LAND-USE classification is an important aspect of urban

Transcription

1 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 14, NO. 1, MARCH Land-Use Classification Using Taxi GPS Traces Gang Pan, Guande Qi, Zhaohui Wu, Daqing Zhang, and Shijian Li, Member, IEEE Abstract Detailed land use, which is difficult to obtain, is an integral part of urban planning. Currently, GPS traces of vehicles are becoming readily available. It conveys human mobility and activity information, which can be closely related to the land use of a region. This paper discusses the potential use of taxi traces for urban land-use classification, particularly for recognizing the social function of urban land by using one year s trace data from 4000 taxis. First, we found that pick-up/set-down dynamics, extracted from taxi traces, exhibited clear patterns corresponding to the land-use classes of these regions. Second, with six features designed to characterize the pick-up/set-down pattern, land-use classes of regions could be recognized. Classification results using the best combination of features achieved a recognition accuracy of 95%. Third, the classification results also highlighted regions that changed land-use class from one to another, and such land-use class transition dynamics of regions revealed unusual real-world social events. Moreover, the pick-up/set-down dynamics could further reflect to what extent each region is used as a certain class. Index Terms Land-use classification, region activeness, social function, taxi traces. I. INTRODUCTION LAND-USE classification is an important aspect of urban planning. It is defined as the recognized human use of land in a city. The granularity of land area in land-use classification ranges from buildings to administrative zones. The concept of land use has been evolving for tens of years from ecological vegetation to urban land use and from coarse classes to detailed classes. Early research [1], [2] on land-use classification attempted to recognize different ecological vegetation such as forests and wetlands. Such land-use classification has broad applications in ecology, studies on the relationship between urbanization and deforestation [3], and farmland changes [4]. Later studies [5], [6] classified urban land into built-up and non-built-up lands to delineate urban region and model urban growth. Built-up regions were extracted to identify the impacts of urban growth in a spatial context and detect urban landcover changes with respect to urbanization [7] [9]. More de- Manuscript received February 4, 2012; revised May 14, 2012; accepted June 19, Date of publication August 13, 2012; date of current version February 25, This work was supported in part by the Qianjiang Talent Program under Grant 2011R10078, the Zhejiang Provincial Natural Science Foundation under Grant Y , and the Fundamental Research Funds for the Central Universities. The Associate Editor for this paper was X. Zhang. G. Pan, G. Qi, Z. Wu, and S. Li (Corresponding author) are with the College of Computer Science and Technology, Zhejiang University, Hangzhou , China ( gpan@zju.edu.cn; qiguande@zju.edu.cn; wzh@zju.edu.cn; shijianli@zju.edu.cn). D. Zhang is with the Department of Telecommunication Network and Services, Institut TELECOM & Maga. SudParis, Evry Cedex, France ( daqingi2r@yahoo.com). Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /TITS tailed classification of urban regions has usually focused on land-covers [10] [12] such as water, railway, and green lands. Most of the studies gave a coarse definition for the regions social function, namely, residential or nonresidential regions [13] [17]. Residential regions are critical because residential change in area and density is thought to be directly related to urbanization [18]. Residential regions can be categorized into different density levels [19] [21] and used to produce population statistics [22]. Most urban land-use classification research has used remotesensing data, particularly satellite images. The first satellite (Landsat-1) for monitoring the Earth s surface was launched by NASA in 1972 with resolution of 79 m. The satellite resolution increased gradually. After 1999, several very high resolution satellites (IKONOS, QuickBird, and OrbView-3) with resolutions of nearly 1 m were launched. The increase of satellite resolution promoted the development of new land-use classification methods and led to better classification results [15], [23], since there was more effective context information available from satellite images. Early land-use classification algorithms relied on the more or less direct relationship between spectral reflectance and the nature of the materials covering the Earth s surface [1], [2]. It was observed that such pixel-based classification got the worse result when the satellite resolution increased [24], because pixel-based methods did not consider spatial context such as local variance in images [25]. Kernel methods consider information of all pixels within a moving window with a given size and achieve more accurate classification results [19], [26]. However, both pixel- and kernel-based techniques need to define artificial image structures, such as pixels and a moving kernel window, whereas actual objects and regions may be irregularly shaped [27]. Object-based classification method is based on the analysis of automatically segmented objects from satellite images and is thus viewed to provide a critical bridge between real-world buildings/ regions and their radiometric characteristics in Earth observation data [28]. Although different classifications of land use have been proposed with different objectives and applications, there are very few works classifying land use based on social functions. In this paper, instead of using remote-sensing techniques, we attempt to exploit large-scale real-world taxi data to reveal the social function of a certain urban area. Concretely, we first use clustering techniques to partition the city map into various areas (zones). Second, we verify that the social function of a certain urban area can be characterized by the temporal and spatial dynamics of the taxi pick-up/set-down number. Third, we use real-time taxi data to predict the social function of a test urban area. Finally, based on the inferred social function of the /$ IEEE

