1. Introduction
Amidst global economic expansion, the past decade has witnessed a marked surge in air travel, with passenger volumes escalating significantly. Airport passenger traffic is expected to keep rising at a rate of 4.5% per year from 2012 to 2031. In response to the heterogeneous spatial and temporal demands of air travelers, airport shuttle bus services have emerged as a pivotal, efficient, and economical transportation alternative. These services facilitate streamlined connectivity between the airport and principal urban centers, effectively mitigating traffic congestion and diminishing parking strain. Additionally, airport shuttle buses substantially augment the quality of passenger service by enhancing accessibility for late-night and long-distance journeys, thereby offering a transportation solution that is not only expedient and economical but also minimizes urban traffic and parking challenges.
Airport shuttles buses face higher operational costs while outperforming regular bus transit services [
1]. These services must therefore balance public service coverage with profitability, a challenge compounded by rising airport passenger traffic and a growing demand for customized travel options. Current research on customized bus services primarily addresses demand analysis, route optimization, and timetable optimization. However, the areas of revenue management and mining user travel behavior during operations remain less explored, representing significant gaps in enhancing the efficiency and quality of transportation services. Differential pricing, also known as price discrimination, entails the strategic setting of disparate prices for identical services within the same temporal framework, facilitated by the identification of unique service attributes and consumer heterogeneity. This approach has been rigorously analyzed and effectively implemented across multiple sectors, including aviation, railways, and e-commerce, demonstrating its versatility and effectiveness. Applying differential pricing strategies to airport shuttles by harnessing online booking data could significantly enhance operational efficiency and fiscal sustainability. Consequently, the strategic utilization of such data to develop differential pricing schemes is imperative for maintaining the viability of airport shuttle services. This method not only ensures an optimal balance between service accessibility and profitability but also catalyzes an improvement in the overall performance of customized transportation services.
To fill existing research gaps, our study examines the order characteristics and user behaviors of airport shuttle buses to develop a differential pricing strategy based on customer price elasticity. Utilizing a dataset from January 2019 to October 2020, which includes 1.5 million user order records for customized bus (CB) routes, we analyze cabin selection behaviors, booking time distributions, and travel pattern features. These insights inform tailored differential pricing strategies. We validate our approach by applying it to real-world corporate order data with fixed external parameters, comparing the generated revenue to that under the existing pricing model. This comparison assesses the impact of our strategy on overall revenue and operational efficiency.
The remainder of this paper is structured as follows:
Section 2 provides an overview of the prevailing research on airport shuttles and differential pricing.
Section 3 outlines the spatial and temporal dimensions of the dataset collected from an airport shuttle bus company and presents an exploratory analysis of user-order data. Building on this foundation, in
Section 4, we develop a differential pricing strategy for airport shuttles, incorporating segmentation of user orders based on diverse travel characteristics. In
Section 5, we assess the effectiveness of this pricing strategy by comparing its revenue outcomes with actual enterprise order revenues. We conclude the paper in
Section 6, summarizing our findings and their implications.
3. Materials
3.1. Data Scope
This research utilized operational data from E-Drive Bus, one of Shanghai’s largest customized bus (CB) service providers, specializing in the battery-electric vehicles (BEVs) primarily serving the Shanghai Hongqiao International Airport. This study’s datasets comprised CB routes and ticketing information spanning from January 2019 to October 2020, which included a comprehensive collection of approximately 1.5 million user order records.
Figure 1 presents a box plot illustrating passenger flow vs. run changes per month for the customized bus company. Before the COVID-19 outbreak in 2019, E-Drive Bus expanded its services by introducing additional bus routes, resulting in an overall upward trend in user orders. However, fluctuations occurred during seasonal holidays such as the Spring Festival and National Day. Peak ridership during this period reached 103,071 passengers per day. The COVID-19 pandemic significantly impacted user engagement, with February 2020 marking a sharp decline to just 5724 orders. Following the outbreak, order volumes gradually recovered, peaking at 72,452, which represented approximately 70% of the pre-pandemic levels.
The CB company’s extensive service network is illustrated in
Figure 2, showcasing coverage across most districts in Shanghai. A notable feature of the network is the prevalence of longer routes that connect suburban areas with the city center. For example, the Auto City (Anting)–Hongqiao Express Line connects the Anting Auto City in Jiading District to the Hongqiao Business District. This route serves as a critical shuttle link to the Hongqiao Airport and plays a key role in facilitating the dispersal of passengers from peripheral areas into the central business hub.
