Measuring the Quality of Arterial Traffic Signal Timing – A Trajectory-based Methodology

Evaluating the benefits from traffic signal timing is of increasing interest to transportation policymakers, operators, and the public as integrating performance measurements with agencies’ daily signal timing management has become a top priority. This dissertation presents a trajectory-based methodology for evaluating the quality of arterial signal timing, a critical part of signal operations that promises reduced travel time and fewer vehicle stops along arterials as well as improved travelers’ perception of transportation services. The proposed methodology could significantly contribute to performance-oriented signal timing practices by addressing challenges regarding which performance measures should be selected, how performance measurements can be performed cost-effectively, and how to make performance measures accessible to people with limited knowledge of traffic engineering. A review of the current state of practice and research was conducted first, indicating an urgent research need for developing an arterial-level methodology for signal timing performance assessments as the established techniques are mostly based on by-link or by-movement metrics. The literature review also revealed deficiencies of existing performance measures pertaining to traffic signal timing. Accordingly, travel-run speed and stop characteristics, which can be extracted from vehicle GPS trajectories, were selected to measure the quality of arterial signal timing in this research.Two performance measures were then defined based on speed and stop characteristics: the attainability of ideal progression (AIP) and the attainability of user satisfaction (AUS). In order to determine AIP and AUS, a series of investigations and surveys were conducted to characterize the effects of non-signal-timing-related factors (e.g., arterial congestion level) on average travel speed as well as how stops may affect travelers’ perceived quality of signal timing. AIP was calculated considering the effects of non-signal-timing-related factors, and AUS accounted for the changes in the perceived quality of signal timing due to various stop circumstances.Based upon AIP and AUS, a grade-based performance measurement methodology was developed. The methodology included AIP scoring, AUS scoring, and two scoring adjustments. The two types of scoring adjustments further improved the performance measurement results considering factors such as cross-street delay, pedestrian delays, and arterial geometry. Furthermore, the research outlined the process for implementing the proposed methodology, including the necessary data collection and the preliminary examination of the applicable conditions. Case studies based on real-world signal re-timing projects were presented to demonstrate the effectiveness of the proposed methodology in enhancing agencies’ capabilities of cost-effectively monitoring the quality of arterial signal timing, actively addressing signal timing issues, and reporting the progress and outcomes in a concise and intuitive manner

Connected and Automated Vehicles in Urban Transportation Cyber-Physical Systems

Understanding the components of Transportation Cyber-Physical Systems (TCPS), and inter-relation and interactions among these components are key factors to leverage the full potentials of Connected and Automated Vehicles (CAVs). In a connected environment, CAVs can communicate with other components of TCPS, which include other CAVs, other connected road users, and digital infrastructure. Deploying supporting infrastructure for TCPS, and developing and testing CAV-specific applications in a TCPS environment are mandatory to achieve the CAV potentials. This dissertation specifically focuses on the study of current TCPS infrastructure (Part 1), and the development and verification of CAV applications for an urban TCPS environment (Part 2). Among the TCPS components, digital infrastructure bears sheer importance as without connected infrastructure, the Vehicle-to-Infrastructure (V2I) applications cannot be implemented. While focusing on the V2I applications in Part 1, this dissertation evaluates the current digital roadway infrastructure status. The dissertation presents a set of recommendations, based on a review of current practices and future needs. In Part 2, To synergize the digital infrastructure deployment with CAV deployments, two V2I applications are developed for CAVs for an urban TCPS environment. At first, a real-time adaptive traffic signal control algorithm is developed, which utilizes CAV data to compute the signal timing parameters for an urban arterial in the near-congested traffic condition. The analysis reveals that the CAV-based adaptive signal control provides operational benefits to both CVs and non-CVs with limited data from 5% CVs, with 5.6% average speed increase, and 66.7% and 32.4% average maximum queue length and stopped delay reduction, respectively, on a corridor compared to the actuated coordinated scenario. The second application includes the development of a situation-aware left-turning CAV controller module, which optimizes CAV speed based on the follower driver\u27s aggressiveness. Existing autonomous vehicle controllers do not consider the surrounding driver\u27s behavior, which may lead to road rage, and rear-end crashes. The analysis shows that the average travel time reduction for the scenarios with 600, 800 and 1000 veh/hr/lane opposite traffic stream are 61%, 23%, and 41%, respectively, for the follower vehicles, if the follower driver\u27s behavior is considered by CAVs

Development and evaluation of cooperative intersection management algorithm under connected vehicles environment

Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various automated and connected vehicle (C/AV) solutions around the globe. Wireless communication technologies such as the dedicated short-range communication (DSRC) protocol are enabling instantaneous information exchange between vehicles and infrastructure. Such information exchange produces tremendous benefits with the possibility to automate conventional traffic streams and enhance existing signal control strategies. While many promising studies in the area of signal control under connected vehicle (CV) environment have been introduced, they mainly offer solutions designed to operate a single isolated intersection or they require high technology penetration rates to operate in a safe and efficient manner. Applications designed to operate on a signalized corridor with imperfect market penetration rates of connected vehicle technology represent a bridge between conventional traffic control paradigm and fully automated corridors of the future. Assuming utilization of the connected vehicle environment and vehicle to infrastructure (V2I) technology, all vehicular and signal-related parameters are known and can be shared with the control agent to control automated vehicles while improving the mobility of the signalized corridor. This dissertation research introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. The Trajectory-driven Optimization for Automated Driving (TOAD) provides an optimal trajectory for automated vehicles while maintaining safe and uninterrupted movement of general traffic, consisting of regular unequipped vehicles. Signal status parameters such as cycle length and splits are continuously captured. At the same time, vehicles share their position information with the control agent. Both inputs are then used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. To determine the most efficient trajectory for automated vehicles, an evolutionary-based optimization is utilized. Influence of the prevailing traffic conditions is incorporated into a control algorithm using conventional data collection methods such as loop detectors, Bluetooth or Wi-Fi sensors to collect vehicle counts, travel time on corridor segments, and spot speed. Moreover, a short-term, artificial intelligence prediction model is developed to achieve reasonable deployment of data collection devices and provide accurate vehicle delay predictions producing realistic and highly-efficient longitudinal vehicle trajectories. The concept evaluation through microsimulation reveals significant mobility improvements compared to contemporary corridor management approach. The results for selected test-bed locations on signalized arterials in New Jersey reveals up to 19.5 % reduction in overall corridor travel time depending on different market penetration and lane configuration scenario. It is also discovered that operational scenarios with a possibility of utilizing reserved lanes for movement of automated vehicles further increases the effectiveness of the proposed algorithm. In addition, the proposed control algorithm is feasible under imperfect C/AV market penetrations showing mobility improvements even with low market penetration rates

System-level Eco-driving (SLED): Algorithms for Connected and Autonomous Vehicles

One of the main reasons for increasing carbon emissions by the transportation sector is the frequent congestion caused in a traffic network. Congestion in transportation occurs when demand for commuting resources exceeds their capacity and with the increasing use of road vehicles, congestion and thereby emissions will continue to rise if proper actions are not taken. Adoption of intelligent transportation systems like autonomous vehicle technology can help in increasing the efficiency of transportation in terms of time, fuel and carbon footprint. This research proposes a System Level Eco-Driving (SLED) algorithm and compares the results, produced by performing microscopic simulations, with conventional driving and the coordination heuristic (COORD) algorithm. The SLED algorithm is designed based on shortcomings and observations of the COORD algorithm to improve the traffic network efficiency. In the SLED strategy, a trailing autonomous vehicle would only request coordination if it is within a set distance from the preceding autonomous vehicle and coordination requests will be evaluated based on their estimated system level emissions impact. Additionally, the human-driven vehicles will not be allowed to change lanes. Average CO2 emissions per vehicle for SLED showed improvements ranging from 0% to 5% compared to COORD. Additionally, the threshold limit to surpass the conventional driving behavior CO2 emissions at 900 vehicles per hour density reduced to 30% using SLED as compared to 40% using the COORD algorithm. Average wait time per vehicle for the SLED algorithm at 1200 vehicles per hour density increased by one to six seconds as compared to the COORD strategy although reduced up to thirty seconds of wait time compared to the conventional driving behavior. This finding can be helpful for policy makers to switch the algorithms based on the requirement i.e. opt for the SLED algorithm if reducing emissions has a higher priority compared to wait and travel time while opt for the COORD algorithm if reducing wait and travel time has a higher priority compared to emissions

Enhancing Mixed Traffic Flow Safety Via Connected and Autonomous Vehicle Trajectory Planning with a Reinforcement Learning Approach

The longitudinal trajectory planning of connected and autonomous vehicle (CAV) has been widely studied in the literature to reduce travel time or fuel consumptions. The safety impact of CAV trajectory planning to the mixed traffic flow with both CAV and human-driven vehicle (HDV), however, is not well understood yet. This study presents a reinforcement learning modeling approach, named Monte Carlo tree search-based autonomous vehicle safety algorithm, or MCTS-AVS, to optimize the safety of mixed traffic flow, on a one-lane roadway with signalized intersection control. Crash potential index (CPI) is defined to quantitively measure the safety performance of the mixed traffic flow. The CAV trajectory planning problem is firstly formulated as an optimization model; then, the solution procedure based on reinforcement learning is proposed. The tree-expansion determination module and rollout termination module are developed to identify and reduce the unnecessary tree expansion, so as to train the model more efficiently towards the desired direction. The case study results showed that the proposed algorithm was able to reduce the CPI by 76.56%, when compared with a benchmark model without any intelligence, and 12.08%, when compared with another benchmark model that the team developed earlier. These results demonstrated the satisfactory performance of the proposed algorithm in enhancing the safety of the mixed traffic flow

On Constant Distance Spacing Policies for Cooperative Adaptive Cruise Control

Cooperative Adaptive Cruise Control (CACC) systems are considered as key potential enablers to improve driving safety and traffic efficiency. They allow for automated vehicle following using wireless communication in addition to onboard sensors. To achieve string stability in CACC platoons, constant time headway (CTH) spacing policies have prevailed in research; namely, vehicle interspacing grows with the speed. While constant distance headway (CDH) spacing policies provide superior potential to increase traffic capacity than CTH, a major drawback is a smaller safety margin at high velocities and string stability cannot be achieved using a one-vehicle look-ahead communication. The hypothesis of this work is to apply CDH only in few driving situations, when traffic throughput is of highest importance and safety requirements can be met due to comparably low velocities. As the most relevant situations where CDH could be applied, we identify starting platoons at signalized intersections. In this paper, we illustrate this idea. Specifically, we compare CTH with CDH regarding its potential to increase the capacity of traffic lights. Starting with the elementary situation of single traffic lights we expand our scope to whole traffic networks including several thousand vehicles in simulation. Using real world data to calibrate and validate vehicle dynamics simulation and traffic simulation, the study discusses the most relevant working parameters of CDH, CTH, and the traffic system in which both are applied.Comment: In preparation for submission to IEEE Transactions on Intelligent Transportation System

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