2 114 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 14, NO. 1, MARCH 2013 test area and context information such as traffic flow density or historic function change, we characterize the activeness of the area corresponding to its social function and track the area function transition. Based on the preceding steps, the main contributions of this paper are as follows: 1) We defined a new problem and a new way for land-use classification. Previous land-use classification research was based on the physical properties of studied objects in remote-sensing data. Classification on social function (used as a synonym for land-use class in this paper) of regions was coarsely defined as residential or nonresidential. We defined the regions land-use classes according to their social function and similar to map legends. Our new method for land-use classification is based on passenger pick-up/set-down dynamics extracted from realworld taxi trace data. In this paper, such pick-up/set-down dynamics simply describe the variation of pick-up/setdown number over time. 2) We observed and verified that there is an inherent relationship between land-use classes and the temporal pattern of taxi pick-up/set-down dynamics. We found that regions with different land-use classes exhibited different pick-up/set-down dynamic patterns. For example, scenic spots have more passengers in daytime than at night and have more passengers during holidays rather than weekdays. 3) We used verified relationship to identify the social function of urban area. According to the verified relationship, we designed six features extracted from the pick-up/ set-down data of different time lengths. Each of the features was evaluated to recognize the regions social function for different classifications. The best combination of features achieves a recognition accuracy of 95% using a support vector machine (SVM) classifier. 4) We could inform the transition in terms of its social function and the activeness of urban area by using passenger pick-up/set-down dynamics. We find that social function of regions is not steady all the time; some regions change from one social function to another. Our classification result could find social function transition of regions, and such transitions are proved to correspond to real-world unusual social events. Activeness measures to what extent a region is used for its social function. In this paper, human flow characteristics are adopted to depict region activeness. As samples of human flow, our passenger pick-up/set-down dynamics directly reflects the activeness of all kinds of regions. The remainder of this paper is organized as follows. Section II reviews related works on land-use classification and taxi trace data. Section III introduces our city-scale taxi trace data and the extraction of pick-up/set-down number. We also discuss the characteristics of distribution of passenger pick-up/ set-down number in this section. In Section IV, we extract regions from the pick-up/set-down data with a refinement of the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm. Features are designed to characterize the regions passenger pick-up/set-down dynamics and then classified in Section V. In Section VI, we show, using pick-up/set-down dynamics, the region activeness and the social function transition. Section VII concludes this paper. II. RELATED WORKS This section briefly reviews related works on taxi trace data and urban land-use classification. Ubiquitous mobility data contain information that is important for the smart environment [29], [30]. In particular, taxi trace data reflect urban traffic behaviors and convey lots of information about a city. Taxi trace data could be used for: 1) rebuilding a city road map [31]; 2) providing information about traffic conditions [32], analyzing potential traffic hotspots [33], and detecting flawed urban planning [34]; 3) indentifying routes for navigation since taxi trace data imply the experienced taxi drivers knowledge about the temporal and spatial traffic conditions. It could be used for driving route recommendations and helping people avoid traffic jams [35], [36]; 4) providing a driving strategy because experienced taxi drivers also have strategies for hunting or waiting for the next passenger. These strategies could be recommended specifically for taxi drivers to find more passengers [37], [38]; and 5) determining an abnormal trajectory since some taxi drivers may be intentionally choosing a longer route to make more of a profit. Such abnormal behaviors could be detected using taxi trace data [39]. Other mobility data may also contain information [40], [41], that can be used for developing intelligent transportation systems [42] [44]. Previous works on urban land-use classification usually employ remote-sensing techniques. They differ from this paper in two ways. First, few works considered fine-grained classification of urban social function. Aubrecht et al. [45] used many classes of the regions social function to build a city model, while they did not give any approach to classifying urban land use. Some of the previous research [46] [48] focused on landcover classification of two kinds of social function, namely, residential and nonresidential [12], [20]. Some work [16], [49] also defined roads (like highway, railway, and so on) as landuse classes. Herold et al. [27] studied three classes of social function: 1) residential; 2) commercial and industrial; and 3) institution. Van de Voorde et al. [17] defined four classes: 1) commercial; 2) industrial; 3) service; and 4) residential. All of these definitions of urban land-use classes are based on visual differences among urban regions. None of the previous work provided a fine-grained classification of the regions social function as this paper does, since remote-sensing data cannot depict enough information. Second, few works can handle the social activeness of all kinds of regions. Social activeness measures the extent to which a region is used for its social function. Previous work depicted the activeness information by visual analysis of building density from remote-sensing images, for example, density level for residential regions [15], [17], [20] and density level for commercial districts [14]. This kind of approach has two limits: 1) Visual analysis of building density is not suitable for the building-scale

3 PAN et al.: LAND-USE CLASSIFICATION USING TAXI GPS TRACES 115 regions, although it may be fine for regions consisting of lots of buildings. 2) It did not depict the activeness of human social activities since building density is not closely related to human mobility. There are quite a few approaches using mobility data for land-use classification in the literature. The first work on landuse classification using mobility data is our previous work [50], of which this paper is an extension. The main differences are the following: 1) a new clustering algorithm is introduced; 2) the land-use classification approach is redesigned for more land-use classes and is more effective; 3) social activeness is presented; and 4) we further investigate the problem and carry out extensive experiments. Recently, Soto and Frias- Martinez [51] reported work on the clustering regions land use using cell phone data. However, they could only get very large zones and did not present any quantitative classification results. Also, Zhang et al. explored the relationship between origin destination (OD) flows and social function of origins and destinations to mine the semantics of OD flows [52]. III. CHARACTERISTICS OF TAXI GLOBAL POSITIONING SATELLITE TRACE DATA SET A. Data Set Description The taxi GPS trace data set used in this paper comes from the Hangzhou City Traffic Bureau. Hangzhou, located in the southeast region of China, is the capital of Zhejiang province and one of the most famous tourist cities in China. The taxi GPS traces were generated over a period of 385 days (from April 1, 2009 to April 20, 2010). During this period, the number of taxis installed with a GPS device increased from 4597 to 7475, whereas the total number of taxis in the city remained almost unchanged. The data set contains approximately three billion records; most of them were sampled at a frequency of about 1 min. Each record consists of the following information: 1) TAXI ID: the unique ID of each taxi; 2) TIME: the sample timestamp YYYY-MM-DD HH:MM: SS ; 3) GPS POSITION: the current longitude and latitude; 4) SPEED: the current taxi speed in km/h; 5) TAXI ORIENTATION: the direction the taxi is heading in, from 0 to 360 in clockwise (North is 0 ); 6) GPS STATE: it is set to 1 if the GPS data is incorrect, and 0 otherwise. In our experiment, all the records with incorrect GPS data are removed. 7) METER STATE: it indicates whether the taxi meter is running, i.e., whether the taxi is occupied. B. Extraction of Pick-Up/Set-Down Number The pick-up/set-down number of passengers in a region is important for depicting the characteristics of human mobility. It characterizes passengers that come to (or leave) this region during a given period. Such mobility information may be related to the properties of the region and reflects the land-use (social function) classes of the region. The number of pick-up/ Fig. 1. Heatmap of pick-up numbers in a local area near the Hangzhou Railway Station. The color bar on the right side illustrates different colors for different pick-up numbers. set-down events is extracted from the mass of taxi traces with the following two steps: 1) Sampling: 4000 taxis were sampled randomly each day, and their traces were retrieved for further analysis, to solve the problem that the number of taxis that install GPS devices does not remain constant in the observed year. 2) Extracting Pick-up/Set-down Events: Transition of the meter state reflects that passengers are picked up or set down. More specifically, a pick-up (set-down) event was extracted from taxi traces when the meter state changes from 0 (1) to 1 (0). C. Distribution of Pick-Up/Set-Down Number Here, we explore the distribution of pick-up/set-down numbers over the city. Based on the statistics of the pick-up number in a week (from April 1, 2009 to April 7, 2009) for each small block (10 10 m 2 ) of the city, we observed the following: 1) Sparsity of pick-ups: Only a few blocks have a couple of passengers picked up, and most of the blocks have very few pick-ups. There are 98.82% of blocks having no passenger picked up; 0.66% of blocks have just one pick-up; and there are only 0.14% of blocks with more than 16 pick-ups in the week. 2) Clustering phenomenon: Blocks with passengers picked up frequently usually cluster with each other, which yield many clusters of high pick-up density within the city. Fig. 1 shows the pick-up number heatmap of a local area near the Hangzhou Railway Station, where the clustering phenomenon is obvious. 3) Disparity of clusters: The clusters formed by blocks with many pick-ups are different in pick-up density and cluster size. As an example, we simply generate the clusters by merging blocks with more than 16 passengers in a fourconnected manner, and the pick-up histogram is shown in Fig. 2. The pick-up density is in a wide range, and the cluster count drops quickly with the increase of pick-up number. Moreover, the clusters have different sizes; the