Table 1 presents statistical comparisons across alternative travel modes, revealing that CB travel time is significantly shorter than public transit but marginally longer than ride-hailing services. Despite CB fares being higher than public transit, they amount to only one-third of ride-hailing costs. These findings underscore CB’s competitive advantage in providing a balanced alternative, optimizing both travel time and cost relative to public transit and ride-hailing services. However, fluctuations in CB pricing could potentially compromise its competitive position against public transit or ride-hailing. Thus, meticulous pricing strategies are critical to ensuring internal profitability and maintaining competitiveness in the market.
3.2. Order Characteristics
The historical order data analyzed in this study include detailed records of passengers’ actual boarding and alighting stations, booking times, riding prices, and seating choices. This research specifically focuses on the differentiated customization of services; therefore, basic pricing factors such as distance and time are intentionally excluded from the scope of the analysis. This approach allows for a concentrated examination of the factors that influence tailored pricing and service strategies.
Currently, ticket pricing for customized buses is uniform across seats, with variations only based on route and boarding/alighting locations. In contrast, air and rail transportation sectors have adopted a more granular pricing approach by offering different prices for different seat classes, taking into account individual passenger needs. This approach enhances operational efficiency. Within a single bus, factors such as seat comfort, convenience of boarding and alighting, views during the ride, and social distancing can significantly affect passenger experience. Recognizing these differences can enable operators to categorize seats, thereby laying the groundwork for implementing personalized pricing strategies that better meet diverse passenger preferences.
To accurately assess user preferences and mitigate potential data distortion from extraneous factors, this study focuses on the seat selection choices of the first four users from each bus shift.
Figure 3 illustrates the limited seating capacity in each vehicle and underscores the importance of maintaining adequate social distancing among passengers. The left side depicts actual bus seats, while the right side shows corresponding data labels, with the last column marked according to numbering rules.
Monthly data from the top four users per shift were normalized to analyze seat selection frequencies, as depicted in
Figure 4 and
Figure 5 spanning from February 2019 to May 2020. The analysis revealed a notable preference for front seats, likely due to perceived enhanced comfort. Seat selection trends also demonstrate seasonal variations. In summer, the first three rows, especially the first row for its superior view, are more popular. Additionally, seats (B, 5) and (B, 8), positioned near the door, are favored for their convenience in boarding and alighting, proximity to the aisle, and increased personal space. Conversely, during winter, there is a noticeable shift in user preferences towards health-conscious seat choices. Users increasingly opt for more dispersed seating arrangements to uphold social distancing, thereby reducing the risk of disease transmission. This seasonal variability in seat preferences underscores the potential for implementing dynamic, seasonally adjusted pricing strategies in customized bus services.
3.3. Booking Characteristics
Customized bus services, which leverage an internet-based reservation system, illustrate that earlier bookings can enhance service delivery. Moreover, the lead time of reservations reflects the urgency of passengers’ travel needs. In the aviation sector, differential pricing strategies based on reservation timing are frequently employed to optimize airline profitability. Therefore, it is advisable for customized bus services to adopt a similar approach. By analyzing variations in reservation timing, targeted interventions can be devised to improve the operational efficacy of these services. This method allows for strategic adjustments in pricing and service deployment, aligning more closely with passenger demands and enhancing operational efficiency.
The data depicted in
Figure 6a illustrate significant variations in the distribution of booking times within the dataset, including multiple peak periods. However, it is evident that the current pricing shown in
Figure 6b for orders does not substantially increase in correlation with the advanced booking times. This observation suggests a potential misalignment between pricing strategies and booking behavior, indicating an opportunity to refine pricing models to better capitalize on booking time variations.
