Understanding taxicab operation behaviors under various management or market policies (i.e., subsidies) is critical to making informed operating decisions for e-hailing companies and for government surveillance. This paper investigates the change of taxicab operation zones in context of an e-hailing app subsidy war in China, which is an important perspective that reflects changes in taxicab behavior, such as how the operation zones of taxicabs under the e-hailing app subsidy war change and how this change affects their trip distance and cruising time. To investigate this issue, this paper utilizes three indexes to elucidate the change of taxicab operation zones, namely, the repetition ratio of operation zone pairs, the area, and the degree of dispersion in the spatial distribution. A case study using taxicab trajectories during all of the important periods of the e-hailing app subsidy war in Shenzhen, China, was conducted and produced several valuable findings; for example, with respect to taxicabs as a whole, the proportion of habitual operation zone pairs among operation zone pairs in neighboring periods is relatively stable under any subsidy policy, and changes in the operation zones have little effect on changes in the average daily trip distance and average daily cruising time. Four groups of taxicabs divided according to initial change patterns in the operation zones present different change patterns during the subsidy war. By comparing these changes before and after the subsidy war, this paper finds that the subsidy war influences the taxicabs in groups I and II, while it has little influence on the taxicabs in groups III and IV, although all groups were affected during the subsidy war. For the taxicab groups in the period with the highest subsidy, the average daily trip distance and average daily cruising time decreased, whereas, in other periods, they presented different patterns.
Taxicabs perform an indispensable function in urban transportation systems. Cabdrivers’ mobility patterns and operating strategies have been widely studied since large-scale taxi GPS trajectories became obtainable due to the rapid development of information and communication technologies (ICT). For example, previous studies analyzed the distance and direction distributions of intraurban trips [
E-hailing services (e.g., Didi in China and Uber in the US) can connect passengers and cabdrivers directly using communication technology since cabdrivers know the passengers’ origins and destinations beforehand. However, several disturbing issues are commonly reported in the taxi industry, including unfair competition, safety concerns, longer working hours, surge pricing, request rejection, and increased difficulties among senior citizens with catching taxis. With the e-hailing app subsidy war that occurred from January to August in 2014 in China, which had a dramatic effect on the taxi industry, it has become feasible to analyze how cabdrivers react to the external policy stimulus and what consequences these different reactions cause. From the perspective of taxicabs, the most direct reflection of the spatial effect of subsidy policy is the change of operation zones. Do the operation zones of taxicabs change under the stimulation of a subsidy war? If so, how does this change affect cabdrivers’ trip distance and cruising time? This paper primarily focuses on how the operation zones of taxicabs under the e-hailing app subsidy war changed and how this change affected their trip distance and cruising time. This paper investigates this question in three dimensions, including the repetition ratio of operation zone pairs, the changes in the area, and the changes in the degree of dispersion in the spatial distribution of operation zones. These three dimensions can well describe the change of taxicab operation zones from the perspective of space. Understanding these questions can provide insights into the effects of subsidy policies on taxicabs’ operation tactics, helping taxi management authorities to develop precise and targeted management tactics for cabdrivers.
To address the research question, three indexes are calculated to indicate the changes in the operation zones, namely, the repetition ratio, the changes in the area, and the changes in the degree of dispersion in the spatial distribution of operation zones. Moreover, this paper calculates the average daily trip distance and the average daily cruising time for each taxicab to investigate the correlation between the spatial change and these two variables. Selected taxis are used to conduct the investigation of the primary research question.
The remainder of this paper is organized as follows. Section
Researchers have great interest in studying location data with spatiotemporal information due to the rapid development and increasing availability of various location-acquisition technologies [
Researchers have analyzed the spatial patterns of taxicabs using three approaches. The first approach is to uncover pattern variations; for example, Chen et al. [
The second approach is to discover the aggregate patterns of taxicabs. For example, Lee et al. [
The third approach is to discover the spatial operation knowledge of taxicabs. For example, Hu et al. [
In short, these studies seldom introduce effective indexes to indicate changes in the operation zones, such as the repetition ratio of operation zone pairs and the spatial changes (i.e., changes in the area and the changes in the degree of dispersion in the spatial distribution) of the operation zones.
Passengers usually hail a taxi on the street in the traditional manner. However, with advanced information and communication technologies, hailing a taxi through smartphones has become very popular; this approach offers passengers a high level of comfort and efficiency, especially during rush hours and rainy days. With the advent of smartphones, an increasing number of smartphone-based e-hailing applications have emerged in recent years, such as Uber and Lyft in the US and Didi and Kuaidi in China. These e-hailing applications provide an information platform that makes communication between drivers and passengers more efficient and convenient. The e-hailing service of taxicabs, as a new form of communication between drivers and passengers, has aroused scholarly interest.
