Modeling and Managing Electric Vehicle Drivers’ Travel Behavior in a Demand-Supply-Coupled Transportation System
Open Access
- Author:
- Song, Yang
- Graduate Program:
- Civil Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- September 26, 2023
- Committee Members:
- Andisheh Ranjbari, Major Field Member
Xianbiao Hu, Chair & Dissertation Advisor
Satadru Dey, Outside Unit & Field Member
Vikash Gayah, Major Field Member
Jay Regan, Professor in Charge/Director of Graduate Studies - Keywords:
- electric vehicles
travel behaviors
demand-supply-coupled
transportation system - Abstract:
- The global shift towards electric vehicles (EVs) holds immense promise for mitigating greenhouse gas (GHG) emissions and advancing sustainable development goals. Nonetheless, the limited market penetration of EVs persists, primarily due to challenges in meeting the demand for replenishment compared to conventional internal combustion engine vehicles (ICEVs). The overarching goal of this dissertation is to develop mathematical models and a management framework for EV drivers’ travel behaviors in a demand-supply-coupled transportation system, with the ultimate aim of facilitating the widespread adoption of EVs. By gaining deeper insights into and effectively managing various aspects of EV driver behaviors, such as charging preferences and route choices, the following benefits can be achieved: meeting the charging demands of EV drivers, optimizing the utilization of charging facility supply, and promoting the adoption of EVs as a preferred mode of travel. Firstly, the charging behavior of EV drivers is modeled based on the given charging facility supply. The existing research efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To fill the gap, an ensemble learning model based on gradient boosting is developed. A total of 18 features are defined and extracted from the multisource data, which cover information on drivers, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5 years of charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. The result shows the proposed model achieved a satisfactory result with an R square value of 0.54 and root mean square error of 0.14, both better than the two benchmark models, the multiple linear regression model and the random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users, or that are further from residential areas. Secondly, the provision of charging infrastructure is formulated as a demand management mechanism accounting for the underlying demand-supply coupled relationship. The existing studies treat each charging station as an independent entity and naively select the candidate locations with the highest individual usage rates. To address this issue, a two-stage learning-based demand-supply-coupled optimization model for the charging station location problem (CSLP) is proposed, aiming to incorporate the concept of EV charging demand management into the planning of charging infrastructures. In stage one, a gradient boosting-based learning model is developed to predict the charging demand of a charging station (CS) based on 15 defined features. Next, in stage two, a demand-supply-coupled CSLP model is developed with the objective of maximizing the total charging usage rates of both existing and newly selected charging stations. The proposed model is solved using a gradient-based stochastic spatial search algorithm. A case study using the same data as the first chapter is performed to test the effectiveness of the proposed model and algorithm. Results show that the proposed method can generate satisfactory charging demand predictions, and can increase charging usage rates by 14%, outperforming two benchmark approaches, namely the Greedy-Based Method and Neighbor-Swap-Based Method. Lastly, the routing behavior, as another aspect of EV driver travel behaviors, is modeled in a community charging setting. The existing research focuses on the EV traffic assignment under the scenario of corridor charging in a small-scale road network, ignoring the link interactions in community charging and path deviations in large-scale road networks. To tackle these challenges, an EV traffic assignment model for large-scale road networks with link interaction in community charging and with path deviations is proposed. First, the mathematical formulation for the EV traffic assignment model considering the interaction among road links connecting to the same CS is proposed, which is further proven to be equivalent to the user equilibrium (UE) condition. Then, a column-generation-based solution algorithm is developed to solve the model, facilitating the complex EV path deviations in a large-scale road network. The result of numerical examples shows that the proposed algorithm could converge in 0.025, 1.71, 4.73 and 91 seconds with a relative gap of no more than 0.0008 on the four testing networks, being the most accurate and fastest compared with the three benchmark algorithms, Frank-Wolfe algorithm, Interaction-Ignored algorithm, and Commercial-Solver-Based algorithm. The sensitivity analysis results show that the total travel cost and the total system dwelling time exhibit a negative correlation with charging supply while displaying a positive correlation with charging demand.