We are developing models to analyze and optimize the cost of transit operations by focusing on the energy impact of the vehicles. For this purpose, we are developing real-time data sets containing information about engine telemetry, including engine speed, GPS position, fuel usage, and state of charge (electrical vehicles) from all vehicles in addition to traffic congestion, current events in the city, and the braking and acceleration patterns. These high-dimensional datasets allow us to train accurate data-driven predictors using deep neural networks, for energy consumption given various routes and schedules. Having these predictors combined with traffic congestion information obtained from external sources will enable the agencies to identify and mitigate energy efficiency bottlenecks within each specific mode of operation such as electric bus and electric car. To make this possible, the project is also developing new distributed computing and machine learning algorithms that can handle data at such a rate and scale.
We are developing microtransit dispatch algorithms that can serve passengers using dynamically generated routes and may expect passengers to make their way to and from common pick-up or drop-off points. Our hypothesis in the project is that the integration of data-driven methods with better operational research methods combined with a socially engaged design will lead to success. A key aspect of this research area is the development of techniques to preserve privacy across multimodal datasets, while also providing sufficient information for analysis and scheduling. This approach is different from commercial alternatives, because most commercial alternatives emphasize only on economic objectives, and as such, those services are often not integrated with public transit and do not address equity issues, which is a critical concern for us. The outcome of the project will be a deployment-ready software system that can be used to design and operate a micro-transit service effectively.
Fixed Line and Paratransit Operations
Lasty, we are developing algorithms to perform system-wide optimization, (the microtransit, fixed line and paratransit) focusing on three objectives: minimizing energy per passenger per mile, minimizing total energy consumed, and maximizing the percentage of daily trips served by public transit. While it is possible to optimize these decisions separately as prior work has done, integrated optimization can lead to significantly better service (e.g., synchronizing flexible courtesy stops with microtransit dispatch for easy transfer). However, this is hard due to uncertainty of future demand, traffic conditions etc. We address these challenges using state-of-the-art artificial intelligence, machine learning, and data-driven optimization techniques. Deep reinforcement learning (DRL) and Monte-Carlo tree search form the core of our operational optimization, which is supported by data-driven optimization for offline planning and by machine learning techniques for predicting demand, maintenance requirements, and traffic conditions.
Smart and Connected Communities
This research effort is part of the broader research that is being conducted in the area of smart and connected communities (SCC). As a research area, SCC is multidisciplinary and lies at the intersection of cyber-physical systems, data science, and social sciences. This research area is enabled by the rapid and transformational changes driven by innovations in smart sensors, such as cameras and air quality monitors, which are now embedded in almost every physical device and system we use, from watches and smartphones to automobiles, homes, roads, and workplaces. Coupled with emerging new modes of networking, new algorithms for data analytics, and new paradigms of distributed computing like fog computing, these sensors create an “Internet of Things” (IoT) that provide endless opportunities for innovation and improving the quality of life, such as improved transportation with reduced congestion and more efficient use of energy and water. The effect of these innovations can be seen in a number of diverse domains, such as transportation, energy, emergency response, and health care, including the transit-related efforts of our team. Read more at the National Science Foundation page.
The team includes members from the Institute of Software Integrated Systems at Vanderbilt University, Cornell University, University of Houston, University of Washington, University of Tennessee at Chattanooga, University of South Carolina, Pacific Northwest National Laboratory, Chattanooga Area Regional Transit Authority and Siemens Corporate Technology. This team consists of people with complementary backgrounds in transit operations, transit optimization, simulations, cyber-physical system, distributed system and software design and artificial intelligence. They have extensive prior experience in building transit and congestion performance indicators using machine-learning models that incorporate exogenous factors, such as weather, traffic, and public events. The research efforts that led to these projects have been ongoing for more than six years starting from the White House’s smart cities initiative and the first Global Cities Team Challenge. The project was started by a collaboration between the Smart and Resilient Computing for Physical Environments Lab (SCOPE), WeGo Nashville and Chattanooga Area Regional Transit Authority.