International Journal of Geo-Information

Article Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data

Huihui Wang 1, Hong Huang 2, Xiaoyong Ni 2 and Weihua Zeng 1,*

1 School of Environment, Normal University, Beijing 100875, ; [email protected] 2 Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China; [email protected] (H.H.); [email protected] (X.N.) * Correspondence: [email protected]

 Received: 16 April 2019; Accepted: 28 May 2019; Published: 30 May 2019 

Abstract: Mobility and spatial interaction data have become increasingly available due to the widespread adoption of location-aware technologies. Examples of mobile data include human daily activities, vehicle trajectories, and animal movements. In this study we focus on a special type of mobility data, i.e., origin–destination (OD) pairs, and propose a new adapted chord diagram plot to reveal the urban human travel spatial-temporal characteristics and patterns of a seven-day taxi trajectory data set collected in Beijing; this large scale data set includes approximately 88.5 million trips of anonymous customers. The spatial distribution patterns of the pick-up points (PUPs) and the drop-off points (DOPs) on weekdays and weekends are analyzed first. The maximum of the morning and the evening peaks are at 8:00–10:00 and 17:00–19:00. The morning peaks of taxis are delayed by 0.5–1 h compared with the commuting morning peaks. Second, travel demand, intensity, time, and distance on weekdays and weekends are analyzed to explore human mobility. The travel demand and high-intensity travel of residents in Beijing is mainly concentrated within the 6th Ring Road. The residents who travel long distances (>10 km) and for a long time (>60 min) mainly from outside the 6th Ring Road and the surrounding new towns of Beijing. The circular structure of the travel distance distribution also confirms the single-center urban structure of Beijing. Finally, a new adapted chord diagram plot is proposed to achieve the spatial-temporal scale visualization of taxi trajectory origin–destination (OD) flows. The method can characterize the volume, direction, and properties of OD flows in multiple spatial-temporal scales; it is implemented using a circular visualization package in R (circlize). Through the visualization experiment of taxi GPS trajectory data in Beijing, the results show that the proposed visualization technology is able to characterize the spatial-temporal patterns of trajectory OD flows in multiple spatial-temporal scales. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.

Keywords: human travel; travel pattern; OD flow; the chord diagram plot; taxi data

1. Introduction Cities are concentrated areas of human activities, and urban spatial structures are closely related to the intracity travel patterns of their residents [1]. Identifying the patterns of intra-urban residents’ trips will help us better understand urban structures and reveal the driving social factors, such as gender and occupation [2]. As questionnaire-based methods are limited by a lack of data, it is difficult to use this traditional method to explore human mobility deeply and accurately. With the rapid development of location-based service (LBS) and information communication technologies (ICT) such as Global Positioning System (GPS) receivers and mobile phones, these technologies support the convenient

ISPRS Int. J. Geo-Inf. 2019, 8, 257; doi:10.3390/ijgi8060257 www.mdpi.com/journal/ijgi ISPRS Int. J. Geo-Inf. 2019, 8, 257 2 of 22 collection of large volumes of individual trajectory data [3–5]. Currently, such data have been widely applied in human mobility pattern research [6–14]. The widespread adoption of big data provides unprecedented opportunities to study various mobility patterns from trillions of trails and footprints. Much research has been conducted to investigate the spatial-temporal patterns of human motion in urban centers including studies in cities such as Rome [15], and Tallinn [16], in addition to Hong Kong [17], [1,18], Wuhan [19,20], and Shenzhen of China [21]. Vehicles equipped with GPS provide an important type of footprint, given that public and private vehicles are the main transportation means for a city’s population. People use vehicles for commuting to and from work, for regular and ad-hoc chores, and for leisure activities [22]. As a main part of the transportation sector, taxis provide accessible and flexible travel services for people living in urban centers. Although taxi trajectories are unable to reflect continuous displacements of specific people, which are crucial for a time geography framework, they describe collective human mobility patterns of intracity travels with accurate positions and time [1]. With information of when and where a customer is picked up or dropped off by taxi, meaningful trips corresponding to displacements between people’s consecutive activities are easy to extract. A careful analysis of these digital footprints from taxi GPS data can provide an innovative strategy to improve the quality of public transit services and facilitate urban public transit planning and operational decision-making [23]. This type of data has high accuracy and good continuity; they are acquired in real-time and support a high degree of automation. GPS taxi data can provide an excellent foundation for revealing urban residents’ travel behavior and analyzing spatial-temporal patterns [24,25]. Recently, GPS-enabled vehicles have been widely adopted to collect real time traffic data [26–29]. In addition to monitoring real-time traffic situations, a large volume of GPS-enabled taxi trajectory data has been used for travel time estimation, dynamic accessibility evaluation, and travel behavior analysis [30–32]. Trajectory and movement data have been studied with various approaches, including visual analysis [33], clustering [34], feature extraction [35], and movement pattern taxonomy [36]. Visual analysis tools enable interactive and intuitive data exploration. Visualization techniques have been used to reveal urban traffic patterns and explore commuting rules of urban residents, thus providing decision-making basis for smart city construction and urban traffic planning. Some scholars analyze and excavate residents’ travel patterns and patterns by visualizing origin–destination (OD) stream data of taxis [37–40]. A study by Yue et al. [37] uses the dynamic taxi data to reveal people’s travel demands and movement patterns in a deeper sense to serve transport management, urban planning, as well as spatial-temporal tailored location search and services. Guo et al. [38] presents a new methodology for detecting location patterns and spatial structures embedded in OD movements; this approach involves steps to group spatial points into clusters, derive statistical summaries, and visualize spatial-temporal mobility patterns. The visual analysis of OD data is an important way to mine inter-regional flow patterns and analyze urban residents’ travel spatial-temporal patterns [41]. At present, there are mainly visual analysis methods for OD data of taxis, including Flow Map, OD Matrix, and OD Map [42–46]. Tobler [42] first used the arrow form of Flow Maps to represent the population migration flow data and to draw the population migration map of the United States. This kind of graph shows the flow direction and the width of the edge indicates the OD flow rate, but this often causes the OD flow line arrows to overlap each other, making the visualization confusing. It is more difficult to visualize the OD stream data of a large number of taxis under the spatial-temporal scales using the Flow Maps. Andrienko [43] first uses the OD Matrix to study the traffic conditions in Milan, Italy. In the OD matrix each row and column corresponds to a region; each cell corresponds to the flow of OD between a pair of regions. Although this method can express flow relations between different regions, it lacks spatial information (region to region) and is not intuitive. In order to compensate for the shortcomings of OD matrix in spatial information representation, Wood et al. [44] proposed an OD map method when studying the migration of the United States. The method uses a regular grid to divide the study region into a two-dimensional matrix. Next, a two-dimensional matrix is nested within each cell. The size of flow from one region to another is represented by the color of another area of the cell that is nested within ISPRS Int. J. Geo-Inf. 2019, 8, 257 3 of 22 the area. The depth of the color indicates the amount of flow. However, this method lacks the ability to express OD flow changes in the case of space-time multiscale. At the same time, domestic and foreign scholars have also built some visual systems to study the temporal and spatial patterns of trajectory data [45–50]. Guo et al. [45] presented an interactive visual analytics system—Triple Perspective Visual Trajectory Analytics (TripVista)—for exploring and analyzing traffic trajectory data. Hurter et al. [47] propose a brush–pick–drop interaction scheme. Their methods are general for 2D trajectory data, but with limited perspectives provided. Slingsby et al. [48] proposed a treemap cartography method to show spatiotemporal traffic patterns. Liu et al. [49] developed a comprehensive visual analytics system to study route diversity based on real trajectory data. Their system provides an intuitive way to compare and evaluate different routes. However, the above method is difficult to visualize trajectory big data in multiscale spatial-temporal, and it is more difficult to reveal the characteristics of OD stream data. In this paper, a large scale study is presented that uses taxi GPS data collected from more than 25,000 drivers for seven consecutive days in Beijing, China. To support this study a new adapted chord diagram plot is introduced (refer to Section 2.3.2) that visualizes taxi OD flows at different granularities on a space-time scale. The new chord diagram plot is implemented using a circular visualization package in R (circlize). The study reveals the spatial-temporal characteristics and patterns of residents’ travel. The spatial distribution patterns of origins and destinations are analyzed first. Then, travel intensity, time, and distance on weekdays and weekends are used to explore human mobility by extracting taxi data. The findings of this study can assist transport managers to better understand spatial variability of resident travel flow across weekdays and weekends, which further provide additional comprehensive travel indicators to facilitate sustainable urban management. The remainder of this paper is organized as follows. Section2 describes the study area, dataset, and methodology used in the paper. The results of travel spatial-temporal characteristics and patterns of urban human are analyzed and discussed in Section3. Conclusions are presented in Section4.

