The distribution of high-quality customers (hereafter HQC) for taxis (including ride-hailing cars) has a significant impact on drivers’ revenue and taxis’ operation efficiency. Based on the taxi global positioning system data, we construct an evaluation model of passengers to discuss the imbalance of consumption in taxi market. The profit margin for each taxi order is calculated, and then a grid-based method is used to distinguish the HQC and the regions with potential higher benefits. We analyze the HQC’s distribution of taxis (including ride-hailing cars) in different areas and in different time periods. We find that the HQC are distributed mainly on the periphery of the main urban area, which indicates that traffic condition is even worse in the urban center because of factors such as congestion. The HQC are more concentrated on workdays and more scattered on nonworkdays, which implies that the public have different travel habits and demands on workdays and nonworkdays. The proportion of HQC in each administrative district or functional zone is not always positively correlated to either the proportion of total orders or the total HQC. This indicates that the distribution of HQC in each administrative district or functional zone is imbalanced. The proportion of orders and that of HQC are roughly the same in the temporal dimension, being higher in the morning and evening rush hours. Compared with the distribution of the HQC of ride-hailing, that of taxis is more imbalanced in the temporal dimension. Relevant departments should further coordinate taxi pricing, strengthen market control, and promote sustainable development in the taxi and ride-hailing markets.
Taxis are an important supplement in urban public transportation. Compared with other traditional traffic modes, they are more comfortable, flexible, and convenient with full time [
The traditional taxi market has typical oligopoly characteristics [
In recent years, with the development of the global positioning system (GPS) and technology for analyzing big data, it is becoming possible to use GPS real-time positional data of taxis to study related problems. Previous research was mainly concerned with (a) using trajectory data to obtain traffic real-time status [
In summary, the previous studies based on taxi GPS trajectory data have focused mainly on issues such as the spatial-temporal distribution of passengers or vehicles, the efficiency of taxi operations, and the profit margins of drivers. Despite the important achievements made by the study of traffic positional data, there are relatively few studies exploring the imbalance of the taxi market from the perspective of passengers’ profit margins. Based on trajectory data, this paper will construct a passenger evaluation model from the perspective of profit margins, proposing the selection conditions of HQC. Finally, this paper takes Beijing as an example to compare the HQC’s spatial-temporal distribution of taxis and ride-hailing vehicles, which can explain the imbalance of the taxi market. That can provide the corresponding data and theoretical support for government regulations and relevant departments to make decisions. In other respects, it can also make an important contribution to the sustainable development of the urban taxi market.
The Didi Service platform contains 565,582 orders for taxis and ride-hailing vehicles in Beijing, including 91,986 orders for traditional taxis and 473,596 orders for online ride-hailing services. Cleaning the data, such as deleting duplicate and inaccurate data, leaves 506,940 valid orders (74,686 taxi orders and 432,254 ride-hailing orders) remaining. The trajectory data of the orders each contain five fields: order ID (after desensitization treatment), time stamp of the trajectory point, the corresponding longitude coordinates, the corresponding latitude coordinates, and instantaneous velocity.
HQC are mainly determined by passengers’ demand in different grids, which are featured by the distribution of OD trips at different locations and time. The distribution of OD trips contributes to the service profit margin of taxis in different regions and different time periods, which leads to an imbalance in the distribution of HQC. As is well-known, both the pick-up locations and the destinations are very important for trips. With the service profit margin calculated, the pick-up locations and the destinations are included in the model. However, when we distinguish the HQC points, only the origins of trips are focused because these origins reflect people’s travel demand to taxis. Therefore, we analyze the distribution of HQC to understand the imbalance of the taxi market based on passengers’ pick-up points. The process of data preprocessing is as follows:
Charge standard of Beijing taxi.
Category | Fare |
---|---|
Base rate (0–3 km) | 13 yuan |
Mileage fee | 2.3 yuan/km |
Low speed fee | Below 12 km/h: added 2 km’s rental per 5 minutes during the morning and evening rush hours, added 1 km’s rental during other times (excl. empty cruise fee) |
Empty cruise fee | Over 15 km carrying passengers one way, added 50% of the basic unit price; round-trip passengers (the starting point and end point are within 2 km (incl. 2 km)) excluding empty cruise fee. |
Night-time charge | 23:00 (incl. 23:00) to 5:00 next day (excl. 5:00) operation: added 20% of the basic unit price. |
carpooling charge | For the carpooling mileage, charged 60% of the payable |
Charge standard of Beijing ride-hailing.