3.4. User Characteristics
Travel pattern characteristics significantly influence user price elasticity; for example, users who travel during peak periods are more likely to accept higher fares. The initial step involves analyzing the distribution of users’ travel distances and frequencies, followed by calculating the number of high-frequency origin–destination (OD) pairs. Furthermore, to delve deeper into user travel behaviors, analyses of hourly order frequency and monthly usage days are undertaken. To facilitate a general understanding, heatmaps are generated to visualize the distribution of travel times within a week, elucidating user travel patterns comprehensively. This analysis also includes the identification of high-frequency travel intervals and differentiates between weekday and weekend travel behaviors. Such detailed examination enhances the understanding of user travel patterns, enabling the development of more precisely targeted pricing strategies that reflect the dynamic nature of travel demand. This facilitates understanding of user behaviors and the development of tailored pricing strategies that reflect the temporal dynamics of travel demand.
Figure 7 illustrates significant variability in travel distances, ranging from under 1 km to a maximum of 95 km. The distribution displays a multi-modal pattern with prominent peaks at 6 and 25 km, which corresponds to the service’s focus on long-distance commuters. Particularly, the peak at 25 km, indicative of residential-to-work commutes, underscores the perceived advantages of customized buses in comfort and punctuality compared to traditional bus services within this distance range. The corresponding order data, including distance and passenger frequency, highlight the preference for customized buses. We utilized the Gaode Map Driving Navigation API to accurately capture the distances based on the boarding and alighting location coordinates for each order.
The daily travel patterns shown in
Figure 8a demonstrate pronounced peak demands during the morning and evening rush hours, predominantly involving short, low-cost journeys. Clustering based on usage days and frequency allows for the identification of high-frequency and low-frequency travelers, enabling tailored service adaptations to effectively meet the diverse needs of these distinct user segments in
Figure 8b.
Figure 9 illustrates the proportion of travel frequencies across different time periods, showing higher demand during morning peaks compared to evenings. This highlights the need for adjusting service frequencies during peak hours to maintain timeliness and reliability. Moreover, notable differences between weekday and weekend travel behaviors indicate varying levels of dependence on the service. These insights are pivotal for developing differential pricing strategies that accommodate diverse demand patterns, thereby improving service efficiency and enhancing customer satisfaction.
In summary, customized bus users exhibit distinct variations in terms of time, space, and frequency of travel. Different travel characteristics influence the sensitivity to service quality and pricing. Therefore, to enhance the operational services and the benefits of customized bus companies, it is essential to segment users based on their historical travel characteristics. By adopting differentiated operational strategies for various user types, companies can achieve a balance between service quality and price management, optimizing both customer satisfaction and operational efficiency.
5. Results
Based on the analysis of differences in user order seat classifications, booking time variations, and travel pattern characteristics, establishing different prices for different categories of users can facilitate the implementation of a final differential pricing strategy.
Implementing a differential pricing strategy for various types of transit users can optimize revenue generation. For users who are highly dependent on customized bus services and lack viable transportation alternatives, increasing prices can enhance overall profits. Conversely, for users with a lower dependence on customized buses, where alternative transportation options are readily available, reducing prices may encourage more frequent use of the service. Based on initial user segmentation, Group 1 (loyal users) and Group 2 (long-distance users) are considered to have a high dependence, and their price elasticity coefficient is smaller than 1, indicating that lowering prices may not significantly increase their usage frequency, but modest price increases are unlikely to drastically reduce their demand. Group 3 (occasional users) and Group 4 (non-weekday users) have alternative travel modes, and their dependence on customized buses is small, so their price elasticity coefficient is large, which should be above 1.
Figure 14 indicates that due to the finite capacity of the vehicle, overall revenue initially rises but eventually levels off. Additionally, employing a price reduction strategy for users with a higher price elasticity
leads to an increase in demand, potentially displacing users with a lower price elasticity
. Notably, the optimal revenue is realized when
and
. This scenario underscores that the greater the differences in user price sensitivity, the more significant the benefits derived from a differential pricing strategy. This finding highlights the effectiveness of tailoring prices to diverse user groups, particularly in contexts where capacity constraints and varying price elasticity interact.
To assess the impact of tiered seating pricing on revenue, it is essential first to consider the variations in price sensitivity among different passengers. If the price for first-class seats increases by gamma , the corresponding selection probability will adjust to , representing the likelihood that a user chooses a first-class seat. Given the limited number of first-class seats, it is necessary to employ a random sampling method to determine the allocation of these seats.
