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ISSN: 2046-0430
CN: CN31-2204/U
e-ISSN: 2046-0449

Understanding Customer Preferences for Autonomous Delivery Vehicles in Instant Delivery: Exploring the Impact of Delivery and Personal Attributes

•Instant delivery services face several challenges, including high labor costs, low efficiency, and the risk of courier accidents.•Despite the increasing popularity of autonomous delivery vehicles (ADVs),...

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Temporal dynamics of pedestrian injury severity: A seasonally constrained random parameters approach

Pedestrians are the most at-risk group in the transportation system, experiencing a troubling and persistent rise in both the frequency and proportion of fatalities over the past decade. In 2022, pedestrian...

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Who is more willing to use shared autonomous vehicles in first-mile-last-mile? A heterogeneity study on carbon incentive policy from China

Encouraging and motivating travelers to opt for more efficient and low-carbon last-mile transportation options is a crucial strategy for increasing the share of public transportation. This study aims...

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Learning to search for parking like a human: A deep inverse reinforcement learning approach

•Maximum entropy-based DIRL for learning parking search behavior.•High-fidelity simulation platform with 987 trajectories from Unity3D.•Hybrid simulation system integrating DIRL and traditional traffic...

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Adaptive emergency evacuation routing: A graph-based approach

Emergency evacuation plays a vital role in disaster management operations. The existing solutions for planning and routing emergency evacuations rely on preplanning based on prior information and lack...

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Are you ready? Understanding the intention to use highly automated on-demand vehicles while considering technology readiness and environmental drive

Highly automated vehicles represent a potentially disruptive technology, with uncertainties about their successful integration into existing transport systems. This study explores psychological factors...

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Incident duration reliability assessment using Monte-Carlo simulation and kernel density estimation of machine learning-based models

Traffic incidents are a major cause of non-recurrent congestion and delays, making accurate incident duration (ID) prediction essential for effective traffic management. While machine learning (ML)...

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Event-triggered prescribed-time control for vehicular platoon systems with unknown disturbances

An event-triggered prescribed-time control scheme is proposed for vehicular platoon systems (VPSs) subject to parameter uncertainties, unknown external disturbances and actuator saturation. By utilizing...

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Collective accessibility impacts of public transport automation on rural areas: The case study of Mühlwald, South Tyrol

Collective autonomous vehicles (AVs) might change the accessibility of rural areas in the following decades. For instance, the operating cost savings triggered by automation could allow upgrading traditional...

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First train schedule optimization for metro systems considering minimum adjustment cost for special event scenarios

During special events, some metro lines and stations may alter their first train schedules to an earlier time, while others remain unchanged. This adjustment can result in longer transfer waiting times...

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Traffic light detection using ensemble learning by boosting with color-based data augmentation

Recent advancements in deep neural networks have significantly improved the detection and recognition of traffic lights for advanced driver assistance systems (ADAS). Traditional methods often rely...

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Using statewide transportation planning model to forecast demand for electric vehicle charging at stations along intercity highways

The availability of Charging Stations (CSs) with adequate capacity is critical for the growth in ownership and usage of Electric Vehicles (EVs). In the United States, the state Departments of Transportation...

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Hierarchical dynamic modeling for highway network real-time risk forecasting with digitalized vehicle data

In traffic safety management, identifying high-risk areas prone to traffic crashes is crucial. Road authorities focus on these high-risk segments to implement strategies that mitigate the impact of...

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Geometric design optimization of speed tables at urban arterials using UAV assistance

One of the primary risk factors at junctions on urban roads is vehicle speed. To curb over-speeding and road crashes at intersections, traffic calming measures are introduced. Current research aims...

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Real-time traffic conflict prediction at signalized intersections using vehicle trajectory data and deep learning

•Uses real-time video data and deep learning for traffic conflict prediction.•Proposes a deep and cross network (DCN) model with lane-level traffic parameters.•Uses SHAP to explain the impact of dynamic...

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Machine learning-based climate zoning and asphalt selection for pavement infrastructure under changing climate: A focused study of Ningxia, China

Climate change poses significant challenges to the durability and performance of asphalt pavements. This study presents a comprehensive analysis of climatic factors in Ningxia, China, to establish a...

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Enhanced in-situ measurement and evaluation methods for subgrade modulus utilizing falling weight deflectometer

Falling Weight Deflectometer (FWD) tests have gained increasing popularity in evaluating the stiffness modulus of pavement subgrade. This paper presents an enhanced method for in-situ testing and evaluation...

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A dictionary-based Bayesian approach to optimizing left-turn restriction locations in grid networks

Left-turn movements at signalized intersections pose significant safety risks for the drivers and efficiency concerns for the traffic operations in urban networks. Restricting left-turn movements at...

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Deployment of Public Charging Stations for BEVs Using an Agent-Based Modeling Approach

A low utilization rate of public chargers and unmatched deployment of public charging stations (CSs) are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation....

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When segment anything model meets inventorying of roadway assets

Automating the identification, localization, and monitoring of roadway assets distributed widely in the roadway network is critical for the traffic management system. It can efficiently provide up-to-date...

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An improved multi-objective method for the selection of driverless taxi site locations

To expedite the large-scale deployment of driverless taxis and advance the autonomous driving industry, research on the location of integrated parking and charging facilities for driverless taxis has...

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Joint optimization of metro train timetable and stopping plan for the first service period: An integrated energy-efficient and time-saving model

This paper addresses the energy conservation challenge in metro systems during the first service period, characterized by large train headways and low passenger demands. A novel train timetabling method...

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Demand-aware distributed pathfinding for repositioning vehicles in shared-use autonomous mobility services

Shared-use autonomous mobility services (SAMS) have the potential to provide accessible and demand-responsive mobility to passengers, while benefitting from autonomous vehicle (AV) technology and bypassing...

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Identifying the key factors of intermodal travel using interpretative ensemble learning

•Development of a novel interpretability-based ensemble learning model to identify key factors affecting intermodal travel.•Differences in feature interpretability between the developed model and the...

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The future of AI in transportation: challenges and opportunities?

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