Normal type | Enjoyed type | |||
---|---|---|---|---|
Mileage | 1.25 yuan/km | 1.6 yuan/km | ||
Duration fee | workdays | non-workdays | workdays | non-workdays |
21:00–06:00 | 21:00–06:00 | 21:00–06:00 | 21:00–06:00 | |
06:00–10:00 | 12:00–15:00 | 06:00–10:00 | 12:00–15:00 | |
17:00–21:00 | others 0.48 yuan /min | 17:00–21:00 | others 0.48 yuan /min | |
others 0.4 yuan /min | others 0.4 yuan /min | |||
Empty cruise fee | 0.64 yuan/km (over 20 km) | 0.68 yuan/km (over 20 km) | ||
Minimum charge | 11 yuan | 12.8 yuan |
We first define what the HQC and HQC points are and then describe how to distinguish and quantify those HQC.
The efficiency of the taxi is related to the distribution of its orders (or its passengers). To quantify accurately the passengers’ distribution of HQC of urban taxis, this paper conducts an evaluation model of passengers based on the service profit margins [
Here,
In the same way, the profit calculation formula of ride-hailing (
Here,
Due to the different pricing standards of taxis and ride-hailing services under different service mileages, the profit calculation formula of taxis and ride-hailing will also be different. And, disregarding the difference in fuel cost between empty cruising and traveling with passengers, this paper discusses and calculates the profit margin based on the service mileage, which is the following three cases [
Here,
Here,
Here,
Based on the service profit margins of orders and combined with the geographical information system (GIS) spatial-temporal analysis method to distinguish the HQC and HQC points, the steps are as follows:
We analyze the distribution of HQC from both space and time dimensions, resulting from HOC’s spatial and temporal dependence. In terms of space, we analyze HQC from the perspective of administrative districts and functional zones, including the comparison of the distribution on workdays with nonworkdays. In terms of time, we study the changing rules of HQC in different times of a day. In particular, we analyze the distribution of HQC in the rush hours of morning and evening. We also compare the service profit margins of HQC in different trip lengths and at different times of a day.
In fact, there will be some sampling bias for some suburb administrative regions (grids) might be less sampled. When there are too few orders in a grid, the average profit margin in the grid might suffer a relatively greater error or become insignificant statistically. However, the bias will not affect our results severely. We are distinguishing the HQC to analyze its distribution in space and time dimensions. The service orders in suburb grids are much less than those in city center, so very fewer orders in suburb grids will not affect the overall distribution of HQC, since their average profit margin might carry a large standard error. For example, the number of HQC in Yanqing District only accounts for 0.003% of all the HQC in Beijing city, which will not severely affect the distribution of HQC in the city.
ArcGIS visualizes the distribution of all passengers and HQC, where the HQC are represented by the red grid, as shown in Figure
The pick-up points of taxis and ride-hailing on Thursday, Friday, and Saturday, respectively. The orders in the legend refer to the service order counts interval in different administrative districts. The background color depth of each administrative district in Beijing is proportional to the number of orders. The red areas are defined as the HQC points in this paper.
In Figure
There are obvious differences in the distribution of HQC between taxis and ride-hailing. The HQC of taxis are distributed mainly in the urban function extended districts and the new urban development districts, such as Chaoyang District, Haidian District, Shunyi District, and Daxing District. In these districts, the HQC account for 55.76% of the total HQC. Chaoyang District is a gathering place for high-end industrial functional areas. Haidian District is a gathering place for educational institutions. These places have a large population density and a large flow of passengers on workdays. The HQC of ride-hailing services are distributed mainly in the traffic and residential areas of the new urban development districts, including Shunyi District, Fangshan District, and Daxing District. In particular, the ride-hailing’s HQC in Shunyi District, Chaoyang District, Daxing District, and Fangshan District account for 58.94% of the total ride-hailing’s HQC. The number of taxis in these areas is small, but there are large travel needs, such as from the place of residence to the nearest public transportation facilities. At the same time, the traffic in the outer suburban districts is smooth, and the carpooling business in the ride-hailing service also provides passengers with greater convenience. So, the HQC in ride-hailing service exhibit a phenomenon of peripheral agglomeration distribution.