2. Materials and Methodology

2.1. Study Area The case study is carried out in Beijing, China, which is located in the North of the 2 Plain (longitude 115.7◦–117.4◦ East and latitude 39.4◦–41.6◦ North). It covers an area of 16,411 km with the gradual declination of altitude from the Northwest to the Southeast. There are 14 districts and 2 counties in the administrative district of Beijing (Figure1). The permanent population of Beijing increased from 15.4 to 21.7 million between 2005 and 2015, of which approximately 86.5% was urban. The GDP increased from 696.9 to 2301.5 billion CNY over the same period [51]. Beijing is one of the most dynamic economic centers in China. The public transit system in Beijing includes buses, subways, taxis, and bicycles. In 2014, the public transport system catered to 45.0% of travel in Beijing overall and, more specifically, 48.0% of travel in its core urban area; taxis provided about 10.0% of the intra-urban trips by transportation [52]. Even though the metro transit dominates Beijing’s public transportation, taxis play an important part in ground transportation in recent years. Traveling by taxi offers flexible routes and is more time-efficient than other modes of transportation. In China taxi trajectories provide a reasonable data source for urban studies, given their capacity to capture a large proportion of urban passenger flows. This research uses a large volume of trip data collected from location-aware devices to analyze the spatial-temporal characteristic and patterns of residents’ trip behaviors in Beijing. ISPRSISPRS Int. Int. J. J. Geo-Inf. Geo-Inf. 20192019,, 88,, x 257 FOR PEER REVIEW 4 4 of of 22 22

Figure 1. The Beijing study area. Figure 1. The Beijing study area. 2.2. Data Sources 2.2. Data Sources Taxis play an important role in intra-urban transportation in Beijing. Many taxi companies have installedTaxis GPS play receivers an important in their role fleets in intra-urban to monitor transportation the real-time in movement Beijing. Many of each taxi taxi.companies This paperhave installedused a data GPS set receivers that contains in their the fleets GPS to trajectories monitor the of morereal-time than movement 27,000 taxis of foreach seven taxi.consecutive This paper used days a(from data Decemberset that contains 25th to the December GPS trajectories 31st in 2012) of more from than an anonymous27,000 taxis taxifor seven company consecutive in Beijing. days The (from data Decemberrecorded each 25th taxi’s to December location, velocity,31st in 2012) timetable, from and an stateanonymous (vacancy taxi or occupancy),company in which Beijing. is acceptableThe data recordedin investigating each taxi’s intra-urban location, travel velocity, patterns. timetabl Tablee, 1and provides state an(vacancy overall or description occupancy), of taxiwhich data. is acceptableThe “Time” in indicates investigating when intra-urban the data are travel recorded, pattern “Latitude”s. Table 1 andprovides “Longitude” an overall provide description the location of taxi data.data ofThe the ‘‘Time’’ taxi vehicle, indicates and when “Speed” the isdata the are instantaneous recorded, ‘‘Latitude’’ velocity of and vehicle, ‘‘Longitude’’ the unit isprovide kilometers the locationper hour. data “Direction” of the taxi represents vehicle, theand driving ‘‘Speed’’ direction, is the instantaneous which is based velocity on North. of vehicle, “State” the represents unit is kilometersthe whether per or hour. not the ‘‘Direction’’ taxi is occupied represents by passengers, the driving “00 ’direction, represents which the taxi is based vehicle on is North. vacant ‘‘State’’ and “10 ’ representsmeans it is the occupied. whether All or thenot trajectoriesthe taxi is occupied have been by cleaned passengers, by removing ‘‘0′’ represents invalid the points taxi causedvehicle byis vacantdata recording and ‘‘1′’ means or transfer it is occupied. errors. Based All the on trajectories the cleaned have data, been the cleaned locations by removing where passengers invalid points were causedpicked by up data (with recording subscript or “O”)transfer and errors. dropped Based off on(with the cleaned subscript data, “D”) the canlocations be identified, where passengers and thus werethe origin picked and up destination (with subscript of a completed “O”) and trip.dropped Each off trip (with can besubscript simplified “D”) to can be abe vector identified, from (X ando,Y thuso,To) theto (X origind,Yd ,Tandd), destination where (X,Y) of denotes a completed the location trip. Each and trip T can time be of simplified a pick-up to event be a vector and a from drop-o (Xffo,Yevent,o,To) torespectively. (Xd,Yd,Td), Awhere total (X,Y) of 88,491,241 denotes tripsthe location have been and extracted T time of from a pick-up the data. event and a drop-off event, respectively. A total of 88,491,241 trips have been extracted from the data. Naturally the trips are more concentratedTable 1. GPS in data central in Beijing urban city. areas than suburban or urban fringes. This can be demonstrated by the density analyses on pick-up points (PUPs) and drop-off points FCD Taxi ID Time Longitude Latitude Speed Direction State (DOPs). Figure 2 depicts the density estimations of all PUPs and DOPs in the seven days. In order to investigateJL the 1000075621 temporal characteristics20121225000014 of trips116.1048 the seven 39.9638days are discretized 11 290into 168 one 0 hour JL 1000075621 20121225000044 116.1040 39.96299 9 200 0 intervals...... PUPs and DOPs represent origins and destinations of various trips, and are critical for us to understandJYJ 13331156462traffic patterns20121225150826 in the city. 116.2918 39.88956 43 268 1 JYJ 13331156462 20121225150937 116.2901 39.88969 45 270 1 ......

ISPRS Int. J. Geo-Inf. 2019, 8, x FOR PEER REVIEW 5 of 22 ISPRS Int. J. Geo-Inf. 2019, 8, 257 5 of 22 Table 1. GPS data in Beijing city.

FCDNaturally Taxi the ID trips are more Time concentrated Longitude in central urban Latitude areas than Speed suburban Direction or urban fringes. State JL 1000075621 20121225000014 116.1048 39.9638 11 290 0 This can be demonstrated by the density analyses on pick-up points (PUPs) and drop-off points (DOPs). JL 1000075621 20121225000044 116.1040 39.96299 9 200 0 Figure… 2 depicts… the density estimations … of all PUPs … and DOPs in … the seven days. … In order … to investigate … theJY temporalJ 13331156462 characteristics 20121225150826 of trips the seven days116.2918 are discretized 39.88956 into 168 43 one hour 268 intervals. PUPs 1 andJY DOPsJ 13331156462 represent origins 20121225150937 and destinations 116.2901 of various trips, 39.88969 and are critical 45 for us 270 to understand 1 traffi…c patterns in… the city. … … … … … …

Figure 2. Spatial distributions of the density of pick-uppick-up points (PUPs) and drop-odrop-offff pointspoints (DOPs).(DOPs). 2.3. Methodology 2.3. Methodology 2.3.1. Temporal Pattern of PUPs and DOPs 2.3.1. Temporal Pattern of PUPs and DOPs In this study, the total numbers of PUPs and DOPs are computed for each hour in the seven In this study, the total numbers of PUPs and DOPs are computed for each hour in the seven days days [3]: [3]: XR,C SP[t] = P[i, j, t], t = 1, 2, ..., T (1) RC, ==i=1,j=1 StP[ ] Pijtt [ , , ], 1,2,..., T (1) ij==XR1,,C 1 SD[t] = D[i, j, t], t = 1, 2, ..., T (2) i=1,j=1 RC, where i and j specify the coordinates of a== pixel. StD[] Dijtt [, ,], 1,2,..., T (2) For seven consecutive days, the dominantij==1, 1 period length for urban mobility patterns has been verified to be 24 h by applying the discrete Fourier-transform (DFT) analysis on PUPs and DOPs [3]. UponWhere this premise, i and j specify the hourly the coordinates average activity of a pixel. number for both weekdays and weekends were calculated:For seven consecutive days, the dominant period length for urban mobility patterns has been P5 verified to be 24 h by applying the discreteλP Fourier-transform[i, j, k 24 + t] (DFT) analysis on PUPs and DOPs [3]. k=1 × Upon this premise, the hourlyPd[i, j , averaget] = activity number (fort = both1, 2, ...,weekdays 24) and weekends were(3) calculated: 5 P7 5 λD[i, j, k 24 + t] k=6 × De[i, j, t] = λPi[, j , k×+ 24 t ](t = 1, 2, ..., 24) (4)  2 d k=1 (3) Pijt[ ,λ , ]== ( t 1,2,...,24) where i and j denote a pixel, denotes the type5 of the activity (Ro for PUPs and Rd for DOPs), k is the ordinal number in a seven-day week, and the superscript d and e specify weekdays and weekends, respectively. 7 2.3.2. Multiscale Analysis Method of ODλ FlowDi[, Based j , k×+ 24 on the t ] Chord Diagram Plot e k=6 (4) The OD flow is a streamlineDijt[ , , where ]== the starting point O ( t(X 1,2,...,24),Y ,T ) of the movement trajectory 2 o o o points to the termination point D (Xd,Yd,Td); it is a directional line with a spatial position. If the OD

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Where i and j denote a pixel, λ denotes the type of the activity (Ro for PUPs and Rd for DOPs), k is the ordinal number in a seven-day week, and the superscript d and e specify weekdays and weekends, respectively.