One group of prior studies investigated the market equilibrium of taxi or related policies. Wang et al. [
The second group of studies focuses on pricing strategy. Salnikov et al. [
In addition, a few studies have investigated the effects of subsidy wars on e-hailing services. For example, Wen et al. [
However, few studies have focused on the question of how the operation zones of taxicabs under an e-hailing app subsidy war change and how this change affects their trip distance and cruising time. Therefore, the present paper investigates these questions.
This section introduces the subsidy war and experimental data, the measurements of the change of taxicab operation zones, and the average daily trip distance and cruising time derived from taxicab trajectories.
A fierce subsidy war between two e-hailing apps, Didi and Kuaidi, was triggered in 2014 in China. Based on the different subsidy policies available to drivers and passengers, this paper divides the time period of the subsidy war into six subperiods, which are listed in Table
Subsidy policies in different periods during the subsidy war (Unit: RMB Yuan).
Period | Duration | Didi | Kuaidi | ||
---|---|---|---|---|---|
Driver | Passenger | Driver | Passenger | ||
1 | Before Jan.9th | 0 | 0 | 0 | 0 |
2 | Jan. 10 th -Feb.16 th | 10 | 10 | 10 | 10 |
3 | Feb.17 th -Mar. 4 th | 10 (50 for new user) | 10-20 | 5-11 | 10-13 |
4 | Mar. 22nd-May 16 th | 10 | 3-5 | 5-11 | 3-5 |
5 | May 17 th -July 8 st | 10 | 0 | 5-11 | 0 |
6 | After Aug.10 th | 0 | 0 | 0 | 0 |
Human travel patterns on holidays vary greatly from those on nonholidays. For example, an enormous number of people leave Shenzhen City to go back their hometowns to celebrate Spring Festival, which has a considerable effect on the level of taxi service in Shenzhen City. In addition, bad weather, such as rainstorms, can significantly influence the demand for taxi services. To reduce the influence of such factors, holidays and bad-weather days are excluded in our datasets. Due to the short time span of the third period, which has only 16 days after excluding holidays and bad-weather days, we choose the same number of days for other periods equally. Finally, Dec. 23-31 in 2013 and Jan. 2, 3-6, 8, and 9 in 2014 were chosen from the first period; Jan. 20, 21, and 23-29 and Feb. 10-16 were selected from the second period; Feb. 17-28 and Mar. 1-4 were chosen from the third period; Mar. 23-28 and Apr. 4, 9, 10, 16, 17, 19-22, and 27 were selected from the fourth period; May 24, 26, 27, and 29 and June 3-5, 11-14, and 26-30 were chosen for the fifth period; and Aug. 11, 15-18, 21, 23-26, and 29 and Sep. 2, 3, 9, 10, and 18 were selected for the sixth period. Let Periods 1, 2, 3, 4, 5, and 6 denote these six periods, respectively.
The data were collected from 9,648 taxicabs equipped with GPS devices that operated in the city of Shenzhen, China, during the selected days in these six periods. All of these taxicab trajectory data cover all six periods. Each record includes the plate number, spatial location (i.e., longitude and latitude), timestamp, operation status (i.e., vacant or occupied), driving direction and velocity of a taxicab. The data do not include the actual identifying information about which taxicab driver uses Didi or Kuaidi app. Therefore, this analysis will reflect this change in the taxicab operation zones under the context of different subsidy policies, which cannot reflect taxicab’s behavior changes directly affected by the Didi or Kuaidi app.
In this paper, we extract two datasets from the raw data, which are a passenger carrying dataset called DATASET #1 and a passenger hunting dataset called DATASET #2. For DATASET #1, the plate number, start time and end time of a trip, travel time, pick-up location and drop-off location of a trip, and the travel distance were collected. For DATASET #2, each record has the plate number, end time of the current trip, start time of the next trip, cruising time, drop-off location of the current trip, pick-up location of the next trip, and cruising distance.
Abnormal data are excluded from the dataset, such as, for DATASET #1, (i) records with a travel time less than 0.5 min, based on the reasonable hypothesis that a taxi trip lasts longer than 0.5 min; (ii) records with a travel distance shorter than 500 m based on the reasonable hypothesis that a taxi trip should be longer than 500 m; and (iii) records with the pick-up and drop-off latitude and longitude value outside of the territory of Shenzhen City. For DATASET #2, the following data are excluded: (i) records with a cruising time of more than 60 min based on the reasonable hypothesis that a taxi can find a passenger within 60 min and (ii) records with the pick-up and drop-off latitude and longitude value outside of the territory of Shenzhen City. After these steps, we prepare two preliminary datasets, both of which contain the same 9,605 taxis.
