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.

No abstract

Distracted driving is known to be one of the main causes of crashes in the United States, accounting for about 40% of all crashes. Drivers’ situational awareness, decision-making, and driving performance are impaired as a result of temporarily diverting their attention from the primary task of driving to other unrelated tasks. Detecting driver distraction would help in adapting the most effective countermeasures. To tackle this problem, we employed a random forest (RF) classifier, one of the best classifiers that has attained promising results for a wide range of problems. Here, we trained RF using the data collected from a driving simulator, in which 92 participants drove under six different distraction scenarios of handheld calling, hands-free calling, texting, voice command, clothing, and eating/drinking on four different road classes (rural collector, freeway, urban arterial, and local road in a school zone). Various driving performance measures such as speed, acceleration, throttle, lane changing, brake, collision, and offset from the lane center were investigated. Using the RF method, we achieved 76.5% prediction accuracy on the independent test set, which is over 8.2% better than results reported in previous studies. We also obtained a 76.6% true positive rate, which is 14% better than those reported in previous studies. Such results demonstrate the preference of RF over other machine learning methods to identify driving distractions.

This study investigates the potential effect(s) of different dynamic message signs (DMSs) on driver behavior using a full-scale high-fidelity driving simulator. Different DMSs are categorized by their content, structure, and type of messages. A random forest algorithm is used for three separate behavioral analyses—a route diversion analysis, a route choice analysis, and a compliance analysis—to identify the potential and relative influences of different DMSs on these aspects of driver behavior. A total of 390 simulation runs are conducted using a sample of 65 participants from diverse socioeconomic backgrounds. Results obtained suggest that DMSs displaying lane closure and delay information with advisory messages are most influential with regards to diversion, while color-coded DMSs and DMSs with avoid route advice are the top contributors potentially impacting route choice decisions and DMS compliance. In this first-of-a-kind study, based on the responses to the pre- and post-simulation surveys as well as results obtained from the analysis of driving-simulation-session data, the authors found that color-coded DMSs are more effective than alphanumeric DMSs, especially in scenarios that demand high compliance from drivers. The increased effectiveness may be attributed to reduced comprehension time and ease with which such DMSs are understood by a greater percentage of road users.

This study investigates travelers' reactions to different types of information, in deciding their parking choice behavior and its effect on circulation time, through a driving simulator and a stated preference (SP) survey. In the simulator-based driving experiments, we develop a 3.47 mile 2 network in the Chinatown area of Washington, D.C., with different scenarios of traffic, driving conditions, and information provision. The parking information is provided using a variable message sign (VMS) and mobile application. In all scenarios, participants can choose from three parking options with different prices and different walking distances to the destination (Verizon Garage, 11th St. Garage, and on-street parking). A sample of 76 participants with diverse socio-economic backgrounds who in total conducted 636 experiments is used. We applied a multinomial logistic regression model, linear regression, and t-test to analyze the collected data. We conclude that types of information and age are important determinants of drivers' parking choice and compliance behaviors. In addition, the results show that the existence of information decreases the circulation time. In addition, the parking choice behavior revealed through the driving simulator is shown to be significantly different from that stated in the survey questionnaire.