2.3.2. Multiscale Analysis Method of OD Flow Based on the Chord Diagram Plot ISPRS Int. J. Geo-Inf. 2019, 8, 257 6 of 22

The OD flow is a streamline where the starting point O (Xo,Yo,To) of the movement trajectory points to the termination point D (Xd,Yd,Td); it is a directional line with a spatial position. If the OD point distribution is mapped to a certain granularity of spatial area, the orientation between OD points point distribution is mapped to a certain granularity of spatial area, the orientation between OD can show the flow of moving objects within the spatial areas. Flow patterns of moving objects on points can show the flow of moving objects within the spatial areas. Flow patterns of moving objects different spatial scales can be analyzed using different partition granularities. By using the definition of on different spatial scales can be analyzed using different partition granularities. By using the OD flow in terms of spatial attributes, this study extends the OD flow to the time dimension. The OD definition of OD flow in terms of spatial attributes, this study extends the OD flow to the time data is a kind of sequence data with time stamps. The directed connection of the OD data start and end dimension. The OD data is a kind of sequence data with time stamps. The directed connection of the time is the OD flow in the time dimension. Mapping the time of an OD point to a time period of OD data start and end time is the OD flow in the time dimension. Mapping the time of an OD point a certain granularity indicates the flow of a moving object in different time periods. By changing to a time period of a certain granularity indicates the flow of a moving object in different time periods. the granularity of partitioning, OD flows at different time scales can be achieved. By changing the granularity of partitioning, OD flows at different time scales can be achieved. Considering that the taxi OD data records the movement of the vehicle in space-time, it is possible Considering that the taxi OD data records the movement of the vehicle in space-time, it is to define its data volume by means of a graph theory method [41]: possible to define its data volume by means of a graph theory method [41]:

QQST== ((,S, T) ) (5) whereWhereS is a setS is of a spatialset of spatial regions regions and T isand aset T is of a timeset of segments. time segments. WhenWhens s S∈orS tor tTT∈(i =(1,in= 2, 1,2,..., ..., n), i )is, spatial-temporali is spatial-temporal granularity. granularity. Therefore, Therefore, there there are i arei i ∈i i ∈ i × possibilitiesii× possibilities for OD for flow. OD flow. The rowThe row and and column column records records represent represent the the start start and and end end positions positions or or times,times, respectively, respectively, forming forming an ani ii×i two-dimensional two-dimensional matrix. matrix. That That is, is, the the possibility possibility of of OD OD streams streams × isis recorded; recorded; each each matrix matrix element element represents represents the the OD OD record record statistics. statistics. This This two-dimensional two-dimensional matrix matrix cancan be be transformed transformed into into a a chord chord diagram, diagram, providing providing a a better better visualization. visualization. The The outer outer ring ring represents represents thethe spatial-temporal spatial-temporal granularity granularity of of the the division division (e.g., (e.g., time time scale scale and and space space scale), scale), and and the the inner inner string string representsrepresents the the amount amount of of migration, migration, as as shown shown in in Figure Figure3 .3. The The rows rows of of the th two-dimensionale two-dimensional matrix matrix representrepresent the the starting starting position position or or time time and and the the columns columns represent represent the the position position or or time time of of the the ending ending point,point, for for example, example, cell cell (3, (3, 4) 4) represents represents the the flow flow amount amount of of 3–4 3–4 (Figure (Figure3). 3). However, However, this this kind kind of of diagramdiagram has has two two important important limitations. limitations. It It cannot cannot show show the the relationship relationship between between the the total total amount amount of of taxitaxi migration migration in in di ffdifferenterent regions, regions, and and it cannot it cannot represent represent the flow the directionflow direction of taxis. of Therefore,taxis. Therefore, it needs it toneeds be improved. to be improved. FigureFigure4 4shows shows the the improved improved the the chord chord diagram diagram plot plot using using the the circlize circlize package package in in R. R. It It can can be be seenseen from from Figure Figure4 4that that the the improved improved chord chord diagram diagram has has better better visual visual e ffeffects.ects. The The text text outside outside the the arc arc representsrepresents the the numbernumber ofof didifferentfferent regions or or time time and and the the width width of of the the arc arc represents represents the the size size of the of themigration migration flow. flow. The The internal internal chord chord band band represents represents the the migration migration of OD data.data. TheThe widerwider thethe chord chord representsrepresents the the larger larger the the migration migration flow. flow. The The chords chords between between di differentfferent arcs arcs represent represent the the migration migration ofof OD OD datadata betweenbetween didifferentfferent spacesspaces or time time periods. periods. The The chords chords between between the the same same arc arc represent represent the theamount amount of ofOD OD data data transferred transferred between between the the same same sp spaceace or or time time period period and the arrowarrow atat thethe endend of of thethe chord chord represents represents the the direction direction of of the the flow. flow.

FigureFigure 3. 3.Two-dimensional Two-dimensional matrix matrix and and visualization visualization e effffectsects of of origin–destination origin–destination (OD) (OD) flow. flow.

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Figure 4. The improved chord graph of OD flows. Figure 4. The improved chord graph of OD flows. According to the chord diagram, the total amount of flow of taxi OD data and the proportion of inflowAccording and outflow to the can chord be clearly diagram, seen inthe any total space amount area orof timeflow period,of taxi OD and data a comparative and the proportion analysis can of alsoinflow be and conducted. outflow The can visualizationbe clearly seen analysis in anyresults space area notonly or time provide period, a reference and a comparative for urban residents’ analysis travelcan also plans, be conducted. but can also The infer visualization their functional analysis zoning results by analyzing not only the provide rules of ODa reference traffic flow for of urban taxis withresidents’ the change travel ofplans, time but between can also di ffinfererent their regions. functional Taxi data zoning are spatialby analyzing position the data rules with of OD a specific traffic timeflow series,of taxis which with the has change characteristics of time ofbetween space-time different and multiscale.regions. Taxi Therefore, data are thisspatial paper position introduces data anwith adapted a specific chord time diagram series, plot which to visualize has characteristics taxi OD flows of inspace-time both the time and scalemultiscale. and spatial Therefore, scale using this thepaper circlize introduces package an inadapted R [53,54 chord]. diagram plot to visualize taxi OD flows in both the time scale and spatialThe scale taxi using data containsthe circlize abundant package information in R [53,54]. on the time scale. This study uses a ring to display 24 h aThe day taxi with data the contains method fromabundant Liu et information al. [49]. As the on shapethe time of the scale. ring This resembles study uses the shape a ring of to the display clock, it24 ish generallya day with considered the method that from the Liu top et of al. the [49]. ring As isthe the shape start of point the ring and resembles the end point the shape of the of time. the Theclock, time it is direction generally is considered represented that by the the top clockwise of the ring direction. is the start Each point arc representsand the end the point corresponding of the time. timeThe time period direction (e.g., 30 is minrepresented or 60 min). by the The clockwise size of the direction. scale on Each the arcarc representsrepresents thethe statisticcorresponding of OD. Heretime period the OD (e.g., data 30 span mi 7n daysor 60 a min). week, The divided size of into the weekdays scale on andthe arc weekends. represents At the samestatistic time, of 24OD. h aHere day the is divided OD data using span 30 7 days min and a week, 60 min divided as diff intoerent weekdays time scales. and The weekends. travel time At ofthe taxi same OD time, has been24 h visualizeda day is divided based using on the 30 circlize min and package 60 min in as R. different time scales. The travel time of taxi OD has been visualizedOn the based spatial on scale,the circlize this paperpackage uses in R. the OD data of 7 days a week, divided into weekdays and weekends.On the spatial Based scale, on R this language, paper uses a spatial the visualizationOD data of 7 analysisdays a week, has been divided conducted into weekdays on the amount and ofweekends. incoming Based and outgoing on R language, traffic and a spatial the net visualization flow in Beijing. analysis Each has arc inbeen the conducted string diagram on the represents amount correspondingof incoming and to outgoing different areastraffic (where and the the net area flow name in Beijing. first letter Each indicates arc in the each string area, diagram e.g., CY represents indicates Chaoyangcorresponding district). to different The scale areas on the (where arc represents the area the name different first OD letter statistics, indicate ands theeach arrow area, at e.g., the endCY ofindicates the chord Chaoyang represents district). the area The where scale on the the trips arc end. represents Further, the the different research OD area statistics, A (the 6thand Ring the arrow Road ofat the Beijing) end of is recursivelythe chord represents partitioned the into area 2 nwhere2n gridsthe trips in four end. quadrants Further, the based research on the area idea A of(the equal 6th × n n granularityRing Road of rule Beijing) meshing is recursively of spatial regions partitioned and quadtree into 2 × coding.2 gridsThen in four the quadrants OD point databased is on mapped the idea to theof equal corresponding granularity the rule number meshing of spatial of spatial grid regions ID according and quadtree to the spatial coding. position, Then the thereby OD point converting data is themapped OD datato the into corresponding sequence data the representednumber of spatial in grid grid units. ID according First, the to study the spatial area A position, is divided thereby into converting the OD data into sequence data represented in grid units. First, the study area A is divided equal-area collections (Figure5a), that is A = (A1,A2,A3,A4), the OD point data of the original movementinto equal-area trajectory collections is divided (Figure into 5a), corresponding that is A = (A subregions,1, A2, A3, A and4), the OD datapoint of data each of subregion the original are statisticallymovement trajectory and visually is divided analyzed. into Then, corresponding according to subregions, the same method, and the the OD subgrid data of is each further subregion divided are statistically and visually analyzed. Then, according to the same method, the subgrid is further into collections of regions (Figure5b), that is A = (A1,A2, ... ,A16), and then statistically and visually analyzesdivided into the ODcollections data of of each regions subarea, (Figure followed 5b), that by analogy;is A = (A1 finally,, A2, …, the A16 study), and areas then arestatistically divided intoand regularvisually 8analyzes8 grids the (Figure OD data5c). Accordingof each subarea, to the IDfollowed of the taxi,by analogy; the ID numbers finally, the of the study spatial areas grids are divided into× regular 8 × 8 grids (Figure 5c). According to the ID of the taxi, the ID numbers of the spatial grids corresponding to the starting point and the ending point are extracted, and the statistics

