Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data

Hindawi Journal of Advanced Transportation Volume 2020, Article ID 7621576, 20 pages https://doi.org/10.1155/2020/7621576

Research Article Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data

Xinmin Liu,1,2 Lu Sun ,1 Qiuxia Sun ,3 and Ge Gao 1,4

1College of Economics and Management, Shandong University of Science and Technology, Qingdao, China 2Qingdao Agricultural University, Qingdao, China 3College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, China 4College of Transportation, Shandong University of Science and Technology, Qingdao, China

Correspondence should be addressed to Lu Sun; [email protected]

Received 3 June 2019; Revised 21 August 2019; Accepted 3 September 2019; Published 13 January 2020

Academic Editor: Gonçalo Homem de Almeida Correia

Copyright © 2020 Xinmin Liu et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Taxi as a door-to-door, all-weather way of travel is an important part of the urban transportation system. A fundamental understanding of temporal-spatial variation and its related inuential factors are essential for taxi regulation and urban planning. In this paper, we explore the correlation between taxi demand and socio-economic, transport system and land use patterns based on taxi GPS trajectory and POI (point of interest) data of Qingdao City. e geographically weighted regression (GWR) model is used to describe the inuence factors of spatial heterogeneity of the taxi demand and visualize the spatial distributions of parameter estimations. Results indicate that during the peak hours, there are some dierences in taxi demand between workdays and weekends. Residential density and housing prices increase the number of taxi trips. Road density, parking lot density and bus station density are positively associated with the taxi demand. It is also found that the higher of the proportion of commercial area and public service area, the greater of the taxi demand, while the proportion of residential area and the land use mix have a negative impact on taxi demand. is paper provides some references for understanding the internal urban environmental factors generating from the taxi travel demand, and provides insights for reducing the taxi vacancy rate, forecasting taxi temporal-spatial demand and urban public transportation system planning.

