Urban Congestion Patterns under High Electric Vehicle Penetration: A Case Study of 10 U.S. Cities

TL;DR

The paper addresses how high electric vehicle penetration reshapes urban congestion by introducing a fixed-class multiclass user equilibrium (MUE) model tailored for GV-EV traffic. It develops a convergent dual-based algorithm and a set of six congestion-pattern metrics to quantify system efficiency, link saturation, and spatial distribution under exogenously specified EV shares $R_e$. Through a case study of 10 U.S. cities with real road-network and OD data, the work shows that EV penetration can reduce total travel time by a city-dependent margin ($2.27\%$–$10.78\%$) and that congestion patterns respond nonlinearly through transition and plateau phases governed by network topology. The findings provide policy guidance emphasizing topology-aware EV promotion, targeted transition-zone interventions, and complementary infrastructure and dynamic management to realize efficient redistribution of traffic under mixed GV-EV fleets.

Abstract

With the global energy transition and the rapid penetration of electric vehicles (EVs), the widening travel cost gap between EVs and gasoline vehicles (GVs) increasingly affects commuters' route choices and may reshape urban congestion patterns. Existing research remains in its preliminary exploratory phase. On the one hand, multi-class models do not account for fixed user class scenarios, which may not align with actual commuters; on the other hand, there is a lack of systematic quantitative analysis based on real-world complex road networks across multiple cities. As a result, the congestion effects induced by heterogeneous GV-EV cost structures may be mischaracterized or substantially underestimated. To address these limitations, this paper proposes a multi-user equilibrium (MUE) assignment model for mixed GV-EV traffic, constructs a dual algorithm with convergence guarantees, and designs multi-dimensional evaluation metrics for congestion patterns. Using 10 representative U.S. cities as a case study, this research explores the evolution trends of traffic congestion under different EV penetration scenarios based on real city-level road networks and block-level commuter origin-destination (OD) demand. The results show that full EV penetration reduces average system travel time by 2.27%--10.78% across the 10 cities, with New Orleans achieving the largest reduction (10.78%) and San Francisco the smallest (2.27%), but the effectiveness of alleviating congestion exhibits urban heterogeneity. Moreover, for cities with sufficient network redundancy, benefits are primarily concentrated during the low to medium EV penetration stage (0-0.5), though cities with topological constraints (e.g., San Francisco) show more limited improvements throughout all penetration levels. This paper can provide a foundation for formulating differentiated urban planning and congestion management policies.

Figures (17)

• Figure 1: Illustrative dual-route network and sensitivity analysis. (a) Network topology with heterogeneous route attributes; b) Changes in the number of vehicles and travel time on the two routes as the GV cost varies. c) Changes in the average travel time and percentage of potential savings for the system as the GV cost varies. d) The impact of EV penetration on average travel time for the system in a set of representative U.S. cities. e) The distribution of GV and EV costs across U.S. cities.
• Figure 2: The spatial distribution of GV and EV costs in the United States. a) Spatial distribution of GV and EV costs. b) Spatial distribution of the sum and subtraction of GV and EV costs. c) Spatial distribution of the ratio of GV cost to EV cost. d) Spatial distribution of potential savings from converting GV cost to EV cost.
• Figure 3: The geographic locations of the selected 10 representative U.S. cities
• Figure 4: Road network and travel demand data for 10 U.S. cities. a) Number of nodes and links. b) Number of TAZs and total demand (trips).
• Figure 5: Probability distributions of commuting distances for 10 U.S. cities. a) Probability distributions of commuting distances for San Francisco, Portland, Las Vegas, New Orleans, and Dallas. b) Probability distributions of commuting distances for Milwaukee, Boston, Philadelphia, Denver, and Honolulu.

Theorems & Definitions (25)

• Definition 3.1: Congestion Pattern
• Definition 3.2: Plateau Phase
• Definition 3.3: Transition Zone
• Proposition 3.4: Variational Inequality Characterization
• proof
• Lemma 3.5: Monotonicity of the Cost Operator
• proof
• Theorem 3.6: Existence and Uniqueness
• proof
• Definition 3.7: Augmented Lagrangian