Location-based services allow users to perform geo-spatial check-in actions, which facilitates the mining of the moving activities of human beings. This paper proposes to recommend time-sensitive trip routes, consisting of a sequence of locations with associated time stamps, based on knowledge extracted from large-scale check-in data. Given a query location with the starting time, our goal is to recommend a time-sensitive route. We argue a good route should consider (a) the popularity of places, (b) the visiting order of places, (c) the proper visiting time of each place, and (d) the proper transit time from one place to another. By devising a statistical model, we integrate these four factors into a goodness function which aims to measure the quality of a route. Equipped with the goodness measure, we propose a greedy method to construct the time-sensitive route for the query. Experiments on Gowalla datasets demonstrate the effectiveness of our model on detecting real routes and cloze test of routes, comparing with other baseline methods. We also develop a system TripRouter as a real-time demo platform.
{"title":"Exploiting large-scale check-in data to recommend time-sensitive routes","authors":"Hsun-Ping Hsieh, Cheng-te Li, Shou-de Lin","doi":"10.1145/2346496.2346506","DOIUrl":"https://doi.org/10.1145/2346496.2346506","url":null,"abstract":"Location-based services allow users to perform geo-spatial check-in actions, which facilitates the mining of the moving activities of human beings. This paper proposes to recommend time-sensitive trip routes, consisting of a sequence of locations with associated time stamps, based on knowledge extracted from large-scale check-in data. Given a query location with the starting time, our goal is to recommend a time-sensitive route. We argue a good route should consider (a) the popularity of places, (b) the visiting order of places, (c) the proper visiting time of each place, and (d) the proper transit time from one place to another. By devising a statistical model, we integrate these four factors into a goodness function which aims to measure the quality of a route. Equipped with the goodness measure, we propose a greedy method to construct the time-sensitive route for the query. Experiments on Gowalla datasets demonstrate the effectiveness of our model on detecting real routes and cloze test of routes, comparing with other baseline methods. We also develop a system TripRouter as a real-time demo platform.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127175651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Mcardle, A. Lawlor, Eoghan Furey, A. Pozdnoukhov
This paper introduces a micro-simulation of urban traffic flows within a large scale scenario implemented for the Greater Dublin region in Ireland. Traditionally, the data available for traffic simulations come from a population census and dedicated road surveys which only partly cover shopping, leisure or recreational trips. To account for the latter, the presented traffic modelling framework exploits the digital footprints of city inhabitants on services such as Twitter and Foursquare. We enriched the model with findings from our previous studies on geographical layout of communities in a country-wide mobile phone network to account for socially related journeys. These datasets were used to calibrate a variant of a radiation model of spatial choice, which we introduced in order to drive individuals' decisions on trip destinations within an assigned daily activity plan. We observed that given the distribution of population, the workplace locations, a comprehensive set of urban facilities and a list of typical activity sequences of city dwellers collected within a national road survey, the developed micro-simulation reproduces not only the journey statistics but also the traffic volumes at main road segments with surprising accuracy.