1. Introduction demand of urban taxi ridership is an important and complex task. Firstly, considering that urban taxi travel is driven by Statistics show that by the end of 2017, there were 584,400 various purposes, and urban functions vary from place to public transport vehicles (tram, rail transit) in China and the place, it is dicult to clearly articulate the determinants asso- number of taxis was 1,395,800, which means that taxi travel ciated with taxi passenger travel [3, 4]. Secondly, unlike other accounted for a large proportion of public transport. However, buses and light rails modes, taxis do not have ¤xed routes and the spatial distribution of residents is imbalanced. erefore, lines, so it is dicult to study taxi travel demand. Under such if drivers do not understand the needs of residents, oversupply circumstances, reducing the vacant rate and waiting time, will lead to unnecessary vacant travel and may cause some making rational use of public resources and exploring the urban problems, such as large amounts of carbon emissions main factors aecting residents’ travel demand have become and trac congestion [1]. Conversely, insucient supply can one of the most important problems we need to solve urgently. lead to long waits for taxi passengers [2]. Both cases seriously In order to solve these issues, studies begin to focus on taxi downgrade the level of service for the taxi industry, such like travel forecast, temporal and spatial pattern of taxi travel. causing inconvenience to residents’ travel, weakening the ser- Some researches also focus on the model development and vice level of taxis, and increasing the cost of daily scheduling. optimization on taxi scheduling system, and the inuence All these bad eects result in the idleness and waste of urban mechanism of taxis a¦er the advent of taxi-haling applications public resources. erefore, it is necessary to dig deep into the [5–7]. Many studies concentrated on the mismatch between needs of residents’ taxi ridership. However, exploring the the potential passengers and empty taxi in spatial, then put 2 Journal of Advanced Transportation forward the route choice model [8–10]. In addition, some travel trajectories. Compared with traditional survey data, it scholars combine the distribution characteristics of travel thus promoting the investigation of urban taxi passengers. demand with the urban spatial structure to consider the factors Taxi GPS trajectory data is used as a type of geospatial activity that affect residents’ taxi travel [6, 11, 12]. e concept of built record, which contains the information reflecting the trajec- environment was proposed by scholars to discuss the influ- tory, such as the time, pick-up and drop-off location of the encing factors of residents’ travel [13–15]. Although the urban passenger during the trip. e data has been used in many built environment factors are complex and diverse, they can studies, such as spatial-temporal distribution of pick-up and be summarized into three aspects: Density, Diversity, and drop-off, hotspots identification and predicting waiting time Design [16], such as population density, employment density, [29–31]. However, few researches focus on urban residents’ land use mix and so on. e above factors are always consid- travel behavior using taxi GPS trajectory data. Actually, taxi ered negative or positive impact on travel demand [13–17]. is a vital travel mode for urban residents, and it plays an impor- However, most of the research objects are mainly on private tant role in reflecting residents’ travel. is paper analyzes the cars, public bicycles and rail transit, etc., to explore the impact spatial-temporal variations of taxi travel demand, and consid- of built environmental factors such as population density and ers the difference in travel demand between workdays and employment density on travel demand [18–20]. Moreover, due weekends during peak hours by using the GPS trajectory data to limited dataset and research methods, studies do not show and POI data. In order to better describe the influence factors spatial distribution differences. For this reason, scholars tend of spatial heterogeneity on the taxi demand, the geographically to ignore the role of taxis in public transportation and the weighted regression model with socio-economic attributes are impact of socio-economic factors on travel demand. In recent used in this paper. years, scholars have begun to pay attention to the status of In summary, although existing researches on the travel taxis in residents’ travel, and have studied the influencing fac- demand of public transportation have been studied, there are tors of taxi trips [4, 21, 22]. In this paper, “Density” factors certain restrictions on the empirical research subjects and such as road density and residential density are used to analyze research methods of the influencing factors. However, data the status of taxis in residents’ travel. used in previous research was survey data, which missed loca- In general, the research methods of the balance of supply tion information. Meanwhile, traditional analysis model and demand for using taxi are dominated by two methods, ignores the instability of the demand spatial distribution. i.e., the four-step method [23] and the ordinary least squares Considering the different travel time and purpose of residents, (OLS) multiple regression model [24]. Compared with the and the distribution of workplace and place of residence are four-step method, the regression model is relatively fast and different in spatial. Temporal-spatial variation of taxi demand cheaper, and is more suitable for more detailed analysis. But distribution should be taken into account in this paper. In view the assumption of the model is that all variables are static of the above, this paper aims to fill this gap by having an empir- throughout the study area. For example, Yang et al. [22] used ical analysis on the taxi demand based on taxi trajectory and the OLS global regression model to study the related factors POI data. e rest of this paper is organized as follows. Section affecting taxi passenger travel, and found land use have a 2 describes the data and methodology, including data preproc- strong correlation with taxi demand. However, the above two essing, the detail of dependent variable and independent var- methods ignore the spatial heterogeneity (e.g. land use types). iables, kernel density estimation and geographically weight Actually, the spatial heterogeneity has an important on resi- regression model. Section 3 presents the temporal-spatial dents’ travel. is is because trips arise from the spatial sepa- distribution of travel demand. Section 4 visualized the influ- ration of people’s desired purposes, and the purposes are encing factors estimated coefficients. Finally, Section 5 we determined by spatial heterogeneity (the different urban func- summarize findings and propose a future research agenda. tional areas). erefore, some scholars proposed to use geographical weighted regression (GWR) model to overcome this problem [25]. is method considers the spatial nonfixed 2. Data and Methodology effect of independent variables due to different research areas, and it introduces the positional features of each sample point 2.1. Data. is paper takes the taxi GPS data of Qingdao City in space and uses the distance between these sample points as from August 2nd to August 8th, 2017 as research object (no an important factor in defining regression weights [12, 26]. special events and national statutory holidays). e devices With the development of Location-Based Services (LBS), record the positions of the taxis, every 30 s during the day, some map service providers (e.g. Google map, Baidu map, generating approximately 140 million records belongs to 8,700 Tencent map, etc.) have gradually opened up map service taxis, which covering more than 80% of the whole city. e application interfaces. It reduces the cost of acquisition of map format of the data structure like . of information or point of interest. It is a kind of point data ere are two status: 0 and 1, 1 for loaded and 0 for empty (no- representing real geographical entities, which mainly contains load). Taxi’s trajectory is composed of several GPS points, the geographical coordinates (latitude and longitude), names, cat- data are arranged in chronological order to form the vehicle’s egories and addresses. Such network open data can be used as trajectory and reflect the information of pick-up and drop-off the main data source for studying the functional structure of location. e identification of the pick-up and drop-off points urban space [27, 28]. GPS (Global Positioning System) data is based on the continuous change of different status, and has a high geographical resolution and can obtain city-wide consists of a continuous, directional linear point distribution Journal of Advanced Transportation 3

Empty Load Empty

Empty D P D P ...0011111111001...100000011... Drop-oPick-upEmpty (0) Load (1) Load D P (a) (b) F¸¹º»¼ 1: Taxi GPS trajectory (a) sequence of GPS trajectory (b) identi¤cation of pick-up and drop-o location. of 0 and 1. e status form a sequence of 0 change to 1, 1 is calling Tencent’s location big data interface (https://www.geoq. the pick-up point, inverse, a sequence of 1 change to 0, 0 is the cn/), and housing prices can be obtained by grabbing home- drop-o point (Figure 1). In the course of taxi driving, GPS pages such as Anjuke and Lianjia (https://qd.anjuke. equipment failure and building occlusion will occur, resulting com/?pi=PZ-360-pc-all-biaoti; https://qd.lianjia.com/?utm_ in data loss or redundancy. It is necessary to detect and delete source=360&utm_medium=pinzhuan&utm_term=- the wrong records to keep the good quality of the data and biaoti&utm_content=biaoti&utm_campaign=biaoti). ensure the accuracy of the results. ree types of data should Trac factors consider the ¤eld of transportation, mainly be removed: (i) records with longitude or latitude as zero; (ii) referring to the state of transportation network con¤guration data outside the scope of study area. e main research area [38, 39]. Road density, bus stop density and parking lot density of this study is Qingdao urban area (Shinan District, Shibei are selected as three sub-variables in trac network [34]. District, Licang District, Laoshan District, Huangdao District, ere are 7,106 bus stations in study area, then calculate den- Chengyang District, shown in Figure 2); (iii) records with sity in each grid cell. Road network data comes from speed higher than 100 km/h. On average, 250,000 records are OpenStreetMap (https://www.openstreetmap.org/#map=10/ obtained per day. 35.8562/119.9185), and saved as a shape¤le. e formula for calculating road density is calculated as 2.2. Methodology. In order to reect the spatial dierence of taxi travel demand, the entire urban area is divided into ∑ = , (1) regions using a grid-based method. Scholars mainly use 1000 1000 m or 500 500 m cells in the literature. In this paper, where is the length of road in cell , and denotes the we select 500 500 m grid cells, which is more accuracy in area of . spatial∗ structure study.∗ Secondly, according the ¤le “DB 3702/ FW JT 010-2014”∗ by Qingdao Municipal Transport Bureau, Land use is also an important factor aecting residents’ the distance between two neighboring stations about 500 m. It travel demand. Generally speaking, dierent land use types is the minimum distance of the accessibility of public service attract dierent human activities, resulting in completely dif- facilities. In this paper, it generated a total of 500 500 m grid ferent passenger patterns [40, 41]. is paper mainly consid- cells in ArcGIS 10.2 system. All pick-up locations of taxi and ers four main types of land use, namely residential, commercial, independent variables are aggregated into corresponding∗ grid public service and other land use. In addition, the mixing of cells, the ¤nal shape¤le of the study area contains more than land use types also aects residents’ travel activities. Land use 1300 cells. e spatial and temporal distribution characteristics data, parking lot and bus station density can be obtained of taxi travel demand are analyzed. en hotspot areas are mainly by POI data, they can be collected by using Amap API identi¤ed by kernel density estimation, and construct GWR port (http://lbs.amap.com/api/webservice/guide/api/search). model to study the relationship between taxi demand and We utilize the entropy of land use, which is calculated as dierent variables at dierent time. Equation (2), to represent the land use mix [42–44]. In this formula, is the proportion of one land use type (residential, commercial, public service, others) in grid cell , and is the 2.2.1. Variable Description. is segment includes dependent number of land use types. e value of ranges from 0 to 1, variables and independent variables. Some research regarded socio-economic, trac and so on as the inuential factors on 0 represent that the land is exclusively dedicated to no-mixed travel demand (independent variables) [20, 32–34]. In this only one type, while 1 represents well mixed. paper, 10 explanatory variables are selected from 3 categories which contains socio-economic, land use and trac factors ∑ (2) as independent variables, and the dependent variable is the =− . number of pick-up points of taxi. e list of the 10 independent ( ) variables is given in Table 1. 2.2.2. Kernel Density Estimation Method. Kernel density is a For socio-economic factors, mainly represent the impact nonparametric method for estimating the probability density of residents’ characteristics on travel demand [19, 26, 35, 36]. function. In this paper, we use this method to ¤nd hotspots of For example, residential density is considered to be the most travel demand in urban areas. It can visually express the change signi¤cant factor aecting travel demand, housing price can of the density distribution of the passenger’s pick-up locations. be a representative indicator of residents’ economic level, and e search radius is used to delineate the proximity thresholds consumption level is relatively high in areas with high housing between things. A speci¤c spatial attenuation function is prices [37]. e data of residential density can be obtained by selected to describe the local spatial association of an event 4 Journal of Advanced Transportation

Chengyang, Licang, Laoshan, Shinan and Shibei district

Licun

Taidong

Huangdao district

Xiangjiang Rd

Jinggangshan Rd

F¸¹º»¼ 2: e study area of Qingdao.