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ISPRS Int. J. Geo-Inf. 2019, 8, x FOR PEER REVIEW 8 of 22 corresponding to the starting point and the ending point are extracted, and the statistics of the flow of eachthe flow taxi of between each taxi di ffbetweenerent regions different are regions calculated; are calculated; the same processing the same processing for all the taxisfor all results the taxis in theresults flow in of the taxis flow between of taxis the between regions the on regions different on spatial different scales. spatial Using scales. the chord Using diagram the chord mentioned diagram above,mentioned the taxi above, OD the data taxi can OD be data visually can analyzedbe visually at analyzed different spatialat different scales. spatial scales.

Figure 5.5. The expression of space multiscale for OD data. 3. Results 3. Results 3.1. Spatial-Temporal Characteristics of PUPs and DOPs 3.1. Spatial-Temporal Characteristics of PUPs and DOPs According to the taxi OD data, the number of PUPs and DOPs in each hour is counted, that is, the numberAccording of timesto the thetaxi state OD data, of passenger the number pick-up of PUPs and and drop-o DOPsff changes in each fromhour is 0 tocounted, 1 and fromthat is, 1 tothe 0 number is calculated of times every the hour.state of One passenger pick-up pick-up and drop-o and ffdrop-offbehavior changes represents from a0 trip.to 1 and A consecutive from 1 to 0 seven-dayis calculated week every taxi hour. OD data One set pick-up is used and to conductdrop-off a behavior comprehensive represents analysis a trip. at diA ffconsecutiveerent hours. seven- It can day week taxi OD data set is used to conduct a comprehensive analysis at different hours. It can be be seen from Figure6 that the temporal sequences represented by curves of SP and SD are very similar. Sevenseen from 24-h Figure cycles 6 can that be the clearly temporal identified. sequences That represented is to say, the by temporal curves of distributions SP and SD are of very both similar. PUPs andSeven DOPs 24-h roughlycycles can repeat be clearly in the identified. seven days. That At is the to say, same the time, temporal urban distributions residents travel of both more PUPs during and theDOPs day roughly than at repeat night. in There the seven are three days. peaks At the during same time, the day,urban especially residents weekdays, travel more at during around the 10:00, day 14:00,than at and night. 19:00, There which are usuallythree peaks correspond during the to going day, especially to work, lunch, weekdays, and goingat around home. 10:00, The 14:00, low peak and usually19:00, which occurs usually at 4:00 andcorrespond 5:00, when to going residents to work, travel lunch, less frequently. and going home. The low peak usually occursThe at global4:00 and temporal 5:00, when distribution residents of PUPstravel and less DOPs frequently. are plotted in Figure7. There are di fferences in the numberThe global of PUPs temporal and DOPs distribution on weekdays of PUPs and and weekends DOPs are (Figure plotted7). in The Figure number 7. There of PUPs are differences and DOPs onin the weekdays number is of generally PUPs and higher DOPs than on theweekdays weekends. and Theweekends number (Figure of PUPs 7). and The DOPs number on theof PUPs weekday and fluctuatesDOPs on weekdays significantly is generally with time, higher showing than similarities the weekends. in the The distribution number of of PUPs time. Theand maximumDOPs on the of theweekday morning fluctuates peak is atsignificantly 8:00–10:00 and with the time, daily showing maximum similarities is at 13:00–14:00. in the Thedistribution morning peaksof time. of taxisThe aremaximum delayed of by the 0.5–1 morning h compared peak is with at 8:00–10:00 the commuting and the morning daily maximum peaks. This is at may 13:00–14:00. be due to The the morning fact that taxispeaks are of moretaxis are sensitive delayed responsive by 0.5–1 h and compared convenient with than the othercommuting transportation morning options, peaks. This and may they be spend due lessto the time fact traveling. that taxis Atare the more same sensitive time, the responsive passengers and are convenient generally than resident other in transportation the urban area options, during and they spend less time traveling. At the same time, the passengers are generally resident in the the commuting period, who mostly have medium and short-distances to travel. Therefore, these urban area during the commuting period, who mostly have medium and short-distances to travel. residents can depart later in the noncommuting time period to avoid traveling during the peak period Therefore, these residents can depart later in the noncommuting time period to avoid traveling during the peak period and avoid early peak traffic jams. This results in a taxi morning peak that occurs later than the normal morning peak. Because taxis are more flexible and quicker than buses,

ISPRS Int. J. Geo-Inf. 2019, 8, 257 9 of 22 ISPRS Int. J. Geo-Inf. 2019, 8, x FOR PEER REVIEW 9 of 22 ISPRS Int. J. Geo-Inf. 2019, 8, x FOR PEER REVIEW 9 of 22 andthe proportion avoid early of peak business traffic trips jams. is This high. results The peak in a taxitravel morning time of peaktaxis thatin the occurs afternoon later thanis also the reflected normal themorningin this proportion point. peak. At of noon, Because business normally taxis trips are islunch hi moregh. time, The flexible therepeak and aretravel fewer quicker time travelers. of than taxis buses, inThe the amount the afternoon proportion of trips is also on of reflectedweekend business intripsdays this isis point. high.obviously At The noon, smaller peak normally travel than time lunchweekdays. of time, taxis thereThe in thereason are afternoon fewer may travelers. be is that also the The reflected travel amount intime thisof oftrips point.the on residents weekend At noon, is daysnormallyrelatively is obviously lunchfree on time, smallerthe weekend, there than are weekdays. fewerand the travelers. purpose The reason Theof the amount may trip beis ofnotthat trips the the same on travel weekend as time the weekdays.of days the isresidents obviously Most isof relativelysmallerthe trips than arefree intended weekdays.on the weekend, for The recreation, reason and the mayplay, purpose be etc. that Theref of the the travelore, trip relatively is time not ofthe thefew same residents residents as the isweekdays.use relatively a taxi toMost free travel. onof thetheAt thetrips weekend, same are intendedtime, and the the morningfor purpose recreation, peak of the ofplay, the trip etc.weeken is notTheref thed wasore, same delayedrelatively as the by weekdays.few 1 h residentscompared Most use with a of taxi the the weekday.to trips travel. are AtintendedThe the number same for time, recreation,of PUPs the morning and play, DOPs etc. peak Therefore,fluctuates of the weeken relativelylittled wi wasth few delayedtime residents from by 12:001 useh compared ato taxi 21:00, to travel.with and thethere At weekday. the was same no Thetime,obvious number the tendency morning of PUPs peakto travel.and of theDOPs However, weekend fluctuates wasthe peak delayedlittle valuewi byth time 1of h PUPs compared from and 12:00 withDOPs to the21:00, in weekday.the and evening there The occurredwas number no obviousofaround PUPs tendency22:00–23:00. and DOPs to fluctuates travel.The main However, little reason with themay time peak be from duevalue 12:00 to ofthe to PUPs 21:00,lack andof and availability DOPs there wasin the of no eveningbuses obvious and occurred tendency metros aroundtoduring travel. this22:00–23:00. However, period of theThe time; peakmain taxis value reason are ofavailable PUPsmay be and and due DOPs in to demand. the in thelack evening of availability occurred of around buses and 22:00–23:00. metros duringThe main this reason period may of time; be due taxis to theare lackavailable of availability and in demand. of buses and metros during this period of time; taxis are available and in demand. 40000 4000035000 3500030000 3000025000 2500020000 Count 2000015000 Count 1500010000 100005000 50000 1 6

