Adaptive traffic signal control for real-world scenarios in agent-based transport simulations

This study provides an open-source implementation of a decentralized, adaptive signal control algorithm in the agent-based transport simulation MATSim, which is applicable for large-scale real-world scenarios. The implementation is based on the algorithm proposed by Lämmer and Helbing (2008), which had promising results, but was not applicable to real-world scenarios in its published form. The algorithm is extended in this paper to cope with realistic situations like different lanes per signal, small periods of overload, phase combination of non-conflicting traffic, and minimum green times. Impacts and limitations of the adaptive signal control are analyzed for a real-world scenario and compared to a fixed-time and traffic-actuated signal control. It can be shown that delays significantly reduce and queue lengths are lower and more stable than with fixed-time signals. Another finding is that the adaptive signal control behaves like a fixed-time control in overload situations and, therefore, ensures system-wide stability

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

Deep learning for real-time traffic signal control on urban networks

Real-time traffic signal controls are frequently challenged by (1) uncertain knowledge about the traffic states; (2) need for efficient computation to allow timely decisions; (3) multiple objectives such as traffic delays and vehicle emissions that are difficult to optimize; and (4) idealized assumptions about data completeness and quality that are often made in developing many theoretical signal control models. This thesis addresses these challenges by proposing two real-time signal control frameworks based on deep learning techniques, followed by extensive simulation tests that verifies their effectiveness in view of the aforementioned challenges. The first method, called the Nonlinear Decision Rule (NDR), defines a nonlinear mapping between network states and signal control parameters to network performances based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past. The NDR is implemented and tested within microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization procedure aiming to reduce delay, CO2 and black carbon emissions. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in optimizing different traffic and environmental objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The second method, called the Advanced Reinforcement Learning (ARL), employs the potential-based reward shaping function using Q-learning and 3rd party advisor to enhance its performance over conventional reinforcement learning. The potential-based reward shaping in this thesis obtains an opinion from the 3rd party advisor when calculating reward. This technique can resolve the problem of sparse reward and slow learning speed. The ARL is tested with a range of existing reinforcement learning methods. The results clearly show that ARL outperforms the other models in almost all the scenarios. Lastly, this thesis evaluates the impact of information availability and quality on different real-time signal control methods, including the two proposed ones. This is driven by the observation that most responsive signal control models in the literature tend to make idealized assumptions on the quality and availability of data. This research shows the varying levels of performance deterioration of different signal controllers in the presence of missing data, data noise, and different data types. Such knowledge and insights are crucial for real-world implementation of these signal control methods.Open Acces

Evaluation of Automatic Vehicle Specific Identification (AVSI) in a traffic signal control system

Automatic Vehicle Specific Identification (AVSI) is a generic name for advanced vehicle detection systems. By automating the identification of vehicles by sensing the presence of vehicles with roadside detection sites or readers, AVSI is assumed to provide vehicle specific information in traffic signal control systems;In the application of AVSI to traffic signal control systems, as a vehicle passes a reader site, the reader records the arrival time and type of the detected vehicle. The reader would then send the information received to a local microprocessor-based traffic signal controller. The controller\u27s built-in signal control logic would then use the information to adjust traffic signal timing to reflect the present traffic stream\u27s characteristics;The purpose of this research is to evaluate the potential benefits of AVSI at an isolated intersection. The evaluation of the applicability of AVSI at an intersection is accomplished by using a new developed microscopic simulation model. This simulation model is coded in SIMAN simulation language. For the purpose of validating the simulation model, a delay study is conducted at an actual intersection. The validation of the model has established a level of confidence in the obtained simulation results;An important element of this simulation model is the development of a new Vehicle Specific Adaptive (VSA) traffic signal control strategy. VSA control strategy adjusts the signal timing based on AVSI traffic information, that is, it examines individual vehicle performance characteristics before extending a phase green time or implementing a new cycle split;Using the simulation model, the incorporated VSA control strategy is tested against a pretimed control system. The simulation results indicates that through the use of AVSI traffic information, the VSA control logic can improve intersection performance by reducing vehicles stopped delay at an intersection

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

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

MAVEN Deliverable 6.4: Integration Final Report

This document presents the work that has been performed in WP6 after D6.3, and therefore focussing on the integration sprints 3-6. It describes which parts of the system are implemented and how they are put together. To do so, it builds upon the deliverables created so far, esp. D6.3 and all other deliverables of the underlying work packages 3, 4 and 5. Another important aspect for understanding the content of this deliverable is D2.1 [4] for the scenario definition of the whole MAVEN project, and the deliverables D6.1 [5] and D6.2 [6], which give an overview on the existing infrastructure and vehicles used in MAVEN