Figure 15 indicates that as the price of first-class seats rises, ticket revenue also increases. However, the growth trend gradually diminishes, because excessively high prices lead some passengers to forego choosing first-class seats. Notably, higher revenues are achieved when the price elasticity is set at
and
. This outcome suggests that passengers with a price elasticity of
, who are more sensitive to price changes, are more likely to opt for first-class seats, thereby enhancing overall revenue. This analysis underscores the importance of strategically managing seat pricing to optimize revenue while considering passenger price sensitivities and the finite availability of premium seating options.
As the departure time for the customized bus approaches, user dependency on the service increases, leading to a decrease in their price elasticity, which is their sensitivity to price changes. Consequently, operators have an opportunity to raise ticket prices to enhance revenue. All the departures scheduled before the average time for all the services are adjusted to the basic discount rate, C, taking into account the time-dependent decay of the price elasticity coefficient, denoted by .
According to
Figure 16, it is observed that a larger
value indicates that as the departure time approaches, users become increasingly dependent on the customized bus service and less sensitive to price changes. Therefore, even after ticket prices are increased, a considerable number of users continue to choose the customized bus, leading to higher overall ticket revenue.
Integrating the strategies based on user group segmentation, seat classification, and booking time, we analyze ticket revenue using various parameters.
Figure 17,
Figure 18 and
Figure 19 depict settings with different user price elasticity coefficients,
and
. In each figure, the rows maintain a constant decay coefficient,
, and the columns keep the same ratio for the increase in the prices of advanced booking orders
C. Within each subplot, the increased ratio for first-class seats,
, varies. These figures also illustrate the overall revenue following adjustments in base prices, showing how these strategies influence financial outcomes under different pricing dynamics and user sensitivities.
When and , examining different rows reveals that a lower attenuation coefficient, , of the price elasticity coefficient results in diminished price sensitivity as the departure time approaches, thereby justifying a price increase to enhance revenue. Within the same row, the analyses show that a higher price increase ratio for orders under the predetermined time threshold, , leads to increased ticketing income. Specifically, at and , a price adjustment ratio of 40% across different user groups yields the maximum comprehensive income, surpassing the operational costs of customized public transportation. However, when is set at 0.7 or 0.9, revenue initially increases but subsequently declines with larger price changes. This trend is attributed to the excessive demand generated by substantial price reductions for users whose price elasticity exceeds 1, which displaces highly dependent users and reduces overall income. This illustrates that simply raising ticket prices does not universally increase total revenue.
Comparative analyses across subgraphs demonstrate that greater differences between user groups significantly enhance the effectiveness of differentiated pricing strategies, thereby increasing revenue. This study assesses income based on nine different parameter sets, depicted in nine subgraphs. As shown in
Figure 17, under various parameter configurations, the optimal differentiated pricing strategy can increase the overall income of customized public transportation by at least 41%, confirming its efficacy.
6. Conclusions
This paper proposes targeted differentiated pricing strategies and analyzes their benefits under different scenarios to provide decision-making insights for improving customized bus operations. This study initially conducts an exploratory analysis of order, booking, and user features using customized bus ticketing data, examining temporal, spatial, and frequency differences. Subsequently, clustering methods are applied to uncover travel characteristics and classify seat classes into first and second class, categorizing passengers into distinct groups. The results demonstrate that integrating differentiated pricing strategies based on seat classification, booking timing, and user travel characteristics can significantly enhance customized public transportation income by at least 41%.
While the effectiveness of these strategies is evident, future research and practical implementation must address several considerations. Firstly, integrating competition from traditional buses, private cars, and taxis into pricing strategy analyses is crucial to optimize strategies and maximize benefits by understanding their influence on passenger decisions. Secondly, while this paper evaluates how differentiated pricing strategies impact revenue for customized public transportation, further discussion on potential limitations and result variability would provide a more comprehensive perspective. Thirdly, ensuring study credibility requires verifying the validity and adaptability of parameter settings to real-world scenarios, as well as validating the experimental results to confirm the anticipated practical outcomes.
In conclusion, this study illustrates the potential of differentiated pricing strategies to substantially increase income in customized public transportation. Future research should concentrate on developing optimization models with precise algorithms to dynamically adjust pricing, adapting to market changes and enhancing revenue management. Furthermore, these strategies extend beyond financial gains, influencing industry norms and customer expectations within the broader transport sector. Addressing these areas in future research will contribute to a more comprehensive understanding and application of strategic pricing in competitive transport markets, fostering sustainable development in public transportation.