0 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126 131 136 141 146 151 156 161 166 1 6

11 16 21 26 31 36 41 46 51 56 61 66 71 Time76 81 86 91 96 PUPsTime DOPs 101 106 111 116 121 126 131 136 141 146 151 156 161 166 PUPs DOPs Figure 6. Curves of SP and SD representing temporal variations of PUPs and DOPs for the entire study Figure 6. Curves of SP and SD representing temporal variations of PUPs and DOPs for the entire Figurearea. 6. Curves of SP and SD representing temporal variations of PUPs and DOPs for the entire study study area. area. 40000 40000 4000035000 4000035000 3500030000 3500030000 3000025000 3000025000 2500020000 2500020000 Count Count 2000015000 2000015000 Count Count 15000 15000 10000 PUPs(Weekday) 10000 DOPs(Weekday) DOPs(Weekend) 100005000 PUPs(Weekday)PUPs(Weekend) 100005000 DOPs(Weekday) DOPs(Weekend) 50000 PUPs(Weekend) 50000 0 0

Time Time Time Time Figure 7. Temporal variance of the PUPsPUPs andand DOPsDOPs activities.activities. Figure 7. Temporal variance of the PUPs and DOPs activities. Based on the taxi data, the demand for travel of residents on weekdays and weekends inin Beijing has beenbeenBased analyzed.analyzed. on the taxi We data, can the see demand from Figure for travel2 2 that that of the theresidents density density on distribution distribution weekdays and ofof pick-uppick-up weekends andand in drop-odrop-off Beijingff haspointspoints been ofof analyzed. taxistaxis in Beijing We can is mainly see from concentrated Figure 2 that in the the 6th density Ring Road, distribution and gradually of pick-up decreases and drop-off toward pointsthethe urbanurban of taxis periphery. periphery. in Beijing However, However, is mainly there thereconcentrated are are also also diff in erencesdi thefferences 6th between Ring between Road, the two, and the whichgraduallytwo, which shows decreases thatshows the thattoward density the theofdensity drop-ourban of ffperiphery. sdrop-offs is higher isHowever, than higher the than densitythere the are ofdensity also pick-ups di offferences pick-ups in some between in hotspots, some the hotspo suchtwo, asts,which thesuch Capitalshows as the that AirportCapital the densityofAirport Beijing, ofof drop-offsBeijing, railway railway stations, is higher stations, China than WorldChinathe density World Trade of Center,Trade pick-ups Center, and in Zhongguancun. someand Zhongguancun. hotspots, Forsuch some Foras thesome large-scale Capital large- Airportcommunities,scale communities, of Beijing, the density railway the density of stations, pick-ups of Chinapick-ups is higher World thanis higher Trade the density thanCenter, the of and drop-odensity Zhongguancun.ffs of spots, drop-offs such For as spots, Huilongguan, some such large- as scaleTiantongyuan,Huilongguan, communities, Tiantongyuan, and Fangzhuang.the density and of Further, Fangpick-upszhuang. it canis higher Further, be seen than it from can the Figurebe density seen8 fromthat of thedrop-offsFigure demand 8 that spots, for the travel demandsuch ofas Huilongguan,for travel of Beijing Tiantongyuan, residents andis mainly Fangzhuang. concentrat Further,ed within it can the be 6th seen Ring from Road. Figure At 8the that same the demandtime, the fortravel travel demand of Beijing in the residents central is urban mainly area concentrat is relatiedvely within larger the than 6th Ringthe outer Road. ring. At the On same the weekday,time, the travel demand in the central urban area is relatively larger than the outer ring. On the weekday,

ISPRS Int. J. Geo-Inf. 2019, 8, 257 10 of 22

BeijingISPRS Int. residents J. Geo-Inf. is2019 mainly, 8, x FOR concentrated PEER REVIEW within the 6th Ring Road. At the same time, the travel demand 10 of 22 in the central urban area is relatively larger than the outer ring. On the weekday, residents’ travel demandresidents’ is mainlytravel demand concentrated is mainly in Capital concentrated Airport of in Beijing, Capital China Airport World of TradeBeijing, Center, China Zhongguancun, World Trade Xinfadi,Center, Yaojiayuan,Zhongguancun, and otherXinfadi, hotspots. Yaojiayuan, Relative and to other other regions, hotspots. these Relative regions to have other a greater regions, demand these forregions travel. have Relative a greater to weekdays, demand for the travel. demand Relative for travel to weekdays, in these areas the demand on the weekend for travel is in lower. these areas on the weekend is lower.

Figure 8. Distribution of residents travel demand of Beijing based on TAZ scale. Figure 8. Distribution of residents travel demand of Beijing based on TAZ scale. A travel intensity indicator is used to measure the travel demand of urban residents, A travel intensity indicator is used to measure the travel demand of urban residents, which which measures how much pressure the residents’ travel places on the transportation system. measures how much pressure the residents’ travel places on the transportation system. The indicator The indicator reflects the amount of travel and the travel time consumption, so travel intensity reflects the amount of travel and the travel time consumption, so travel intensity is defined as the is defined as the product of the number of trips and the average travel time of a one-way trip. product of the number of trips and the average travel time of a one-way trip. Travel intensity is a Travel intensity is a comprehensive index of residents’ travel demand, travel capacity, and level of comprehensive index of residents’ travel demand, travel capacity, and level of urban transport urban transport services. In order to better reflect the form of Beijing residents’ travel, the taxi OD services. In order to better reflect the form of Beijing residents’ travel, the taxi OD data was further data was further used to analyze the travel intensity of residents on the weekday and weekend in used to analyze the travel intensity of residents on the weekday and weekend in Beijing based on the Beijing based on the TAZ (Traffic Analysis Zone) scale. The identified resident travel ODs were TAZ (Traffic Analysis Zone) scale. The identified resident travel ODs were spatialized, each line spatialized, each line representing one trip. Travel time and related records are in the attribute table of representing one trip. Travel time and related records are in the attribute table of ArcGIS layer. From ArcGIS layer. From Figure9, it can be seen that the maximum travel intensity of taxis at weekdays in Figure 9, it can be seen that the maximum travel intensity of taxis at weekdays in Beijing was 61 and Beijing was 61 and the maximum intensity at weekends was 128. This may be due to the increase in the maximum intensity at weekends was 128. This may be due to the increase in the number of the number of recreational and leisure entertainments by urban residents on weekends. At the same recreational and leisure entertainments by urban residents on weekends. At the same time, high- time, high-intensity travel in Beijing is mainly concentrated within the 6th Ring Road, and a single intensity travel in Beijing is mainly concentrated within the 6th Ring Road, and a single trip back and trip back and forth is mainly located outside the 6th Ring Road. This mainly comes from some new forth is mainly located outside the 6th Ring Road. This mainly comes from some new cities in Beijing, cities in Beijing, such as Pinggu, Yanqing, Miyun, and Huairou. It can also be seen that compared with such as Pinggu, Yanqing, Miyun, and Huairou. It can also be seen that compared with the weekday, the weekday, the number of residents who choose to travel outside the 6th Ring Road on weekends the number of residents who choose to travel outside the 6th Ring Road on weekends has increased, has increased, and travel between the central urban area and the new city has become more frequent. and travel between the central urban area and the new city has become more frequent. According to the taxi GPS data, the travel time/distance between residents on weekdays According to the taxi GPS data, the travel time/distance between residents on weekdays and and weekends in Beijing are analyzed. The average travel time/distance on weekdays in Beijing weekends in Beijing are analyzed. The average travel time/distance on weekdays in Beijing are 36.7 are 36.7 min and 8.7 km, respectively, and the average travel time and distance on weekends is min and 8.7 km, respectively, and the average travel time and distance on weekends is 34.3 min and 34.3 min and 8.1 km. As shown in Figure 10, the residents who travel more than 60 min on weekdays 8.1 km. As shown in Figure 10, the residents who travel more than 60 min on weekdays and weekends and weekends mainly came from the new towns of Beijing, such as Pinggu, Miyun, and Huairou. mainly came from the new towns of Beijing, such as Pinggu, Miyun, and Huairou. Trips less than 10 Trips less than 10 min are mainly from the inner area of the . At the same time, compared min are mainly from the inner area of the 5th Ring Road. At the same time, compared to weekdays, to weekdays, residents on weekends have a longer travel time and a wider range. It may be due to residents on weekends have a longer travel time and a wider range. It may be due to most residents most residents choose to have fun or play on weekends. As shown in Figure 11, the residents who choose to have fun or play on weekends. As shown in Figure 11, the residents who traveled long traveled long distances (>10 km) were mainly from outside the 6th Ring Road and the surrounding distances (>10 km) were mainly from outside the 6th Ring Road and the surrounding new towns of new towns of Beijing (e.g., Pinggu, Miyun, and Huairou), while those who travelled a short distance Beijing (e.g., Pinggu, Miyun, and Huairou), while those who travelled a short distance (<5 km) travel (<5 km) travel were mainly from within the 5th Ring Road area. The circular structure of the travel were mainly from within the 5th Ring Road area. The circular structure of the travel distance distance distribution also confirms the single-center urban structure of Beijing. distribution also confirms the single-center urban structure of Beijing.