The traffic analysis zone (TAZ) is the analysis unit in this study. TAZ is usually constructed by census block information, where trips begin and end in transportation planning. This study uses TAZs to represent the operation zones of taxicabs from the perspective of taxicab service and the requirement of policy making for transportation planning. As shown in Figure
Traffic analysis zones in the city of Shenzhen, China.
First, we introduce the definitions of operation zone and operation zone pairs. The operation zone represents a zone where a taxi has ever performed a pick-up or drop-off passengers, which can be described as a sequence
Examples of the change of a taxicab’s operation zones. The rectangles represent TAZs and each TAZ is given a unique ID ranging from 1 to 5. A red arrow represents this taxicab has completed trips from the start TAZ of the arrow to the end TAZ of the arrow. In addition, the trip number is given beside the arrow in red number. The change of taxicab operation zones between neighboring periods can be derived by comparing the taxicab operation zones in current period (a) and taxicab operation zones in next period (b).
In this section three indexes are introduced to uncover the change of taxicab operation zones in context of the e-hailing app subsidy war, namely, the repetition ratio of operation zone pairs, the area and the degree of dispersion in the spatial distribution of operation zones.
A repetition ratio indicator is introduced to depict the proportion of repeatedly appearing operation zone pairs of each taxi between two neighboring periods. Each relation among origins and destinations of a taxicab can be described by the adjacency matrix
The repetition ratio of the taxis’ operation zone pairs is calculated in two different ways, i.e., unweighted and weighted. The unweighted repetition ratio of the taxis’ operation zone pairs can be derived by calculating the ratio of the number of repeated operation zone pairs to the total number of distinct pairs:
Considering that operation zone pairs with higher visiting frequency should make crucial contributions to the repetition ratio of operation zone pairs, we measure the visiting frequency of each pair as a weight to calculate the weighted repetition ratio of the operation zones:
For the convenience of understanding, we calculate this index for the example in Figure
The taxi-origin and taxi-destination relations can be described by two adjacency matrices
The area of the taxis’ operation zones is calculated in two different ways, i.e., unweighted and weighted. First, the unweighted area of the operation zones of taxi
Considering that operation zones with higher visiting frequency play a more crucial role than other operation zones, this paper sums the number of pick-ups and drop-offs within each operation zone and uses the sum as the weight to calculate the weighted area of the operation zones:
For the convenience of understanding, we calculate this index for the example in Figure
Several indexes are widely used to measure the spatial dispersion in landscape studies; for example, the proximity index [
Similarly, we consider that operation zones with higher visiting frequency should make greater contributions to the degree of dispersion in the spatial distribution of the taxi operation zones. We measure the visiting frequency of each merged zone by summing the numbers of internal pick-ups and drop-offs, and we use the visiting frequency as the weight to calculate the weighted nearest-neighbor index (WNNI):
This paper assesses the effect of changes in the taxicab operation zones on their cost (average daily cruising time) and income (average daily trip distance). The average daily trip distance of each taxi in a period can be derived by averaging the sum of the trip distances in the period of subsidy war. The change in the average daily trip distance of each taxi between two neighboring periods can be obtained by subtracting its average daily trip distance in the current period from the next period. Similarly, this paper derived the average cruising time and the change in the average cruising time.
In this section, the overall changes in the taxicab operation zones in Shenzhen City within the context of the e-hailing app subsidy war are analyzed. Moreover, the change of operation zones in different groups of taxicabs and the influence of this change on their average daily cruising time and average daily trip distance are investigated, and finally, the changes in the patterns of weighted and unweighted areas and the NNI in different periods of this subsidy war are elucidated.
Figure
Distributions of the unweighted repetition ratio (a) and weighted repetition ratio (b) between different neighboring periods.
This paper analyzes the spatial change in the operation zones from the perspectives of the change in the area and the change in the degree of dispersion in the spatial distribution. To analyze the change of operation zones of taxicabs in the context of the subsidy war, the selected taxicabs are divided into four groups (see Table
Grouping result of taxicabs.
Group | Area | NNI | Taxicab count | Percentage |
---|---|---|---|---|
I | Increase | Increase | 2034 | 21.18% |
II | Increase | Decrease | 2608 | 27.15% |
III | Decrease | Increase | 2773 | 28.87% |
IV | Decrease | Decrease | 2190 | 22.80% |
To investigate the change patterns of these taxicabs in each group during other neighboring periods, this paper lists the taxicab percentages of the four patterns within each group in Table
The taxicab percentage of four patterns within each group during neighboring periods.
