**5. Conclusions**

As ridesharing (including taxi) services are often run by private companies, profitability is the top priority in operation. This leads to an increase in drivers' refusal to take passengers to areas with low demand where they have difficulties finding subsequent passengers, causing problems such as extended waiting time when hailing a vehicle for passengers bound for these regions. This problem differs depending on time and region. In late-night hours and in suburban regions, the imbalance between supply and demand is especially widened, worsening the problem. In order to address this problem, solutions were proposed in this study through a dynamic pricing strategy using reinforcement learning algorithms.

This study used Seoul city's taxi data to find appropriate fare surge rates for ridesharing services between 10:00 p.m. and 4:00 a.m. In reinforcement learning, the outcome of centrality analysis was applied as the weight affecting drivers' destination choice probability. Moreover, the reward function used during learning was adjusted according to whether or not a passenger waiting time value was applied. Profit was used as the reward value. By applying a negative reward for the passenger's waiting time, a more appropriate surge fare level could be identified. Across the region, the average surge level amounted to 1.6. Regions located on the outskirts of the city in predominantly residential regions such as Gangdong-gu, Dongjak-gu, Eunpyeong-gu, and Gangseo-gu showed a higher surge. On the contrary, central areas, such as Gangnam-gu, Jongno-gu, and Jung-gu, had a lower surge. The findings showed that the supply of ridesharing services in low-demand regions could be increased by as much as 7.5% using surge fares, thereby reducing regional equity problems related to ridesharing services in Seoul to a grea<sup>t</sup> extent.

This study conducted a reinforcement learning-based dynamic pricing simulation to respond to the regional equity problem of ridesharing (including taxi) services in Seoul. A novel approach was presented using dynamic pricing as a way to mitigate the spatial equity problem by affecting ridesharing supply, unlike most previous dynamic pricing studies which simply targeted higher profitability. Notably, it was shown that a surge rate change in fares could reduce the indirect refusal of drivers to take passengers to unpreferred areas. With additional real-time ridesharing user data, the Deep Q-Network(DQN) technique can be adopted to conduct a smaller-scale spatial analysis of ridesharing services. Furthermore, with more knowledge on fare sensitivity by user group, the dynamic pricing approach proposed in this study can significantly contribute to resolving the spatial equity problem in mobility services in the future.

**Author Contributions:** Conceptualization, K.Y.H.; data curation, J.S.; investigation, Y.J.C. and M.H.K.; methodology, J.S.; project administration, M.H.K.; software, J.S.; writing—original draft, J.S.; writing—review and editing, K.Y.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by a gran<sup>t</sup> (20TLRP-B148970-03) from the Transportation and Logistics R&D Program funded by the Ministry of Land, Infrastructure, and Transportation of the Korean government.

**Acknowledgments:** This paper is a modification and expansion of Jae In Song's doctoral dissertation.

**Conflicts of Interest:** The authors declare no conflict of interest.