4 116 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 14, NO. 1, MARCH 2013 Fig. 2. Pick-up density histogram. Clusters are simply obtained by merging the blocks with more than 16 pick-ups. largest cluster contains 452 blocks, whereas the smallest clusters contain only one block. The three properties of pick-up distribution provide insights for the extraction of regions. First, we focus on blocks with many pick-ups since blocks with few pick-ups contain little information of human mobility. The first property shows that there are very few blocks with many pick-ups. Second, such blocks, according to the second property, yield many regular clusters. Such regular clusters are rough approximations to real-world irregular regions. In this paper, these regions are extracted with clustering methods. Third, the difference of pickup density and size among clusters makes region extraction more challenging. Some adaptive mechanism is required to achieve an effective region extraction. IV. REGION EXTRACTION: DBSCAN-BASED CLUSTERING Regions with high pick-up density are informative in conveying human mobility cues, which is related to the social function of regions. This paper focuses on regions with high pick-up density. From the algorithmic view, these regions can be defined as convex hulls of clusters within the set of pick-up/ set-down points. The challenge of extracting clusters comes from the disparity of clusters. We refine the traditional DBSCAN clustering algorithm [53] to solve this problem. A. Terminology and Notations For facilitating further description, several definitions and terms are listed below, which are much similar to the DBSCAN algorithm [53] Definition 1: The point set to be clustered is S, S = {p : p =(x, y)}, where p represents any pick-up position, x is the longitude, and y is the latitude of p. Definition 2: The d-neighborhood of a point p is denoted by N d (p) ={q : q S, L 2 (p, q) <d}, where L 2 (p, q) is the Euclidean distance between p and q. Definition 3 Average Neighbor Number: The average neighbor number of points in a point set R is denoted as Nd (p) aver d (R) =,p R. R Definition 4 Directly Density Reachable: p is directly density reachable from a point q with respect to MinPts and d if and only if p N d (q) and N d (q) MinPts. Definition 5 Density Reachable: p is density reachable from a point q with respect to MinPts and d if there exists a chain of points p 1 = p, p 2,...,p n = q, where p i is directly density reachable from p i+1 for i = 1,...,n 1. Definition 6 Cluster: A cluster C with respect to MinPts and d is a nonempty subset of S satisfying the following. 1) p, q, ifq C and p is density reachable from q with respect to d and MinPts, then p C; 2) p, q C, o C, so that both p and q can be density reachable from o with respect to d and MinPts. Definition 7 Convex Hull and Area: Define a convex hull of a cluster of points C as P = conv(c). This convex hull is a polygon; its area is denoted as area(p ). Definition 8 Polygon Distance: Define the distance between two polygons P i and P j as dist p (P i,p j )=min(l 2 (p i, p j )), where p i is any point located in P i, and p j is any point located in P j. B. IDBSCAN: Iterative DBSCAN Clustering Algorithm DBSCAN is a widely used density-based clustering algorithm. It is simple and thus efficient for large-scale data. However, the original DBSCAN algorithm essentially fixes a unique density threshold for all the clusters. It is not effective for extracting clusters with different pick-up/set-down density. Thus, we refine the DBSCAN algorithm by adaptively setting the density threshold of clusters and iteratively extracting them with reasonable size. The refined algorithm is named as IDBSCAN. Given the point set S and d, our IDBSCAN algorithm retrieves the final cluster set as follows: Algorithm: Iterative DBSCAN 1: Initialize: S C = {S}; A b = m 2 2: While C S C, s.t. Area(conv(C)) >A b 3: S L = C; 4: if S L S, MinPts = aver d (S L ); 5: for all p S L : 6: S p = {p}, S L = S L \ S p ; 7: While q S p, s.t. N d (q) MinPtsand S L N d (q) 8: S p = S p N d (q),s L = S L \ N d (q); 9: end While 10: S C = S C {S p }; 11: end for 12: S C = S C \{C}; 13: end While Steps 3 12 (the inner loop) is the original DBSCAN algorithm. The previous definition of clusters provides a direct way to divide all points into clusters. If started from an in-class point p, all the point densities reachable from p will be put in the cluster that p belongs to. By setting MinPts and d, the