Group | Pattern | Area | NNI | Period 1-2 | Period 2-3 | Period 3-4 | Period 4-5 | Period 5-6 |
---|---|---|---|---|---|---|---|---|
I | A | Increase | Increase | 100.00% | 8.21% | 49.36% | 27.29% | 8.11% |
B | Increase | Decrease | 0 | 22.86% | 30.14% | 41.49% | 24.63% | |
C | Decrease | Increase | 0 | 29.45% | 15.63% | 20.65% | 24.29% | |
D | Decrease | Decrease | 0 | 39.48% | 4.87% | 10.57% | 42.97% | |
| ||||||||
II | A | Increase | Increase | 0 | 16.60% | 50.61% | 25.73% | 9.66% |
B | Increase | Decrease | 100.00% | 13.11% | 28.14% | 41.72% | 23.50% | |
C | Decrease | Increase | 0 | 49.46% | 17.22% | 19.98% | 23.73% | |
D | Decrease | Decrease | 0 | 20.82% | 4.03% | 12.58% | 43.10% | |
| ||||||||
III | A | Increase | Increase | 0 | 19.58% | 3.79% | 11.83% | 40.53% |
B | Increase | Decrease | 0 | 15.15% | 53.62% | 25.82% | 8.37% | |
C | Decrease | Increase | 100.00% | 40.35% | 29.90% | 42.84% | 20.12% | |
D | Decrease | Decrease | 0 | 19.19% | 13.09% | 19.58% | 22.25% | |
| ||||||||
IV | A | Increase | Increase | 0 | 34.98% | 50.64% | 25.48% | 7.90% |
B | Increase | Decrease | 0 | 19.45% | 29.73% | 42.97% | 19.91% | |
C | Decrease | Increase | 0 | 32.79% | 16.16% | 19.68% | 23.79% | |
D | Decrease | Decrease | 100.00% | 12.79% | 3.47% | 11.87% | 48.40% |
To further investigate the operation changes of these four groups, this paper lists the changes in the operation zones, the average daily trip distance and the cruising time in each group between all neighboring periods in Table
The spatial changes in the operation zones, the changes in the average daily trip distance and the cruising time in each group between all of the neighboring periods.
Group | Period 1-2 | Period 2-3 | Period 3-4 | Period 4-5 | Period 5-6 |
---|---|---|---|---|---|
Unweighted area (Unit: km2) | |||||
I | 30.76 | -25.20 | 41.35 | 25.80 | -34.53 |
II | 40.19 | -25.56 | 40.35 | 23.85 | -33.98 |
III | -35.61 | 4.71 | 46.82 | 25.59 | -41.43 |
IV | -31.29 | 4.07 | 44.92 | 26.22 | -42.17 |
NNI | |||||
I | 0.27 | -0.10 | 0.16 | -0.02 | -0.14 |
II | -0.41 | 0.15 | 0.18 | -0.04 | -0.15 |
III | 0.36 | -0.17 | 0.19 | -0.04 | -0.18 |
IV | -0.27 | 0.18 | 0.18 | -0.03 | -0.16 |
Average daily trip distance (Unit: km) | |||||
I | 20.78 | -10.98 | 9.63 | 7.69 | 13.43 |
II | 24.08 | -9.36 | 9.35 | 6.94 | 13.71 |
III | -8.91 | -3.11 | 16.00 | 3.04 | 11.22 |
IV | -9.49 | -1.68 | 12.91 | 3.65 | 10.77 |
Average daily cruising time (Unit: hour) | |||||
I | 0.46 | -0.36 | -0.21 | -0.01 | -0.01 |
II | 0.60 | -0.38 | -0.21 | -0.02 | -0.07 |
III | -0.16 | -0.23 | 0.00 | -0.06 | 0.04 |
IV | -0.08 | -0.26 | -0.04 | -0.05 | -0.04 |
The change trends of each group’s average daily cruising distance (a), operation zones area (b), and NNI (c) compared with Period 1.
To reflect the changes before and after the subsidy war, this paper compares the change trends of each group’s operation zones area (Figure
To assess the effect of the number of pick-ups and drop-offs on the spatial changes in the operation zones, the weighted area and WNNI are used to compare the difference in the unweighted areas and the NNIs in different periods. This number can help reflect the habitual operating preferences of cabdrivers. Figure
Histograms of the unweighted area of the operation zones (in blue) and weighted area of the operation zones (in red) of all of the selected taxis in each period. The blue vertical line represents the mean value of the unweighted area of the operation zones, and the red vertical line represents the mean value of the weighted area of the operation zones.
