Connected Vehicles at Signalized Intersections: Traffic Signal Timing Estimation and Optimization

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

Optimal scheduling of connected and autonomous vehicles at a reservation-based intersection.

Reservation-based intersection control has been evaluated with better performance over traditional signal controls in terms of intersection safety, efficiency, and emission. Controlling connected and autonomous vehicles (CAVs) at a reservation-based intersection in terms of improving intersection efficiency is performed via two factors: trajectory (speed profile) and arrival time of CAVs at the intersection. In an early stage of the reservation-based intersection control, an intersection controller at the intersection may fail to find a feasible solution for both the trajectory and arrival time for a CAV at a certain planning horizon. Leveraging a deeper understanding of the control problem, reservation-based intersection control methods are able to optimize both trajectory and arrival time simultaneously while overcoming the infeasible condition. Furthermore, in order to achieve real-time control at the reservation-based intersection, a scheduling problem of CAV crossing the intersection has been widely modeled to optimize the intersection efficiency. Efficient solution algorithms have been proposed to overcome the curse of dimensionality. However, a control methodology consisting of trajectory planning and arrival time scheduling that can overcome the infeasible condition has not been explicitly explained and defined. Furthermore, an optimal control framework for joint control of the trajectory planning and arrival time scheduling in terms of global intersection efficiency has not been theoretically established and numerically validated; and mechanisms of how to reduce the time complexity meanwhile solving the scheduling problem to an optimal solution are not fully understood and rigorously defined. In this dissertation, a control method that eliminates the infeasible problem at any planning horizon is first explicitly explained and defined based on a time-speed-independent trajectory planning and scheduling model. Secondly, this dissertation theoretically defines the optimal control framework via analyzing various control methods in terms of intersection capacity, throughput and delay. Furthermore, this dissertation theoretically analyzes the mechanism of the scheduling problem and designs an exact algorithm to further reduce the time complexity. Through theoretical analyses of the properties of the scheduling problem, the reasons that the time complexity can be reduced are fundamentally explained. The results first validate that the defined control framework can adapt to extremely high traffic demand scenarios with feasible solutions at any planning horizon for all CAVs. Under extensive sensitivity analyses, the theoretical definition of the optimal control framework is validated in terms of maximizing the intersection efficiency. Moreover, numerical examples validate that a proposed scheduling algorithm finds an optimal solution with lower computation time and time complexity

Traffic Management System for the combined optimal routing, scheduling and motion planning of self-driving vehicles inside reserved smart road networks

The topic discussed in this thesis belongs to the field of automation of transport systems, which has grown in importance in the last decade, both in the innovation field (where different automation technologies have been gradually introduced in different sectors of road transport, in the promising view of making it more efficient, safer, and greener) and in the research field (where different research activities and publications have addressed the problem under different points of view). More in detail, this work addresses the problem of autonomous vehicles coordina tion inside reserved road networks by proposing a novel Traffic Management System (TMS) for the combined routing, scheduling and motion planning of the vehicles. To this aim, the network is assumed to have a modular structure, which results from a certain number of roads and intersections assembled together. The way in which roads and intersections are put together defines the network layout. Within such a system architecture, the main tasks addressed by the TMS are: (1) at the higher level, the optimal routing of the vehicles in the network, exploiting the available information coming from the vehicles and the various elements of the network; (2) at a lower level, the modeling and optimization of the vehicle trajectories and speeds for each road and for each intersection in the network; (3) the coordination between the vehicles and the elements of the network, to ensure a combined approach that considers, in a recursive way, the scheduling and motion planning of the vehicles in the various elements when solving the routing problem. In particular, the routing and the scheduling and motion planning problems are formulated as MILP optimization problems, aiming to maximize the performance of the entire network (routing model) and the performance of its single elements - roads and intersections (scheduling and motion planning model) while guaranteeing the requested level of safety and comfort for the passengers. Besides, one of the main features of the proposed approach consists of the integration of the scheduling decisions and the motion planning computation by means of constraints regarding the speed limit, the acceleration, and the so-called safety dynamic constraints on incompatible positions of conflicting vehicles. In particular, thanks to these last constraints, it is possible to consider the real space occupancy of the vehicles avoiding collisions. After the theoretical discussion of the proposed TMS and of its components and models, the thesis presents and discusses the results of different numerical experiments, aimed at testing the TMS in some specific scenarios. In particular, the routing model and the scheduling and motion planning model are tested on different scenarios, which demonstrate the effectiveness and the validity of such approach in performing the addressed tasks, also compared with more traditional methods. Finally, the computational effort needed for the problem solution, which is a key element to take into account, is discussed both for the road element and the intersection element

A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy