IATSS Research最新文献

The present study aimed to propose new pedestrian delay models for Signalized Midblock Crosswalks (SMC) for mixed traffic conditions. A detailed study of the literature revealed that most of the existing pedestrian delay models were developed for signalized intersections. Thus, the need for the study was established and data were collected at eight SMC in Hyderabad, one of the most densely populated metropolitan cities in India, using video-graphic technique. Two delay models were developed based on the compliance behaviour and non-compliance behaviour of pedestrians. Both models have two components i.e., waiting delay and crossing delay where the latter has two subset components i.e., frictional delay and pedestrian-vehicle interaction delay. The bidirectional effect (pedestrian-pedestrian interaction while crossing the road) of pedestrians was addressed as frictional delay while the non-compliance behaviour by pedestrians and vehicles was addressed as pedestrian-vehicle interaction delay in the present models. The waiting delay component was defined by modifying the Webster delay model for non-uniform pedestrian arrivals. The proposed delay models yielded an error of 5% and 7% for compliance behaviour model and non-compliance behaviour model respectively. The proposed models can be used for optimizing the signal timings and defining Level of Service (LOS) of facilities.

The primary condition for society to benefit from autonomous vehicle (AV) advantages is the acceptance of these vehicles by people. In this regard, the factors which affect the acceptance of these vehicles among different countries should be identified. In previous studies, a major focus has been on developing autonomous vehicle acceptance models, neglecting the moderating variables' effect on these models. The main aim of this research is to investigate the effect of moderating variables including demographic characteristics, psychological characteristics, traffic experience collision, and travel/driving behavior on the autonomous vehicle acceptance model (AVAM). The AVAM was developed via structural equations modeling by participating 553 Tehrani citizens by extending the unified theory of acceptance and use of technology. It was indicated that the negative relationship of the perceived risk on the intention of using AVs has been higher for individualistic people, culprit drivers with a history of more than one property damage-only collision, and those without a driving license. Also, the results showed that emphasis on the advantages and benefits of autonomous vehicles in collectivist people as compared to individualistic people would lead to a greater intention to use these vehicles.

Centerline rumble strips are low-cost effective countermeasures installed on the center of the highway segments to reduce crashes, especially roadway departure crashes. For safety evaluation of centerline rumble strips, methodologies such as naïve before-after analysis and cross-sectional study with Empirical Bayes have been widely utilized. The implementation of these methodologies may be limited due to the lack of relevant control groups, and/or other temporal variations in crashes such as seasonality and serial autocorrelation. This study aims to explore Intervention Time Series Analysis approach as an alternative method for the safety evaluation of centerline rumble strips on rural-two-lane undivided highways in Louisiana. Two different methodologies are explored in the intervention time series approach including the Forecast modeling technique and the Auto-regressive Integrated Moving Average intervention model. The forecast models are based on the exponential smoothing technique, state-space framework, and neural network model. The database consists of monthly observations of total and target crashes on 312 highway segments of 1274 miles in length in which centerline rumble strips were installed during the 2010–2012 period. The time frame 2005–2012 is defined as the pre-intervention period whereas the time frame 2013–2017 is defined as the post-intervention period. The analysis revealed that the Auto-regressive Integrated Moving Average intervention model performed better in terms of error estimates including root means square error, mean absolute error, and mean absolute percentage error. The proposed Auto-regressive Integrated Moving Average intervention model reveals a 17.75% total and 40.54% target crash reduction on the selected rural-two-lane undivided highway segments during the post-intervention period. All the findings are found statistically significant at a 95% confidence level.

Most road crashes at work are caused by Driver Behavioral Drift (DBD). This DBD has become a recurring issue on congested road sections.

In this context, this study proposes a method called (MASOCU-DBD) which allows to analyze this DBD problem in two steps: assessment of the dynamics of DBD occurrence using a model called BM-NSA and analysis of DCC using a Cost-Benefit Analysis (CBA) weighted by the Analysis Hierarchical Process (AHP).

The application of the MASOCU-DBD on a road section of an Algerian city's entry highlighted the problem of the DBD in terms of its occurrence and uselessness in the studied section.

The merit of the proposed method is that it uses multi-criteria analysis tools (AHP and CBA) as well as a mathematical model (BM-NSA) to analyze professional drivers' behavioral deviations.