The spatial dispersion distribution of the operation zones with a high number of pick-ups and drop-offs is another important characteristic that reflects the change in the operation zones. It is reasonable that the operation zones with high weights could be used to describe the degree of dispersion in the spatial distribution of all of the operation zones. Figure
Histograms of the maximum number of pick-ups and drop-offs within an operation zone of each taxi in every period.
The distribution of WNNI with respect to periods (a) and thresholds (b).
Pearson’s correlation coefficient R [
The R2 and p-value for the correlation analysis between the change in the operation zones and the change in the average daily trip distance or average daily cruising time in each period.
Period 1-2 | Period 2-3 | Period 3-4 | Period 4-5 | Period 5-6 | |
---|---|---|---|---|---|
Unweighted area of operation zones and the average daily trip distance | |||||
R2 | 0.3493 | 0.1106 | 0.1534 | 0.1884 | 0.1823 |
p-value | | 1.0096e-24 | | | |
Unweighted area of operation zones and the average daily cruising time | |||||
R2 | 0.1821 | 0.0649 | 0.0466 | 0.0874 | 0.0041 |
p-value | | 3.3171e-14 | 1.2470e-10 | 4.9607e-19 | 3.4868e-1 |
NNI of operation zones and the average daily trip distance | |||||
R2 | 0.0209 | 0.0015 | 0.0227 | 0.0334 | 0.0352 |
p-value | 4.3059e-4 | 1.5593e-0 | 8.0890e-5 | 6.1279e-7 | 7.7453e-7 |
NNI of operation zones and the average daily cruising time | |||||
R2 | 0.0190 | 0.0015 | 0.0068 | 0.0077 | 0.0004 |
p-value | 6.5916e-4 | 1.5817e-0 | 5.1891e-1 | 6.3006e-1 | 4.0837e-0 |
This paper uses the taxicab trajectories in Shenzhen City to elucidate the change of taxicab operation zones within the context of e-hailing apps subsidy war. The results reveal interesting findings about the aggregate level of taxicabs: (i) the subsidy policies for taxicabs and passengers have an influence on the operation zones of taxicabs; (ii) the proportion of habitual operation zone pairs among operation zone pairs in neighboring periods is relatively stable under any subsidy policy during this subsidy war; and (iii) the changes in the operation zones have little effect on the changes in the average daily trip distance and average daily cruising time.
From the perspective of taxicab groups, there are also important findings: (i) the initial increase or decrease in patterns of the area and the degree of dispersion in the spatial distribution of the operation zones of the taxicabs in the beginning of the subsidy war could be used to reflect the difference in further operation zone changes between groups during the subsidy war. This group division rule is helpful for similar investigation tasks. (ii) In Period 3, which had the highest subsidy, all of the selected taxicabs travelled with decreased areas of operation zones and less passenger hunting time, but they had a decreased average daily trip distance at the same time. In this period, taxicabs could find their passengers efficiently and did not require a greater trip distance to maintain their incomes than in other periods because they could receive high subsidies from the e-hailing apps. (iii) When the subsidy to the passengers decreased, all of the groups of taxicabs increased their areas of operation zones in Periods 3-4 and 4-5 and had a high dispersion of their operation zones, especially in Period 4; as a result, they increased their average daily trip distance to maintain their incomes. (iv) When the subsidy war ended, some taxicabs may have returned to their prewar work status.
Based on the findings from all taxicabs and grouped taxicabs, this study could inform the operating decisions of e-hailing companies and government surveillance. For example, when e-hailing companies plan their future specific subsidy policies, the impact of the planned policies on taxis can be estimated to some extent. For the taxi management authority, the derived knowledge of the changes in the taxicabs’ operation zones and habitual operating preferences under the stimulus of policies could help to develop precise and targeted management tactics for different groups of taxicabs to improve urban mobility and public transportation services.
This paper focuses only on the influence of the subsidy war on cabdrivers, whereas, in future research, it is necessary to deeply investigate the interactions between cabdrivers and passengers, since the subsidy war not only stimulated cabdrivers’ behaviors but also influenced passengers’ daily travel patterns. To better understand these patterns, it is necessary to integrate the taxi trajectory data with the detailed order data of the e-hailing apps.
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.
The research was supported in part by the National Natural Science Foundation of China (Grants nos. 41771473 and 41231171), the National Key Research and Development Program of China (2017YFB0503802), and the Innovative Research Funding of Wuhan University (2042015KF0167).