{"title":"City-scale traffic simulation from digital footprints","authors":"G. Mcardle, A. Lawlor, Eoghan Furey, A. Pozdnoukhov","doi":"10.1145/2346496.2346505","DOIUrl":"https://doi.org/10.1145/2346496.2346505","url":null,"abstract":"This paper introduces a micro-simulation of urban traffic flows within a large scale scenario implemented for the Greater Dublin region in Ireland. Traditionally, the data available for traffic simulations come from a population census and dedicated road surveys which only partly cover shopping, leisure or recreational trips. To account for the latter, the presented traffic modelling framework exploits the digital footprints of city inhabitants on services such as Twitter and Foursquare. We enriched the model with findings from our previous studies on geographical layout of communities in a country-wide mobile phone network to account for socially related journeys. These datasets were used to calibrate a variant of a radiation model of spatial choice, which we introduced in order to drive individuals' decisions on trip destinations within an assigned daily activity plan. We observed that given the distribution of population, the workplace locations, a comprehensive set of urban facilities and a list of typical activity sequences of city dwellers collected within a national road survey, the developed micro-simulation reproduces not only the journey statistics but also the traffic volumes at main road segments with surprising accuracy.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126974201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, researches on recommendation of urban Points-Of-Interest (POI), such as restaurants, based on social information have attracted a lot of attention. Although a number of social-based recommendation techniques have been proposed in the literature, most of their concepts are only based on the individual or friends' check-in behaviors. It leads to that the recommended POIs list is usually constrained within the users' or friends' living area. Furthermore, since context-aware and environmental information changes quickly, especially in urban areas, how to extract appropriate features from such kind of heterogeneous data to facilitate the recommendation is also a critical and challenging issue. In this paper, we propose a novel approach named Urban POI-Mine (UPOI-Mine) that integrates location-based social networks (LBSNs) for recommending users urban POIs based on the user preferences and location properties simultaneously. The core idea of UPOI-Mine is to build a regression-tree-based predictor in the normalized check-in space, so as to support the prediction of interestingness of POI related to each user's preference. Based on the LBSN data, we extract the features of places in terms of i) Social Factor, ii) Individual Preference, and iii) POI Popularity for model building. To our best knowledge, this is the first work on urban POI recommendation that considers social factor, individual preference and POI popularity in LBSN data, simultaneously. Through comprehensive experimental evaluations on a real dataset from Gowalla, the proposed UPOI-Mine is shown to deliver excellent performance.
{"title":"Urban point-of-interest recommendation by mining user check-in behaviors","authors":"J. Ying, E. H. Lu, Wen-Ning Kuo, V. Tseng","doi":"10.1145/2346496.2346507","DOIUrl":"https://doi.org/10.1145/2346496.2346507","url":null,"abstract":"In recent years, researches on recommendation of urban Points-Of-Interest (POI), such as restaurants, based on social information have attracted a lot of attention. Although a number of social-based recommendation techniques have been proposed in the literature, most of their concepts are only based on the individual or friends' check-in behaviors. It leads to that the recommended POIs list is usually constrained within the users' or friends' living area. Furthermore, since context-aware and environmental information changes quickly, especially in urban areas, how to extract appropriate features from such kind of heterogeneous data to facilitate the recommendation is also a critical and challenging issue. In this paper, we propose a novel approach named Urban POI-Mine (UPOI-Mine) that integrates location-based social networks (LBSNs) for recommending users urban POIs based on the user preferences and location properties simultaneously. The core idea of UPOI-Mine is to build a regression-tree-based predictor in the normalized check-in space, so as to support the prediction of interestingness of POI related to each user's preference. Based on the LBSN data, we extract the features of places in terms of i) Social Factor, ii) Individual Preference, and iii) POI Popularity for model building. To our best knowledge, this is the first work on urban POI recommendation that considers social factor, individual preference and POI popularity in LBSN data, simultaneously. Through comprehensive experimental evaluations on a real dataset from Gowalla, the proposed UPOI-Mine is shown to deliver excellent performance.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126270927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Nanni, R. Trasarti, Giulio Rossetti, D. Pedreschi
A basic task of urban mobility management is the real-time monitoring of traffic within key areas of the territory, such as main entrances to the city, important attractors and possible bottlenecks. Some of them are well known areas, while while others can appear, disappear or simply change during the year, or even during the week, due for instance to roadworks, accidents and special events (strikes, demonstrations, concerts, new toll road fares). Especially in the latter cases, it would be useful to have a traffic monitoring system able to dynamically adapt to reference areas specified by the user. In this paper we propose and study a solution exploiting on-board location devices in private cars mobility, that continuously trace the position of the vehicle and periodically communicate it to a central station. Such vehicles provide a statistical sample of the whole population, and therefore can be used to compute a summary of the traffic conditions for the mobility manager. However, the large mass of information to be transmitted and processed to achieve that might be too much for a real-time monitoring system, the main problem being the systematic communication from each vehicle to a unique, centralized station. In this work we tackle the problem by adopting the general view of distributed systems for the computation of a global function, consisting in minimizing the amount of information communicated through a careful coordination of the single nodes (vehicles) of the system. Our approach involves the use of predictive models that allow the central station to guess (in most cases and within some given error threshold) the location of the monitored vehicles and then to estimate the density of key areas without communications with the nodes.
