Use of Traffic Simulation to Analyze the Changes in City Mobility During the COVID-19 Pandemic

It seems that the COVID-19 pandemic, which started in December 2019, will have longer and more profound consequences on our lives than initially foreseen. Among the most obvious are everyday decisions about the mode of transport. From related research, it can be seen that the most affected transport mode is public transport, which had the greatest decline. The reason for lesser use of public transport is in complete closure of public transport in some parts of the world. However, where this measure has not been applied, the reason for the reduction is people's fear of infection when using public transport or any shared modes of transportation. The fear stems from the fact that the COVID-19 virus is spreading extremely fast in densely populated rooms. All these changes are affecting the changes in city mobility. Related research shows a decrease of mobility in general and an increase in the use of individual modes of transportation. Distinct changes can be observed in different environments as compared to previous travel behaviour.

An Extended Car-Following Model in Connected and Autonomous Vehicle Environment: Perspective from the Cooperation between Drivers

The development of connected and autonomous vehicle (CAV) technology has received increasing attention in recent years. Although car-following behavior in mixed traffic with CAVs and human-driven vehicles (HDVs) is a core component of microscopic traffic simulation, intelligent traffic systems, etc., the current study of car-following behavior in mixed traffic has some limitations. Furthermore, actual data do not support its applicability to the Chinese traffic environment. To address this gap, this paper designs and organizes a car-following experiment in mixed traffic in Beijing, extracts the trajectory data of CAVs and HDVs based on video recognition, and reconstructs the extracted trajectory data using the Lagrangian theory and Kalman filter theory to ensure the accuracy of the data. Based on this data set, this paper develops an extended car-following model. The model considers the cooperation between drivers by reformulating the prospect theory (PT). The root mean square percentage error (RMSPE) is selected to calibrate and validate the parameters of the proposed model, and the results show that there is significant heterogeneity between CAVs and HDVs in mixed traffic, and the proposed model captures this heterogeneity well. The model presented in this paper provides theoretical support for microscopic traffic simulation in mixed traffic.

Basic Study on Expressway Congestion Mitigation using Cellular Automata

Traffic congestion has become a serious social problem in Japan. In particular, traffic congestion causes enormous time and economic losses on expressways, which are intended to facilitate smooth traffic flow. It also causes environmental problems and a decrease in logistics efficiency, so efforts to eliminate or reduce traffic congestion are essential. The elimination and mitigation of traffic congestion on highways is a factor in reducing traffic accidents and fatalities. In recent years, with the improvement of computing capabilities, research on traffic congestion reduction and mitigation has been conducted using traffic simulation. Traffic simulation allows us to reproduce and evaluate traffic phenomena that occur on roads on a computer. The Kakuto Tunnel is located on the border between Ebino City in Miyazaki Prefecture and Hitoyoshi City in Kumamoto Prefecture. The Kakuto Tunnel has insufficient traffic capacity and is frequently congested on holidays. In this study, we created a traffic simulator assuming the Kakuto Tunnel. In the simulation, we examined the effectiveness of increasing the number of lanes as a method of reducing traffic congestion.