T¾¿À¼ 1: Candidate list of explanatory variables.

Variables Description Residential density e number of people per square kilometer in each grid cell Socio-economic Housing price e average housing price per square kilometer in each grid cell Road density e length of road segments per square kilometer in each grid cell Trac Bus station density e number of bus stops per square kilometer in each grid cell Parking lot density e number of parking lots per square kilometer in each grid cell Residential area e ratio of the total oor area used for residential purposes in each grid cell Commercial area e ratio of the total oor area used for commercial purposes in each grid cell Land use Public service area e ratio of the total oor area used for public service purposes in each grid cell Other land use e ratio of the total oor area used for other purpose in each grid cell Land use mix e degree of mixture of land use in each grid cell with each event within the coverage of the search radius, indicating the relationship between the spatial closeness of 1 − () = , (3) the object and the proximity distance. e calculation process ℎ =1 ℎ of the kernel density estimation ¤rst centers on the point where is a sequence of independent and identically 1,..., feature, creates a circular surface with a given search radius. distributed random variables with , let the probability density en counts the number of point features within the circular function be , is the kernel function, means − /ℎ surface and divides by the area of the circle. e density value the number of samples, is smoothing ℎ = 500 > 0 at center is the largest, and the further away from the center parameter, also called bandwidth. e larger the kernel density point, the smaller the density value until the density value at value, the denser the point, and the higher the probability of the radius is zero [45, 46]. e format of is as follows: occurrence of a regional event. () Journal of Advanced Transportation 5

2.2.3. Geographically Weighted Regression Method. e decreases, number of passengers taking taxi also decreases. In classical linear regression model is based on the least square terms of time trends, the overall appearance is two peaks in method (OLS) to estimate the parameters. According to the morning and evening. From 8:00 to 11:00 in the morning, the ¤rst law of Tobler geography, all attribute values on a as the number of residents moving to business work increases, geographic surface are related to each other, but closer values then rises sharply to reach the ¤rst peak of the day which is are more strongly related than are more distant ones [47]. e the morning peak (09:00-10:00). From 16:00 to 18:00 in the distribution of taxi travel demand is spatially related to urban evening, due to the number of vehicles on the road during the spatial pattern, and its inuencing factors also have spatial peak hours of the work, trac congestion caused the taxi travel geographic attributes. e assumption that the residual term time become longer and the taxi trips changed signi¤cantly, is independent in OLS model will not be satis¤ed. But GWR showing a “ ” trend. A¦er 21:00, as the road gradually model allows some unstable data to be simulated directly, and unblocked, the number of passengers on the taxi began to replaces global parameter estimation with local parameter increase and reached the second peak period (21:00-22:00). estimation. It is more conducive to exploring the spatial A¦er 23:00, there are fewer residents on the street, and the variation characteristics and spatial rules of taxi travel demand demand for travel has gradually decreased. Passengers have [24]. It expressed as follows: gradually declined with the night, reaching the lowest point of the day at 04:00, in line with people’s daily travel rules. e maximum trips during the week is Friday, followed by the =0 , + , + , (4) weekend. =1 As shown in Figure 4, there some dierences between working days and weekends in terms of overall taxi demand. where is estimated coecient, represent the location , e overall distribution of taxi demand on weekdays and of , is independent variable, is a residual. Some methods weekends is similar, but there are signi¤cant dierences in exist that used to model the location of the estimated coe- local variation patterns during the a¦ernoon (dotted line cir- cient, such as ¤xed distance threshold, the Gaussian kernel cle). ere are two periods (0:00-4:00 and 14:00-10:00) the function and bi-square function. e coecients are calibrated demand for weekends and much greater than weekdays, while for each grid cell and vary from locations. In our case, we use the morning (5:00-11:00) is less than the working day. e Gaussian kernel function to estimate the spatial eects: result reects the travel habits of many people nowadays, most 2 people would like to have a rest on weekends morning a¦er exp working days, so they delay the time outside. At the same time, = − (5) people are more likely to join in leisure activities, such like represent the distance between observation and location social, going to the cinema and other entertainments. ese , which is calculated as the orthodromic distance in this study. activities always last until midnight. As the public transpor- When one of the samples is found, the weight of the other tation have been shut down, they will choose taxi back to res- sample point decreases as increases, and is bandwidth, idence places, and the taxi demand will increase. which refers to the nonnegative attenuation parameter of the functional relationship between weight and distance. e 3.2. Results of Spatial Distribution of Taxis. From the temporal larger the bandwidth, the slower the weight decays with the distribution results, we found that the urban residents in increase of the distance. Qingdao have some dierences in time, and the morning and evening peaks are 9:00-10:00 and 21:00-22:00 respectively. From the perspective of trac demand, the distribution 3. Results and Analysis of pick-up points in the peak period is visually analyzed using kernel density estimation. It can be seen that there are 3.1. Results of Temporal Distribution of Taxis. According to dierences in hotspots during peak hours on weekdays and the status attribute of GPS data, the temporal distribution weekends. of the passengers at dierent time granularities is counted It can be seen from Figures 5 and 6 that the location of the at intervals of 1 hour, record the change of the number of hotspots in the morning and evening peaks is similar, showing taxi passengers in one day, and comparing the changes in the a trend of “middle height, low circumference”, and higher ker- number of trips on workdays and weekends. ere are 8,600 nd th nel density values in the Shinan District and Shibei District of taxis in Qingdao from August 2 to August 8 , 2017 were the city. Licang District and Huangdao District have a lower used to record the passengers’ records, and the regular pattern density. For the reasons why the dierence in the spatial dis- of the taxi passengers in dierent days were obtained (Shown tribution of taxi demand, we need further analysis. in Figures 3 and 4). For the weekday morning peak and evening peak, evening It can be seen from Figure 3 that the distribution of pas- peak is more signi¤cant, and the hotspots of the early peak is sengers at each time of the week is closely related to the travel smaller than the range of the secondary hotspots. During the rules of residents, and consistent with the regularity of the morning rush hour, the main hotspots on the workdays are passenger volume at each time. e number of passengers in similar, but the scope is dierent (Figure 5(a)). As can be seen the day varies from 1 to 2 hours, and the trips during the day from the Figure 5, the main hotspots areas are mainly distrib- is at a high level. ere are certain uctuations in dierent uted near the Qingdao Railway Station in the Shinan District. time periods. At night, as the ow of people on the street During the early peak period, the ow of people is relatively 6 Journal of Advanced Transportation

16 000 14 000

s 12 000 ip

tr 10 000

er of 8 000 300 000

mb 6 000 250 000 s Nu 4 000 ip 200 000 tr 2 000 150 000 er of 0 mb 100 000

0:001:002:003:004:005:006:007:008:009:0010:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00 Nu 50 000 Time of day 2017.08.02 (Wed.) 0 .) .) .) t.)

2017.08.03 ( ur.) ri ur.) ed on un.) 2017.08.04 (Fri.) ues.) 2017.08.05 (Sat.) 2017.08.06 (Sun.) 2017.08.07 (Mon.) 2017.08.04 (F 2017.08.05 (Sa 2017.08.06 (S 2017.08.02 (W 2017.08.08 (T 2017.08.03 ( 2017.08.08 (Tues.) 2017.08.07 (M

F¸¹º»¼ 3: e distribution of number of trips for taxi in one week.

16,000

14,000

12,000 s

ip 10,000 tr 8,000 er of mb

Nu 6,000

4,000

2,000

0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:0010:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00 Time of day Weekdays Weekends

F¸¹º»¼ 4: Variation of trips in weekdays and weekends. dense, forming the most important hotspot area. In addition Similarly, the weekends peak hotspots are more pro- to the railway station, a small range of taxi passengers gathered nounced, while the early peaks have smaller range (Figure 6). around Qingdao Metro Line 3 of the Fushan institute station e hotspots in the morning rush hour on Saturday are mainly in the Shinan District. Secondly, there are more guests near concentrated near the railway station. Qingdao Railway Station Weihai road in Shibei District. Qingdao Taidong Commercial is the largest railway station in Qingdao, and is responsible for Pedestrian Street which is one of the ¤ve major shopping dis- passenger and cargo transportation tasks in Qingdao and sur- tricts in Qingdao. In addition, near the Licun Metro Station rounding areas. In addition to the hotspots at the railway station, in Licang District, where it is the most prosperous area in the morning peak on Sunday also formed a hotspot around Licang District. e secondary hotspots are mainly distributed Taidong Pedestrian Street. e pick-up point for the Saturday around the ¤rst-level hotspots, as well as Qingdao Liuting night peak is mainly along the metro May Fourth Square Station International Airport in Chengyang District, Jinggangshan to Yan’er island road Station, which locate with the new CBD of road in Huangdao District and the area surrounded by Qingdao. It is a good place for residents to go shopping on week- Changjiang middle road (including Guanting, Jiajiayuan shop- ends through the large shopping malls such as e Mixc, ping mall, etc.), and Qingdao North Railway Station, etc. Hisense Plaza, Belle Plaza and MYKAL. e hotspot of Licang Journal of Advanced Transportation 7

