IATSS Research最新文献

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.

The color of road signs is a critical aspect of road safety, as it helps drivers quickly and accurately identify and respond to these signs. Properly colored road signs improve visibility during the day and make it easier for drivers to make informed decisions while driving. In order to ensure the safety and efficiency of road traffic, it is essential to maintain the appropriate color level of road signs.

The objective of this study was to analyze the color status and daylight chromaticity of in-use road signs using supervised machine learning models, and to explore the correlation between road sign's age and daylight chromaticity. Three algorithms were employed: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The data used in this study was collected from road signs that were in-use on roads in Sweden.

The study employed classification models to assess the color status (accepted or rejected) of the road signs based on minimum acceptable color levels according to standards, and regression models to predict the daylight chromaticity values. The correlation between road sign's age and daylight chromaticity was explored through regression analysis. Daylight chromaticity describes the color quality of road signs in daylight, that is expressed in terms of X and Y chromaticity coordinates.

The study revealed a linear relationship between the road sign's age and daylight chromaticity for blue, green, red, and white sheeting, but not for yellow. The lifespan of red signs was estimated to be around 12 years, much shorter than the estimated lifespans of yellow, green, blue, and white sheeting, which are 35, 42, 45, and 75 years, respectively.

The supervised machine learning models successfully assessed the color status of the road signs and predicted the daylight chromaticity values using the three algorithms. The results of this study showed that the ANN classification and ANN regression models achieved high accuracy of 81% and R² of 97%, respectively. The RF and SVM models also performed well, with accuracy values of 74% and 79% and R² ranging from 59% to 92%. The findings demonstrate the potential of machine learning to effectively predict the status and daylight chromaticity of road signs and their impact on road safety in the Swedish context.

Pedestrian distraction is a major causal factor reported in pedestrian fatalities worldwide. Even though many observational studies and laboratory-based research have been conducted to examine the influence of pedestrian distraction on road safety, there is little understanding of the determinants of pedestrian beliefs that influence distracted walking behaviour. The present study examines the associations of pedestrian beliefs related to engagement in mobile phone distraction with psychological factors such as mobile phone addiction, boredom proneness, and mindfulness. Five hundred and fifty-one participants completed a questionnaire about their distraction beliefs (behavioural, normative, and control), mobile phone addiction, boredom proneness, and mindfulness. A Structural Equation Model (SEM) was developed to investigate the influence of mobile phone addiction, boredom proneness, and mindfulness on the three types of beliefs. Mobile phone addiction was significantly associated with behavioural beliefs (factor loading = 0.38) and control beliefs (factor loading = 0.23) but not with normative beliefs. Further, significant associations of boredom proneness were observed with all three types of beliefs: behavioural (factor loading = 0.15), normative (factor loading = 0.13), and control (factor loading = 0.15). Mindfulness showed significant relationships with normative beliefs (factor loading = 0.13) and control beliefs (factor loading = 0.11) but not with behavioural beliefs. This study is the first attempt to investigate the predictors of pedestrian distraction beliefs in the Indian context. The findings can assist the policymakers in understanding the pedestrian psychology behind their distracted walking behaviour.

The use of advanced models to investigate the determinants of injury severity outcomes for motorized and non-motorized-involved crashes are sparse. Therefore, random-parameter ordered probit models with heterogeneity in means and variances were developed to estimate factors affecting injury severity for motorized and non-motorized-involved crashes. Data covering a five-year period comprising 5976 and 634 cases for motorized and non-motorized-involved crashes respectively, was retrieved from the database of the National Road Safety Authority, State Insurance Company and Driver and Vehicle Licensing Authority in Ghana and used for model estimation. The results show that factors have varying significant effects on injury severity outcomes for motorized and non-motorized models. Marginal effects indicate that old age occupants, head-on-collision, exceeding a posted speed limit of 100 km/h and crash during weekends contributed greatly to the likelihood of severe injury outcomes in motorized model. Additionally, male non-motorists, non-use of helmet, rear-end collision, right-angle collision and crash on urban roads and during weekends, contributed significantly to the severe injury outcomes of non-motorized models. The direction of effect of the factors on severe injury was observed to have varying degrees of estimated coefficients. The difference in estimated coefficients shows that crashes involving non-motorized vehicles were more likely to result in severe injury compared to motorized vehicles. The motorized model had heterogeneity in means of five (5) random parameters observed, while the non-motorized model had heterogeneity in means of four (4) random parameters observed with two variables affecting the variance of three random parameters. Based on the results, various countermeasures were proposed to enhance road traffic safety.