Electric vehicle recharging options in urban areas
Discrete choice modeling to estimate user preference
Bibliographic Data
| ID | 21233640 |
|---|---|
| Authors | Lorenzo Sica (0000-0003-2791-5465, Politecnico di Torino), Angela Carboni (0000-0003-1780-1157, Politecnico di Torino, corresponding author), Francesco Deflorio (0000-0003-4109-8019, Politecnico di Torino), Cristiana Botta (0000-0003-1198-4386, LINKS Foundation) |
| Year | 2025 |
| Volume | 121 |
| Pages | 106175 |
| Publication date | 2025-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2025.106175 |
| OpenAlex | W4406893766 |
| Language | EN |
| Citations received | 7 |
| References cited | 29 |
Discrete choice models can support electric charging infrastructure deployment. • Charging preference for owners of electric cars are less affected by charging prices. • Owners of electric cars consider comfort and sustainability in charging choice. • Charging options in parking areas are preferred in case of comfort improvements. • Electric price policy may have an impact on public charging usage near home or work. Electric mobility has grown in recent years, and this requires a proper charging infrastructure according to the needs of users and all stakeholders involved in the decision-making processes. To support user-centric planning of recharging infrastructure in cities, this study investigated user behavior in charging activities. We analyzed user choices regarding different charging options during routine activities: private and public infrastructures near home or near work, and equipped parking areas available close to other places (such as shopping centers). A stated choice survey was used to collect data from European cities for a discrete choice experiment. Models were specified, calibrated, and then analyzed in key scenarios for cities to analyze user charging choices according to the main factors (such as the cost and time for charging) and show the effect of different policies on the use of public charging points. Furthermore, segmentation analysis was performed to evaluate the effect of specific socioeconomic characteristics of users on their choices. According to the scale of the attributes obtained, the most influential factor was the charging cost. The effect of price neutralization was more significant for public charging options close to home and work, whereas comfort improvements were more effective for parking areas
Discrete choice · Econometrics · Economics · Preference · Statistics · Transport engineering · Computer Science · Electric Vehicles and Infrastructure · Engineering · Environmental Science · Mathematics · Smart Grid Energy Management · Transportation and Mobility Innovations
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| Citations per year | 7 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |