Traffic Equilibrium for Mixed Traffic Flows of Human-Driven Vehicles and Connected and Autonomous Vehicles in Transportation Networks under Tradable Credit Scheme

Abstract

In this paper, the traffic equilibriums for mixed traffic flows of human-driven vehicles (HDV) and connected and autonomous vehicles (CAV) under a tradable credit scheme (TCS) are established and formulated as two variational inequality (VI) problems with exogenous and endogenous CAV penetration rate, respectively. A modified Lagrangian dual (MLD) method embedded with a revised Smith’s route-swapping (RSRS) algorithm is proposed to solve the problems. Based on the numerical analysis, the impacts of CAV penetration and the extra expense of using a CAV on network performance are investigated. A novel driveway management, autonomous vehicle/credit charge (AVCC) link, is put forward to improve the efficiency of TCS. Under the TCS with exogenous CAV penetration rate, a logit-based model is applied to describe the stochastic user equilibrium for mixed traffic flow. It is found that the penetration of CAV gives rise to a better network performance and it can be further improved by the deployment of AVCC link. Under the TCS with endogenous penetration rate, a nested-logit model is applied to describe travelers’ choices of vehicle types and routes. It is found that the deployment of AVCC links can slow down the decline rate of CAV penetration with increasing expense and thus ensure a lower average travel time for CAVs. In both cases, the deployment of AVCC links can stimulate credit trading and drop down its unit price.

Investigating the Impacts of Transaction Cost under a Tradable Credit Scheme with Heterogenous Users

“…Since the ME credit price is exactly the Lagrangian multiplier with respect to the credit conservation constraint, algorithms that can acquire solutions to both the primal and the dual problems are theoretically able to solve VI problem (12). In the literature, a gradient projection method was proposed by Han and Cheng [34] for solving equilibrium credit price and successfully applied in solving minimization model [35] and VI model [19]. In this section, a more efficient trial-and-error method is developed to solve the credit price in VI problem (12).…”

“…Integrating link-based discrete credit charging scheme into the discrete network design problem, Wang et al [18] proposed a mixed-integer nonlinear bilevel programming model to improve the network performance from the perspectives of both transport network planning and travel demand management. For autonomous vehicle (AV) management, Zhang et al [19] investigated the traffic equilibriums for mixed traffic flows of human-driven vehicles and connected autonomous vehicles in the context of TCS, and Seilabi et al [20] developed a scheme for travel demand and lane management strategies in the AV transition era. ere are also some works including a case study based on experiments [21][22][23][24].…”

Reliability-Based Mixed Traffic Equilibrium Problem Under Endogenous Market Penetration of Connected Autonomous Vehicles and Uncertainty in Supply

“…Wang et al (2019) proposed a VI-based model for mixed traffic of UE-seeking CAVs and SUE-seeking HVs. Zhang et al (2020) considered the perception error of travelers and established a nested logit (NL) model to describe the combined vehicle and path choices under tradable credit schemes. Xie and Liu (2022) adopted the demand proportion of CAVs to reflect their information quality and varying perception error.…”

Optimal Tradable Credit Scheme Design with Recommended Credit Price

“…Following this, a significant tranche of research has been conducted to seek the applicability of TCS in traffic management. e extensions include user heterogeneity [14][15][16][17][18], day-to-day dynamic [19][20][21], network design [22,23], bottleneck management [12,[24][25][26][27][28][29][30], multimodal network [31][32][33], environmental issue [34][35][36][37], equity issue [38,39], public-private partnership [40,41], and autonomous vehicle management [42,43]. is paper aims to investigate one aspect that has hitherto received little attention, namely, the optimal credit scheme design in the context of probit-based stochastic user equilibrium (SUE).…”