Table 4 Overview of the implemented functions for an Agent-based modeling in neighborhoods mobility
Refs. | Market mechanism | Policies modelled | User behavior | Efficiency | Method |
|---|---|---|---|---|---|
Category: park-and-ride | |||||
 Zhou et al. (2019) | Determination of necessary vehicles fleet |  | Actual driving behavior | High vehicle utilization with minimal waiting time for drivers | GIS-Data and empirical driver information from Nagoya, Japan |
 Zhou et al. (2021) | Determination of necessary vehicles fleet |  | Actual driving behavior | High vehicle utilization with minimal waiting time for drivers | GIS-Data and empirical driver information from Kozoji Newtown, Japan |
Category: microgrid with battery electric vehicle | |||||
 Surmann et al. (2020) | Increasing self-consumption in the neighborhood to reduce the charging costs (bidirectional charging) of battery-electric vehicles | Communication, billing, and decision-making do not require a centralized authority | The user decides loading mode: maximum SOC, cost-optimized or performance-optimized | Less electricity is drawn from the external power grid | GIS-Data, empirical driver information |
 Xydas et al. (2016) | Demand Response: Relief of the network node through price curve |  | Battery-electric vehicles are Responsive or not responsive to the price signal |  | GIS-Data, empirical driver information |
Category: charging station and charging characteristics | |||||
 Yagües-Gomà et al. (2014) | Determination of the necessary battery size for the vehicle fleet |  |  | Two charging modes for the battery-electric vehicle: single charge end of the day or mul-tiple charges when the battery is less than 20% and capped at 80% | Travel input data is obtained from Barcelona’s driving survey, Battery data for LI-Ion |
 Lin et al. 2018) | Reduce the cost of the charging infrastructure |  | seven trip reasons: end journey, home, work, shopping, dine, pick/drop, and public recreation | Case 1: standard charging (level 2) and case 2: two location rapid charging (level 3 up to 240 kW) | GIS-Data, empirical driver information, for location spots are defined |
 Liu and Bie (2019) | Reduce the cost of the charging infrastructure |  |  | Analyze the different charging stations AC, DC, and ADC | theory based model with driver profile, GIS-Data |