A Two-Stage Stochastic Programming Approach for the Electric Vehicle Public Charging Station Location Problem Under Uncertain Dynamic Household Activity-Travel Demand

In this paper, the authors propose a novel two-stage stochastic mixed integer programming (TSMIP) algorithm with recourse for locating plug-in electric vehicle (PEV) public charging stations in conjunction with an advanced activity-based model of charging demand. A chi-square automatic interaction detector (CHAID)-based dynamic decision tree is used to estimate charging demand under uncertainty represented by a set of scenarios. The dynamic decision tree represents some measure of uncertainty since it consists of a series of nodes and branches that specify the condition states and personal profiles (i.e., deterministic part), and the leaf nodes with probabilistic action states that lead to particular choice behavior (i.e., stochastic part). The contributions of this study can be listed as follows: (i) Charging demand is directly estimated from multi-day activity-travel diary data of PEV users; (ii) Given the uncertain nature of demand inherited from the probabilistic decision tree, a two-stage stochastic programming model is proposed to solve the strategic location-allocation optimization problem of PEV public charging stations; (iii) A novel scenario-generation method combining decision tree and multiple scenario trees is proposed, which results in statistically well-defined models; (iv) The proposed approach is demonstrated for the city of Eindhoven, The Netherlands using activity-based travel demand model ALBATROSS.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ADB40 Standing Committee on Transportation Demand Forecasting.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Kim, Seheon
    • Rasouli, Soora
    • Timmermans, Harry
    • Yang, Dujuan
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 19p

Subject/Index Terms

Filing Info

  • Accession Number: 01697536
  • Record Type: Publication
  • Report/Paper Numbers: 19-04091
  • Files: TRIS, TRB, ATRI
  • Created Date: Mar 1 2019 3:51PM