Traffic congestion at arterial intersections and freeway bottlenecks degrades the air quality and threatens the public health. Conventionally, air pollutants are monitored by sparsely distributed Quality Assurance Air Monitoring Sites. Sparse mobile crowd-sourced data, such as cellular network and Global Positioning System (GPS) data, contain large amount of traffic information, but have low sampling rate and penetration rate due to the cost limit on data transmission and archiving. The sparse mobile data provide a supplement or alternative approach to evaluate the environmental impact of traffic congestion. This research establishes a framework for traffic-related air pollution evaluation using sparse mobile data and traffic volume data from California Performance Measurement System (PeMS) and Los Angeles Department of Transportation (LADOT). The proposed framework integrates traffic state model, emission model and dispersion model. An effective tool is developed to evaluate the environmental impact of traffic congestion for both arterials and freeways in an accurate, timely and economic way. The proposed methods have good performance in estimating monthly peak hour fine particulate matter (PM 2.5) concentration, with error of 2 ug/m3 from the measurement from monitor sites. The estimated spatial distribution of annual PM 2.5 concentration also matches well with the concentration map from California Communities Environmental Health Screening Tool (CalEnviroScreen), but with higher resolution. The proposed system will help transportation operators and public health officials alleviate the risk of air pollution, and can serve as a platform for the development of other potential applications.

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Transportation related activities has substantially increased the mobility of people and goods, but also brings impacts to the environment, such as greenhouse gas (GHG) emissions, air pollution, energy consumption, and health problems. This growing energy consumption and emission crisis has drawn tremendous attention from the public agencies, industry and researchers. Different solutions have been proposed to reduce the energy consumption from the transportation sector. For example, alternative energy sources, such as electricity and hydrogen, have been proposed as more energy-efficient and environmental-friendly fuel sources. In addition to eco-friendlier fuel sources, wireless communication and automated driving technology can also be applied to develop connected eco-friendly transportation systems. Connected and automated vehicles (CAVs) have shown the capability to improve traffic mobility and energy efficiency via vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication. Among all CAV applications, Eco Approach and Departure (EAD) at Signalized Intersections is particularly promising for fuel saving and emission reduction in urban area, as drivers would effectively reduce stops and idling and avoid unnecessary acceleration and deceleration by receiving signal timing information in advance.In real-world traffic, signal timing and traffic conditions are quite dynamic and uncertain in the scope of different road networks. Due to the existence of different varieties of vehicle classes and vehicle engine types, the algorithm developed for eco-driving also needs to be powertrain-specific. In this dissertation, advanced eco-trajectory planning algorithms have been developed to address four challenges: 1) Driving in urban areas with uncertain queue conditions due to the limitation of the communication and sensing range; 2) Developing an adaptive Eco-Approach and Departure (EAD) strategy to minimize the expected energy consumption when passing an actuated signalized intersection; 3) Extending the intersection-based eco-driving algorithm to corridor level for global optimal performance; and 4) Customizing eco-driving algorithms to other vehicles, such as electric trucks, under different traffic volumes and vehicle fleet mix.

As part of the City of Riverside’s Smart-City initiative, UC Riverside researchers have developed an Innovation Corridor testbed for enabling shared electric connected and automated transportation research. This Innovation Corridor testbed is located in Riverside California, and consists of a six-mile section of University Avenue between the UC Riverside campus and downtown Riverside. The testbed supports various transportation modes including passenger vehicles, trucks, transit (e.g., RTA buses), bicycles, and various forms of micro-mobility. This corridor is continuously being instrumented with various infrastructure equipment to support research in shared electric connected and automated transportation. Specifically for this project, the corridor has been equipped with roadside communications equipment and advanced traffic signal controllers at several key intersections, to help improve safety, mobility and environmental sustainability. With this initial instrumentation, we have then conducted connected vehicle experimentation that utilize the signal phase and timing (SPaT) data from these intersections to smooth traffic flow and reduce emissions. For this Innovation Corridor, a high-fidelity simulation environment was also developed to evaluate potential connected vehicle strategies. A variety of Eco-Approach and Departure (EAD) connected vehicle experiments have been conducted and evaluated, both in simulation and in the real-world. As part of the simulation ecosystem, we have compared the energy and emissions modeling results to see which best matches the real-world measurements.

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Connected and automated vehicle technology could bring about transformative reductions in traffic congestion, greenhouse gas emissions, air pollution, and energy consumption. Connected and automated vehicles (CAVs) can directly communicate with other vehicles and road infrastructure and use sensing technology and artificial intelligence to respond to traffic conditions and optimize fuel consumption. An eco-approach and departure application for connected and automated vehicles has been widely studied as a means of calculating the most energy-efficient speed profile and guiding a vehicle through signalized intersections without unnecessary stops and starts. Simulations using this application on roads with fixed-timing traffic signals have produced 12% reductions in fuel consumption and greenhouse gas emissions. But real-world traffic conditions are much more complex—uncertainties and the limited sensing range of automated vehicles create challenges for determining the most energy-efficient speed.

To account for this uncertainty, researchers from the University of California, Riverside, propose a prediction-based, adaptive connected eco-driving strategy. The proposed strategy analyzes the possible upcoming traffic and signal scenarios based on historical data and live information collected from communication and sensing devices, and then chooses the most energy-efficient speed. This approach can be extended to accommodate different vehicle powertrains and types of roadway infrastructure. This research brief summarizes findings from the research and provides research implications.

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The Eco-Approach and Departure (EAD) application has been proved to be environmentally efficient for a Connected and Automated Vehicles (CAVs) system. In the real-world traffic, traffic conditions and signal timings are usually dynamic and uncertain due to mixed vehicle types, various driving behaviors and limited sensing range, which is challenging in EAD development. This research proposes an adaptive strategy for connected eco-driving towards a signalized intersection under real world conditions. Stochastic graph models are built to link the vehicle and external (e.g., traffic, signal) data and dynamic programing is applied to identify the optimal speed for each vehicle-state efficiently. From energy perspective, adaptive strategy using traffic data could double the effective sensor range in eco-driving. A hybrid reinforcement learning framework is also developed for EAD in mixed traffic condition using both short-term benefit and long-term benefit as the action reward. Micro-simulation is conducted in Unity to validate the method, showing over 20% energy saving.

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Surface transportation systems (e.g., arterial roadways with signalized intersections) are inherently inefficient, particularly at higher traffic volumes. In general, both the infrastructure (e.g., traffic signals) and the vehicles operate independently, with little coordination between them. Previous research has shown that implementing strategies that take advantage of infrastructure-tovehicle communication can improve overall mobility and reduce environmental impacts, e.g., the Eco-Approach and Departure (EAD) application that takes advantage of communicating signal phase and timing information to the vehicles. In this paper, the authors build upon this past research to develop a new cooperative traffic operation approach that takes advantage of not only infrastructure-to-vehicle communications, but also vehicle-to-infrastructure communications. This effort integrates a dynamic traffic signalization algorithm together with EAD algorithm to achieve even greater traffic efficiency. The research was carried out in a high-fidelity simulation environment and shows upwards of 15% fuel savings and 85% reductions in waiting time.

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