5 PAN et al.: LAND-USE CLASSIFICATION USING TAXI GPS TRACES 117 Fig. 3. Comparison of IDBSCAN and DBSCAN in four cases. (a) Two clusters with similar density separated with each other very well. (b) Two clusters with different density. (c) Two clusters with similar density are close. (d) Two clusters are overlapped too much. The horizontal lines mean the density threshold for cluster extraction. DBSCAN algorithm fixes a density threshold and gets the clusters above the density threshold. However, for heterogeneous data, such a unique density threshold may result in clusters with size varying in a large range. Too large regions may have multiple social functions, while too small regions will have few passengers. The outer loop is our refinement of the original DBSCAN algorithm. The refinement could segment a large cluster into smaller clusters with higher pick-up density since the refinement repeatedly runs DBSCAN on clusters larger than a bound size with increasing density threshold. Such refinement prevents getting very small clusters since it begins with large clusters (gotten with an initial low density threshold) and then decreases in size gradually. It also prevents getting very large clusters because the area is restricted with an upper bound. According to our observation on the taxi trace data of Hangzhou, this bound is empirically set to be large ( m 2 ). Fig. 4. Effect of varying two parameters d and MinPts of IDBSCAN. C. Evaluation We illustrate 4 typical cases to demonstrate the advantage of IDBSCAN compared with DBSCAN, shown in Fig. 3. For the first case shown in Fig. 3(a), two clusters with similar density separated with each other very well; both algorithms get a good clustering result. For the second case shown in Fig. 3(b), the density of the two clusters is very different. The DBSCAN algorithm gets clusters with very large size, whereas our algorithm can iteratively run the clustering procedure with increasing density threshold and find clusters with the desired size. For the third case, shown in Fig. 3(c), two clusters stay close, and DBSCAN cannot separate them. However, if the total area of the two clusters is larger than the area bound, our algorithm can separate them. If the two clusters lie very closely and the total area is small, then our algorithm may also fail, as shown in Fig. 3(d). To sum up, DBSCAN has difficulty in setting an appropriate value for d and MinPts, whereas our algorithm can adaptively select the two parameters for each cluster. Moreover, we evaluate the effect of the two parameters d and MinPts. We hope to investigate how the parameters will affect the number of the extracted clusters. The experiment with different d and MinPts using one-week pick-up data (from April 1, 2009 to April 7, 2009) is carried out. The result is shown in Fig. 4. It demonstrates that the parameter d can be used to adjust the number of extracted clusters, whereas the parameter MinPts has little effect on the cluster number. When d increases from 1 to 7, the cluster number keeps growing. This is because, for a given MinPts, a larger d value means an initial lower density and more clusters are extracted.

6 118 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 14, NO. 1, MARCH 2013 TABLE I STATISTICS OF THE EXTRACTED REGIONS However, the cluster number decreases a little when d increases from 7 to 9, which is caused by those clusters close to each other. When d is small, these clusters will be extracted separately; when d is too large, these clusters merge into a larger cluster. D. Result of Region Extraction We use the IDBSCAN algorithm to extract clusters from the taxi GPS trace data described in Section III. The parameter setting is as follows: d = 5, and MinPts = 5. There are 952 regions extracted. However, the social function of these regions is still unknown. To build a data set with social function for study, we employ the manual labeling strategy. Six experienced taxi drivers were invited to label the social function for the extracted regions. They were asked to label only those regions with relatively single (or pure) social function. We ended up with 534 regions with a labeled social function. These regions belong to eight kinds of social function, i.e., coach/train station, campus, hospital, scenic spot, commercial district, entertainment district, office building, and residential district. The data demography is listed in Table I. V. L AND-USE CLASSIFICATION The pick-up/set-down dynamics is to describe the variation of pick-up/set-down number over time. First, we reveal the inherent relation between land-use classes and temporal patterns of pick-up/set-down dynamics. Second, to evaluate how useful the pick-up/set-down dynamics would be for land-use classification, six features are designed. Four typical classifiers are then employed to recognize the social function (land use) of regions. Moreover, we explore which features when combined could achieve the optimal classification performance. Moreover, we evaluate how much time of historical data the feature extraction requires to obtain reasonable classification accuracy. A. Definitions and Terminology For a region R, in this paper, the pick-up/set-down dynamics is defined as a temporal sequence consisting of pick-up/ set-down numbers in the time interval of an hour. Thus, the dynamics of a day can be denoted by a 24-dimensional vector. In the case of many days, it can be denoted by a dynamics matrix, each of whose columns is for one day. For convenience in further discussion, we give the terminologies and definitions as follows: U the pick-up dynamics matrix of a region; D the set-down dynamics matrix of a region; a j,k an element of the dynamics matrix A, describing the pick-up/set-down number within the jth hour of the kth day; a j,: row vector of matrix A; a :,k column vector of matrix A; S h day set of holidays; S cardinality of the set S; v L 2 norm of the vector v; N(v) normalization of a vector v, denoted as N(v) = (v/ v ); u./v element-wise division of two vectors u and v. B. Relationship Between Land Use and Passenger Dynamics Fig. 