Research

Intelligent Transportation Systems (ITS)



Back-Pressure Based Traffic Signal Control for Urban Vehicular Networks

Back-pressure algorithm, which works as water flows through pipe networks according to pressure gradients, has been increasingly attractive to reduce traffic congestion for urban vehicular networks. Recent work has shown the performance superiority of back-pressure based traffic scheduling algorithms, such as throughput optimality, distributed implementation, low computational complexity, etc. However, these back-pressure based traffic scheduling algorithms either assume each road can hold infinite vehicles (infinite road capacity) or need to have prior knowledge of vehicle turning ratios, all of which are not realistic for applications. In this paper, we propose a back-pressure based traffic scheduling algorithm that can efficiently reduce traffic congestion for realistic urban vehicular networks with finite road capacity and without prior knowledge of vehicle turning ratios. we have implemented BPTSA by simulation and will show the evaluation result. Besides, the comparison with non-optimized fixed cycle traffic light will be also presented.

発表論文等

  • Ying Liu, Gao Juntao*, Yishan Lin, 伊藤 実*, “優秀ポスター賞,” 第25回マルチメディア通信と分散処理ワークショップ, 2017.10.12.


Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network

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.


Stochastic Optimization



Bias Based General Framework for Delay Reduction in Backpressure Routing Algorithm

In queueing networks, it is well known that the throughput-optimal backpressure routing algorithm results in poor delay performance for light and moderate traffic loads. To improve delay performance of backpressure routing algorithm, available works exploit various information of queueing networks, such as queue length, shortest path and packet delay, to direct packets to shorter routes to their destinations. Despite different forms of these works, they share the common characteristic: using bias to help backpressure routing to reduce packet delay. From this observation, we propose in this paper a bias based general framework to reduce packet delay of backpressure routing algorithm. Our framework is general in the sense that it not only covers many bias based variants of backpressure routing algorithm as special cases but also enables edge-cutting methods, like deep learning, to be adopted to further improve delay performance. We prove that our general framework also retains the throughput-optimality property of backpressure routing algorithm.



Optimal Scheduling for Incentive WiFi Offloading under Energy Constraint

WiFi offloading is a promising solution to alleviating the heavy traffic burden of cellular networks due to data explosion. However, since WiFi networks are intermittently available, a mobile device user in WiFi offloading usually needs to wait for WiFi connection and thus experiences longer delay of packet transmission. To motivate users to participate in WiFi offloading, cellular network operators give incentives (rewards like coupons, e-coins) to users who wait for WiFi connection and transmit packets through WiFi networks. In this paper, we aim at maximizing users’ rewards while meeting constraints on queue stability and energy consumption. However, we face scheduling challenges from random packet arrivals, intermittent WiFi connection and time varying wireless link states. To address these challenges, we first formulate the problem as a stochastic optimization problem. We then propose an optimal scheduling policy, named Optimal scheduling Policy under Energy Constraint (OPEC), which makes online decisions as to when to delay packet transmission to wait for WiFi connection and which wireless link (WiFi link or cellular link) to transmit packets on. OPEC automatically adapts to random packet arrivals and time varying wireless link states, not requiring a priori knowledge of packet arrival and wireless link probabilities. As verified by simulations, OPEC scheduling policy can achieve the maximum rewards while keeping queue stable and meeting energy consumption constraint.