Modeling Crowd Flows Network to Infer Origins and Destinations of Crowds from Cellular Data

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1 Modeling Crowd Flows Network to Infer Origins and Destinations of Crowds from Cellular Data Ai-Jou Chou Gunarto Sindoro Njoo Wen-Chih Peng ABSTRACT Massive spatiotemporal data is generated every day and everywhere make it possible for modeling citywide human mobility. However, few studies have investigated where the origins and destinations of the crowd flows in the urban areas. Therefore, the aim of this paper is to infer origins and destinations of crowds. We present a dynamic weighted directed graph to model the mobility of crowds, called the crowd flows network. In particular, we use a cellular dataset of approximately 7 million spatiotemporal records from Chunghwa Telecom in Taiwan to demonstrate the practicality of our proposed crowd flows network. Results reveal that crowd flows network captures meaningful features from the cellular data and the findings can be seen as the first step towards the more comprehensive understanding of citywide crowds flows mobility. efficient and would not pose any privacy problems. However, checkin data is known for its sparsity problem and bias towards certain kind of users [5]. Finally, the cellular data is widely available as the data is collected by the telecom company whenever the user utilizes communication services. Unlike GPS, it neither incurs high energy consumption nor violates user s privacy. However, it has more data compared to the check-in data. The only limitation of the cellular data is the accuracy of the user s location is not fine grained. CCS CONCEPTS Information systems Spatial-temporal systems; (a) Modeling the destinations (b) Modeling the origins KEYWORDS Crowd flows modeling; urban computing; cellular data; 1 INTRODUCTION In recent years, there has been a rapid proliferation of smartphones which is not only changing the way people behave and interact but also generating massive spatial- and temporal-aware data, such as cellular data from telecommunication providers, GPS data from location-based applications, and geo-tagged contents published in the online social networks. The spatial- and temporal-aware data might be used as a proxy to depict the movement of people in the densely populated cities. For example, business managers would like to know where potential customers come from, carhailing companies would inquire traffic demands in particular area of the city, and telecommunication providers are interested in where the speed dropping experiences arise from. These problems are highly pertinent to the crowd flows in the city. In particular, there are three types of spatiotemporal data that can be leveraged to understand the crowds movement in the urban level and each data source exhibits different characteristics. GPS data extracted from smartphones has very high density yet it incurs high energy consumption and sometimes might not be available due to the lack of signal or due to privacy issue. On the other hand, check-in data is a data that is provided by the users willingly; it is more energy UrbComp 18, August 2018, London, UK Copyright held by the owner/author(s). Figure 1: The illustration of the origins and destinations modeling of the crowds movement. There are two types of crowd flows can be observed in the movement data: inflow and outflow [8]. To forecast the inflow and outflow of crowds, a deep-learning based method called ST-ResNet [7] has been applied on the taxicab GPS data and bike rental data. Because the inflow and outflow can be measured by different data sources, the authors in [6] explored a multi-view deep-learning based framework for the problem of predicting taxi demand. The limitation of the above studies is that their models only consider the GPS data of a single transportation mode, that is, the crowd flow data only comes from people who take a particular transportation mode, i.e. people on a taxicab and people using a bike-sharing system. In contrast, the cellular data we use records the approximate location and timestamp of the crowd in various transportation modes because user s devise connects to the base station actively or passively whenever they are stationary, walking, taking public transportation and driving. Moreover, the biggest difference between the above studies and ours is that they only discuss crowd flows that flow in and out of a region, the number of flows in each and every region, and we are looking at crowd flows that flow in or out of a region when given a destination or an origin, which makes our problem different and more practical. In the light of the next location prediction, [1 4] are some stateof-the-arts in the area. However, these research mostly focused on the personal mobility modeling and location recommendation from check-in data. While, our work focuses on exploring the origins

