Multitype Recharge Facility Location for Electric Vehicles

On‐road emission inventories in urban areas have typically been developed using traffic data derived from travel demand models. These approaches tend to underestimate emissions because they often only incorporate data on household travel, not including commercial vehicle movements, taxis, ride hailing services, and other trips typically underreported within travel surveys. In contrast, traffic counts embed all types of on‐road vehicles; however, they are only conducted at selected locations in an urban area. Traffic counts are typically spatially correlated, which enables the development of methods that can interpolate traffic data at selected monitoring stations across an urban road network and in turn develop emission estimates. This paper presents a new and universal methodology designed to use traffic count data for the prediction of periodic and annual volumes as well as greenhouse gas emissions at the level of each individual roadway and for multiple years across a large road network. The methodology relies on the data collected and the spatio‐temporal relationships between traffic counts at various stations; it recognizes patterns in the data and identifies locations with similar trends. Traffic volumes and emissions prediction can be made even on roads where no count data exist. Data from the City of Toronto traffic count program were used to validate the output of various algorithms, indicating robust model performance, even in areas with limited data.

Section: Resultsmentioning

confidence: 99%

Methodology for spatio‐temporal predictions of traffic counts across an urban road network and generation of an on‐road greenhouse gas emission inventory

Ganji

Shekarrizfard

Harpalani³

et al. 2019

“…Traditional studies attempted to determine the optimal serving facility locations to capture maximum customer flow as well as to minimize the number of facilities required to cover a given flow (Berman et al, 1992;Hodgson, 1990). For more information on location design problems see: (Daskin, 1996;Owen and Daskin, 1998;Snyder, 2006;Ouyang et al, 2009;Wang and Ouyang, 2013;Jung et al, 2016;Zhang, Rey et al, 2018;Wang et al, 2019). Some other studies provided joint design problems that simultaneously consider facility location problem with other network elements (Melkote and Daskin, 2001;Shen et al, 2003;Yao et al, 2010).…”

Section: Literature Reviewmentioning

confidence: 99%

A simulation‐based optimization model for infrastructure planning for electric autonomous vehicle sharing

Zhao

Cui

2019

New transportation technologies (e.g., electric autonomous vehicles (EAVs)) and operation paradigms (e.g. car sharing) are discussed, researched and to a small degree also deployed in recent years in response to rising energy crises and aggravating traffic congestions. In this research, we present a station-based car-sharing service system that integrates both EAV technologies and car sharing operations. Based on the simulation model, a dynamic and timecontinuous optimization model seeking a near-optimum design of charging station location and EAV deployment is developed. By discretizing the model, we proposed a Monte Carlo simulation model to evaluate the total system cost for a given location and vehicle deployment design. A heuristic approach based on the Genetic Algorithm is developed to solve the system design of station location and vehicle deployment. A numerical test in Yantai city, China is conducted to illustrate the effectiveness of the proposed model and to draw managerial insights into how the key parameters

“…According to the review conducted by Zhu, Fu, and Ma (), studies on network sensor location can be categorized into studies on flow‐observability‐oriented sensor location, studies on flow‐estimation‐oriented sensor location, and those on travel‐time‐estimation‐oriented sensor location. Here, the network sensor location problem belongs to the facility location problem, such as the location problem of freight facility (Hajibabai, Bai, & Ouyang, ; Xie & Ouyang, ), railroad wayside defect detection (Ouyang, Li, Barkan, Kawprasert, & Lai, ), and recharge facility for electric vehicles (Zhang, Rey, & Waller, ), in operations research area. The flow‐observability‐oriented sensor location aims to determine how many sensors are needed as well as where they should be located for the unique determination of the unobserved link flows.…”

Section: Literature Reviewmentioning

confidence: 99%

Road side unit location optimization for optimum link flow determination

Liang

2019

This study addresses the problem of road side unit (RSU) location optimization for optimum link flow determination. The error of link flow determination using RSU information comes from two sources: measurement error and inference error. The inference error is caused by the propagation and accumulation of measurement error during the link flow inference process. The direct problem formulation aiming to minimize the total error is impossible because the minimization of inference error has to be indirectly formulated as the minimization of the cumulative number of unobserved links. Further, for a given number of RSU, the decrease in the cumulative number of unobserved links results in the increase of the number of observed links, and thus results in the increase of measurement error due to data packet queueing delay. Therefore, for a given RSU installation budget, the balance between the two types of error needs to be optimized in order to achieve the optimum link flow determination accuracy. To fulfill this goal, the RSU location optimization problem is formulated as a bi‐objective nonlinear binary integer programming. This programming is constrained by complete link flow determination requirements. In order to accelerate computation, an efficient ε‐constraint method is designed to generate Pareto optimal frontier. The subproblem solved at each iteration is linearized using piecewise linear approximation and solved using a constraint generation method. The proposed model and the solution algorithm are evaluated through numerical examples. The results reveal that the Pareto optimal solutions achieved by the proposed model are at least not inferior to a non‐Pareto solution obtained in the baseline scenario. Further, both the measurement error and inference error associated with some of the Pareto optimal points are lower than those associated with the non‐Pareto optimal solution.