Modeling the interdependencies of the transportation and communication systems

With the introduction of machine connectivity (vehicles, bicycles, devices) there is a need to capture the interdependencies of the transportation and communication systems. As part of this effort the CSM is developing various low-computational communication system abstractions and integrating these abstractions with the INTEGRATION software (a software being developed in CSM). Current work includes the modeling of X2I DSRC communication modeling and 4G/LTE C-V2X (Release 14) communication modeling. The communication models have been implemented in the INTEGRATION software and work is currently underway to model various CV applications accounting for the interdependencies of these systems. This work is funded by Ford and the US Department of Energy.

A Study of the Impact of Ridesharing on Public Transit Ridership

Since ridesharing services were first introduced to the market in 2010, its market share more than tripled. With such a rapid increase in market share, the relationship between the ridesharing industry and public transit started to gain attention from policy makers, transportation planners, and researchers. The current body of research on ridehailing services is limited by its novelty and the corresponding lack of publicly available ridehailing trip data. The conclusions are conflicting. The majority of existing research used stated preference data to identify the relationship between ridesharing and public transit by asking participants’ choices. Our proposed research will explore the patterns of ridesharing trips, modeling the relationship between public transit and ridesharing, and propose regulation policies using objective datasets. With an extra-large-scale ridesharing trip data at an individual ridesharing trip level combining with real-time public transit information, the proposed research will be the first to explore the impacts of ridesharing on public transit without using stated preference survey. We will also explore ways to integrate ridesharing with public transit by linking ridesharing with public transit.

Estimating Traffic Stream Density Using Connected Vehicle Data

The number of on-road vehicles has increased rapidly over the past few decades, leading to serious traffic congestion in many areas. An efficient way of solving traffic congestion is improving traffic management strategies using advanced technologies and advanced traffic signal control systems that optimize traffic signal timings in real-time. Knowing the number of vehicles on a specific roadway segment is crucial in developing efficient adaptive traffic signal controllers; however, it is difficult to measure traffic density directly in the field.

This research aims to estimate the total number of vehicles on signalized approaches using only connected vehicle (CV) data. The estimate outcomes can be provided to traffic signal controllers to optimally determine the allocation of green time for each traffic signal phase, leading to better intersection performance measures. Different estimators (filters) using CV data will be developed to estimate the total number of vehicles on signalized links, such as Kalman and particle filters. One concern with using CVs is measuring their level of market penetration (LMP). The LMP is defined as the ratio of the total number of CVs to the total number of vehicles. Providing accurate LMP estimates should improve the estimation accuracy of the vehicle counts. Therefore, in this research, a machine-learning model will be developed to provide real-time estimates of the LMP values. Then, the developed filtering model will be combined with the developed machine learning model to improve the vehicles count estimation accuracy. In addition, an adaptive filtering technique will be developed to enable real-time estimates of statistical parameters of the system noise rather than using predefined values for the entire simulation. Finally, this research will examine the impacts of traffic demand level on the estimation model, considering both under- and over-saturated conditions.

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.

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.