A5069178501- Traffic and Road Safety
- Human-Automation Interaction and Safety
- Urban Transport and Accessibility
- Autonomous Vehicle Technology and Safety
- Evacuation and Crowd Dynamics
- Traffic control and management
- Air Quality and Health Impacts
- Vehicle emissions and performance
- Reliability and Maintenance Optimization
- Energy, Environment, and Transportation Policies
- Traffic Prediction and Management Techniques
- Neural and Behavioral Psychology Studies
- Power System Reliability and Maintenance
- Transportation Planning and Optimization
- Mining Techniques and Economics
- Advanced Manufacturing and Logistics Optimization
- Consumer Retail Behavior Studies
- Data Visualization and Analytics
- Behavioral Health and Interventions
- Advanced Text Analysis Techniques
- Urban and Freight Transport Logistics
University of Leeds
2021-2023
Delft University of Technology
2023
Chalmers University of Technology
2023
The Quebec Population Health Research Network
2022
University of Wisconsin–Milwaukee
2013
One of the current challenges automation is to have highly automated vehicles (HAVs) that communicate effectively with pedestrians and react changes in pedestrian behaviour, promote more trustable HAVs. However, details how human drivers interact at unsignalised crossings remain poorly understood. We addressed some aspects this challenge by replicating vehicle-pedestrian interactions a safe controlled virtual environment connecting high fidelity motion-based driving simulator CAVE-based lab...
When humans share space in road traffic, as drivers or vulnerable users, they draw on their full range of communicative and interactive capabilities. Much remains unknown about these behaviors, but need to be captured models if automated vehicles are coexist successfully with human users. Empirical studies user behavior implicate a large number underlying cognitive mechanisms, which taken together well beyond the scope existing computational models. Here, we note that for all putative...
Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in field automated driving. Pedestrian crossing influenced by various interaction factors, including time to arrival, waiting time, presence zebra crossing, and properties personality traits both pedestrians drivers. However, these factors have not been fully explored for use predicting outcomes. In this paper, we machine learning predict decision, initiation (CIT), duration (CD) at...
Recent developments in vehicle automation require simulations of human-robot interactions the road traffic context, which can be achieved by computational models human behavior such as game theory. Game theory provides a good insight into user considering agents' interdependencies. However, it is still unclear whether conventional suitable for modeling vehicle-pedestrian at unsignalized locations or if more complex like behavioral are needed. Hence, we compared four game-theoretic based on...
Current research on vehicle-pedestrian interactions focuses the reaction of one actor other than interaction two actors, and considering impact real-time behaviour both actors each other. To address this issue, current study replicated a natural to virtual environment by connecting high-fidelity driving simulator CAVE-based pedestrians' simulator. Behaviours from in response were observed indifferent situations including crossing locations five time gaps. The proposed method enabled...
AbstractRoad traffic mortalities (RTMs) and injuries are among the leading causes of human fatalities worldwide, particularly in low-and middle-income countries like Iran. Using an interrupted time series analysis, we investigated three interventional points (two government-mandated fuel price increases increased ticket fines) for their potential relation to RTMs. Our findings showed that while overall trend RTMs was decreasing during study period, multiple individual provinces smaller...
Cell phone use while driving is a common contributing factor in thousands of road traffic injuries every year globally. Despite extensive research investigating the risks associated with cell driving, social media campaigns to raise public awareness and number laws banning this behaviour remains prevalent throughout world. The current study was conducted Iran, where are leading causes death disability, drivers continue their phones, despite legislative bans restricting behaviour. A total 255...
As we move towards a future with Automated Vehicles (AVs) incorporated in the current traffic system, it is crucial to understand driver-pedestrian interaction, order enhance AV design and optimization. Previous research these interactions, which has primarily used naturalistic observations or single-actor virtual reality simulations, been limited by its inability draw causal conclusions, also due lack of real human-human interactions. Our study addresses limitations employing high-fidelity...
When humans share space in road traffic, as drivers or vulnerable users, they draw on their full range of communicative and interactive capabilities. Much remains unknown about these behaviors, but need to be captured models if automated vehicles are coexist successfully with human users. Empirical studies user behavior implicate a large number underlying cognitive mechanisms, which taken together well beyond the scope existing computational models. Here, we note that for all putative...
One of the current challenges automation is to have highly automated vehicles (HAVs) that communicate effectively with pedestrians and react changes in pedestrian behaviour, promote more trustable HAVs. However, details how human drivers interact at unsignalised crossings remain poorly understood. We addressed some aspects this challenge by replicating vehicle-pedestrian interactions a safe controlled virtual environment connecting high fidelity motion-based driving simulator CAVE-based lab...
The progress in technology development over the past decades, both with respect to software and hardware, offers vision of automated vehicles as means achieving zero fatalities traffic. However, promises this new – an increase road safety, traffic efficiency, user comfort can only be realized if is smoothly introduced into existing system all its complexities, constraints, requirements. SHAPE- IT will contribute major undertaking by addressing research questions relevant for introduction...
Modelling human-robot interaction in the road traffic context is an evolving yet understudied area. Recent developments vehicle automation require simulations of such interactions, which can be achieved by computational models human behaviour as game theory. Game theory a modelling paradigm that provides good insight into user considering agents’ interdependencies. However, it still unclear whether conventional suitable for vehicle-pedestrian interactions at unsignalised locations or if more...
Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in field automated driving. Pedestrian crossing influenced by various interaction factors, including time to arrival, waiting time, presence zebra crossing, and properties personality traits both pedestrians drivers. However, these factors have not been fully explored for use predicting outcomes. In this paper, we machine learning predict decision, initiation (CIT), duration (CD) at...
Understanding driver-pedestrian interactions at unsignalized locations has gained additional importance due to recent advancements in vehicle automation. Naturalistic observations can only provide correlational data, of limited value for understanding and modeling the mechanisms underlying road user interaction. Therefore, controlled studies virtual reality (VR) are an important complement, but conventional methods accommodate a single human participant. Recently, there been surge interest...
With the outbreak of COVID-19 pandemic and subsequent imposition mobility restrictions in many nations, traffic volumes driving behaviors have changed worldwide. This study aims to investigate impact fuel price on volume offenses (speeding, tailgating, illegal overtaking) Iran’s provincial aggregated data period March 21, 2019 May 20, 2020. A time-series analysis was conducted capture effects interventions level trend, followed by a spatial autocorrelation among provinces identify that...