ISPRS Int. J. Geo-Inf. 2019, 8, x FOR PEER REVIEW 11 of 22 ISPRSISPRS Int.Int. J. Geo-Inf. 2019, 8, 257x FOR PEER REVIEW 1111 of of 22 ISPRS Int. J. Geo-Inf. 2019, 8, x FOR PEER REVIEW 11 of 22

Figure 9. Distribution of travel intensity in Beijing based on the TAZ scale. Figure 9. Distribution of travel intensity in Beijing based on the TAZ scale. Figure 9. Distribution of travel intensity in Beijing based on the TAZ scale.

Figure 10. Distribution of travel time in Beijing based on the TAZ scale. Figure 10. Distribution of travel time in Beijing based on the TAZ scale. Figure 10. Distribution of travel time in Beijing based on the TAZ scale. Figure 10. Distribution of travel time in Beijing based on the TAZ scale.

Figure 11. Distribution of travel distance in Beijing based on the TAZ scale. Figure 11. Distribution of travel distance in Beijing based on the TAZ scale. 3.2. Multitime ScaleFigure Patterns 11. Distribution of Urban Resident of travel Traveldistance in Beijing based on the TAZ scale. 3.2. Multitime ScaleFigure Patterns 11. Distribution of Urban Resident of travel Traveldistance in Beijing based on the TAZ scale. 3.2. MultitimeFigure 12 showScale Patterns the patterns of Urban of urban Resident resident Travel travel at different time scales (e.g., 30 min. and 60 min.) on3.2. weekdays MultitimeFigure 12 andScale show weekends. Patterns the patterns of OnUrban weekdays,of urbanResident resident theTravel morning travel at peak different of the time OD flows scales appears (e.g., 30from min. 7:00and to60 11:00min.)Figure and on fromweekdays 12 14:00show toandthe 16:00 patternsweekends. in the of afternoon. urbanOn weekdays, resident The peak travelthe of morning the at different OD inpeak the time eveningof the scales OD of the(e.g.,flows taxi 30 appears ismin. from and 21:00from 60 Figure 12 show the patterns of urban resident travel at different time scales (e.g., 30 min. and 60 tomin.)7:00 23:00. to on 11:00 Itweekdays may and be from dueand 14:00 toweekends. the to fact16:00 that On in the theweekdays, afternoon. city’s public the The morning transport peak of peak the subways ODof the in ceasedtheOD eveningflows operation appears of the in taxi from this is min.) on weekdays and weekends. On weekdays, the morning peak of the OD flows appears from period7:00from to 21:00 of11:00 time, to and 23:00. causing from It may 14:00 residents be to due 16:00 to tochoose inthe the fact afternoon. taxis that travelthe city’s The to make peakpublic of a transpor substantialthe OD tin subways the increase. evening ceased On of weekends, theoperation taxi is 7:00 to 11:00 and from 14:00 to 16:00 in the afternoon. The peak of the OD in the evening of the taxi is thefromin this OD 21:00 period peak to 23:00. appears of time, It may at causing 8:00~12:00 be due residents to the in thefact to morning that choos thee city’s andtaxis 15:00~17:00 publictravel transporto make in thet subwaysa substantial afternoon. ceased Theincrease. operation peak On of fromin this 21:00 period to 23:00. of time, It may causing be due residents to the fact to that choos thee city’staxis publictravel transporto maket asubways substantial ceased increase. operation On in this period of time, causing residents to choose taxis travel to make a substantial increase. On

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weekends, the OD peak appears at 8:00~12:00 in the morning and 15:00~17:00 in the afternoon. The thepeak taxi of OD the flows taxi inOD the flows evening in the is at evening 22:00~00:00. is at Compared22:00~00:00. with Compared weekdays, with the weekdays, overall travel the volume overall ontravel weekends volume has on decreased,weekends has while decreased, travel time while of the travel morning time of peak the hasmorning been delayedpeak has by been nearly delayed 1 h. Afterby nearly 15:00, the1 h. number After 15:00, of residents the number has gradually of reside increased,nts has residents’gradually nightlife increase continuedd, residents’ until nightlife around 22:00continued to 23:00 until at around night. At22:00 this to time, 23:00 with at night. the continuousAt this time, suspension with the continuous of public transportationsuspension of public such astransportation bus, subway, such and otheras bus, transportation, subway, and other taxis transp are onlyortation, a small taxis number are only of travel a small tools, number and demand of travel hastools, increased and demand substantially. has increased substantially. AsAs shown shown in in FigureFigure 12 12,, the the distributiondistribution of of OD OD flows flows tends tends to to fall fall inin adjacentadjacent oror samesame timetime periodsperiods atat didifferentfferent time time scales. scales. When When thethe timetime scalescale is is 3030 min,min, therethere areare nearly nearly 50.0% 50.0% of of the the ODOD pairspairs thatthat fallfall onon thethe originaloriginal timetime period,period, andand anotheranother 40.0%40.0% fallfall onon thethe adjacent adjacent time time period. period. ItIt cancan bebe seenseen thatthat nearlynearly halfhalf ofof thethe tripstrips ofof residentsresidents maymay bebe withinwithin halfhalf anan hourhour usingusing taxis,taxis, andand mostmost ofof thethe tripstrips areare resolvedresolved within within one one hour. hour. At At thethe samesame time,time, wewe cancan seesee thatthat traveltravel inin shortshort timetime takestakes up the majority. ConsideringConsidering thethe popularizationpopularization ofof bus,bus, subwaysubway andand otherother traveltravel tools,tools, andand thethe relatively relatively expensive expensive priceprice of of taxis, taxis, the the use use of of taxis taxis by by urban urban residents residents is is mostly mostly short short trips. trips.

Figure 12. Multitime scale visualizations for taxi OD data in Beijing based on the chord diagram plot. Figure 12. Multitime scale visualizations for taxi OD data in Beijing based on the chord diagram 3.3. Spatialplot. Multiscale Patterns of Urban Resident Travel As shown in Figure 13, the chord diagram plot presents a complete inter-area OD flow system of taxis in Beijing. Each chord starts from the region of pick-ups and ends in the district of drop-offs.

ISPRS Int. J. Geo-Inf. 2019, 8, 257 13 of 22

The direction of the flows is illustrated using arrowheads on each chord; it shows the visualization of OD flow in various regions of Beijing on weekdays and weekends. Residents’ travel in Beijing is mainly concentrated in the central area of the 6th Ring Road. The largest areas are CY and HD, followed by XC, FT, and DC. Areas with less travel are mainly concentrated in the new town of Beijing, such as PG and MTG, HR, YQ, MY, and other ecological conservation development areas. At the same time, it can be found that more than 50% of district and county OD inflows are mainly concentrated in the region, including CY and HD of the urban functional expansion zone, CP and FS of the new urban development zone, and some districts and counties of the ecological conservation development areas. Compared with the weekdays, the number of residents on weekends has decreased significantly, but the overall trend has little change. However, the outflow of taxi OD in some districts and counties has exceeded the situation in this region, such as HR, TZ, and SJS. Furthermore, the extended chord diagram plot used to analyze the unidirectional flow pattern of OD net travel traffic in various regions reveals interesting results. The net travel traffic value between two regions is calculated by the difference of two bilateral flow sizes, where only the positive net flow values are shown. For example, the average daily OD trip flow from HD to CP is 4000 trips, while there are only 1000 trips from CP to HD. The average net travel flow between these two regions is 3000 (4000 1000 = 3000) trips, and the net travel direction − is from HD to CP. The length of the axes in Figure 13c,d represents the sum of the total net in-flows and total net out-flows. The bilateral net flows circular plot allows us to clearly identify the unbalanced inter-region travel corridors. The largest bilateral net migration on weekdays is the flow from XC to FT as shown in Figure 14 (and not from DC to CY as shown in Figure 13a), and the largest bilateral net migration on weekend is the flow from CY to SY as shown in Figure 14 (and not from CY to DC as shown in Figure 13b). For the major trip areas, such as FT, CP, and SY, the net flow values capture the intensity of net inflows. Similarly, for those major travel-sending areas such as CY, HD, and XC, higher net out flows are observed. ISPRSISPRS Int. Int. J. J. Geo-Inf. Geo-Inf. 20192019, ,88, ,x 257 FOR PEER REVIEW 1414 of of 22 22 ISPRS Int. J. Geo-Inf. 2019, 8, x FOR PEER REVIEW 14 of 22

Figure 13. OD flows in various regions of Beijing based on the chord diagram plot. FigureFigure 13. 13. OD OD flows flows in invarious various regions regions of ofBeijing Beijing based based on on the the chord chord diagram diagram plot. plot.

