Blue phase: Optimal network traffic control for legacy and autonomous vehicles

Abstract

With the forecasted emergence of autonomous vehicles in urban traffic networks, new control policies are needed to leverage their potential for reducing congestion. While several efforts have studied the fully autonomous traffic control problem, there is a lack of models addressing the more imminent transitional stage wherein legacy and autonomous vehicles share the urban infrastructure. We address this gap by introducing a new policy for stochastic network traffic control involving both classes of vehicles. We conjecture that network links will have dedicated lanes for autonomous vehicles which provide access to traffic intersections and combine traditional green signal phases with autonomous vehicle-restricted signal phases named blue phases. We propose a new pressure-based, decentralized, hybrid network control policy that activates selected movements at intersections based on the solution of mixed-integer linear programs. We prove that the proposed policy is stable, i.e. maximizes network throughput, under conventional travel demand conditions. We conduct numerical experiments to test the proposed policy under varying proportions of autonomous vehicles. Our experiments reveal that considerable trade-offs exist in terms of vehicle-class travel time based on the level of market penetration of autonomous vehicles. Further, we find that the proposed hybrid network control policy improves on traditional green phase traffic signal control for high levels of congestion, thus helping in quantifying the potential benefits of autonomous vehicles in urban networks.

Performance Evaluation of Modified Cyclic Max-Pressure Controlled Intersections in Realistic Corridors

“…Levin et al ( 23 ) gave an MP control with AIM and dynamic lane reversal capable controller with proof of stability and showed significant performance improvement compared with first-come-first-served control. Rey and Levin ( 24 ) developed a new traffic network control policy based on an MP algorithm for CAVs, and they also gave a proof of stability. Yen et al ( 25 ) compared the fairness and vulnerabilities against cyber-attacks of four different BP based controllers.…”

Backpressure control with estimated queue lengths for urban network traffic

“…In light of the work by [4] and [12], Taale et al [15] and Sun et al [6] tested both a time-slotted BP and a cyclic BP in their experiments, and interestingly, they found that the time-slotted BP works better than the cyclic BP in general. More recently, [16] proposed a BP that can accommodate both human-driven and autonomous vehicles.…”

Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data

“…As shown in Figure 1, the detection time of the first vehicle and the time interval between the first vehicle and the subsequent vehicle n are taken as the analysis parameters, and the recursive formula is obtained as shown in equation (1). e vehicle detection time, the time interval between vehicles, and the number of vehicles constitute the main influencing variables for analysing the accuracy of vehicle link travel time estimation:…”

“…e travel time is always one kind of quantitative indicators to analyse traffic network performance based on link travel time or route travel time. Even in the near future with the emergence of autonomous vehicles in urban traffic networks and the mixed flow, travel time plays an important role on solving autonomous traffic control problem such as the blue phase design [1]. In addition, travel time is one of the crucial factors affecting drivers' behaviour when they lead or follow vehicle platoon in the mixed flow with autonomous vehicles and human driven vehicles, and some works on the dedicated lane management consider the vehicle time factors such as headway to improve lane capacity [2].…”