Mixed traffic conditions present a complex problem for analysis by transportation engineers and policymakers, due to the inherent heterogeneity and lane indiscipline. The chaotic arrangements make it difficult to identify and analyze basic riding behaviour such as following and filtering. Moreover, the smaller lateral dimensions of Motorized Two Wheelers (MTWs) and their higher manoeuvreability add to the difficulty. Presence of different types of vehicles in traffic mix, following the leader with or without obliqueness, filtering through tight pores between leader vehicles, etc. are some of the aspects that require special attention. In this respect, this study is aimed at investigating the attributes related to following and filtering manoeuvres of MTWs in such disordered traffic conditions. Real world data from two Indian cities was used for the analysis, which showed that the behaviour of MTWs is heavily influenced by the type of leader vehicle(s), Clear Lateral Gap (CLG), speed, etc. Safety analysis carried out using Time-To-Collision (TTC) showed that about 8.2% of the interactions were risky. Support Vector Machine (SVM) technique was used to investigate the choice of filtering based on Clear Lateral Gap (CLG) and relative speed. Moreover, analysis of the observed parameters was conducted to obtain their specific distributions based on leader vehicle type, following regime and choice of filtering. The analysis will give directions for further research on developing driving behaviour models of MTWs in mixed traffic. The results will also find potential application in traffic flow theories, safety studies, microsimulation, implementation of MTW infrastructure, etc.

Road traffic crashes have increased over the years leading to greater injury severity among children who are mostly vehicle occupants in high-income countries. This adversely affects the healthy development of children and might lead to death. However, studies in the literature have focused on predicting crash injuries among adults while children have different crash injury risks as well as crash kinematics compared to adults. To address this gap, this paper presents a new dataset for child occupant crash injury severity prediction collected over 8 years (2012 to 2019) in the United Arab Emirates (UAE). The performance of state-of-the-art machine learning algorithms was then evaluated using the proposed dataset. In addition, feature selection techniques and logistic regression model were employed to extract the most significant features for crash injury severity prediction among child occupants. Furthermore, the impact of data balancing approaches on the prediction performance was analyzed as the dataset is highly imbalanced. The experimental results showed that Adaboost, Bagging REP, ZeroR, OneR, and Decision Table algorithms predicts child occupant injury severity with the highest accuracy. Child occupant seating position, emirate, crash location, crash type and crash cause were observed as significant features that predicts injury severity by both the feature selection and logistic regression models.

The autonomous vehicle (AV) is predicted to reduce the number of accidents and fatalities on the roads caused by human-related error and lessen traffic congestion caused by stop-and-go behavior. The susceptibility of autonomous vehicles (AVs) to potential hacking and data exploitation has generated significant concerns regarding cybersecurity and privacy risks within the domain of perceived safety. This study aimed to empirically test a comprehensive model of perceived safety in AVs, incorporating cognitive safety, emotional safety, and privacy cybersecurity. The responses of 466 participants were analyzed using a structural equation modeling (SEM) approach. The findings indicated that a significant majority of the respondents expressed their intention to utilize high-level autonomous vehicles (AVs) in the future. Specifically, 31.1% of the participants expressed an intention to use a level 2 AV, while 8.8% indicated their preference for a level 5 AV. In terms of perceived safety, privacy cybersecurity emerged as the most influential predictor, followed by emotional safety and cognitive safety. The analysis of causal relationships between the variables further revealed that privacy cybersecurity had the greatest impact on both emotional safety and cognitive safety, highlighting its critical role in shaping the overall perception of safety in AVs. Finally, this study can provide insight into how the drivers perceives AVs safety, which can be useful for government organizations, transportation agencies, and AV developers in shaping AV safety.

This paper summarizes current trends in the research and development of e-Mobility and energy coupled simulation to deal with electric vehicle integration into power systems, optimal charging infrastructure design, and regional energy and environmental impact assessments. Small trials and simulations were introduced as a case study. The car probe and floating population data are input into various electric vehicle dynamic models, in which variable vehicle speed and state-of-charge are precisely considered. Then, the synergic impacts on the mobility and energy sides are evaluated through co-simulation of the road traffic and distributed power system models.

Despite numerous studies on motorcycle safety, especially the compliance of helmet use laws in both developed and developing countries across the globe, little is known about the mediating role of traffic law enforcement and road safety education specifically on the relationship between helmet use influencing factors and helmet usage in general. The aim of this study was to examine the mediating role of traffic law enforcement and road safety education on the relationship between helmet usage influencing factors and motorcycle helmet usage. A total of 358 respondents from a university community that uses a motorcycle on daily bases to and from the community and for other important trip purposes completed a self-reported questionnaire in the Upper West Region of Ghana where motorcycles are predominantly used as a transportation mode. To test for the various hypotheses of this study, we developed a hypothesized single multiple mediated structural equation model and several sub-mediated models using AMOS 26.0. The results showed that the perceived safety of the helmet, weather conditions, and convenience of helmet use have positive significant impacts on helmet use. The study also found a full mediation role of the combined effect of traffic law enforcement and road safety education on the relationship of helmet use influencing factors investigated and helmet use. The study concludes that new traffic law enforcement and road safety education strategies need to be adopted to help improve upon the low prevalence of helmet use within the study area.