We consider a transportation system of heterogeneously connected vehicles, where not all vehicles are able to communicate. Heterogeneous connectivity in transportation systems is coupled to practical constraints such that (i) not all vehicles may be equipped with devices having communication interfaces, (ii) some vehicles may not prefer to communicate due to privacy and security reasons, and (iii) communication links are not perfect and packet losses and delay occur in practice. In this context, it is crucial to develop control algorithms by taking into account the heterogeneity. In this paper, we particularly focus on making traffic phase scheduling decisions. We develop a connectivity-aware traffic phase scheduling algorithm for heterogeneously connected vehicles that increases the intersection efficiency (in terms of the average number of vehicles that are allowed to pass the intersection) by taking into account the heterogeneity. The simulation results show that our algorithm significantly improves the efficiency of intersections as compared to the baselines.
Traffic phase scheduling: Design and development of traffic phase scheduling algorithms have a long history; more than 50 years @cite_22 . Thus, there is huge literature in the area, especially on the design of optimal pre-timed policies @cite_22 @cite_6 @cite_1 , which activate traffic phases according to a time-periodic pre-defined schedule. These policies do not meet expectations under changing arrival times, which require adaptive control @cite_3 . The adaptive control mechanisms including @cite_4 , @cite_5 , @cite_8 , @cite_19 , @cite_15 and @cite_21 , optimize control variables, such as traffic phases, based on traffic measures, and apply them on short term.
@cite_4 @cite_22 @cite_8 @cite_21 @cite_1 @cite_3 @cite_6 @cite_19 @cite_5 @cite_15
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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. 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. A framework is given for discussing how to adjust load in order to handle periodic processes whose timing parameters vary with time. The schedulability of adjustable periodic processes by a preemptive fixed priority scheduler is formulated in terms of a configuration selection problem. Specifically, two process transformations are introduced for the purpose of deriving a bound for the achievable utilization factor of processes whose periods are related by harmonics. This result is then generalized so that the bound is applicable to any process set and an efficient algorithm to calculate the bound is provided. When the list of allowable configurations is implicitly given by a set of scalable periodic processes, the corresponding period assignment problem is shown to be NP-complete. The authors present an approximation algorithm for the period assignment problem for which some encouraging experimental results are included. > We study a processing system comprised of parallel queues, whose individual service rates are specified by a global service mode (configuration). The issue is how to switch the system between various possible service modes, so as to maximize its throughput and maintain stability under the most workload-intensive input traffic traces (arrival processes). Stability preserves the job inflow–outflow balance at each queue on the traffic traces. Two key families of service policies are shown to maximize throughput, under the mild condition that traffic traces have long-term average workload rates. In the first family of cone policies, the service mode is chosen based on the system backlog state belonging to a corresponding cone. Two distinct policy classes of that nature are investigated, MaxProduct and FastEmpty. In the second family of batch policies (BatchAdapt), jobs are collectively scheduled over adaptively chosen horizons, according to an asymptotically optimal, robust schedule. The issues of nonpreemptive job processing and non-negligible switching times between service modes are addressed. The analysis is extended to cover feed-forward networks of such processing systems nodes. The approach taken unifies and generalizes prior studies, by developing a general trace-based modeling framework (sample-path approach) for addressing the queueing stability problem. It treats the queueing structure as a deterministic dynamical system and analyzes directly its evolution trajectories. It does not require any probabilistic superstructure, which is typically used in previous approaches. Probability can be superposed later to address finer performance questions (e.g., delay). The throughput maximization problem is seen to be primarily of structural nature. The developed methodology appears to have broader applicability to other queueing systems.