2 UrbComp 18, August 2018, London, UK Ai-Jou Chou, Gunarto Sindoro Njoo, and Wen-Chih Peng Table 1: Example of mobility trace rid timestamp coordinates 1 01/13/17 8:30AM (25.038, ) 2 01/13/17 8:34AM (25.038, ) 3 01/13/17 9:04AM (25.045, ) 4 01/13/17 9:13AM (25.051, ) 5 01/13/17 9:16AM (25.051, ) and destinations of crowd flows using cellular data in the citylevel. Figure 1 illustrates the problem that we tackles in our work. Instead of predicting the outflow volume of a particular region (i.e., shown in pale yellow) in Figure 1(a), we aim to infer the possible destinations of the crowds. Similarly, instead of knowing the inflow volume of the center region, we aim to infer the origins of the crowds (as shown in Figure 1(b)). The key contributions of this work are explained as follows. We propose a method to model the mobility of the crowds based on a dynamic weighted directed graph called crowd flows network. A crowd flows network characterizes the transition of the crowds between origin and destination regions as well as describes the inflow and outflow time series between regions. The practicality of the proposed methodology is demonstrated through the case studies. To the best of our knowledge, this is the first work to model crowd flows network to infer the possible origins and destinations by using cellular data. While our work is still at the early stage, the findings from the crowd flow network could have broad implications for understanding citywide crowds movement. 2 PROBLEM DEFINITION 2.1 Preliminaries We denote the set of regions by L = {l 0,l 1,...,l N } which partition a city into N non-overlapping rectangle areas and the set of time slots T = {t 0, t 1,..., t M } which divide a day into M time slots by using t minutes as the length of a time slot. The mobility trace of a user u is a time-ordered sequence S = record 1, record 2,..., record n. Each record is a tuple (timestamp, coordinates) where (1) timestamp is the timestamp string (2) coordinates is associated with geographical coordinates representing the location of the user at the timestamp. Table 1 shows an example of mobility trace. Given a mobility trace S, the region set L and the time slot set T, a projection trajectory S = r 1, r 2,..., r m is a mobility trace binding with L and T. For a record r i = (t i,l i ) that projected from (timestamp, coordinates), r i satisfies: (1) t i timestamp < t i+1 ; (2) coordinates lies within the l i. We treat coordinates lies outside L as an outlier and discard the record. 2.2 Origins and Destinations of Crowds Considering a large amount of users U accompanied by generated trajectories, we aim to infer origins and destinations of U at any time periods by modeling the crowd flows around the urban space. Definition 1 (Origin and Destination). If two consecutive records i and j in the trajectory of user u satisfies (1) r i r j ; (2) t i t j, we regard r i as the origin and r j as the destination. (a) Toy example of a crowd flows network (b) Adjacency matrix Figure 2: An illustration of crowd flows network and the adjacency matrix. The boxed row indicates Region b as origin with destinations (a, c) and corresponding outflow (3, 10). The boxed column indicates Region c as destination with origins (b, d) and corresponding inflow (10, 1). Inferring crowd flows destinations. Given a region l as the origin, current time period p and time threshold τ, the problem aims to identify a set of regions where users U will move into after τ time slots. Inferring crowd flows origins. Given a region l as the destination, current time period p and time threshold τ, the problem aims to identify a set of regions where users U from and will move into region l after τ time slots. 3 METHODOLOGY Our goal is to infer the origins and destinations of crowds in the city. First, we discuss modeling the crowd flows into directed networks through large scale cellular data. Second, we present our crowd flows network reflects several static and dynamic properties between crowds, regions and time. 3.1 Crowd Flows Network Formulation A crowd flows network is a graph where nodes represent regions. Two nodes are connected by a directed edge at a specific time period if there have individuals moving from one region to another region during that time period. Edge weights represent the number of individuals moving during that time period. The crowd flows network characterizes the dynamics of people moving around an urban space. Nodes can further hold attributes such as coordinates of center point, POI list, etc. Figure 2(a) shows a toy example of a crowd flows network including five nodes. 2(b) is the corresponding adjacency matrix. Rows in the adjacency matrix represents the node as origin and crowd flows move into which nodes. The sum of values in a row means the total outflows of the node. Similarly, columns in the adjacency matrix represents the node as destination and crowd flows move from which nodes. The sum of values in a column means the total inflows of the node. Based on Definition 1 in Section 2, a pair of origin and destination and the time information including start time, end time and travel time from user trajectory can be collected. Remember that we have discretized time into fixed-size time slots. The crowd flow network is then defined as follows.