Next, we analyze the distribution of HQC in different administrative districts. Figure
The distribution of orders and HQC in different administrative districts.
The higher the P-HQC-OAD in administrative districts (the red line), the more the high efficiency orders and the more the possibilities that drivers will obtain high profits from orders. Although the order number has a significantly positive correlation with the HQC, the distribution of HQC in each administrative district is imbalanced. From Figure
Next, we will analyze the distribution of HQC in different functional zones. According to the functional attributes in different areas of the city and the classification of Baidu POI industry, all orders are divided into 12 functional zones. The functional zones include shopping, traffic, education, finance, hotels, residence, catering, life service, culture, leisure, medical treatment, and government. Figure
The proportion of orders and HQC in different functional zones.
In each functional zone, the higher the P-HQC-OFZ (the red line), the higher the probability of the HQC appearing and the higher the possibility that drivers will make greater profits. Compared with taxis, the HQC of ride-hailing in each functional zone is relatively balanced. Specifically, the P-HQC-OFZ is relatively stable, accounting for approximately 4.43% in each functional zone (the proportion in traffic is relatively high, at 20.60%). However, the HQC of taxis in various functional zones is imbalanced. For example, in government and traffic functional zones, the P-HQC-OFZ is 34.92% and 32.82%, respectively. However, in life service and residential functional zones, the P-HQC-OFZ is 17.06% and 13.65%, respectively. In other words, drivers can get high-efficiency orders easily in the traffic functional zone. In particular, the possibility of drivers receiving orders is low in the government functional zone, but drivers are most likely to receive high-efficiency orders.
Next, the distribution of HQC in the temporal dimension is analyzed. Figure
The temporal distribution of taxi (ride-hailing) orders and HQC.
The greater the value of P-HQC-O per hour, the higher the probability of HQC in this period. From Figure
It is of great significance for not only the taxi and ride-hailing markets but also urban planning departments to understand the residents’ travel rules in the morning and evening rush hours. The HQC of taxis (including ride-hailing vehicles) during morning and evening rush hours will be analyzed by selecting 7:00–9:00, 17:00–19:00 on workdays and 10:15–11:45, 14:15–18:45 on nonworkdays. Figure
The proportion of orders and HQC in different functional zones on workdays and nonworkdays (taxi and ride-hailing).
Figure
The service profit margins of trips for the HQC orders in different trip lengths and at different times of a day are shown in Figure
The daily trend of HQC in different trip lengths. We categorize all the trips into three classes, namely, short trips (i.e., the trips that are less than 3 km), long trips (i.e., the trips that are more than 15 km), and intermediate trips (i.e., the trips that are more than 3 km and less than 15 km).
Taxi
Ride-hailing
The distributions of orders and HQC in different administrative districts. Note: P-O refers to the proportion of orders in the total orders in the 16 administrative regions. P-HQC refers to the proportion of HQC in the total HQC. P-HQC-OAD refers to the proportion of HQC in the total orders of the corresponding administrative districts.
The distributions of orders and HQC in different functional zones. Note: P-HQC-OFZ refers to the proportion of HQC in the total orders of the corresponding functional zones.
The service profit margins of the short trips (<3 km) and long trips (>15 km) are at a relatively high level, while those of the intermediate trips (3-15 km) are at a relatively low level. For example, the service profit margins of the trips of “0-3 km” and “>15 km” are 1.78 and 1.94 times higher than those of the trips of “3-15 km,” respectively. The service profit margin of long trips at night is higher than that in daytime. For instance, on average, their profit margin from night to early morning (such as 0:00-5:00) is 1.21 times more than that in daytime (take 8:00-18:00 as an example). Comparing the profit margins of the short trips with long trips, we can find that the short trips tend to have greater profit margins in daytime while the long trips tend to have greater profit margins at night and in early morning. We speculate that this is because the road condition is much more congested in day time than in night and early morning, for the profit margins of short trips are mainly determined by the base rate, while those of long trips are mainly determined by the mileage fee and empty cruise fee.