5 plots the daily pick-up pattern for four kinds of region land use as examples. The daily pick-up pattern shows the mean pick-up number in each hour of a day averaged over a year for each region. Meanwhile, the standard variance of the pickup number among the same kind of regions is plotted in thin color shadow. We explicitly separate weekdays and holidays. The figure shows that different land-use classes differ greatly in peak value, daily fluctuation, and weekday holiday disparity. C. Feature Extraction A good feature will very much help the land-use classification of a region. This paper designs and investigates 6 pick-up/set-down features, which are extracted from the pick-up/set-down dynamics matrix U/D, to explicitly depict the characteristics of a region R. All the features are computed using the historical data of a certain time length, for example, three-month taxi GPS data. 1) Daily pick-up feature (I): The daily pick-up feature provides information about the pick-up number in each hour of a day. The feature can be denoted as a 48-dimensional vector, composed of the holiday part (24 dimensions) and the weekday part (24 dimensions). The holiday part for the region R is calculated as vh I k S = h N(u :,k ) S h and the weekday part is computed similarly. 2) Daily set-down feature (II): Similar to the daily pickup feature, this feature is the set-down number per hour averaged over days, with holidays and weekdays, respectively. It also has 48 dimensions. 3) Pick-up/set-down difference feature (III): It represents the difference of the pick-up number and the set-down number in each hour. It is denoted as a 48-day vector composed by the holiday part and the weekday part; the holiday part is obtained by v III h = k S h (N(u :,k ) N(d :,k )). S h

7 PAN et al.: LAND-USE CLASSIFICATION USING TAXI GPS TRACES 119 Fig. 5. Daily pick-up pattern (in line) for four classes of regions along with their standard variance (in shadow). (a) Station. (b) Scenic Spot. (c) Commercial District. (d) Entertainment District. Fig. 6. Classification results of six features and four algorithms. I, II, III, IV, V, and VI indicate the different kinds of features. Four different lengths of data for feature extraction are tested (a) One year. (b) Half a year. (c) Four months. (d) One month. 4) Pick-up/set-down ratio feature (IV): It measures the ratio of the pick-up number to the set-down number in each hour of a day. Its holiday part is calculated as v IV h = k S h (N(u :,k )./N(d :,k )). S h 5) Weekly pick-up feature (V): The weekly pick-up feature for a region R depicts the variation of pick-up number of each day in a week. It is a 7-dimension vector calculated as vi V = C u j,7k+i, i = 1, 2,...,7, k 0 j k where C is the factor for normalizing the vector. 6) Weekly set-down feature (VI): v VI C N ( j k d j,7k+i), i = 1, 2,...,7, k 0. i = D. Experiments We test the land-use classification performance using 534 regions with eight kinds of social functions (land use): 1) station; 2) campus; 3) hospital; 4) scenic spot; 5) commercial district; 6) entertainment district; 7) office building; and 8) residential district. The data demography is shown in Table I. They are extracted from the nearly one-year real-world taxi trace data of Hangzhou City, and the associated social function is labeled by six professional taxi drivers. Four classical classifiers are evaluated for land-use classification. They are linear-kernel SVM, k-nearest neighbor, linear discriminate analysis, and three-layer BP (backward propagation neural network). All the parameters for the algorithms are optimized. 1) Experimental Setup: We employ the tenfold crossvalidation policy. All the regions belonging to the same land use are randomly divided into ten folds as evenly as possible (the difference between the region number of any two folds is not larger than one). In each round, nine folds are for training classifiers and the rest for validation. Thus, any region for validation will never simultaneously appear in the training set of regions. We repeat this procedure ten times. The advantage of this policy is that all regions are used for both training and validation in an interleave way, and each region is used for validation exactly once. The six kinds of features depend on the time length of data for extraction. The longer time length will let features convey more land-use information but make them less sensitive to temporal change of land use. In the experiment, given a time length for feature, we partition the data of each region into several parts of the same time length. Each part of the data is to generate an individual feature vector, which serves as an independent training or validation sample for evaluation. 2) Experiment 1 Evaluation of Features and Algorithms: This experiment is to evaluate the discriminative capability of the six kinds of features and the classification performance of the four algorithms. For each kind of feature and each algorithm, four different time lengths of data for feature extraction are tested: one year, half a year, four months, and one month. The experiment result is shown in Fig. 6. As for the discriminative capability of a single feature, feature I is the best among the six features, for almost all four time lengths and four algorithms. The best performance achieves 88.62%, with the SVM classifier and 4-mo data for feature extraction. Features V and VI are the worst ones,