3 Modeling Crowd Flows Network to Infer Origins and Destinations of Crowds from Cellular Data UrbComp 18, August 2018, London, UK (a) Contextual Neighbors (b) Semantic Neighbors Figure 4: An illustration of contextual and semantic neighbors. In the left figure, red regions are contextual neighbors of the green region. In the right figure, all red regions are semantic neighbors each others since they have a common destination. Figure 3: Crowd flows network constructed from real world cellular data with the corresponding city map. For simplicity, we ignored the direction of edges in the network. Definition 2 (Crowd flows network). Given a time window size w and a time threshold τ, we aggregate the pairs of origin and destination from users U for every w time slots if the travel time is equal to τ. The crowd flows network is further defined as G k = (V, Ek ). V is the stationary set of regions. Ek is the set of edges over a period k. An edge (vi, v j, w) Ek points from the origin vi to the destination v j associated with a weight w [1, ) indicating the number of individuals. Note that (vi, v j, w) is different from (v j, vi, w) since edge is directed. 3.2 Network Properties Connection Structure. Intuitively, people driving car or taking subway can arrive farther regions in a short time in the city. Since the travel time is fixed to τ when we construct a crowd flows network, the network reflects this intuition and implicitly depict the transport infrastructures. In the Figure 3, the dense horizontal part is a highway corresponding the black line in the middle on the map. We observed nodes on the highway region are linked to other more distant nodes and have wider east-west range. Therefore, the length of connection in the network can be seen as a speed factor and the direction of connection reflects the general direction of transport. For instance, node represents a train station region whose connection exhibits a southwest-northeast structure meets the railway Region Importance. Region importance is a measure based on global node ranking in the crowd flows network. The higher the region importance, the more likely crowds moving into and out of the region. Therefore, the region is possibly a commercial intersection or a transport hub. The lower the region importance, the more likely the region is sparsely inhabited or inaccessible. The region importance is tied closely with crowd flows movement Contextual Neighbors. A node is contextual neighbor of another node means they are directly connected in the crowd flows network. When a direct edge existed between two regions, the region which the edge point from is an origin and the region which the edge point to is a destination, together they present a crowd flow movement behavior. Regions are not arbitrary connected, intuitively, two region with close geographical distance are more likely connected. Also, we found that two region connected by transportation network are often connected and their edge weight even higher than two geographically close regions with the same origin regions. On the other hand, contextual relationships are pertinent to time influence. For example, people tend to have a lunch near the working place at noon but in the evening people tend to go far away to shopping malls, theatres and bars to relax after work Semantic Neighbors. Semantic neighbors indicate that two nodes are not necessarily connected, but they share common contextual neighbors in the crowd flows network. Comparing to contextual neighbors, semantic neighbors are implicit in the crowd flows network. In reality, semantic relationship reflects similar region functionality between two regions. A real illustration of this is Figure 4(b), red regions have semantic relationship each other and their common neighbors are MRT stations on the green line in the middle. In fact, these red regions are residential area along the MRT green line. 4 EXPERIMENTS 4.1 Dataset We use a cellular dataset from Chunghwa Telecom, which is the largest telecommunication provider in Taiwan. The dataset contains cellular data about 314,000 users from 12/31/2016 to 01/13/2017 for the city Taipei and New Taipei, so-called Greater Taipei area. We divided the area into 49 x 49 regions. The size of each region is 0.5km x 0.5km. 4.2 Case Studies Inferring K destinations of crowd flows. Given a specific origin and K, we infer K destinations from the crowd flows network. Figure 5 shows four origins and the query results. K is set as 30 and query period is on 01/09/2017 from 8 a.m. to 10 a.m. Time threshold τ is set as 5 minutes. Four origins are located in Shilin district, Songshan district, Banqiao district and the vicinity of Taipei 101. The query results show that K destinations which have edge

