Relay Requirement and Traffic Assignment of Electric Vehicles

Xie, Chi; Jiang, Nan

doi:10.1111/mice.12193

Cited by 54 publications

(44 citation statements)

References 27 publications

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“…Previously, a diversity of network equilibrium models was developed to capture travelers’ behavioral realism and system‐level traffic performance (Unnikrishnan et al., ; Yang & Huang, ; Ziliaskopoulos, Waller, Li, & Byram, ). For example, Cen, Lo, Li, and Lee (); Chen, Liu, and Yin (); He, Yin, and Zhou (); Xie and Jiang (); and Zhang, Rey, and Waller () modeled the network equilibrium considering driving ranges of electric vehicles and deployment of recharging facilities. Chen, He, Yin, and Du () developed a mathematical framework for the optimal design of AV zones in a general network.…”

Section: Introductionmentioning

confidence: 99%

A network traffic assignment model for autonomous vehicles with parking choices

Zhang

Liu

Waller

2019

Computer aided Civil Eng

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This article is the first in the literature to investigate the network traffic equilibrium for traveling and parking with autonomous vehicles (AVs) under a fully automated traffic environment. Given that AVs can drop off the travelers at their destinations and then drive to the parking spaces by themselves, we introduce the joint equilibrium of AV route choice and parking location choice, and develop a variational inequality (VI)‐based formulation for the proposed equilibrium. We prove the equivalence between the proposed VI model and the defined equilibrium conditions. We also show that the link flow solution at equilibrium is unique, even though both the route choices and parking choices are endogenous when human‐occupied AV trips (from origin to destination) and empty AV trips (from destination to parking) are interacting with each other on the same network. We then develop a solution methodology based on the parking‐route choice structure, where we adjust parking choices in the upper level and route choices in the lower level. Numerical analysis is conducted to explore insights from the introduced modeling framework for AV network equilibrium. The results reveal the significant difference in network equilibrium flows between the AV and non‐AV situations. The results also indicate the sensitivity of the AV traffic pattern to different factors, such as value of time, parking pricing, and supply. The proposed approach provides a critical modeling device for studying the traffic equilibrium under AV behavior patterns, which can be used for the assessment of parking policies and infrastructure development in the future era of AVs.

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Section: Introductionmentioning

confidence: 99%

A network traffic assignment model for autonomous vehicles with parking choices

Zhang

Liu

Waller

2019

Computer aided Civil Eng

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“…As the driving distance limit increases, more paths are eligible to carry flows and a larger value should be used to generate more feasible paths during the assignment process. However, as indicated in [38], the change in the number of feasible paths does not always increase with the distance limit, since each subpath of the generated shortest paths would be feasible when the distance limit is large enough.…”

Section: Numerical Analysismentioning

confidence: 99%

“…Modeling a traffic network with realistic refueling behaviors may require accommodating different routing objectives (e.g., minimization of travel time, charging time, and/or fuel consumption), different refueling services (e.g., batterycharging service or battery-swapping service), and different types of vehicles (e.g., GVs and BEVs) [38]. All these factors result in different path travel cost perception and route choice behaviors.…”

Section: Journal Of Advanced Transportationmentioning

confidence: 99%

“…Most of the previous studies focused on user equilibrium (UE) problems with BEVs. Among these studies, Jiang et al [13] first introduced a path-constrained deterministic traffic assignment problem and further extended this work by considering trip chain and range anxiety analysis [36][37][38][39]. Zheng et al [40] presented a bilevel model to locate charging facility and minimize all users cost in the upper level and to find path-constrained equilibrium BEV flows in the lower level.…”

Section: Journal Of Advanced Transportationmentioning

confidence: 99%

“…Hence, a feasible path used by GVs can be decomposed into several parts for BEVs according to whether a charging action should be taken by BEV drivers at each charging station. To model BEVs paths, three notions, namely, subpath, pure subpath, and feasible subpath, proposed by Xie and Jiang [38], are introduced in the formulation of the lower level stochastic assignment problem and three charging action based scenarios are analyzed as follows.…”

Section: Preliminaries a Feasible Path For Gvs Between A Givenmentioning

confidence: 99%

See 2 more Smart Citations

Location Design of Electric Vehicle Charging Facilities: A Path-Distance Constrained Stochastic User Equilibrium Approach

Jing

Ramezani

et al. 2017

Journal of Advanced Transportation

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Location of public charging stations, range limit, and long battery-charging time inevitably affect drivers' path choice behavior and equilibrium flows of battery electric vehicles (BEVs) in a transportation network. This study investigates the effect of the location of BEVs public charging facilities on a network with mixed conventional gasoline vehicles (GVs) and BEVs. These two types of vehicles are distinguished from each other in terms of travel cost composition and distance limit. A bilevel model is developed to address this problem. In the upper level, the objective is to maximize coverage of BEV flows by locating a given number of charging stations on road segments considering budget constraints. A mixed-integer nonlinear program is proposed to formulate this model. A simple equilibrium-based heuristic algorithm is developed to obtain the solution. Finally, two numerical tests are presented to demonstrate applicability of the proposed model and feasibility and effectiveness of the solution algorithm. The results demonstrate that the equilibrium traffic flows are affected by charging speed, range limit, and charging facilities' utility and that BEV drivers incline to choose the route with charging stations and less charging time.

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An edge scanning method for the continuous deviation‐flow refueling station location problem on a general network

Abbaas

Ventura

2021

Networks

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This study addresses the continuous deviation-flow refueling station location problem on a general network. Instead of having a finite number of candidate locations, we consider any point in the network as a candidate location. In addition, vehicles are allowed to deviate from their prescribed (shortest) paths to refuel. At the beginning, we focus on the location of a single refueling facility, which is a relevant problem in the initial stages of development of a refueling infrastructure for alternative fuels in a transportation network. The objective is to maximize the traffic flow covered (in roundtrips per time unit). We propose an exact algorithm that determines the endpoints of all refueling segments on each edge of the network that cover the corresponding origin-destination flows. Then, the set with the best endpoints is shown to be optimal and can be used to determine the entire set of optimal locations. Network reduction rules and a network decomposition procedure are also discussed to reduce the size of the problem and improve the computational efficiency. Later, the entire set of endpoints is used in a set covering model to locate multiple refueling stations under the assumption that every vehicle only needs to refuel once on each way of the round trip. Moreover, we limit the allowed refueling deviation distance from the shortest path. Finally, a numerical example is provided to illustrate the proposed methodology.

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Relay Requirement and Traffic Assignment of Electric Vehicles

Cited by 54 publications

References 27 publications

A network traffic assignment model for autonomous vehicles with parking choices

A network traffic assignment model for autonomous vehicles with parking choices

Location Design of Electric Vehicle Charging Facilities: A Path-Distance Constrained Stochastic User Equilibrium Approach

An edge scanning method for the continuous deviation‐flow refueling station location problem on a general network

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