Connected Cities with Smart Transportation
project posts that is managed in CIP
This project developed real-time distributed network control techniques capable of utilizing various types of real-time traffic data, from both fixed and mobile sources. The work is divided into two major parts: traffic state estimation when data is limited and adaptive control.
This project aims to develop LOS analysis procedures that estimate or measure the average search time for selected types of parking facilities. Additionally, alternative evaluation methodologies for parking operations based on the IOT will be explored. The new smart cities approach to measure customer service is dubbed PMS to distinguish it from the LOS analysis procedure. Cities are interested in making better use of smart meter usage data.
This project aims to understand the key parameters that play a role in achieving responsive behaviors in workers. Through wearable sensors and representations of traffic loads from real data acquired from hardware in the loop systems and work zones in virtual reality, data on workers’ behavioral and physiological responses to warnings issued under various realistic scenarios and varying warning mechanisms will be studied.
Although lots of research have been conducted on multi-vehicle cooperative perception, few studies have considered combining information from vehicle onboard sensors and roadside sensors. This project argues that it would be highly beneficial to utilize roadside sensors for cooperative driving perception.
Overview Transportation agencies have been enforcing illegal overweight trucks at the weighing stations and by the Police enforcement units. It was found that only 8.6% of the actual overweight trucks
This project will increase the safety profile and ease-of-use of the VISION (Visually Impaired Smart Service System for Spatial Intelligence and Onboard Navigation) platform toward ‘connected’ dynamic navigation in complex urban environments, providing a new level of security to the end user and permitting one to break down significant barriers to employment and social interaction.
Autonomous vehicles (AV) and connected vehicles (CV) technology has been much of the focus of transportation industry lately, and they will likely make a vast impact on the future of transportation systems. This project will combine AV and CV technologies for connected and autonomous vehicles (CAVs) to reduce congestion and improve network performance and safety by developing new tools and methods using reinforcement learning and nonlinear and optimal control techniques.
Game theory is a powerful tool for security risk analysis that has been extensively used in various engineering systems, and game-theoretic approaches have been applied to studying the security of routing in transportation and communications. This project will be the basis for a synthesis of game theory and queuing theory, essential for capturing the interaction between the queuing dynamics and players decisions, in order to protect the ITS system from spoofing and attacks.
Dr. Zhou and Dr. Ozbay found that, if the traffic flow parameters are time-varying and/or the knowledge of these parameters are biased, the performances of a traffic state estimator that has assumed them to be known and fixed-valued can be significantly downgraded. Moreover, only augmenting these parameters into the state vector and then resorting to nonlinear recursive estimation techniques such as extended Kalman filter (EKF) cannot solve the issue. This is because, under a CTM-based traffic estimator, the critical density is unobservable under free-flow conditions, and hence biased initial knowledge of the critical density can cause false switching of the working model of the estimator and distort the estimation afterward.
This perception of transit can be captured through the sentiment of posts made by users on social media. Performance metrics based on user perception can provide the service provider a customer-facing view of their service and help enhance their service and address the user concerns, especially from a public health standpoint in a post-COVID lockdown world, appropriately.