4 UrbComp 18, August 2018, London, UK (b) Shilin Ai-Jou Chou, Gunarto Sindoro Njoo, and Wen-Chih Peng (c) Songshan (a) Origins (a) Inflow of top 4 destinations (d) Banqiao (e) Taipei 101 Figure 5: Inferring K destinations (τ = 5 minutes) (b) Region map (a) Shilin (b) Taipei 101 Figure 7: Top destinations of the different time period from Region Figure 6: Inferring K destinations (τ = 10 minutes) connected to the origin at the center. The destination with darker color means more people moving from the origin to the destination. Figures 5 reveal destinations of crowds are influenced by the transportation infrastructures as well as the region functionality in the city. All of them contain Taipei mass rapid transit (MRT) stations serving greater Taipei. Figure 5(a) shows Shilin station is on the read line, Songshan station is a terminal station of the green line, Banqiao and Taipei 101 station are on the blue line. Moreover, Songshan and Banqiao stations are also train stations (TRA) served as part of conventional rail in Taiwan. Banqiao is also a High-Speed Rail (HSR) station served as a high-speed rail along the west coast of Taiwan. Therefore, Banqiao and Songshan connect to remote destinations in Figure 5(c) and Figure 5(d) which are the other TRA and HSR stops. Furthermore, the connection structures of Shilin, Songshan, and Banqiao match the railway route on the map in Figure 5(a). Figure 5(e) shows destinations in the vicinity of Taipei 101 are limit to nearby regions which is one of the most crowded districts in Taipei Destinations of different time threshold. We experiment with different time threshold τ to test our proposed crowd flows network with different travel time between origin and destination. Figure 6 presents the query results of Shilin and Taipei 101 when τ is set as 10 minutes. K and query period are same as the previous section. Comparing to Figure 5(b) when τ is set as 5 minutes, destinations of crowds in Figure 6(a) are reached more distinct regions. The farthest destination of Shilin is Fuxinggang station on the red line. The travel time released by the Taipei Metro Company between Shilin and Fuxinggang station is about 14 minutes meets our time threshold setting Destinations of different time period. We aim to present contextual neighbors changed over time by our proposed crowd flows network. The case can be seen as a commuting behavior of crowds. Based on our crowd flows network, outflow and inflow time series between any two regions are edge weights at the different time period. Given Region 1543 as an origin and its top 4 destinations Region 1447, 1400, 1586, and Four inflow time series of the destinations are plotted in different colors in Figure 7. Figure 7(a) shows Region 1447 and Region 1440 have higher crowd inflows from the origin 1543 in the morning. Figure 7(b) shows the route is from suburbs to core city. However, in the evening, Region 1586 and Region 1732 turn to have higher crowd inflows from the origin Figure 7(b) shows the route is reversely from core city to suburbs. This is an example of the commuting behavior of crowds in urban space. We found such similar results on the regions located between suburbs and the core city. 5 CONCLUSIONS In this paper, we present a method to infer origins and destinations of the crowds by modeling crowd flow network from large-scale cellular data. The crowd flows network described in this work

5 Modeling Crowd Flows Network to Infer Origins and Destinations of Crowds from Cellular Data UrbComp 18, August 2018, London, UK could serve as the basis for understanding the interactions between regions, crowds, and time in the urban space. The four properties related to the crowd flow network illustrated are of importance in explaining urban dynamics. For evaluation, we have conducted several case studies with three different scenarios: inferring top K destinations, querying destinations of different time threshold, and querying destinations of the different time period. The results demonstrate that the crowd flows network is interpretable and distill valuable information from massive spatial- and temporalaware data. For future work, we are exploring network embedding methods properly preserving the properties of crowd flows network in other to predict origins and destinations of crowds. REFERENCES [1] Shanshan Feng, Gao Cong, Bo An, and Yeow Meng Chee POI2Vec: Geographical Latent Representation for Predicting Future Visitors. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. [2] Jing He, Xin Li, Lejian Liao, Dandan Song, and William K Cheung Inferring a Personalized Next Point-of-Interest Recommendation Model with Latent Behavior Patterns. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (2016), [3] Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan Predicting the Next Location : A Recurrent Model with Spatial and Temporal Contexts. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (2016), [4] Xin Liu, Yong Liu, and Xiaoli Li Exploring the context of locations for personalized location recommendations. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, Vol Janua [5] Gang Wang, Sarita Y Schoenebeck, Haitao Zheng, and Ben Y Zhao "Will Check-in for Badges": Understanding Bias and Misbehavior on Location-based Social Networks. In Proceedings of the Tenth International AAAI Conference on Web and Social Media (ICWSM) [6] Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Didi Chuxing, and Zhenhui Li Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. [7] Junbo Zhang, Yu Zheng, and Dekang Qi Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. [8] Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang Urban Computing: Concepts, Methodologies, and Applications. ACM Transactions on Intelligent Systems and Technology 5, 3 (2014), 1 55.