Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
This project aims to develop a comprehensive holistic model of urban transportation demand given multiple available modes, including for-hire vehicles and their shared options. The model will enable assessment of the impact of shared mobility on urban transportation mode choice, which can be further translated into economic, social, and environmental impacts.
The research team has already established an online transportation platform, named the Digital Roadway Interactive Visualization and Evaluation Network (DRIVE Net). DRIVE NET can be used for sharing, integration, visualization, and analysis of transportation-related data. The proposed research aims to extend the functions of DRIVE Net by developing an AI platform for network-wide congestion detection and prediction using multi-source data.
This research project aimed to develop a data-driven approach for modeling cities, with a focus on pedestrian dynamics, which play a fundamental role in urban planning. It focused on detecting and counting objects such as pedestrians, cars, and bicycles in visual data sources that can provide insight into how people move around a city. The research team used an image database made up of tens of millions of images produced by Brooklyn-based start-up Carmera as its main data source.
The knowledge base from this data can be used to support design of portfolios of service options for a city. Given all the myriad of different options and existing public data, can we design a framework that can identify operating strategies that dominate in one or more sustainability criteria and quantify their performances within a portfolio of projects for city agencies to evaluate?
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.
Access to real-time information on flooding can improve resiliency and efficiency by allowing residents to identify navigable transportation routes and make informed decisions to avoid exposure to floodwater contaminants. While there exist commercially available sensors that detect the presence of water inside homes, there is an unmet need for hyperlocal information on the presence and depth of street-level floodwater.
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 work will be conducted with the research group 6-t: Bureau de Recherche, based in Paris. The project aims to better understand and compare the consumption practices and mobility behaviors of the residents living in two of the major cities in the world (Paris and NYC). The work will be conducted through a simultaneous survey in both cities, analysis, stakeholder meeting and narrative.