{"title":"Efficient distributed computation of human mobility aggregates through user mobility profiles","authors":"M. Nanni, R. Trasarti, Giulio Rossetti, D. Pedreschi","doi":"10.1145/2346496.2346511","DOIUrl":"https://doi.org/10.1145/2346496.2346511","url":null,"abstract":"A basic task of urban mobility management is the real-time monitoring of traffic within key areas of the territory, such as main entrances to the city, important attractors and possible bottlenecks. Some of them are well known areas, while while others can appear, disappear or simply change during the year, or even during the week, due for instance to roadworks, accidents and special events (strikes, demonstrations, concerts, new toll road fares). Especially in the latter cases, it would be useful to have a traffic monitoring system able to dynamically adapt to reference areas specified by the user.\u0000 In this paper we propose and study a solution exploiting on-board location devices in private cars mobility, that continuously trace the position of the vehicle and periodically communicate it to a central station. Such vehicles provide a statistical sample of the whole population, and therefore can be used to compute a summary of the traffic conditions for the mobility manager. However, the large mass of information to be transmitted and processed to achieve that might be too much for a real-time monitoring system, the main problem being the systematic communication from each vehicle to a unique, centralized station.\u0000 In this work we tackle the problem by adopting the general view of distributed systems for the computation of a global function, consisting in minimizing the amount of information communicated through a careful coordination of the single nodes (vehicles) of the system. Our approach involves the use of predictive models that allow the central station to guess (in most cases and within some given error threshold) the location of the monitored vehicles and then to estimate the density of key areas without communications with the nodes.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"42 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122853645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wen He, Deyi Li, Tianlei Zhang, Lifeng An, Mu Guo, Guisheng Chen
The widely use of GPS-enabled devices has provided us amount of trajectories related to individuals' activities. This gives us an opportunity to learn more about the users' daily lives and offer optimized suggestions to improve people's trip styles. In this paper, we mine regular routes from a users' historical trajectory dataset, and provide ridesharing recommendations to a group of users who share similar routes. Here, regular route means a complete route where a user may frequently pass through approximately in the same time of day. In this paper, we first divide users' GPS data into individual routes, and a group of routes which occurred in a similar time of day are grouped together by a sliding time window. A frequency-based regular route mining algorithm is proposed, which is robust to slight disturbances in trajectory data. A feature of Fixed Stop Rate (FSR) is used to distinguish the different types of transport modes. Finally, based on the mined regular routes and transport modes, a grid-based route table is constructed for a quick ride matching. We evaluate our method using a large GPS dataset collected by 178 users over a period of four years. The experiment results demonstrate that the proposed method can effectively extract the regular routes and generate rideshare plan among users. This work may help ridesharing to become more efficient and convenient.
