Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to improve the efficiency of traffic flow in complex urban road networks. In this approach, a scheduling agent is associated with each intersection. Each agent senses the traffic approaching its intersection and in real-time constructs a schedule that minimizes the cumulative wait time of vehicles approaching the intersection over the current look-ahead horizon. In this paper, we propose a cooperative algorithm that utilizes both connected and autonomous vehicles (CAV) and schedule-driven traffic control to create better traffic flow in the city. The algorithm enables an intersection scheduling agent to adjust the arrival time of an approaching platoon through use of wireless communication to control the velocity of vehicles. The sequence of approaching platoons is thus shifted toward a new shape that has smaller cumulative delay. We demonstrate how this algorithm outperforms the original approach in a real-time traffic signal control problem.
Traditionally, there are three general approaches to control traffic signal: a) fixed timing; b) actuated; and c) adaptive. The earliest implementations are based on a fixed timing method optimized using historical traffic data offline. The later advancements have used actuated or adaptive signals. Then, if all cars are equipped with wireless communication technologies, e.g., Dedicated Short Range Communications (DSRC), to communicate with a centralized infrastructure, we can optimize the traffic flow by ordering the phases of traffic signal more efficiently. In @cite_15 @cite_2 information from equipped vehicles is used to determine demands and optimize the cycle length and green splits of a traffic signal once every cycle. In @cite_8 the presence of platoons is detected using V2I communication and a mixed integer non-linear program is solved to produce the optimal phasing sequence. However, this approach does not scale well for generating long horizon plans and does not deal with uncertainty of traffic states.
@cite_8 @cite_15 @cite_2
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The operation of traffic signals is currently limited by the data available from traditional point sensors. Point detectors can provide only limited vehicle information at a fixed location. The most advanced adaptive control strategies are often not implemented in the field because of their operational complexity and high-resolution detection requirements. However, a new initiative known as connected vehicles allows the wireless transmission of the positions, headings, and speeds of vehicles for use by the traffic controller. A new traffic control algorithm, the predictive microscopic simulation algorithm, which uses these new, more robust data, was developed. The decentralized, fully adaptive traffic control algorithm uses a rolling-horizon strategy in which the phasing is chosen to optimize an objective function over a 15-s period in the future. The objective function uses either delay only or a combination of delay, stops, and decelerations. To measure the objective function, the algorithm uses a micro... Adaptive traffic signal control, which adjusts traffic signal timing according to real-time traffic, has been shown to be an effective method to reduce traffic congestion. Available works on adaptive traffic signal control make responsive traffic signal control decisions based on human-crafted features (e.g. vehicle queue length). However, human-crafted features are abstractions of raw traffic data (e.g., position and speed of vehicles), which ignore some useful traffic information and lead to suboptimal traffic signal controls. In this paper, we propose a deep reinforcement learning algorithm that automatically extracts all useful features (machine-crafted features) from raw real-time traffic data and learns the optimal policy for adaptive traffic signal control. To improve algorithm stability, we adopt experience replay and target network mechanisms. Simulation results show that our algorithm reduces vehicle delay by up to 47 and 86 when compared to another two popular traffic signal control algorithms, longest queue first algorithm and fixed time control algorithm, respectively. Wireless vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions. A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (e.g., vehicle speed and location or traffic signal state and timing). Unless controlled, the aggregate of these broadcasts will congest the channel under dense traffic scenarios, reducing the effectiveness of collision avoidance applications that use transmitted information. Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology. This paper defines a new adaptive congestion control algorithm that can be applied to the message rate of devices in this vehicular environment. While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full-precision control inputs that are available on the wireless channel. The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters. This “perfect” convergence avoids the limit cycle behavior that is inherent to binary control. We also discuss several practical aspects associated with implementing LIMERIC, including guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust convergence under all conditions, convergence with asynchronous updates, and using channel load to determine the aggregate message rate that is observable at a receiver. This paper also extends the convergence analysis for two important cases, i.e., measurement noise in the input signal and delay in the update process. This paper illustrates key analytical results using MATLAB numerical results and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high-density scenarios. A novel concept for a decentralized adaptive traffic signal control in urban networks using in future available vehicle to infratructure (V2I) communication data is presented. The phase-based strategy takes advantage of the improved detection data and optimizes each time interval of 5 seconds the phase sequence in order to reduce the total queue length within a forecast horizon of 20 seconds. For optimization the methods of dynamic programming and complete enumeration are used. The methods are embedded in the simulation environment of the microscopic traffic simulator AIMSUN NG. The market penetration level is the critical factor that impacts the quality of the new signal control. Hence, various penetration levels are modelled. For reference TRANSYT-7F is used.