In this paper, we propose a novel two-stage stochastic mixed integer programming (TSMIP) 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, and (iv) the proposed approach is demonstrated for the city of Eindhoven, The Netherlands using activity-based travel demand model ALBATROSS.
|Title of host publication||Proceedings of the 99th annual meeting of the Transportation Research Board|
|Publication status||Published - 2019|
|Event||98th Annual Meeting of the Transportation Research Board - Walter E. Washington Convention Center, Washington, D.C., United States|
Duration: 13 Jan 2019 → 17 Jan 2019
|Conference||98th Annual Meeting of the Transportation Research Board|
|Period||13/01/19 → 17/01/19|