This study investigates the effect of the coronavirus (COVID-19) pandemic on public transport ridership in Baltimore and nine other U.S. cities similar to Baltimore, in terms of population and service area, during the first five months of 2020. The analysis is based on ridership numbers, vehicle revenue hours, and vehicles operated in maximum service. A compliance analysis was done between 2020 and 2019, as well as a monthly analysis of 2020 by mode and type of services. In comparison to 2019, the ridership decreases from March, the start of the pandemic, while all ten cities experienced the most decrease in ridership in April.
This paper presents a framework for online highway travel-time prediction using traffic measurements that are likely to be available from vehicle infrastructure integration (VII) systems, in which vehicle and infrastructure devices communicate to improve mobility and safety. In the proposed intelligent VII system, two artificial intelligence (AI) paradigms, i.e., artificial neural networks (ANNs) and support vector regression (SVR), are used to determine future travel time based on such information as the current travel time and VII-enabled vehicles' flow and density. The development and performance evaluation of the VII-ANN and VII-SVR frameworks, in both the traffic and communications domains, were conducted using an integrated simulation platform for a highway network in Greenville, SC. In particular, the simulation platform allows for implementing traffic surveillance and management methods in the traffic simulator PARAMICS and for evaluating different communication protocols and network parameters in the communication network simulator, Network Simulator version 2 (ns-2). This paper's findings reveal that the designed communications system can support the travel-time prediction functionality. The findings also demonstrate that the travel-time prediction accuracy of the VII-AI framework was superior to a baseline instantaneous travel-time prediction algorithm, with the VII-SVR model slightly outperforming the VII-ANN model. Moreover, the VII-AI framework was shown to perform reasonably well during nonrecurrent congestion scenarios, which have traditionally challenged sensor-based highway travel-time prediction methods.Index Terms-Artificial intelligence (AI), traffic simulation, travel-time prediction, vehicle infrastructure integration (VII).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.