Modeling the influence of charging cost on electric ride-hailing vehicles
Transportation Research Part C: Emerging Technologies, ISSN: 0968-090X, Vol: 160, Page: 104514
2024
- 1Citations
- 15Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations1
- Citation Indexes1
- CrossRef1
- Captures15
- Readers15
- 15
Article Description
Major transportation network companies (TNCs) have promised to shift to 100% electric vehicles (EVs) in the next two decades, which places an increasing need to investigate the issues of ride-hailing services provided by EVs. Existing studies that model the EV charging systems and the TNC service systems omit the influences of the charging costs (i.e., electricity rate, and value of waiting time) on driver supply and passenger demand, which results in inaccurate prediction of system dynamics. This study is the first attempt to understand the influence of the electricity rate on the demand/supply of ride-hailing services and its implications. We compute the charging cost as the sum of the electricity cost based on the charging volume and the values of the expected waiting time. Specifically, we construct a queueing model framework to calculate the expected waiting time with M/M/k/C and synchronized M/M/1 queues, which models the charging and ride-hailing service processes separately. The experiment results from a two-symmetric-unit network show that the system performance metrics, such as platform profit and ratios of passengers served, have decreasing trends with increasing electricity rates. These trends shift when electricity rates and wage/trip fare rates change simultaneously, indicating the TNC platforms are able to achieve high profits by adjusting wage/fare rates to handle changes in electricity rates. Similar performance trends are validated by increasing electricity rates on large-scale experiments based on real-world trip demand. We further undertake sensitivity analysis and conclude that as the passenger demand increases, the system’s performance metrics, such as platform profit and the percentage of served passengers, gradually converge, within the constraints of the number of EVs and their battery capacity; the usage frequencies of charging stations follow the Pareto Principle, where roughly 15% of stations could serve most of the charging demand.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0968090X24000354; http://dx.doi.org/10.1016/j.trc.2024.104514; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85185195388&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0968090X24000354; https://dx.doi.org/10.1016/j.trc.2024.104514
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know