**2. Literature Review**

### *2.1. Studies Analyzing Spatially Marginalized Areas in Terms of Transportation Services*

In their study, Lee et al. [11] identified the mobility of di fferent marginalized groups using smart card data and evaluated the mobility of groups highly reliant on public transportation. Building upon this, the study also categorized regions into di fferent types according to their need for mobility improvement and concluded that mainly the outskirt areas of a city require improvements. Ha et al. [12] located areas marginalized from public transportation services in Seoul, using real travel characteristics gathered using Google and T map navigation application programming interfaces (APIs), and they analyzed areas with high priority in service enhancement. Data, such as travel characteristics and socioeconomic indices, were analyzed, identifying Gangbuk-gu, Seongbuk-gu, Seodaemun-gu, Jungnang-gu, and the southeast zone as areas requiring improvements in public transportation. Han [13] deduced spatially marginalized areas by assessing user mobility and the service level of providers. Moreover, the study reviewed the potential equity issue caused among di fferent groups and suggested ways to improve it. According to the study, overall public transportation showed a satisfactory level from the users' perspective; however, there was a large deviation in terms of the

supply level by region. Notably, on the outskirts of the city, including Nowon-gu and Gwanak-gu, the gap between supply and demand appeared to be wide and, thus, most urgently requiring an improvement in supply.

From an accessibility perspective, Lee et al. [14] developed various indices for the connectivity, directness, and diversity of public transportation using transportation card data and assessed each transportation zone. This study confirmed that, with a higher connectivity of public transportation, it had more routes and better directness. On the contrary, in zones located on the outskirts of the city, marginalization from public transportation was vivid. Kim et al. [15] and Yoon et al. [16] studied regional marginalization and inequity by considering not just spatial accessibility, but also social classes. Kim et al. overlaid and compared socioeconomic characteristics and city zones using location data of public transportation in Daegu. According to their analysis, the low accessibility and environmental inequity of socially disadvantaged people (the aged, recipients of national basic livelihood benefits, etc.) in suburban areas were confirmed. Yoon et al. calculated the inequity index of socially marginalized people on the basis of a Gini-style index and the methods of accessibility measure developed by Curie. As for public transportation accessibility, the regional gap was bigger for the subway than the bus. When this was overlaid on top of the data for the socially disadvantaged group, inequity was confirmed to be greater for the subway than the bus.

There were studies assessing equity depending on regional di fferences in infrastructure. Kim et al. [17] analyzed disadvantaged regions by overlaying service areas of public transportation and confirmed that suburb areas were mainly in a disadvantageous position. Furthermore, when considering socioeconomic characteristics, the study concluded that there was a gap in public transportation infrastructure among regions. Lee et al. [18] used Seoul Metropolitan Household Travel Survey data to measure regional equity among di fferent income brackets depending on the levels of transportation infrastructure. The study showed that lower spatial equity led to longer total traveling time. Bin et al. [19] carried out a spatial cluster analysis at the administrative unit (Eup, Myeon, and Dong) level with transportation infrastructure indicators and travel behavior to assess equity in Gyeonggi-do province (excluding Seoul Metropolitan City and Incheon City). The results clearly showed gaps between areas closer to Seoul and those on the outskirts of the city. In particular, equity at the infrastructure level in northern Gyeonggi-do province was low.

### *2.2. Dynamic Pricing Studies on Ridesharing Service*

Before reviewing previous literature, dynamic pricing can be defined as a strategy in which prices change flexibly for the same product or service depending on the market situation [20–22]. This strategy is mainly employed with respect to e-commerce, flight tickets, and hotel booking and demand management. This results in the optimization of selling products and services in an environment where the price can be easily adjusted. With regard to dynamic pricing for ridesharing services, there were studies on profitability improvement and the determination of an appropriate price through a pricing strategy [23–30], studies analyzing the elements of a pricing strategy a ffecting customers or drivers [31–33], and a study based on reinforcement learning [34].

First, Banerjee et al. [23] validated the performance of dynamic pricing by suggesting a ridesharing model considering two aspects: the stochastic dynamics of the market and the strategic decisions of the drivers, passengers, and platform. According to the analysis, depending on supply and demand conditions, flexible pricing resulted in increased total utility. Zeng et al. [24] researched the dynamic pricing strategy in accordance with potential users considering the destination of taxies. Markov Decision Process (MDP) was established by considering the cost of pick-ups at the destination. The total utility was enhanced compared to the fixed cost case. Hall et al. [25] studied the economic utility of surge pricing by analyzing Uber data.

When prices rise in line with the surge price algorithm, the delta a ffected the improving profitability of drivers, supply, and e fficiency. Moreover, if surge pricing was not employed during peak hours, passenger waiting time was extended as drivers did not pick up passengers and passenger utility dropped.

In an analysis of the e ffects of dynamic pricing on drivers through Uber cases, Chen et al. identified that the surge price has a negative impact on passengers and a positive impact on drivers. Furthermore, the study found unfairness along the regional border of surge pricing. Chen et al. [32] studied changes in the number of Uber drivers depending on changes in surge price. The analysis confirmed that there was a higher rate of vehicle operation, as well as changes in operating hours, during the time period in which higher profit was expected from the drivers' perspective. Kooti et al. [33] analyzed the impact of dynamic pricing and income on the behavior of drivers, and they found that drivers operating vehicles during peak hours earned a higher income compared to nonpeak-hour drivers.

Wu et al. [34] simulated the application of dynamic pricing to ridesharing services. They compared four pricing methodologies, namely, (1) statistic pricing, (2) proportional pricing, (3) batch updates, and (4) reinforcement learning with the goal of profit maximization in a single Origin-Destination(OD) scenario. The simulation found that pricing based on reinforcement learning increased the total profit and individual profit of drivers the most.

### *2.3. Summary of the Review*

Previous studies on mobility-disadvantaged regions mainly focused on evaluating areas marginalized from public transportation from a user's perspective utilizing big data, such as smart card data, household travel surveys, and Geographic Information System Data Base (GIS DB: location data for bus and subway, thematic transportation map, etc.). These studies identified that public transportation services in these regions were not adequately provided. On the other hand, they paid little attention to the late-night mobility inconvenience in regions after public transportation services had ended.

As for dynamic pricing in ridesharing services, studies were carried out mainly using Uber cases. As Uber discloses operation data, several studies related to dynamic pricing were conducted. In particular, the surge-based pricing analysis of Uber showed that it helped increase driver supply and operational profit. On the contrary, if a single-fare system was applied without flexibility, overall utility decreased due to a lower rate of matching and a longer waiting time when suppliers decided not to take passengers. However, there were no studies measuring regional di fferences in dynamic pricing; thus, it is necessary to determine ways of addressing drivers' refusal to take passengers when taking regional di fferences into account.

Accordingly, a study evaluating the dynamic pricing solution is required to improve the quality of mobility services in disadvantaged regions in Seoul.