{"title":"Mining regular routes from GPS data for ridesharing recommendations","authors":"Wen He, Deyi Li, Tianlei Zhang, Lifeng An, Mu Guo, Guisheng Chen","doi":"10.1145/2346496.2346510","DOIUrl":"https://doi.org/10.1145/2346496.2346510","url":null,"abstract":"The widely use of GPS-enabled devices has provided us amount of trajectories related to individuals' activities. This gives us an opportunity to learn more about the users' daily lives and offer optimized suggestions to improve people's trip styles. In this paper, we mine regular routes from a users' historical trajectory dataset, and provide ridesharing recommendations to a group of users who share similar routes. Here, regular route means a complete route where a user may frequently pass through approximately in the same time of day. In this paper, we first divide users' GPS data into individual routes, and a group of routes which occurred in a similar time of day are grouped together by a sliding time window. A frequency-based regular route mining algorithm is proposed, which is robust to slight disturbances in trajectory data. A feature of Fixed Stop Rate (FSR) is used to distinguish the different types of transport modes. Finally, based on the mined regular routes and transport modes, a grid-based route table is constructed for a quick ride matching. We evaluate our method using a large GPS dataset collected by 178 users over a period of four years. The experiment results demonstrate that the proposed method can effectively extract the regular routes and generate rideshare plan among users. This work may help ridesharing to become more efficient and convenient.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123196075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lijun Sun, Der-Horng Lee, A. Erath, Xianfeng Huang
Rapid tranit systems are the most important public transportation service modes in many large cities around the world. Hence, its service reliability is of high importance for government and transit agencies. Despite taking all the necessary precautions, disruptions cannot be entirely prevented but what transit agencies can do is to prepare to respond to failure in a timely and effective manner. To this end, information about daily travel demand patterns are crucial to develop efficient failure response strategies. To the extent of urban computing, smart card data offers us the opportunity to investigate and understand the demand pattern of passengers and service level from transit operators. In this present study, we present a methodology to analyze smart card data collected in Singapore, to describe dynamic demand characteristics of one case mass rapid transit (MRT) service. The smart card reader registers passengers when they enter and leave an MRT station. Between tapping in and out of MRT stations, passengers are either walking to and fro the platform as they alight and board on the trains or they are traveling in the train. To reveal the effective position of the passengers, a regression model based on the observations from the fastest passengers for each origin destination pair has been developed. By applying this model to all other observations, the model allows us to divide passengers in the MRT system into two groups, passengers on the trains and passengers waiting in the stations. The estimation model provides the spatio-temporal density of passengers. From the density plots, trains' trajectories can be identified and passengers can be assigned to single trains according to the estimated location. Thus, with this model, the location of a certain train and the number of onboard passengers can be estimated, which can further enable transit agencies to improve their response to service disruptions. Since the respective final destination can also be derived from the data set, one can develop effective failure response scenarios such as the planning of contingency buses that bring passengers directly to their final destinations and thus relieves the bridging buses that are typically made available in such situations.
{"title":"Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system","authors":"Lijun Sun, Der-Horng Lee, A. Erath, Xianfeng Huang","doi":"10.1145/2346496.2346519","DOIUrl":"https://doi.org/10.1145/2346496.2346519","url":null,"abstract":"Rapid tranit systems are the most important public transportation service modes in many large cities around the world. Hence, its service reliability is of high importance for government and transit agencies. Despite taking all the necessary precautions, disruptions cannot be entirely prevented but what transit agencies can do is to prepare to respond to failure in a timely and effective manner. To this end, information about daily travel demand patterns are crucial to develop efficient failure response strategies. To the extent of urban computing, smart card data offers us the opportunity to investigate and understand the demand pattern of passengers and service level from transit operators.\u0000 In this present study, we present a methodology to analyze smart card data collected in Singapore, to describe dynamic demand characteristics of one case mass rapid transit (MRT) service. The smart card reader registers passengers when they enter and leave an MRT station. Between tapping in and out of MRT stations, passengers are either walking to and fro the platform as they alight and board on the trains or they are traveling in the train. To reveal the effective position of the passengers, a regression model based on the observations from the fastest passengers for each origin destination pair has been developed. By applying this model to all other observations, the model allows us to divide passengers in the MRT system into two groups, passengers on the trains and passengers waiting in the stations. The estimation model provides the spatio-temporal density of passengers. From the density plots, trains' trajectories can be identified and passengers can be assigned to single trains according to the estimated location.\u0000 Thus, with this model, the location of a certain train and the number of onboard passengers can be estimated, which can further enable transit agencies to improve their response to service disruptions. Since the respective final destination can also be derived from the data set, one can develop effective failure response scenarios such as the planning of contingency buses that bring passengers directly to their final destinations and thus relieves the bridging buses that are typically made available in such situations.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121401345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Barbara Furletti, L. Gabrielli, C. Renso, S. Rinzivillo
The huge quantity of positioning data registered by our mobile phones stimulates several research questions, mainly originating from the combination of this huge quantity of data with the extreme heterogeneity of the tracked user and the low granularity of the data. We propose a methodology to partition the users tracked by GSM phone calls into profiles like resident, commuters, in transit and tourists. The methodology analyses the phone calls with a combination of top-down and bottom up techniques where the top-down phase is based on a sequence of queries that identify some behaviors. The bottom-up is a machine learning phase to find groups of similar call behavior, thus refining the previous step. The integration of the two steps results in the partitioning of mobile traces into these four user categories that can be deeper analyzed, for example to understand the tourist movements in city or the traffic effects of commuters. An experiment on the identification of user profiles on a real dataset collecting call records from one month in the city of Pisa illustrates the methodology.