Weekday 09:00-10:00 2017.08.02 09:00-10:00 2017.08.03 09:00-10:00

2017.08.04 09:00-10:00 2017.08.07 09:00-10:00 2017.08.08 09:00-10:00

km 0510 20 Kernel Density High Low (a) Weekday 21:00-22:00 2017.08.02 21:00-22:00 2017.08.03 21:00-22:00

2017.08.04 21:00-22:00 2017.08.07 21:00-22:00 2017.08.08 21:00-22:00

km 0510 20 Kernel Density High Low (b) F¸¹º»¼ 5: Kernel density of weekdays peak period (a) weekdays 09:00-10:00 (b) weekdays 21:00-22:00. 8 Journal of Advanced Transportation

km 0 5 10 20

Kernel Density High Low

F¸¹º»¼ 6: Kernel density of weekends peak period.

District is near the Licun metro station, which includes Suning road and Changjiang road, there are shopping malls such as Life Plaza, Laoshan department store, WeiKe Plaza and other MYKAL, JUSCO, etc., these commercial places will attract more shopping centers. ere also locate with many colleges in Licang people to gather here. District, so students are always gathered here. e hotspots in Huangdao District are mainly around the Xiangjiang road trac 3.3. Results of Geographically Weighted Regression. Before bureau, the Zijinshan road and the Jinggangshan road metro the regression analysis, in order to ensure the rationality of station. e surrounding area of Zijinshan road is a large col- the model, the spatial autocorrelation and collinearity of the lection of catering industry, and the intersection of Jinggangshan alternative independent variables should also be discussed. Journal of Advanced Transportation 9

T¾¿À¼ 2: Correlation matrix.

Resi- Bus Public Housing Road Parking Residen- Commer- Other Land use dential station service price density lot density tial area cial area land use mix density density area Residential 1 density Housing 0.218 1 price Road density 0.222 0.342 1 Bus station 0.380 0.437 0.465 1 density Parking lot 0.408 0.479 0.497 0.442 1 density Residential 0.047 0.090 0.033 0.164 −0.084 1 area Commercial 0.133 0.201 0.203 0.214 0.205 −0.102 1 area Public 0.169 0.147 0.087 0.124 0.128 −0.126 0.038 1 service area Other land −0.141 −0.193 −0.057 −0.196 −0.123 −0.093 0.444 0.432 1 use Land use 0.223 0.442 0.338 0.458 0.366 0.156 0.446 0.287 0.252 1 mix

T¾¿À¼ 3: Estimation results for global models (OLS).

Coecient t-test VIF Variables Weekday Weekend Weekday Weekend Weekday Weekend Residential density 0.000 0.000 8.704 8.699 1.245 1.217 Socio-economic Housing price 0.000 0.000 7.111 5.723 1.555 1.485 Road density 0.000 0.000 4.027 4.943 1.424 1.403 Trac Bus station density 0.109 0.114 2.590 2.034 2.909 2.759 Parking lot density 0.041 0.043 8.259 6.904 2.724 2.631 Residential area 3.528 4.064 −0.793 −0.292 1.431 1.512 Commercial area 2.365 2.993 0.365 0.612 2.306 2.563 Land use Public service area 1.970 2.534 0.646 0.683 2.287 2.506 Other land use 1.721 2.268 −0.676 −0.262 2.478 2.692 Land use mix 1.460 1.816 −0.519 −1.322 2.092 2.025

T¾¿À¼ 4: Comparison between global model and the GWR model.