Figure 14. Distribution of OD net flows in various regions of Beijing. FigureFigure 14. 14. Distribution Distribution of ofOD OD net net flows flows in invarious various regions regions of ofBeijing. Beijing.

ISPRS Int. J. Geo-Inf. 2019, 8, 257 15 of 22

ISPRS Int. J. Geo-Inf. 2019, 8, x FOR PEER REVIEW 15 of 22 From the above analysis, it can be seen that residents’ travel of Beijing is mainly concentrated in From the above analysis, it can be seen that residents’ travel of Beijing is mainly concentrated in the central area of the 6th Ring Road. Therefore, the streets in the 6th Ring Road of Beijing are taken the central area of the 6th Ring Road. Therefore, the streets in the 6th Ring Road of Beijing are taken as the object of the study. The grid area is used to divide the study area into 2 2, 4 4, and 8 8 regular as the object of the study. The grid area is used to divide the study area into× 2 ×× 2, 4 × 4, ×and 8 × 8 grid, as shown in Figure 15. The different divisions represent different spatial scales. Here, the OD regular grid, as shown in Figure 15. The different divisions represent different spatial scales. Here, data of seven days (i.e., one week) in Beijing are used, divided by the weekdays and the weekend, the OD data of seven days (i.e., one week) in Beijing are used, divided by the weekdays and the to carry out the visual analysis to the OD flow of the taxis in different spatial scale. weekend, to carry out the visual analysis to the OD flow of the taxis in different spatial scale.

FigureFigure 15.15. Division of space re regiongion in Beijing.

The streets of the 6th Ring Road in Beijing are firstly divided into 2 2 regular grids (Figure 16a,d). The streets of the 6th Ring Road in Beijing are firstly divided into× 2 × 2 regular grids (Figure As16a,d). shown As in shown Figure in 16 Figurea,d, the 16a,d, NO. 4 the of theNO.4 study of the area st hasudy the area largest has the flows largest of trips, flows which of trips, is mainly which due is tomainly the fact due that to this the area fact contains that this most area of contains the areas most in Beijing, of the includingareas in Beijing, Tiananmen including Square, Tiananmen Chaoyang District,Square, FengtaiChaoyang District, District, and Fengtai , District. and The Daxing travel District. volume The of thetravel other volume three of grid the areasother isthree also relativelygrid areas large, is also and relatively the diff large,erence and between the differe thesence areas between is small. these At areas the sameis small. time, At comparedthe same time, with thecompared weekdays, with the the volume weekdays, of trips the in volume the four of regions trips in has the decreased four regions on weekends, has decreased but the on overall weekends, trend wasbut notthe significant.overall trend It was can alsonot significant. be seen from It can the abovealso be that seen travel from activitiesthe above in that the travel same areaactivities account in the for thesame majority area account and cross-regional for the majority travel and is cross-regi less underonal this travel spatial is scale.less under this spatial scale. Further,Further, the the streets streets of of the the 6th6th RingRing RoadRoad inin BeijingBeijing areare divideddivided into 4 × 44 regular regular grids grids based based on on × thethe method method of of recursive recursive partitioning partitioning (Figure(Figure 1616b,e).b,e). As shown in Figure 16b,e,16b,e, the the NO.11 NO.11 of of the the study study areaarea has has the the largest largest flowsflows ofof trips,trips, whichwhich isis mainlymainly due to the fact that this area area contains contains Tiananmen ,Square, many many tourist tourist attractionsattractions inin thethe surroundingsurrounding area and many transportation stations stations including including thethe Beijing Beijing Railway Railway StationStation and the Beijing Beijing South South Railway Railway Station. Station. The The daily daily traffic traffi ofc ofthe the residents residents is ishuge. huge. This This is followed is followed by areas by areas NO.10, NO.10, NO.7, NO.7, and NO.6. and NO.6. The NO.10 The NO.10of study of area study includes area includes a large avolume large volume of travel of areas travel such areas as suchLugouqiao, as Lugouqiao, Yangfang, Yangfang, and Wanshou and Wanshou Road. And Road. this Andarea also this includes area also includesthe Beijing the West Beijing Railway West RailwayStation and Station various and research various institutions. research institutions. Therefore, Therefore,the amount the of amounttrips is oflarge trips in is this large area. in In this the area. No. 7 In of the the No. area, 7 there of the ar area,e large there travel are areas large including travel areas Wangjing including and WangjingSanlitun; andthe Sanlitun;No. 6 of the the area No. is 6 also of the relatively area is large, also relatively mainly because large, mainlyit contains because many it colleges contains and many universities colleges such as Peking University, Tsinghua University, and Renmin University of China, as well as the Beijing North Station. Therefore, the amount of taxi trips is also large. Compared with the weekdays,

ISPRS Int. J. Geo-Inf. 2019, 8, 257 16 of 22 and universities such as Peking University, Tsinghua University, and Renmin University of China, asISPRS well Int. as J. the Geo-Inf. Beijing 2019, North 8, x FOR Station. PEER REVIEW Therefore, the amount of taxi trips is also large. Compared 16 ofwith 22 the weekdays, the volume of trips in these regions has decreased on weekends, but the overall trend the volume of trips in these regions has decreased on weekends, but the overall trend was not was not significant. It can also be seen from the above that travel activities in the same area account for significant. It can also be seen from the above that travel activities in the same area account for the the majority and cross-regional travel is less under this spatial scale. At the same time, it can also be majority and cross-regional travel is less under this spatial scale. At the same time, it can also be seen seen that residents in the above areas have more activities in the same area. Compared with these areas, that residents in the above areas have more activities in the same area. Compared with these areas, thethe tra trafficffic flow flow in in other other areas areas is is getting getting smaller smaller and and smallersmaller asas theythey are getting farther from the the central central urbanurban area. area. On the basis of 4 4 grid, it is further divided into 8 8 regular grids. With the increasing On the basis of 4× × 4 grid, it is further divided into 8× × 8 regular grids. With the increasing intensityintensity of of the the segmentation, segmentation, the the tra ffitrafficc flow flow between between diff erentdifferent regions regions becomes becomes clearer clearer (Figure (Figure 16c,f). As16c,f). shown As in shown Figure in 16 Figurec,f, the 16c,f, NO. the 28–30 NO. and 28–30 NO. and 35–38 NO. of 35–38 the study of the area study has area the has largest the flowslargest of flows trips, whichof trips, is mainly which dueis mainly to the due fact to that the these fact that areas th areese mainlyareas are centered mainly oncentered Tiananmen on Tiananmen Square. The Square. area includesThe area almost includes all of almost High-speed all of RailwayHigh-speed Station, Railway Railway Station, Station, Railway Airports, Station, and Airports, other transportation and other hubstransportation of Beijing. hubs It also ofcovers Beijing. most It also of covers the tourism, most of shopping, the tourism, and shopping, other entertainment and other entertainment venues. It is thevenues. core area It is ofthe Beijing. core area At of the Beijing. same At time, the itsame is also time, the it areais also where the area Beijing where residents Beijing residents have the have most travelthe most demand. travel Compared demand. Compared with the weekdays, with the week the volumedays, the of volume trips in of these trips regions in these has regions decreased has ondecreased weekends, on butweekends, the overall but trendthe overall was nottrend significant. was not significant. At the same At time,the same it can time, also it becan seen also that be withseen increasing that with increasing distance from distance the central from the urban central areas, urban the areas, traffi cthe flow traffic in other flow areasin other is gettingareas is smallergetting andsmaller smaller. and smaller.

Figure 16. Spatial multiscale visualizations for taxi OD data in Beijing based on the chord diagram plot. Figure 16. Spatial multiscale visualizations for taxi OD data in Beijing based on the chord diagram plot. From the above analysis the further refinement of the urban spatial scale supports the straightforward identification of the core areas of the city using the visualization analysis method From the above analysis the further refinement of the urban spatial scale supports the ofstraightforward OD flows. The greateridentification the scale of the of division,core areas the of clearerthe city the using flow the of visualization traffic between analysis regions method becomes. of AtOD the flows. same The time, greater this method the scale can of also division, be used the toclearer see the the tra flowffic flowof traffic between between diff erentregions areas. becomes. It can characterizeAt the same the time, OD this flow method of taxi can trajectory, also be used flow to direction, see the traffic and the flow properties between characteristicsdifferent areas. ofIt can OD flowcharacterize in different the spatial-temporal OD flow of taxi scales.trajectory, It can flow reveal direction, the spatial-temporal and the properties patterns characteristics of OD flow of dataOD flow in different spatial-temporal scales. It can reveal the spatial-temporal patterns of OD flow data at multiple spatial-temporal scales so as to better understand of the flow pattern of urban human between inner areas of the city.

