IATSS Research最新文献

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.

This work analyses influence of road, weather and crash-specific factors on crash severity outcomes for low-speed urban midblock sections and intersections, for day and night time, using Backpropagation–Artificial Neural Network (BP–ANN). Five-year crash data (2015–2019) from 82Km urban road network of Patna, India was used for the study. The road factors include pavement width, distress condition, marking; shoulder type, condition; road section type as mid-block, intersection and intersection control. Weather factors include season of crash, fog or rain at crash time. Crash factor include collision partner, type and crash time. The most appropriate BP–ANN model architecture was estimated using Misclassification-Rate. It was observed that midblock segments witness higher severities during daytime, whereas intersections witness higher severities during night. Controlled intersections are safer compared to un-controlled intersections. Pavement distress greatly increase the chance of higher severities. Narrow roads record greater severities during day due to lack of surveillance.

Road traffic crashes are a leading cause of serious injuries and fatalities globally and place unnecessary developmental and economic burdens on low- and middle-income countries (LMIC) as they account for the vast majority of the world's road related deaths. This is typically due to both the increased frequency of dangerous crash types and the increased severity of said crash types. Rollover crashes while quite rare are a particularly dangerous crash type among other various crash types. In the case of Namibia, rollover crashes reportedly accounted for 34% of both road related injuries and fatalities in Namibia for 2020. When compared to high-income countries the issue of rollover crash severity in Namibia and like sub-Saharan African (SSA) countries becomes apparent. Therefore, it crucial to understand the contributing factors and their associated effects on rollover crash severities in these countries. This study aims to investigate and identify the significant factors influencing crash severities and their associated impact magnitudes on single-vehicle rollover crashes in Namibia by adopting a mixed logit with heterogeneity in means and variances approach to account for unobserved heterogeneity in the data. Although it is not without its limitations the dataset used in this study includes single-vehicles rollover crash instances from 2014 to 2016 within Namibia and is able to provide unique details for the crash observations including various driver, environmental, roadway, and vehicle characteristics. Results from this study indicate several factors including weekends, open roadways, and minibuses to be significantly increasing the crash severity of single-vehicle rollover crashes. Additionally, results provide a basis for which researchers and policy makers can understand rollover crashes in Namibia and adopt an appropriate approach to address this issue, such as, Safe Systems. Such an approach would include but not be limited to the implementation of roadside features, educational campaigns, speed enforcement, and vehicle standards policy.

Floods are a significant issue across Southeast Asia, with Thailand, particularly the Nakhon Si Thammarat Municipality, being heavily affected due to its unique conditions such as heavy rainfall, rapid urbanization, and low-lying coastal position. This study utilizes Geographic Information Systems (GIS) to analyze the flood-prone areas of Nakhon Si Thammarat Municipality, assess its road networks, and identify optimal evacuation locations and routes. Various data layers such as slope angle, elevation, distance from roads, rainfall, TWI, NDVI, land use, soil texture, distance from rivers, stream density, and road density are integrated using the frequency ratio method. The findings reveal areas with very high flood susceptibility, which span 198.78 sq. km (56.74%) at the high frequency level, 299.43 sq. km (37.63%) at the low frequency level, and 284.62 sq. km (53.88%) at the moderate frequency level. During flood scenarios, travel times saw an average increase to 21.4 min, which is a fourfold surge compared to regular conditions, highlighting the impact of floods on evacuation strategies. Upon evaluating the road network under flood conditions and applying network analysis techniques, efficient and safe evacuation routes were determined. The results, which underscore a fourfold increase in travel time during flood scenarios, present valuable insights for emergency management authorities across Thailand and Southeast Asia to devise comprehensive evacuation plans, thereby enhancing regional resilience against future flood events. Furthermore, the methodologies and findings are adaptable and can be applied to other flood-prone regions within Southeast Asia, contributing to improved disaster preparedness and response.

