A bi‐level model to optimize road networks for a mixture of manual and automated driving: An evolutionary local search algorithm

Abstract

This paper presents a bi‐level model to optimize automated‐vehicle‐friendly subnetworks in urban road networks and an efficient algorithm to solve the model, which is relevant for the transition period with vehicles of different automation levels. We formulate the problem as a network design problem, define solution requirements, present an effective solution method that meets those requirements, and compare its performance with two other solution algorithms. Numerical examples for network of Delft are presented to demonstrate the concept and solution algorithm performances. Results indicate that our proposed solution outperforms competing ones in all criteria considered. Furthermore, our findings show that the optimal configuration of these subnetworks depends on the level of demand; lower penetration rates of automated vehicles call for less dense subnetworks, and thereby less investments. Nonetheless, a large proportion of benefits are already achievable with low‐density subnetworks. Denser subnetworks can deliver higher benefits with higher penetration rates.

Optimizing Road Networks for Automated Vehicles with Dedicated Links, Dedicated Lanes, and Mixed-Traffic Subnetworks

“…In transport literature, strategic decisions regarding road networks are commonly modeled as bilevel network design problems (NDPs) [27,28]. Studies that propose optimal network design concepts for AVs have followed this framework as well [19,21,25]. erefore, we model the optimal deployment of AV-ready subnetworks, dedicated AV links, and dedicated AV lanes as a bilevel NDP.…”

“…e deployment of AV-ready subnetworks, dedicated AV links, and dedicated AV lanes affects link travel times. e most common approach to derive the travel time function of links with AVs in mixed traffic for macroscopic static traffic assignment models is considering shorter driving gaps for AVs [25,[29][30][31]. In this approach, the Bureau of Public Roads (BPR) travel time function is used along with a total link flow equivalent based on passenger car equivalent (PCE) values, which is the sum of class flows (RVs and AVs) multiplied by a scaling parameter (i.e., the PCE value) representing their driving time headways.…”

“…Madadi et al [24] suggested an AV-ready subnetwork where the infrastructure is enhanced to allow efficient use of ADS in mixed traffic. Furthermore, they proposed a model for optimal deployment of AV-ready subnetworks [25] and studied the evolution of these subnetworks over time with a multistage model [26]. e AV-ready subnetwork concept entails selecting a subset of roads that have the potential to meet certain design and quality requirements, upgrading them to meet these standards, and constructing a subnetwork (within the road network) that can facilitate uninterrupted, safe, and efficient operation of AVs in mixed traffic (i.e., on the same lane as RVs).…”

“…e requirements include machine-readable and uniform lane markings and road signs, high road surface quality, high-definition digital maps, and V2I communication infrastructure [25]. is can ensure safety for all road users and increase link capacity.…”

Promoting Autonomous Vehicles Using Travel Demand and Lane Management Strategies

“…Wu et al (2020) examined the system optimal design of a small network (with HDV streets and AV expressways) under congestion pricing in a bid to minimize the cost of system travel time. Using a bi-level framework, Madadi et al (2020) investigated metropolitan agency decisions for road link retrofit, for example, installing machine-readable road signs and lane markings, to accommodate AVs. At the upper level, the total cost of link retrofit and total system travel time were minimized, and at the lower level, travelers route choice decisions were optimized using a logit-based stochastic user equilibrium model.…”