction from cell phone use will decrease by 5 %, and self-reported distraction will decrease by 9 %. The results of this study can be used to detect distracted driving and find the best strategies to overcome this problem. The results also suggest that there should be a greater degree of awareness of distraction prevention technologies and education on the use of these technologies among different groups to reduce the number of fatalities, injuries, and crashes due to distraction.

Automated vehicles (AVs) are not yet widely accepted by the public, and the COVID-19 pandemic has the potential to lead to a shift in attitudes toward travel modes and vehicles because of travel restrictions and the risk of viral transmission. Therefore, it is necessary to understand how AVs are being accepted by the public during the pandemic. This study investigated public acceptance of AVs across two periods of the COVID-19 epidemic prevention and control in China via 19-question online surveys, including the travel modes, AVs acceptance, and sociodemographic. A total of 429 responses were collected. Results showed that the public acceptance of AVs was on the positive side, but was diverse in items: the average extra cost willing to pay for a fully AV was 28,855.88 CNY (4116.39 USD), and 26.8% of respondents were not willing to pay for it. Respondents agreed on the benefits of AVs and are concerned about legal liability for drivers and fuel economy, and had a positive attitude of commercial AVs. Most acceptance items had differences between the pandemic periods, indicating that people were more willing to accept AVs during period with higher risk of infection. However, only the difference in perceived benefits of AV of ensuring social distance was statistically significant. Gender, age, and ownership of vehicle had greater effects on AVs acceptance, while driving ability and driving experience had small effects on it. This survey can provide insights for studies examining the acceptance of AVs across time, and exploring factors influencing AVs acceptance.

This paper introduces a new visual dataset and framework to facilitate computer-vision-based traffic monitoring in high density, mixed and lane-free traffic (TRAMON). While there are advanced deep learning algorithms that can detect and track vehicles from traffic videos, none of the existing systems provides accurate traffic monitoring in mixed traffic. The mixed traffic flows in developing countries often includes the types of vehicles that are not widely known by the existing visual datasets. The computer vision algorithms also face difficulties in detecting and tracking a high density of vehicles that are not following lanes. This paper proposes a large-scale visual dataset of >282,000 labelled images of traffic vehicles, as well as a comprehensive framework and strategy to train common deep-learning-based computer vision algorithms to detect and track vehicles in high density, heterogeneous and lane-free traffic. A systematic evaluation of results shows that TRAMON, the proposed visual dataset and framework, performs well and better than the common visual dataset at all traffic densities.

Studies on the age and generation characteristics of traffic accidents primarily focus on the tendency of deaths and severe injuries, whereas the faults or drivers who caused the accidents are not considered. Using license holders as a parameter for measuring accident risk when evaluating the number of primary-party accidents is challenging because it includes those who possess licenses but do not drive. In previous studies, the age characteristics in traffic accidents were evaluated based on different age groups and generational characteristics. Therefore, a Bayesian age–period–cohort analysis was performed in this study to isolate the effects of age, period, and generation on the number of traffic crashes. This approach can identify the gender/age of the driver, who may be the primary contributor to an accident, as well as the risk of traffic accidents in younger and older drivers. The results show that 1) age imposes a more significant effect than the duration and cohort. In the case of single-vehicle accidents, 2) the effect of age was significantly more prominent for males over 80 years old and females over 70 years old.

The use of two-wheelers (TWs) has gained popularity as an alternative to personal vehicles due to their flexibility, fuel economy, ease of parking, and size, especially in congested cities. However, TWs are considered vulnerable road users due to their higher riding risk compared to other modes. This study proposes a novel framework to extract latent and dependent heterogeneous risk factors that affect the crash severity of TWs. By combining eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP) analysis, this study investigates the factors affecting TW crash severity, providing both local and global interpretability. The XGBoost method is employed to model crash severity, while SHAP analysis facilitates the derivation of explanations from the model, enhancing our understanding of the contributing factors. The French crash dataset for TWs between 2014 and 2017 is utilized for this analysis. The findings highlight that the department of the crash, road category, urbanization level, TW category, and age of the user significantly influence TW crash severity. Furthermore, severe injuries are more likely to occur in TW crashes associated with rural areas, older riders, riders not wearing helmets, run-off-road crashes, and crossing roads. The insights derived from this study can be leveraged to develop targeted interventions that address the identified risk factors and promote the safety of TW riders. By focusing on these key factors, policymakers and stakeholders can implement effective measures to reduce the severity of TW crashes and enhance the overall safety of TW users.