Increasing usage of private cars and low service quality of public transport is an acute problem in many developing cities. The prerequisite is to meet the mobility needs, especially for low-income people, to ensure adequate capacity by the service provided. Paratransit is an indispensable mode of public transport, especially where the mass transit system is insufficient. Rapid increase in urban population, per capita income along existing transport infrastructure has stimulated their usage as a cheap and convenient public transport mode. Quality of service is considered as one of the most significant means to assess transit performance. To observe the performance of public transportation in roadway systems, overall passenger perceived service quality (SQ) has always been the most significant means of measurement. This research aims to establish a relationship between SQ variables describing the service of paratransit by Structural Equation Modeling (SEM) based on users' perceptions. An interview survey was conducted off-board to 2025 paratransit users at twenty paratransit routes in Dhaka metropolitan area (Bangladesh). Results show that the attribute of integration with supporting modes has the highest loading, inferring it as the most significant aspect of SQ. Attributes loading value may be explained according to the importance perceived by the users. Several SE models were developed using 21 service variables from 2000 questionnaires. Upon developing different models, the best model with three latent constructs was identified as the main characteristics for explaining the entire set of physical and service performance elements of the paratransit service. Three latent constructs were ‘Quality of transport’, ‘Transit performance’ and ‘Service quality’. Among 21 SQ variables, ‘Security of passenger’, ‘seat comfort level’, and ‘riding safety’ have been found to impart the greatest influence on the overall perceived SQ. The study findings support the data collected from paratransit users. This study may help the paratransit operators to determine variables that are decisive for SQ and their relation with the overall perceived SQ by the users. Understanding SQ variables, influencing passenger perception makes it easier to design and deliver good quality service.

Ascertaining the underlying pattern of road traffic crashes (RTCs) and identifying hotspots is essential for improving safety on the road network. Researchers have employed various statistical modelling and spatial methods to predict crash frequency and identify their hotspots on the road network. In India, the road network length has been increasing, especially the expressway network length. The increase in the network length has also increased RTCs. Hence, it is essential to assess the crash pattern and identify hotspots on the intercity expressways in India. This study aims to identify the fatal crash hotspots on the selected intercity expressway using geospatial methods. First, in this study, hotspot sections were identified using ordinary kriging (OK) and, kernel density estimation (KDE), network kernel density estimation (NKDE) methods. Next, the employed techniques were compared to know their predictive effectiveness in identifying the hotspots. The study used the fatal crash data from August 2012 to October 2018 for the selected 165 km intercity expressway. Outcomes of the geospatial methods revealed some of the common hotspots are identified by both methods. The comparative analysis indicated that the NKDE method is more effective in identifying the hotspots in smaller segments than the other two methods. Consequently, this research's outcomes would facilitate intercity expressway-owning agencies to select a practical and readily applicable hotspot identification methodology in LMICs.

Although a large number of studies have tried to explore the relationship between built environment and pedestrian crash severity in developed countries, there is a lack of similar studies in the context of developing countries. Methodologically, the contributory factors influencing pedestrian crash severity are commonly identified through global logistic regression (GLR) models. However, these models are unable to capture the spatial variation in the relationships between the dependent and independent variables. The local logistic regression model, such as geographically weighted logistic regression (GWLR), can potentially overcome this issue. The application of local logistic regression to model pedestrian crash severity is absent in the literature. Therefore, this study aimed to apply the GWLR technique to explore spatially heterogeneous relationships between natural and built environment-related factors and pedestrian crash severity in Dhaka, the capital city of a developing country: Bangladesh. First, using secondary pedestrian crash data, a GLR model was developed to identify significant contributory factors influencing pedestrian crash severity. Results of the model showed that the probability of fatal pedestrian crash occurrence increased at night, in unlit locations, and during adverse weather conditions. In addition, the likelihood of a fatal crash decreases when medians exist on roads and around institutional land use. Also, the chance of fatal crashes increased on straight and flat roads and at locations with more bus stops. Finally, this study explored spatial variation in the effect intensity of these significant variables across the study area using the GWLR technique. High intensity variation across the study area was found for road geometry and institutional land use factors. On the other hand, low intensity variation was found for light conditions and the presence of median factors. This technique can be applied in any area, and the results would help provide insights into the spatial dimension of traffic safety.