Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Mechanical Engineering

Committee Member

Dr. Ardalan Vahidi, Committee Chair

Committee Member

Dr. Joshua Summers

Committee Member

Dr. Mohammed Daqaq

Committee Member

Dr. Paul Venhovens

Committee Member

Dr. Andre Luckow


Summary: While traffic signals ensure safety of conflicting movements at intersections, they also cause much delay, wasted fuel, and tailpipe emissions. Frequent stops and goes induced by a series of traffic lights often frustrates passengers. However, the connectivity provided by connected vehicles applications can improve this situation. A uni-directional traffic signal to vehicle communication can be used to guide the connected vehicles to arrive at green which increases their energy efficiency; and in the first part of the dissertation, we propose a traffic signal phase and timing estimator as a complementary solution in situations where timing information is not available directly from traffic signals or a city’s Traffic Management Center. Another approach for improving the intersection flow is optimizing the timing of traditional traffic signals informed by uni-directional communication from connected vehicles. Nevertheless, one can expect further increase in energy efficiency and intersection flow with bi-directional vehicle-signal communication where signals adjust their timings and vehicles their speeds. Autonomous vehicles can further benefit from traffic signal information because they not only process the incoming information rather effortlessly but also can precisely control their speed and arrival time at a green light. The situation can get even better with 100%penetration of autonomous vehicles since a physical traffic light is not needed anymore. However, the optimal scheduling of the autonomous vehicle arrivals at such intersections remains an open problem. The second part of the dissertation attempts to address the scheduling problem formulation and to show its benefits in microsimulation as well as experiments.

Intellectual Merit: In the first part of this research, we study the statistical patterns hidden in the connected vehicle historical data stream in order to estimate a signal’s phase and timing (SPaT). The estimated SPaT data communicated in real-time to connected vehicles can help drivers plan over time the best vehicle velocity profile and route of travel. We use low-frequency probe data streams to show what the minimum achievable is in estimating SPaT. We use a public feed of bus location and velocity data in the city of San Francisco as an example data source. We show it is possible to estimate, fairly accurately, cycle times and duration of reds for pre-timed traffic lights traversed by buses using a few days worth of aggregated bus data. Furthermore, we also estimate the start of greens in real-time by monitoring movement of buses across intersections. The results are encouraging, given that each bus sends an update only sporadically (≈ every 200 meters) and that bus passages are infrequent (every 5-10 minutes). The accuracy of the SPaT estimations are ensured even in

presence of queues; this is achieved by extending our algorithms to include the influence of queue delay. A connected vehicle test bed is implemented in collaboration with industry. Our estimated SPaT information is communicated uni-directionally to a connected test vehicle for those traffic signals which are not connected. In the second part of the dissertation, another test bed, but with bi-directional communication capability, is implemented to transfer the connected vehicle data to an intelligent intersection controller through cellular network. We propose a novel intersection control scheme at the cyber layer to encourage platoon formation and facilitate uninterrupted intersection passage. The proposed algorithm is presented for an all autonomous vehicle environment at an intersection with no traffic lights. Our three key contributions are in communica-tion, control, and experimental evaluation: i) a scalable mechanism allowing a large number of vehicles to subscribe to the intersection controller, ii) reducing the vehicle-intersection coordination problem to a Mixed Integer Linear Program (MILP), and iii) a Vehicle-in-the-Loop (VIL) test bed with a real vehicle interacting with the intersection control cyber-layer and with our customized microsimulations in a virtual road network environment. The proposed MILP-based controller receives information such as location and speed from each subscribing vehicle and advises vehicles of the optimal time to access the intersection. The access times are computed by periodically solving a MILP with the objective of minimizing intersection delay, while ensuring intersection safety and considering each vehicle’s desired velocity. In order to estimate the fuel consumption reduction potential of the implemented system, a new method is proposed for estimating fuel consumption using the basic engine diagnostic information of the vehicle-in-the-loop car.

Broader Impacts: This research can transform not only the way we drive our vehicles at signalized intersec-tions but also the way intersections are managed. As we evaluated in a connected test vehicle in the first part of the dissertation, our SPaT estimations in conjunction with the SPaT information available directly from Traffic Management Centers, enables the drivers to plan over time the best vehicle velocity profile to reduce idling at red lights. Other fuel efficiency and safety functionalities in connected vehicles can also benefit from such information about traffic signals’ phase and timing. For example, advanced engine management strategies can shut down the engine in anticipation of a long idling interval at red, and intersection collision avoidance and active safety systems could foresee potential signal violations at signalized intersections. In addition, as shown in the second part of the dissertation, when a connected traffic signal or intersection con-troller is available, intelligent control methods can plan in real-time the best timings and the lengths of signal phases in response to prevailing traffic conditions with the use of connected vehicle data. Our MILP-based intersection control is proposed for an all autonomous driving environment; and right now, it can be utilized in smart city projects where only autonomous vehicles are allowed to travel. This is expected to transform driving experience in the sense that our linear formulations minimizes the intersection delay and number of stops significantly compared to pre-timed intersections.



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