Optimal Signal Design for Mixed Equilibrium Networks with Autonomous and Regular Vehicles

Abstract

A signal design problem is studied for efficiently managing autonomous vehicles (AVs) and regular vehicles (RVs) simultaneously in transportation networks. AVs and RVs move on separate lanes and two types of vehicles share the green times at the same intersections. The signal design problem is formulated as a bilevel program. The lower-level model describes a mixed equilibrium where autonomous vehicles follow the Cournot-Nash (CN) principle and RVs follow the user equilibrium (UE) principle. In the upper-level model, signal timings are optimized at signalized intersections to allocate appropriate green times to both autonomous and RVs to minimize system travel cost. The sensitivity analysis based method is used to solve the bilevel optimization model. Various signal control strategies are evaluated through numerical examples and some insightful findings are obtained. It was found that the number of phases at intersections should be reduced for the optimal control of the AVs and RVs in the mixed networks. More importantly, incorporating AVs into the transportation network would improve the system performance due to the value of AV technologies in reducing random delays at intersections. Meanwhile, travelers prefer to choose AVs when the networks turn to be congested.

Optimal Design of Transportation Networks with Automated Vehicle Links and Congestion Pricing

“…In recent years, a number of studies have focused on the development of algorithms for NDP and road pricing, including sensitivity-analysisbased [9,29], cutting constraint [30], linearization [31,32], and relaxation [27,33] algorithms. Some metaheuristic algorithms have also been applied to solve these kinds of problems.…”

“…We can obtain vehicle flows in the initial network by solving UE problems (4)- (12); when setting candidate AV link set̃= ⌀ without congestion pricing. After solving this UE problem for vehicle flows, we find that no AVs run on links (5,6), (6,5), (6,9), (7,8), (8,7), and (8,3) in the initial network. So we exclude these links from the candidate link set and form the following candidate link set: = {(1, 5) , (1,6) , (2,5) , (2, 6) , (5, 7) , (5, 9) , (6,8) , (9, 7) , (9,8) , (7,3) , (7,4) , (8,4)} .…”

“…AV link flow CV link flow (1,5) 35.000 0.000 (1,6) 0.000 35.000 (2,5) 35.000 0.000 (2,6) 0.000 35.000 (5,6) 0.000 0.000 (6,5) 0.000 0.000 (5,7) 55.774 0.000 (5,9) 14.226 0.000 (6,8) 0.000 42.768 (6,9) 0.000 27.232 (9,7) 4.450 18.119 (9,8) 9.775 9.113 (7,8) 0.000 18.119 (8,7) 0.000 0.000 (7,3) 30.000 0.000 (7,4) …”

“…Two of the anticipated advantages (relative to human-driver vehicle control) are increased road network capacity and the freeing up of driver-occupant time to engage in leisure activities or economically productive (nondriving) tasks [8]. Jiang [9] studied optimal signal design for mixed equilibrium networks with autonomous and regular vehicles. With the rapid increase of car ownership, the problem of traffic emission and lack of land for parking becomes a serious issue [10], and AV can substantially reduce traffic emission and save land for parking.…”

Commute Equilibrium for Mixed Networks with Autonomous Vehicles and Traditional Vehicles

“…For a taxi company, a taxi fleet with AVs can be more easily operated and its operational efficiency can be substantially enhanced due to the application of information technology in AVs. In turn, travelers can enjoy comfortable AV services and reduction of travel time due to the improvement of traffic flow by reducing random delays [4]. With the implementation of AV-type taxi service, the ownership of private cars would decrease and less land demand would be required for car parking.…”