ISPRS Int. J. Geo-Inf. 2019, 8, 257 17 of 22 at multiple spatial-temporal scales so as to better understand of the flow pattern of urban human between inner areas of the city.

3.4. Spatial-Temporal Patterns of Urban Human Travel with Taxi OD Data As an important flow subject in different areas of the city, taxis can be used to represent the inter-regional connectivity patterns. The OD flow with the characteristics of start–stop and direction can characterize the inter-area connectivity. Therefore, the hidden behavior patterns in the trajectory data can be revealed by analyzing the OD data of taxis, which will be helpful to study the rules of urban human travel. This paper uses OD data for seven days in one week in Beijing, divided into weekdays and weekends. The OD data at different spatial scales during the 7:00–10:00 and 17:00–20:00 for morning and evening rush hours of Beijing were visually analyzed to explore spatial-temporal patterns of urban human travel. Figure 17 show the spatial-temporal patterns of OD data at different scales on weekdays and weekends. As shown in Figure 17, the areas with the largest volume of taxis in the morning and evening rush hour of Beijing are mainly centered on Tiananmen Square, and extend to the from South to North, including Beijing Railway Station, Beijing South Railway Station, Beijing West Railway Station, and many other transportation sites. The Beijing West Railway Station, which has the largest flow of traffic in Beijing, plus the Beijing Railway Stations and Beijing South Railway Stations, result in huge travel needs to residents entering and leaving the stations. As one of the main tools of transportation in the city, taxis are an important mode of transportation for passengers to enter and leave the railway stations. Therefore, the travel volume in this area is the largest. It was mainly reflected in NO. 4 of Figure 16a, and the proportion of outer rings is the largest. While the proportion of outer rings of NO. 11 and NO. 10 in Figure 16b is also larger. It can be seen that Beijing West Railway Station and Beijing South Railway Station have the largest scale of operations among the many railway stations in the city, and the corresponding passenger traffic is also the largest. Compared to the weekends, we can see that these areas have a large amount of travel between 7:00 and 10:00 on weekdays. However, the number of trips decreased from 17:00 to 20:00, the overall trend has changed little. The main reason is that the period from 7:00 to 10:00 on weekdays belongs to the morning peak of work. The residents have a large number of trips, and the amount of trips at the time will be reduced on weekends. Most residents would chose to entertain and go shopping during the period from 17:00 to 20:00 on weekends, resulting in an increase in the number of trips during the period compared with the weekdays. At the same time, it can also be seen that residents in these areas have more activities in the same area. With the increasing distance from the central urban areas, the traffic flow in other areas is getting smaller and smaller. At the same time, it can be seen that compared with , the amount of taxis entering Tongzhou District is the smallest; in particular, the amount of taxi travel is almost negligible in the period of 17:00–20:00. The Capital Airport of Beijing is located in Shunyi District (NO. 2 of 2 2 grid), and the amount of trips in the area is also relatively large. Tongzhou District is mostly × a residential area, so the demand of the taxis is relatively small. In comparison with the outer ring of NO. 7 in 4 4 grid at different time periods, it can be seen that the travel volume experiences little × change between the morning and evening rush hours on weekdays. On the weekends, the amount of the travel in the period of 17:00–20:00 is higher than the period of 7:00–10:00. This may be due to the fact that the area contains well-known areas such as Wangjing and Sanlitun. These areas have a large amount of travel, especially during the period of 17:00–20:00, resulting in a large amount of taxis entering and leaving from 17:00 to 20:00 on weekends. ISPRS Int. J. Geo-Inf. 2019, 8, 257 18 of 22 ISPRS Int. J. Geo-Inf. 2019, 8, x FOR PEER REVIEW 18 of 22

Figure 17. Multiscale expression of taxi OD data space in the rush hour (morning and evening). Figure 17. Multiscale expression of taxi OD data space in the rush hour (morning and evening).

ISPRS Int. J. Geo-Inf. 2019, 8, 257 19 of 22

4. Discussion and Conclusions The initial aims of this research focus on how to explore and analyze the characteristics and patterns of urban residents’ travel using taxi GPS data. We propose solutions to these problems by establishing a set of simple, universal, and effective methods. In this paper, we use trajectory data of GPS-equipped taxis in Beijing, China to extract a large volume of trips of anonymous customers and to explore urban human travel spatial-temporal characteristics and patterns. The spatial distribution patterns of PUPs and DOPs on weekdays and weekends are firstly analyzed. In addition, travel demand, intensity, time, and distance on weekdays and weekends are used to explore human mobility by extracting taxi data. Furthermore, this study introduces and applies a new adapted chord diagram plot to visualize taxi OD flows with different space-time scales, revealing the travel patterns of residents. The method can characterize the volume, direction and properties of OD flows in multiple spatial-temporal scales; it is implemented using a circular visualization package in R (circlize). Through the visualization experiment of taxi GPS trajectory data in Beijing, the results show that the proposed visualization technology is able to characterize the spatial-temporal patterns of trajectory OD flows in multiple spatial-temporal scales. This approach utilizes the massive taxi trajectory data from complicated real-world traffic situations to accurately reflect the true spatial-temporal characteristics and patterns of urban travel. PUPs and DOPs play different roles in traffic generation and attraction; they consider the problem from different perspectives [23]. For example, PUPs represent generation of travel demand from a passenger’s perspective, while they also reflect the attraction for taxi drivers to look for riders. Therefore, it is critical to explore their spatial-temporal characteristics and patterns in depth. Previous studies are limited in their ability to visualize multiscale spatial-temporal trajectories in big data, and it is more difficult to reveal the characteristics of OD stream data [42–50]. The methods in this study have the advantage of being able to explore and analyze spatial-temporal characteristics and patterns of urban resident travel in a unique way. The results above verify that the set of methods, including the proposal for the new adapted chord diagram plot method, is a valid tool for analyzing the urban road traffic characteristics and patterns of urban resident travel. The methods proposed in this paper and the corresponding results provide a new perspective for understanding spatial variability of resident travel flow across weekdays and weekends that is essential for sustainable traffic management. The urbanization process in China has been rapid, and urban structures of world-level cities, such as Beijing, are becoming more complex. Big data provide opportunities to conduct empirical research on urban human travel characteristics and patterns. Furthermore, exploring the corresponding urban structures can break ground for new theoretical studies and contribute to urban and transportation planning. The methods provided in this study are also suitable for the analysis of other cities with similar data sources. However, it should be noted that taxi data inevitably encounter issues of representativeness, that is, mobile users and taxi passengers are not random samples of the population. Another representativeness issue of the taxi data is that residents can choose different transportation modes, such as driving a private car, taking a bus or metro, or taking a taxi for various trip purposes. It is natural that different modes are associated with different patterns. Some travel, such as long-distance commuting, is hardly reflected by taxi trajectories. Most existing data are only able to show city characteristics from a specific perspective. Further studies may expand the data source to include private vehicles, bus, and metro trips. This combination of diverse data can describe human mobility and urban structure in more dimensions, providing a multifaceted picture of urban dynamics. In addition, the conclusions drawn from taxi trip data could also contribute to the improvement of other transportation modes because of their complementary relationships. Therefore, we suggest that taxi trips constitute a relatively stable proportion of intracity travels; they provide a large data volume that is highly precise. Taxi data are reasonable to represent intracity spatial interactions and to reveal city structure. The data source may also be expanded on the temporal dimension. The availability ISPRS Int. J. Geo-Inf. 2019, 8, 257 20 of 22 of long-period human mobility data can make it possible to detect changes in the urban structure and validate the effect of policies.

Author Contributions: Conceptualization, Hong Huang and Weihua Zeng; Data curation, Xiaoyong Ni; Funding acquisition, Weihua Zeng; Methodology, Huihui Wang and Weihua Zeng; Resources, Hong Huang and Xiaoyong Ni; Visualization, Huihui Wang; Writing—original draft, Huihui Wang. Funding: This research was supported by the Reform and Development Research Program of Ministry of Science and Technology “Imperative and significant problems to addressing climate change after Paris Conference” (No. YJ201603). Acknowledgments: This research was supported by the Reform and Development Research Program of Ministry of Science and Technology “Imperative and significant problems to addressing climate change after Paris Conference” (No. YJ201603). The authors would like to thank the researchers from HeFei University of Technology for their support and help, as well as the anonymous reviewers for their helpful and constructive comments. Conflicts of Interest: The authors declare no conflicts of interest.

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