Objective: With the advent of connected and automated vehicle (CAV) technology, there is an increasing need to evaluate driver behavior with the advent of connected and automated vehicle (CAV) technology. This study introduced a red-light violation warning (RLVW) system using CAV technology in a driving simulator environment, to evaluate driver braking behavior when the light changes suddenly from green to yellow. Methods: Researchers recruited a total of 93 participants from diverse socio-economic backgrounds for this study and created a virtual network of downtown Baltimore. An eye tracking device was used to observe distractions and head movements. A Lognormal accelerated failure time (AFT) distribution model was used for this analysis, to calculate speed reduction times from the moment the traffic light changes from green to yellow, to the point where a minimum speed was reached. Results: It was observed that speed reduction times were significantly higher in the presence of a RLVW system, requiring a longer period of time to come to a complete stop at the red light. Inferences can be drawn from the jerk analysis that, the RLVW system results in a highly unsafe jerk at the onset of the warning. Without the RLVW system though, a highly uncomfortable positive jerk occurs closer to the signal, which is due to sudden acceleration, as the participants possibly slowed down a lot initially. Gaze analysis showed that the system was able to attract the attention of the drivers, as the majority of the drivers noticed the displayed warning. Conclusions: The findings suggest that the presence of an RLVW system sends a clear message to the driver about the change in traffic light and gives the driver ample time to adapt their initial approach speed to stop at the signal, avoiding potential intersection crashes.

A single unit of information is an answer to any anticipated question a motorist may ask. Such questions include: “What happened? Where? What do I do?” This study, a first of its kind, analyzes the optimum number of units of information Dynamic Message Signs (DMSs) should display to influence driver speeding behavior. A 155-mi² virtual road network of the Baltimore-Washington Parkway (MD-295) in Maryland was developed for use with a medium-fidelity driving simulator, and 65 participants took part in the study. Six scenarios featured DMSs displaying 2–7 units of information, interchangeably, and a total of 296 simulation sessions were conducted. Mean speeds are calculated over five different phases: the initial speed area, visible area, readable area, lost legibility area and post DMS area. Analysis of variance (ANOVA) and post-hoc analysis showed that participants tend to accelerate as soon as they lose sight of the DMS displaying 2–3 units of information and continue to do so after they pass the DMS. An ordinary least squares (OLS) regression analysis reveals that participants older than 55 slow down the most when they encounter DMSs with 6–7 units of information. Participants in the age group of 26–35 tend to increase speeds, especially when a DMS displays 2–4 units of information. This suggests that the comprehension time is low when there are fewer units of information on a DMS. Too little information may be unclear or ambiguous whereas too much may be hard to comprehend and cause drivers to slow down.

Speeding in a work zone needs to be tackled effectively to enhance work zone safety. This study evaluates the influence of three different work zone signs—speed photo enforced signs, dynamic speed display signs and reduced speed limit signs—on driver speeding behavior using a medium-fidelity driving simulator. The speed photo enforced and reduced speed limit signs were placed at intervals before the start of the work zone and in the work zone. A virtual work zone network was built to replicate the Baltimore–Washington Parkway (MD-295). A total of 66 participants from different socioeconomic backgrounds participated in 264 driving simulation sessions. The speeds were measured over four phases: initial speed area, sign visible area, sign readable area and post sign area. An analysis of variance and post hoc analysis showed that the speed photo enforced sign was the most effective of the three signs. The second speed photo enforced sign within the work zone was more effective at reducing speed than the sign before the start of the work zone, especially in the post sign area. It was observed that female participants tend to slow down more when encountering a speed photo enforcement sign.

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.

In contemporary times, bike sharing programs are gaining importance as an influential transportation mode in both urban and rural areas. They are also used as a vital transportation mode on university campuses which serve as a healthy and environmentally-friendly transportation system. However, having an appropriate location for a bike station is important, so as to maximize the benefits of the service. This study used an origin–destination (O-D) matrix to identify appropriate bike station locations at the Morgan State University campus. The O-D matrix analysis identifies three locations Cumming Hall/University Health Center, Rawling Hall, and Center for Built Environment and Infrastructure Studies as the most appropriate locations to start a pilot, which will serve most of the campus (students, faculty, staff) and connect them to the maximum number of facilities at Morgan State University. The O-D matrix takes into account the occupancy or population of individual buildings based on enrollment over the past four years, the distance to the center of the campus where maximum facilities including the graduate and undergraduate offices are located, and the frequency of the university shuttle connecting most of the buildings. This methodology can be replicated and used on other university campuses and will help further bike sharing programs.

With the advent of Advanced Driver Assistance Systems (ADAS), there is an increasing need to evaluate driver behavior while using such technology. In this unique study, a forward collision warning (FCW) system using connected vehicle technology, was introduced in a driving simulator environment, to evaluate driver braking behavior and then the results are validated using data from field tests. A total of 93 participants were recruited for this study, for which a virtual network of South Baltimore was created. A one sample t-test was conducted, and it was found that the mean reduction in speed of 15.07 mph post FCW, is statistically significant. A random forest, machine learning algorithm was found to be the best fit for ranking the most important variables in the dataset by order of importance. Field data obtained from the University of Michigan Transportation Research Institute (UMTRI), substantiated the FCW findings from this driving simulator study.