8 120 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 14, NO. 1, MARCH 2013 TABLE II FEATURE SELECTION RESULTS Fig. 8. SVM classification by different features with varying time length for feature extraction. Fig. 7. Visualization of 3 classes of regions after the dimensionality reduction to 3 with MDS. whose classification accuracy is generally below 60%. The results demonstrate that the daily pick-up/set-down information (features I, II, III, and IV) is very helpful. As for classifiers, in many cases, SVM performs best in land-use classification for all six features and four time lengths thanks to its powerful generalization capability. The BP network has the lowest accuracy among the four algorithms. 3) Experiment 2 Feature Selection: Different features may convey different but complementary information on land use. The feature selection experiment attempts to find the best combination of features for classification. The new combined feature is generated by concatenating the two to six original feature vectors. The best feature combination is selected using the forward backward feature selection algorithm [54] and the brutal force algorithm, respectively. The forward backward algorithm iteratively executes a forward step and then a backward step until convergence. The forward (backward) step repeatedly adds (deletes) the best (worst) feature unless the classification accuracy decreases. The brutal algorithm enumerates all the feature combinations (2 6 in total) and finds the best one. The four classifiers are all evaluated. We choose the time length of 4 mo for feature extraction. The feature selection result is shown in Table II. For the cases of four classifiers, the two feature selection methods always get the same feature selection result: feature I + feature II. The two feature combination achieves the best classification accuracy of 95.65% when the SVM classifier is used. This combination is even better than combining all the features. To visualize the feature I + II space, we use multidimensional scaling (MDS) to reduce the feature to three dimensions and plot all the three classes of social function, residential district, hospital, and scenic spot in Fig. 7. 4) Experiment 3 Varying Time Length for Feature Extraction: The fewer time length of data for feature extraction, the less cost to build a land-use classifier, while possibly worse classification accuracy. This experiment is to find how the performance changes with the time length for feature extraction. We vary the time length of data from ten days to half a year. The SVM classifier is used. Fig. 8 shows the experimental results with the SVM classifier by different features. It finds that the accuracy using any feature always decreases when the time length decreases. The performance decrease becomes quick when the time length comes below 30 days. For the best feature combination (feature I + feature II), the classification performance keeps above 90% when the time length is larger than 30 days, and the best result is achieved with the time length of 4 mo. VI. REGION DYNAMICS AND SOCIAL ACTIVENESS A. Region Dynamics A region s social function does not remain consistent all the time. It may change when buildings in the region are disused for reconstruction or change their utilization. For example, the land-use class of a region will change from station to residential district if the train station in the region is replaced by apartment buildings. We call such dynamic change of land use over time as region dynamics. The land-use classification result of a region over time can be used to depict the region dynamics. We employed the time length of 1 mo for feature extraction and classified land use of all the regions of each month. We found that the land use of many regions changed stably during the period of April 2009 to March Transition of social function is usually caused by unusual social events, such as traffic regulation, building reconstruction, and new center opening. For five transition examples, we further investigate the underlying social events that cause a change in land use. The social events are listed in Table III. We can see that regions 2 and 3 changed their land use mainly because of a building closing, the land-use transition of regions 1 and 5 was mainly caused by the opening of new big center, and the

9 PAN et al.: LAND-USE CLASSIFICATION USING TAXI GPS TRACES 121 TABLE III SOCIAL EVENTS BEHIND THE SOCIAL FUNCTION TRANSITION illustrates a famous scenic spot, the Xixi wetland, during different days. The Xixi wetland has peaks in the spring and during October, because people like to go for holidays in warm seasons (e.g., spring plus October). 2) Spatial variation of social activeness. Social activeness also varies spatially, different in regions at the same time. As an example, Fig. 9(b) shows the social activeness of some regions in the main city zone on April 1, Social activeness of regions is described in color density. In this figure, the regions within red circle are more socially active than the other regions. This is because it is the commercial area of the city with lots of large shopping malls. Fig. 9. Social activeness of some regions. (a) Social activeness variation among different times in the Xixi wetland. (b) Social activeness of some regions in the main city zone on April 1, The color density indicates their activeness. transition of region 4 was due to traffic regulation (not strictly obeying the prohibition regulation at night makes it change to the land use of entertainment). B. Social Activeness It is usually in a different degree that regions are utilized for a social function, depending on time and location. We call this kind of utilization activeness degree for a region as social activeness. It depicts to what extent a region is used for a social function. The regions with a similar social function