We distinguish HQC and HQC points from pick-up locations of passengers based on service profit margin of each order and then analyze the distribution of HQC and HQC points. In this paper, we provide a new perspective to analyze the imbalance of taxi market from the distribution of HQC. The balance of the taxi market has an important impact on the sustainable development of the city. Therefore, it is especially important to fully understand the spatial-temporal distribution of taxi’s high-quality customers (HQC). Based on global positioning system trajectory data, this paper constructs an evaluation model of passengers from the perspective of profit margins. Then, we present the selection criteria of HQC and discuss the distribution laws of HQC in different areas and different time periods. The conclusions are as follows.
The HQC of taxis and ride-hailing are distributed mainly in the periphery of the main urban area, which indicates that traffic condition is even worse in the urban center because of factors such as congestion. In central urban areas of Beijing (such as Dongcheng District and Xicheng District), there are more passengers and more traffic demands. Due to the well-developed public transportation, HQC in these areas are few and scattered. There are significant differences in the distribution of HQC between workdays and nonworkdays. The HQC on workdays are in a state of aggregation (concentrated in the functional zones of residence, leisure, financial, traffic, and education). On the other hand, the HQC are distributed more widely and more evenly on nonworkdays. There are also obvious differences in the distribution of HQC between taxis and ride-hailing. For example, the traffic in the outer suburban districts is smooth, and the carpooling service of the ride-hailing also provides passengers with greater convenience, which results in the distribution of ride-hailing’s HQC being in aggregation in the outer periphery.
The proportion of orders in the total orders, or P-O, of taxis (including ride-hailing cars) in the administrative district is positively correlated with the proportion of HQC in the total HQC, or P-HQC. However, the proportion of HQC in the total orders of the corresponding administrative districts, or P-HQC-OAD, is inversely proportional to the orders and HQC in most cases, which indicates that the HQC in each administrative district is imbalanced. The distribution of HQC in each functional zone is also imbalanced, especially the HQC of taxis. The government and traffic function zones have the highest proportion in P-HQC-OFZ, with 34.92% and 32.82%, respectively. And the functional zones of the life service and residence are second.
The P-O per hour and the P-HQC per hour of taxis and ride-hailing show a similar daily changing trend. In the morning and evening rush hours, the two proportions account for a relatively high proportion and have a significant positive correlation. The P-HQC-O per hour of taxis is significantly higher than that of ride-hailing, which indicates that drivers of taxis are more likely to accept high-margin orders. The peak change of ride-hailing is stable on the whole, which indicates that ride-hailing pricing is more reasonable. The extreme point of the taxi's P-HQC-O per hour appears in the evening rush hours, and the peak value is significantly higher than in other periods, indicating that taxis’ HQC in the temporal dimension exhibits an imbalance. The relevant departments should further coordinate taxi pricing, strengthen market control, and promote balanced development in the taxi and ride-hailing markets.
Understanding the spatiotemporal distribution of HQC can benefit the taxi drivers a lot. For example, it can improve the information asymmetry between taxi drivers and the HQC, which would reduce their seeking time of HQC. In other words, it is in favor of improving the operational efficiency of taxi, which can increase the drivers’ income and improve the service quality. In sum, the distribution of HQC of online ride-hailing services is of great significance to improve the operational efficiency of the online ride-hailing services market. To promote sustainable development in the online ride-hailing service market, it is recommended that the relevant departments coordinate the supply of taxis with different service modes to rationally optimize resource allocation according to the market demand in different time periods and different regions. At the same time, they should also coordinate the supply of vehicles in the rush hours of morning and evening. Finally, the relevant departments should utilize big data on the location of urban traffic and strengthen the construction of the big data platform on traffic to grasp the real-time traffic situation accurately and elaborately from a global perspective, which can also provide guidance for methods of management and data-driven decision-making.
See Figures
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare no conflicts of interest.
This work was supported by the programs of National Social Science Foundation of China (Grant: 16CJY056).