Connected Vehicle Infrastructure University Transportation Center
The Teacher Transportation Institute (TTI) at Morgan State University was implemented from July 13, 2015 to July 29, 2015. The professional development took place over a two-week period. The session was used by the teachers to conduct research and plan for the final project development and presentations. The teachers were given a pre-program survey to determine their expectations of the TTI professional development. It also established what the teachers knew about transportation and transportation-related careers. One participant, a school counselor, registered for the TTI to become more informed of career and educational opportunities in the transportation industry for her students. Teachers who are more informed about the connection between STEM and the transportation industry and are aware of the trends in transportation and safety developments can communicate career opportunities available to their students.
TTI participants discussed what STEM is and its importance to post-secondary education and successful careers in transportation and related fields. The program engaged the teachers in hands-on, inquiry-based lessons that included the use of engineering principles and technology. The teachers visited transportation and transportation-related work sites to become familiar with current trends in transportation. They gained insights into careers and the work environment of engineers, transportation planners, information technology professionals and social scientists in the transportation industry. Guest speakers also discussed opportunities and careers in the transportation industry.
Participants discussed the impact of the traffic roundabout and connected vehicles on the environment and safety. Participants conducted traffic feasibility studies and used scientific and mathematical principles to analyze their data. They were taught to construct a scale of a traffic roundabout. The teachers also experienced driving scenarios behind the wheel of a driving simulator and became more aware of the hazards of distracted driving.
The TTI participants received Continuing Education Units (CEUs) from Morgan State University’s Center for Professional Development and Continuing Studies for attending the entire program. The CEUs will be used by the teachers for recertification of teaching credentials and/or promotion.
Click to read the full report here.
The final report for Prototyping and Evaluating a Smart Phone Dynamic Message Sign Application in the CVI-UTC Testbed, submitted by Dr. Brian L. Smith, Dr. Jiaqi Ma, and Dr. Hyungjun Park, has been released.
Report Abstract:
Traveler Information Systems are designed and operated by transportation agencies to provide travelers with real-time traffic information, enabling them to make better travel decisions. One of the most commonly used ways to provide real-time, en route traveler information to motorists is through Dynamic Message Signs (DMSs). Despite their effectiveness, they are costly and limited in terms of the amount of information they can deliver. The wide availability of smart mobile devices can provide traveler information through in-vehicle devices (without incurring huge infrastructure costs) and (in a more flexible manner) to selected individuals and locations without geographical constraints. Research was conducted to comprehensively develop and evaluate this concept and a summary of tasks and findings is presented below.
First, this research proposed the concept of a Virtual Dynamic Message Sign (VDMS) system utilizing a smartphone-based application to demonstrate and summarize user experience for future deployment. The user survey revealed a positive attitude among participants toward a VDMS system in terms of both usefulness and satisfaction; the average ratings were −0.90 and −0.81 respectively on a −2 to 2 (Totally agree to Totally disagree) five-point Likert scale. The survey also indicated that most drivers (81.0%) perceived VDMS as a safer way to receive information. Many drivers (66.7%) also felt more comfortable receiving an audible message from a VDMS system rather than a text message on a DMS. The results indicate great user acceptability and the potential for such systems to be deployed by public agencies in the future.
This research also aimed to address the question of whether a VDMS conveys information at least as effectively as existing DMSs. A mixed, repeated-measure experiment was designed using a driver simulator to examine (1) the impacts of driver age, (2) information transmission mode, (3) amount of information, and (4) driving complexity on message comprehension, distraction, and perceived difficulty.
Forty-two people were recruited and each of them participated in a test under different combinations. Participant performance was measured in terms of message comprehension, distraction, and self-reported message difficulty level. Results revealed that VDMS generally performs better than DMS across different amounts of information, under different driving conditions, and regardless of driver age. VDMS proved significantly better than DMS in message comprehension under relatively complex conditions. It reduced reaction time to unexpected stimuli (as measured with a reduced time-to-brake of 0.39 seconds), and made the same messages easier to process and retain for drivers than DMS.
Based on these results, it is recommended that transportation agencies give careful consideration to VDMS as a future strategy for delivering public traffic information in a connected vehicle environment.
Click here to learn more about this project and read the final report.
The final report for Infrastructure Safety Assessment Using Connected Vehicle Data, submitted by Dr. Brian L. Smith, Robert Kluger, and Dr. Hyungjun Park, has been released.
Report Abstract:
The goal of the Infrastructure Safety Assessment in a Connected Vehicle (CV) Environment project was to develop a method to identify infrastructure safety “hot spots” using CV data. Using these basic safety messages to detect hot spots may allow for quicker discovery than traditional methods, such as police-reported crashes. The basic safety message may be able to detect events that police normally cannot obtain, including unreported crashes and near-crashes.
The project successfully explored some models and algorithms to detect crashes and near-crashes and also designed a methodology to apply to hot spot identification. With the data available, conclusive results were not achieved; however, the models showed some potential. Three techniques were tested to predict crashes using vehicles’ kinematic data. To predict where a crash was occurring, multivariate adaptive regression splines, classification and regression trees, and a novel pattern matching approach were all tested. The models were able to identify the majority of 13 known crashes with different amounts of false positives. The pattern matching approach outperformed a simple acceleration threshold by identifying nearly 70% of crashes in a crash-only test set and 74% of near-crashes in a near-crash only test set. On the training set, it was able to identify more crashes than the thresholds without increasing the number of false positives observed. Based on the work described in this report, the CVI-UTC is fully prepared to apply the methodology to data collected on the field test bed.
Click here to learn more about this project and read the final report.