are different in social activeness, and a region has different social activeness at different times. For example, some scenic spots have more tourists than others and a certain scenic spot may have more passengers in spring than in winter. Such social activeness reflects human flow information. It can be exploited to find popular spots for drivers, tourists, and passengers. We simply compute the social activeness of a region as the total pick-up number of a day. The results with the pick-up data from April 1, 2009 to March 31, 2010 show the following: 1) Temporal variation of social activeness. The social activeness of a region may vary in different days. Fig. 9(a) VII. CONCLUSION AND DISCUSSIONS In this paper, we explore and prove the potential use of taxi trace data in land-use classification. The data used in this paper are large-scale real-world taxi trace data of a big city with a population of 6 million. First, an improved clustering algorithm (iterative DBSCAN) is presented to extract regions, according to the characteristics of the data. Second, the land-use classification using the taxi trace data is proposed. Six kinds of features are designed, and four classifiers are integrated into the evaluation. The performance of different features and classifiers is evaluated, and a best feature combination is also achieved. With the large-scale data, our approach can achieve an accuracy of 95% for land-use classification. Third, the dynamic transition of a region s land use can be detected and reveal corresponding social events. Our work still has some limits for land-use classification. First, we cannot address regions that have few taxi passengers. The taxi passenger flow is only a small part of the whole human flow and this results in some regions having fewer passengers. However, if the trace data of personal cars are available, our method can be easily applied to complementary trace data to handle more regions. Second, our work currently only addresses regions with pure land use. We do not consider regions with multiple land-use classes, which will be focus of future work. ACKNOWLEDGMENT The authors would also like to thank the Hangzhou City Traffic Bureau for providing the taxi trace data, which became the basis for their research. They would also like to thank the six professional drivers for their manual labeling of land use.

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11 PAN et al.: LAND-USE CLASSIFICATION USING TAXI GPS TRACES 123 [51] V. Soto and E. Frias-Martinez, Robust land use characterization of urban landscapes using cell phone data, in Proc. 1st Workshop Pervasive Urban Appl., Pervasive, 2011, pp [52] W. Zhang, S. Li, and G. Pan, Mining the semantics of origin destination flows using taxi traces, in Proc. 4th Int. Workshop LBSN, Pittsburgh, PA, Sep. 8, [53] M. Ester, H. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in Proc. KDD, 1996, pp [54] T. Zhang, Adaptive forward-backward greedy algorithm for learning sparse representations, IEEE Trans. Inf. Theory, vol. 57,no.7,pp , Jul Zhaohui Wu received the B.Sc. and Ph.D. degrees in computer science from Zhejiang University, Hangzhou, China, in 1988 and 1993, respectively. He is currently a Professor with the Department of Computer Science, Zhejiang University. His research interests include distributed artificial intelligence, semantic grid, and pervasive computing. Dr. Wu is a Standing Council Member of the China Computer Federation. Gang Pan received the B.Sc. and Ph.D. degrees in computer science from Zhejiang University, Hangzhou, China, in 1998 and 2004, respectively. He is currently a Professor with the College of Computer Science and Technology, Zhejiang University. He has published more than 90 refereed papers. He visited the University of California, Los Angeles, Los Angeles, during His research interests include pervasive computing, computer vision, and pattern recognition. Dr. Pan has served as a Program Committee Member for more than ten prestigious international conferences, such as IEEE International Conference on Computer Vision and IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Daqing Zhang received the Ph.D. degree from the University of Rome La Sapienza, Rome, Italy, in He is currently a Professor on ambient intelligence with TELECOM SudParis, Evry, France. He has published more than 140 referred journal and conference papers. His interests include context-aware computing, social and community intelligence, pervasive elderly care, mobile social networking. Dr. Zhang is the Associate Editor for four leading journals including ACM Transactions on Intelligent Systems and Technology. He has been a frequent Invited Speaker in various international events on ubiquitous computing. He has served as the General Co-Chair or Program Co-Chair for more than ten international conferences. Guande Qi received the B.Sc. degree in life science from Zhejiang University, Hangzhou, China, in 2008, where he is currently working toward the Ph.D. degree with the Department of Computer Science and Technology. His research interests include machine learning and data mining. Shijian Li (M 10) received the Ph.D. degree from Zhejiang University, Hangzhou, China, in In 2010, he was a Visiting Scholar with the Institute Telecom SudParis, Evry, France. He is currently with the College of Computer Science and Technology, Zhejiang University. He has published over 40 papers. His research interests include sensor networks, ubiquitous computing, and social computing. Dr. Li serves as an Editor of the International Journal of Distributed Sensor Networks and as Reviewer or PC Member of more than ten conferences.