ABSTRACT
To realize a sustainable transportation system, it is necessary to estimate the environmental load caused by transportation. Here transportation demand affects carbon dioxide emissions directly. In general, traffic simulations or scenario-based evaluations have been used to predict transportation demand. However, the COVID-19 pandemic that began in late 2019 has changed transportation demand drastically, and such changes have not been considered in conventional simulation models. Therefore, it is important to quantify the impact of the pandemic on transportation demand and its magnitude. In this study, we developed a model focused on describing the changes in transportation demand caused by the COVID-19 pandemic in Japan. We developed a model using system dynamics because this method is effective in describing socio-technical systems such as transportation demand. Based on related studies, we categorized transportation demand by purpose and modeled it based on the cause-and-effect relationship between the amount of transportation and the prevalence of infectious diseases. To verify the developed model, we compared actual data of 2020 in Japan with the output of the model. We set scenarios with varying parameter values that contribute significantly to changes in transportation demand, such as individual awareness of the pandemic. As a result, the developed model was verified at the behavioral level. This model can be used in developing future transportation systems. © 2022 Elsevier B.V.. All rights reserved.
ABSTRACT
COVID-19 has arisen great control challenges to Governments and decision-makers. In 2020, the COVID-19 pandemic has spread around the world, causing nearly 123 million of confirmed cases (March 22, 2021). With the fact that cities are densely populated and public transport is a place that gathers a great number of populations, questions of the impact of urban mobility on COVID-19 propagation and the impact of protection measures on COVID-19 propagation are to be addressed. This research paper presents our novel transport based approach for modeling and simulating COVID-19 disease centered on the SUMO traffic simulator. Conventional approaches will be presented firstly, we discuss their pros and cons and we give a comparison. Based on their comparison, we noticed that mathematical, spatiooral, cellular automata and agent-based models cannot represent many transport aspects related to transport restrictions (e.g., barriers and reduction of vehicles capacities). We detail then the proposed approach in which we describe the required data, which are Open Street Map data, traffic data, individuals' data, pandemic and restrictions data. We are currently using this approach for developing a COVID-19 simulator based on the SUMO traffic simulator. Obtained intermediate results confirmed that the proposed approach addresses well the above-mentioned questions. © 2021 IEEE.
ABSTRACT
As India is in its developing stage and the traffic on the other side in India is very heterogeneous or mixed in its nature and the average growth rate of vehicles in India is about 8%. With the increase rate of urbanization in India it will lead to the considerable traffic and travel growth on the roads which will result in vehicular delays, long queues and traffic congestion. So, in this paper with the help of traffic simulation software, i.e. VISSIM, three simulation of an unsignalized intersection {Dadour and Una-Jahu, Nerchowk Rd. (NH-21),H.P} will be analyzed and will compare them on the basis of vehicular delays and long queues. These three simulation will be analyzed on the basis of real world traffic data which is less from the expectations due to the pandemic covid-19, theoretical traffic data (increase in real data by 30%) and theoretical traffic data {with traffic signals as theoretical data follows warrant 1 (Min. Vehicular Volume) shown in IRC:93:1985}. Result showed that with increase in vehicular data there was not so much variation in vehicular delays, whereas there was an increase in long queues or queue stops and whilst third simulation (with traffic lights) is done it shows that it overcomes the queue stops of the intersection. © 2022, Springer Nature Singapore Pte Ltd.