Center for Sustainable Mobility
The Center for Sustainable Mobility (CSM) conducts research relevant to society’s transportation mobility, energy, environmental, and safety needs. The center translates the results of research into realistic and workable applications, creates and provides tools needed to apply developed knowledge and processes, and educates qualified engineers to meet today’s transportation demands and tomorrow’s transportation challenges in the following areas: transportation network control; large-scale transportation system modeling; traffic state prediction using large data and artificial intelligence techniques; transit bus real-time routing and scheduling; vehicle energy and environmental modeling; connected and automated vehicle control; and eco-transportation applications. CSM publications can be accessed here.
ARPA-E Traveler Response Architecture using Novel Signaling for Network Efficiency in Transportation (TRANSNET) Project
The Center for Sustainable Mobility teamed with Xerox' Palo Alto Research Center (PARC) to develop the Collaborative Optimization and Planning Transportation Energy Reduction (COPTER) controller, a complete solution for comprehensive transportation network modeling. This project leveraged PARC's competencies in the model-based control of complex systems and human cognitive modeling, CSM's recognized leadership in transportation modeling and control, and Xerox's substantial incumbency as a provider of transportation service solutions to U.S. cities. CSM built a large-scale, agent-based, multi-modal model (cars, buses, trains, bicycles and walking) that was used to model over 3 million travelers in the greater Los Angeles area during morning and evening peak periods. According to the results, when 10% of the greater Los Angeles population received COPTER messages during peak periods, 55% of the message recipients (i.e., 5.5% of peak period travelers) accepted the recommendations, resulting in up to a 4% reduction in energy and 20% reduction in delay over the entire area.
Developing an Eco-Cooperative Automated Control System (Eco-CAC)
This work is funded by the Department of Energy Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, Energy Efficient Mobility Systems Program under award number DE-EE0008209. Given that the transportation sector accounts for 69% of the nation’s petroleum consumption and 33% of the nation’s CO2 emissions, any reductions in the energy consumed by the transportation sector will have significant environmental benefits. Connected Vehicle (CV) systems comprise sets of applications that connect vehicles to each other and to the roadway infrastructure using vehicle-to-vehicle and vehicle-to-infrastructure communications. CVs produce cooperative, network-wide benefits through the exchange of information and have the potential to drastically improve the efficiency and sustainability of our transportation system. This project aims to substantially reduce vehicle fuel/energy consumption by integrating vehicle control strategies with CV applications. Specifically, CSM is developing a novel integrated control system that (1) routes vehicles in a fuel/energy-efficient manner and balances the flow of traffic entering congested regions, (2) selects vehicle speeds based on anticipated traffic network evolution to avoid or delay the breakdown of a sub-region, (3) minimizes local fluctuations in vehicle speeds (also known as speed volatility), and (4) enhances the fuel/energy efficiency of various types of vehicles.
Developing Collaborative Connected Automated Vehicle Lane Selection and Merging Algorithms
This effort is being conducted in collaboration with Toyota and as part of the University Mobility Equity Center (UMEC). The research entails developing algorithms to identify the optimum lane allocation of CAVs along freeways and developing collaborative lane changing algorithms using game theory. It is anticipated that this research will improve the flow of traffic along freeway sections.
Traffic Signal Control within a Connected Vehicle Environment
This on-going effort entails developing real-time traffic signal control algorithms that use CV data. The effort has resulted in the development of a de-centralized cycle-free Nash bargaining traffic signal controller. The controller is currently being tested on different networks and has produced very promising results.
Multi-modal Network-wide Traffic State Prediction
The objective of this collaborative research effort with Ford is to develop a multi-modal trip departure and routing system considering a desired time of arrival at one’s destination while accounting for traveler response to these recommendations.
- Anne Deekens