OLS GWR Weekday Weekend Weekday Weekend AICc 12166.605853 9753.084300 12035.744212 9709.16244 2 0.330356 0.315992 0.387968 0.360411 Adjusted 2 0.328114 0.313254 0.387504 0.342968

Multiple independent variables are used in regression the correlation coecients for each of the independent analysis, and these variables may be related to each other. variables with other independent variables. erefore, Multicollinearity refers to the distortion or diculty in Pearson correlation is used to test the validity of the model of estimating the model estimates due to the existence of independent variable. It is observed that all correlations are precise correlations or high correlations between explanatory less than 0.5 (Table 2). variables in linear regression models. Before proceeding to OLS linear regression are ¤rst used to investigate signi¤- regression modeling, variables with higher correlation should cant factors that inuence the urban taxi ridership and results be eliminates. Correlation matrix were developed to examine are represented in Table 3. Ten independent variables are 10 Journal of Advanced Transportation

Low

High (a) (b) (c)

F¸¹º»¼ 7: e distribution of main POIs (a) residential density (b) road density (c) land use mix. reveals to be closely relate to the urban taxi ridership in both possible explanation people with higher income may own their weekdays and weekends models. It was found that the variance private cars thus reduce the choice of hailing a taxi. ination factor (VIF) was less than 7.5 and indicated that the independent variables are well selected, so that the multicol- 4.2. Impact of Tra c Factors on Taxi Demand. In general, the linearity issue is avoid. impact of road density, bus station density and parking lot As shown in Table 4, the adjusted 2 is 0.387504 for week- density also have positive impact on taxi travel. Parking lot day model and 0.342968 for weekend model, which corre- density has the greatest impact on taxi travel demand, followed sponds to 0.05939 and 0.029714 improvement in the amount by bus station density (Figure 9). e impact of road density on of variation explained compared to OLS linear regression. In taxi demand varies greatly on weekdays, with little dierence addition, the reduction in AICc value is superior of GWR at between weekends. Places with higher road density may generate same day. more taxi trips, and these places usually have a higher population density. Weekend coecient value is lower compared to 4. Discussions weekdays, which means that although the correlation coecient is generally positive. e road density is lower, but more taxi trips GWR model is used to analyze the spatial variation in the may be generated, such as Laoshan scenic area surrounding on impact of road density, residential density, land use and other weekend where are fewer bus stops and bus lines. Areas with indicators on taxi travel demand, as well as the impact of dif- higher density of bus stations may generate more taxi trips, but ferent peak periods on weekdays and weekends. Here, we ¤nd the intensity on the weekend is reduced. is phenomenon can that the coecient in 5 weekdays and weekends has similar be explained in two ways. Firstly, places with bus stops are o¦en trends, so we select 4th, August as weekday and 6th, August crowded and tend to attract a large number of passengers, which as weekend to discuss the results. To better understand the in turn attracts and generates more taxi trips. Secondly, taxis may results of each variable, the distribution of main POIs in each be widely used for connecting, and passengers can travel to and gird cell shown in Figure 7. from the bus station’s departure point and destination by taxi. e reason why the density of parking lot has a greater impact 4.1. Impact of Socio-Economic Factors on Taxi Demand. Both on trac demand is related to the location of the parking lot. the impact of residential density and housing prices have e spatial pattern of parking lots is generally located in hotels, positive eect on residents’ taxi travel, and the impact of shopping malls, transportation hubs and other buildings, so the residential density is greater than housing prices (Figure 8). For crowds in these places are relatively dense and will encourage residential density, the coecient of weekend peaks is greater some passengers to choose taxis. than the weekday, and the impact range of weekend is wider, but the impact of housing price on taxi travel is opposite. e 4.3. Impact of Land Use on Taxi Demand. Similar pattern is impact of housing price on taxi demand is relatively consistent, observed for the proportion of residential area, other land use with changes on the weekend and the trend has gradually and land use mix, they have negative eect on taxi demand, shi¦ed from south to north. From a spatial perspective, the and other variables are positive (Figure 10). e lower the impact of residential density on taxi demand is more sensitive proportion of residential area, the fewer people choose to travel in parts of Shinan and Shibei District, mainly concentrated by taxi. From the status quo of land use in Qingdao, the area in Qingdao Railway Station and the Metro Line 2, Qingdao with a large living area is mainly in Shinan and Shibei District, CBD, and Taidong Commercial Pedestrian Street, especially where the housing prices are high and public transportation for weekend. e impact of housing price on the sensitive area relatively developed. erefore, majority people choose public is mainly in Huangdao. Moreover, although somewhere the transportation in these places. Residential density negatively housing price is high, the coecient is relatively small. One on taxi ridership on weekday and weekend morning peak, Journal of Advanced Transportation 11