{"title":"Identifying users profiles from mobile calls habits","authors":"Barbara Furletti, L. Gabrielli, C. Renso, S. Rinzivillo","doi":"10.1145/2346496.2346500","DOIUrl":"https://doi.org/10.1145/2346496.2346500","url":null,"abstract":"The huge quantity of positioning data registered by our mobile phones stimulates several research questions, mainly originating from the combination of this huge quantity of data with the extreme heterogeneity of the tracked user and the low granularity of the data. We propose a methodology to partition the users tracked by GSM phone calls into profiles like resident, commuters, in transit and tourists. The methodology analyses the phone calls with a combination of top-down and bottom up techniques where the top-down phase is based on a sequence of queries that identify some behaviors. The bottom-up is a machine learning phase to find groups of similar call behavior, thus refining the previous step. The integration of the two steps results in the partitioning of mobile traces into these four user categories that can be deeper analyzed, for example to understand the tourist movements in city or the traffic effects of commuters. An experiment on the identification of user profiles on a real dataset collecting call records from one month in the city of Pisa illustrates the methodology.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130126524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
People often have the demand to decide where to wait for a taxi in order to save their time. In this paper, to address this problem, we employ the non-homogeneous Poisson process (NHPP) to model the behavior of vacant taxis. According to the statistics of the parking time of vacant taxis on the roads and the number of the vacant taxis leaving the roads in history, we can estimate the waiting time at different times on road segments. We also propose an approach to make recommendations for potential passengers on where to wait for a taxi based on our estimated waiting time. Then we evaluate our approach through the experiments on simulated passengers and actual trajectories of 12,000 taxis in Beijing. The results show that our estimation is relatively accurate and could be regarded as a reliable upper bound of the waiting time in probability. And our recommendation is a tradeoff between the waiting time and walking distance, which would bring practical assistance to potential passengers. In addition, we develop a mobile application TaxiWaiter on Android OS to help the users wait for taxis based on our approach and historical data.
