Abstract The COVID-19 pandemic and its containment measures have significantly impacted both physical and virtual accessibility, disproportionately affecting disadvantaged populations and exacerbating transport inequities. This study systematically reviews 63 peer-reviewed publications, supplemented by key reports from professional institutes and international organizations, to examine how transport equity was addressed during the pandemic and to identify strategies for improvement. Four key strategies are proposed: developing targeted emergency response plans to ensure access for disadvantaged populations; enhancing support for alternatives to private car travel, such as micromobility, shared mobility, and public transit; reconfiguring land use and promoting mixed-use development to improve accessibility; and advancing an equity-focused digital transformation to address disparities in virtual accessibility. The study suggests future research directions, including cross-regional and longitudinal studies, and integrating subjective and objective measures to better understand the combined effects of physical and virtual accessibility on transport behaviors during the pandemic.

Abstract Since regulation will be essential to steer the implementation of vehicle automation in a sustainable direction and automated vehicle services are now being commercially rolled out, urgency increases for governments to gain knowledge of potential user profiles, and insights into mode choice behavior that could guide policy-making. For this purpose, this study conducted an online stated choice survey in Flanders, Belgium, collecting a sample of 645 completed questionnaires. Respondents choose between their current mode of transport and a new hypothetical service with stop-based pooled automated vehicles (AVs) that would operate on-demand and pick-up and drop-off travelers at designated stops for trips they did for work or school, leisure activities and doing groceries. Discrete choice models with different nesting structures were estimated and compared. The results show that for work/school and leisure trips, the stop-based pooled AV service is perceived most resembling to the bus, but also shares substantial similarities with the car, whereas for grocery trips the resemblance is predominantly with bus with little association to the car. Interestingly, accessing the pick-up point and waiting for the pooled AVs is more acceptable than for the bus. Further, the value of travel time of stop-based pooled AV service appears to be less than half of the private car, and almost twice as large as for the bus, across all three different trip purposes: work/school, leisure and groceries.

Abstract This study addresses the challenge of estimating traffic states for road links. We propose an innovative approach that leverages partial trajectory data captured by camera-equipped probe vehicles traveling in the opposite lane. The methodology combines state-of-the-art computer vision algorithms for extracting vehicle trajectories from street-view video sequences with a novel estimation technique based on the Cell Transmission Model (CTM) and Genetic Algorithms (GA). Our approach first calibrates Fundamental Diagram (FD) parameters using observed cell densities, then estimates boundary conditions for all space-time diagrams. We validate the method using simulated traffic data from three different types of links and parameter settings. Results show that the proposed methodology can estimate traffic densities in unobserved regions, even with limited data availability. This research contributes to the field by introducing a cost-effective, high-resolution traffic data collection method and a robust estimation technique for comprehensive traffic state information. While the study shows promising results, it also identifies areas for improvement, including refining models, optimizing processes, and testing with real-world data to enhance accuracy and scalability.