N Weekday 09:00-10:00 Weekday 21:00-22:00

Residential Density

km 015 020 Coecient .000749 - .000780 Coecient .000754 - .000787 .000536 - .000656 .000781 - .000816 .000423 - .000665 .000788 - .000819 .000657 - .000716 .000817 - .000856 .000666 - .000715 .000820 - .000856 .000717 - .000748 .000857 - .000933 .000716 - .000753 .000857 - .000922 Weekday 09:00-10:00 Weekday 21:00-22:00

Housing Price

Coecient .000136 - . 000151 Coecient .000136 - .000153 .000083 - . 000099 .000152 - . 000195 .000080 - . 000097 .000154 - .000197 .000100 - . 000117 .000196 - . 000257 .000098 - . 000115 .000198 - .000255 .000118 - . 000135 .000258 - . 000292 .000116 - . 000135 .000256 - .000289

N Weekend 09:00-10:00 Weekend 21:00-22:00

Residential Density km 015 020 Coecient .000818 - .000852 Coecient .000988 - .000998 .000509 - .000645 .000853 - .000874 .000681 - .000793 .000999 - .001009 .000646 - .000758 .000875 - .000896 .000794 - .000904 .001010 - .001018 .000759 - .000817 .000897 - .000916 .000905 - .000987 .001019 - .001028 Weekend 09:00-10:00 Weekend 21:00-22:00

Housing Price

Coecient .000108 - .000117 Coecient .000099 - .000100 .000085 - .000095 .000118 - .000163 .000091 - .000095 .000101 - .000105 .000096 - .000100 .000164 - .000236 .000096 .000106 - .000176 .000101 - .000107 .000237 - .000285 .000097 - .000098 .000177 - .000213

F¸¹º»¼ 8: Spatial distribution for the coecients of socio-economic factors for weekday and weekend. 12 Journal of Advanced Transportation

N Weekday 09:00-10:00Weekday 21:00-22:00

Road Density

km 015 0 20 Coecient .000950 - .000988 Coecient .000943 - .000981 .000767 - .000840 .000989 - .001017 .000486 - .000823 .000982 - .001016 .000841 - .000897 .001018 - .001048 .000824 - .000883 .001017 - .001049 .000898 - .000949 .001049 - .001144 .000884 - .000942 .001050 - .001144 N Weekend 09:00-10:00 Weekend 21:00-22:00

Road Density

km 015 0 20 Coecient .001232 - .001285 Coecient .000871 - .000916 .000923 - .001081 .001286 - .001332 .000625 - .000735 .000917 - .000951 .001082 - .001178 .001333 - .001369 .000736 - .000824 .000952 - .000975 .001179 - .001231 .001370 - .001430 .000825 - .000870 .000976 - .001018 (a) Figure 9: Continued. Journal of Advanced Transportation 13

N Weekday 09:00-10:00 Weekday 21:00-22:00

Parking Lot Density

km 015 0 20 Coecient .320866 - .335808 Coecient .318832 - .331905 .208929 - .253998 .335809 - .352663 .138658 - .272339 .331906 - .344850 .253999 - .286090 .352664 - .373317 .272340 - .295926 .344851 - .365467 .286091 - .320865 .373318 - .411526 .295927 - .318831 .365468 - .401484

Weekend 09:00-10:00 Weekend 21:00-22:00 N

Parking Lot Density km 015 0 20 Coecient .295693 - .298666 Coecient .496120 - .497444 .253733 - .277884 .298667 - .303202 .470588 - .476439 .497445 - .498697 .277885 - .292118 .303203 - .308328 .476440 - .480535 .498698 - .500257 .292119 - .295692 .308329 - .315426 .480536 - .496119 .500258 - .503843 N Weekday 09:00-10:00 Weekday 21:00-22:00

Bus Station Density

km 015 0 20 Coecient .288216 - .307318 Coecient .288635 - .316237 .197896 - .236909 .307319 - .329348 .200454 - .244739 .316238 - .345933 .236910 - .264327 .329349 - .358363 .244740 - .267041 .345934 - .377435 .264328 - .288215 .358364 - .392226 .267042 - .288634 .377436 - .412967

N Weekend 09:00-10:00 Weekend 21:00-22:00

Bus Station Density km 015 0 20 Coecient .220048 - .233459 Coecient .026961 - .034421 .170427 - .192849 .233460 - .245603 .003228 - .017540 .034422 - .047729 .192850 - .208614 .245604 - .255820 .017541 - .021840 .047730 - .097226 .208615 - .220047 .255821 - .267561 .021841 - .026960 .097227 - .110081 (b) F¸¹º»¼ 9: Spatial distribution for the coecients of transport factors for weekday and weekend. 14 Journal of Advanced Transportation