{"title":"Where to wait for a taxi?","authors":"Xudong Zheng, X. Liang, Ke Xu","doi":"10.1145/2346496.2346520","DOIUrl":"https://doi.org/10.1145/2346496.2346520","url":null,"abstract":"People often have the demand to decide where to wait for a taxi in order to save their time. In this paper, to address this problem, we employ the non-homogeneous Poisson process (NHPP) to model the behavior of vacant taxis. According to the statistics of the parking time of vacant taxis on the roads and the number of the vacant taxis leaving the roads in history, we can estimate the waiting time at different times on road segments. We also propose an approach to make recommendations for potential passengers on where to wait for a taxi based on our estimated waiting time. Then we evaluate our approach through the experiments on simulated passengers and actual trajectories of 12,000 taxis in Beijing. The results show that our estimation is relatively accurate and could be regarded as a reliable upper bound of the waiting time in probability. And our recommendation is a tradeoff between the waiting time and walking distance, which would bring practical assistance to potential passengers. In addition, we develop a mobile application TaxiWaiter on Android OS to help the users wait for taxis based on our approach and historical data.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127619486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human activity behaviors in urban areas mostly occur in interior places, such as department stores, office buildings, and museums. Understanding and characterizing human spatio-temporal interactive behaviors in these indoor areas can help us evaluate the efficiency of social contacts, monitor the frequently asymptomatic diseases transmissions, and design better internal structures of buildings. In this paper, we propose a new temporal quantity: 'Participation Activity Potential' (PPA) to feature the critical roles of individuals in the populations instead of their degrees in the corresponding complex networks. Especially for the people with high degrees (hubs in the network), Participation Activity Potential which is directly from the statistics of their daily interactions, can easily feature the rank of their degree centrality and achieve as high as 100% accuracy rating without building the corresponding networks by high-complexity algorithms. The effectiveness and efficiency of our new defined quantity is validated in all three empirical data sets collected from a Chinese university campus by the WiFi technology, a small conference and an exhibitions by the RFID technology.
{"title":"Characterizing large-scale population's indoor spatio-temporal interactive behaviors","authors":"Yi-Qing Zhang, Xiang Li","doi":"10.1145/2346496.2346501","DOIUrl":"https://doi.org/10.1145/2346496.2346501","url":null,"abstract":"Human activity behaviors in urban areas mostly occur in interior places, such as department stores, office buildings, and museums. Understanding and characterizing human spatio-temporal interactive behaviors in these indoor areas can help us evaluate the efficiency of social contacts, monitor the frequently asymptomatic diseases transmissions, and design better internal structures of buildings. In this paper, we propose a new temporal quantity: 'Participation Activity Potential' (PPA) to feature the critical roles of individuals in the populations instead of their degrees in the corresponding complex networks. Especially for the people with high degrees (hubs in the network), Participation Activity Potential which is directly from the statistics of their daily interactions, can easily feature the rank of their degree centrality and achieve as high as 100% accuracy rating without building the corresponding networks by high-complexity algorithms. The effectiveness and efficiency of our new defined quantity is validated in all three empirical data sets collected from a Chinese university campus by the WiFi technology, a small conference and an exhibitions by the RFID technology.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130384613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Trajectory similarity search studies the problem of finding a trajectory from the database such the found trajectory most similar to the query trajectory. Past research mainly focused on two aspects: shape similarity search and semantic similarity search, leaving personalized similarity search untouched. In this paper, we propose a new query which takes user's preference into consideration to provide personalized searching. We define a new data model for this query and identify the efficiency issue as the key challenge: given a user specified trajectory, how to efficiently retrieve the most similar trajectory from the database. By taking advantage of the spatial localities, we develop a two-phase algorithm to tame this challenge. Two optimized strategies are also developed to speed up the query process. Both the theoretical analysis and the experiments demonstrate the high efficiency of the proposed method.
{"title":"User oriented trajectory similarity search","authors":"Haibo Wang, Kuien Liu","doi":"10.1145/2346496.2346513","DOIUrl":"https://doi.org/10.1145/2346496.2346513","url":null,"abstract":"Trajectory similarity search studies the problem of finding a trajectory from the database such the found trajectory most similar to the query trajectory. Past research mainly focused on two aspects: shape similarity search and semantic similarity search, leaving personalized similarity search untouched. In this paper, we propose a new query which takes user's preference into consideration to provide personalized searching. We define a new data model for this query and identify the efficiency issue as the key challenge: given a user specified trajectory, how to efficiently retrieve the most similar trajectory from the database. By taking advantage of the spatial localities, we develop a two-phase algorithm to tame this challenge. Two optimized strategies are also developed to speed up the query process. Both the theoretical analysis and the experiments demonstrate the high efficiency of the proposed method.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122986854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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