This work proposes a deep neural network approach known as the column-structured deep neural network (COL-DNN-R) for predicting crowd density in an indoor environment using historical Wi-Fi traces of individual visitors. With a structure designed to minimize feature engineering, COL-DNN accepts raw features such as crowd density, opening and closing hours and peak visitor counts for extracting features. The extracted features are used by a regression model R for predicting the crowd densities. Standard regression models such as MLP, RF and SVM can be used as R. Experiments are performed to investigate the effect of feature representation and model structure on the prediction accuracy. Experiment results show the best prediction accuracy is obtained using features extracted by COL-DNN and using MLP as the regression model, i.e., R = MLP.
{"title":"Predicting Indoor Crowd Density using Column-Structured Deep Neural Network","authors":"Akihito Sudo, Teck-Hou Teng, H. Lau, Y. Sekimoto","doi":"10.1145/3152341.3152349","DOIUrl":"https://doi.org/10.1145/3152341.3152349","url":null,"abstract":"This work proposes a deep neural network approach known as the column-structured deep neural network (COL-DNN-R) for predicting crowd density in an indoor environment using historical Wi-Fi traces of individual visitors. With a structure designed to minimize feature engineering, COL-DNN accepts raw features such as crowd density, opening and closing hours and peak visitor counts for extracting features. The extracted features are used by a regression model R for predicting the crowd densities. Standard regression models such as MLP, RF and SVM can be used as R. Experiments are performed to investigate the effect of feature representation and model structure on the prediction accuracy. Experiment results show the best prediction accuracy is obtained using features extracted by COL-DNN and using MLP as the regression model, i.e., R = MLP.","PeriodicalId":168922,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130192018","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}
Understanding the daily movement of humans in space and time on different granularity levels is of critical value for urban planning, transport management, health care and commercial services. However, population's daily travel behavior data was collected by travel surveys that are infrequent, expensive, and disable to reflect changes in transportation. The demand for capturing, modeling and reproducing human travel behavior in different scenarios pose a challenge on the significant delays. In this study, we propose an inverse reinforcement learning based formulation for training an agent model that enables modeling complex decision-making with consideration of a dynamic environment on the urban granularity level. The modeling framework is based on the Markov decision process to represent an individual's decision making. To obtain the travel behavior characteristics of real humans, we apply the proposed approach to a real-time GPS dataset collected via a smart phone application with more than 2 million daily users to model the people travel behavior for different daily scenarios (i.e., weekdays, weekends, and national holidays) in the Tokyo metropolitan area. It is found that the developed model can generate individual's daily travel plan. In addition, by aggregating the agent travel behavior on the city-wide scale, the urban daily travel demand can be obtained and used for estimate the hourly population distribution. The result of this work can also be regarded as a synthetic mobility dataset, avoiding many of the privacy concerns surrounding real GPS data.
{"title":"Modeling and reproducing human daily travel behavior from GPS data: A Markov Decision Process approach","authors":"Y. Pang, K. Tsubouchi, T. Yabe, Y. Sekimoto","doi":"10.1145/3152341.3152347","DOIUrl":"https://doi.org/10.1145/3152341.3152347","url":null,"abstract":"Understanding the daily movement of humans in space and time on different granularity levels is of critical value for urban planning, transport management, health care and commercial services. However, population's daily travel behavior data was collected by travel surveys that are infrequent, expensive, and disable to reflect changes in transportation. The demand for capturing, modeling and reproducing human travel behavior in different scenarios pose a challenge on the significant delays. In this study, we propose an inverse reinforcement learning based formulation for training an agent model that enables modeling complex decision-making with consideration of a dynamic environment on the urban granularity level. The modeling framework is based on the Markov decision process to represent an individual's decision making. To obtain the travel behavior characteristics of real humans, we apply the proposed approach to a real-time GPS dataset collected via a smart phone application with more than 2 million daily users to model the people travel behavior for different daily scenarios (i.e., weekdays, weekends, and national holidays) in the Tokyo metropolitan area. It is found that the developed model can generate individual's daily travel plan. In addition, by aggregating the agent travel behavior on the city-wide scale, the urban daily travel demand can be obtained and used for estimate the hourly population distribution. The result of this work can also be regarded as a synthetic mobility dataset, avoiding many of the privacy concerns surrounding real GPS data.","PeriodicalId":168922,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128266431","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}
J. Heo, Kyong-Mee Chung, Sanghyun Yoon, S. Yun, J. Ma, Sungha Ju
Higher Education is facing disruptive innovation that requires, among other things, provision of a more effective and customized education service to individual students. In the field of Learning Analytics (LA), there has been much effort, in the form of the collection and thorough analysis of a variety of student-related datasets, to optimize learning performance and environments by means of personalization. The datasets include traditional questionnaire surveys, learning management system (LMS) log data of learner activities, and, more recently in the wake of the big-data-analytics trend, unstructured datasets such as SNS activities, text data, and other transactional data. Spatial data, however, is rarely considered as a key dataset, despite its high potential for characterization of students and prediction of their performances and conditions. In this context, the authors propose a new, spatial-data-driven student-characterization research framework. This vision paper describes spatial computing in its three, descriptive, predictive, and prescriptive modeling stages as well as its three challenges: (1) technical spatial data acquisition issues; (2) legal and administrative issues; (3) expansion of the application domains in which spatial data can contribute to improved modeling quality. With respect to each challenge, the on-going efforts are briefly introduced in order to substantiate the feasibility of the proposed research framework.
{"title":"Spatial-Data-Driven Student Characterization in Higher Education","authors":"J. Heo, Kyong-Mee Chung, Sanghyun Yoon, S. Yun, J. Ma, Sungha Ju","doi":"10.1145/3152341.3152343","DOIUrl":"https://doi.org/10.1145/3152341.3152343","url":null,"abstract":"Higher Education is facing disruptive innovation that requires, among other things, provision of a more effective and customized education service to individual students. In the field of Learning Analytics (LA), there has been much effort, in the form of the collection and thorough analysis of a variety of student-related datasets, to optimize learning performance and environments by means of personalization. The datasets include traditional questionnaire surveys, learning management system (LMS) log data of learner activities, and, more recently in the wake of the big-data-analytics trend, unstructured datasets such as SNS activities, text data, and other transactional data. Spatial data, however, is rarely considered as a key dataset, despite its high potential for characterization of students and prediction of their performances and conditions. In this context, the authors propose a new, spatial-data-driven student-characterization research framework. This vision paper describes spatial computing in its three, descriptive, predictive, and prescriptive modeling stages as well as its three challenges: (1) technical spatial data acquisition issues; (2) legal and administrative issues; (3) expansion of the application domains in which spatial data can contribute to improved modeling quality. With respect to each challenge, the on-going efforts are briefly introduced in order to substantiate the feasibility of the proposed research framework.","PeriodicalId":168922,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133277258","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}
Photos shared by users on public websites (Flickr, Panoramio) provide a resource for mining behavioural data. When the photos are associated with locations and time stamps, we can reconstruct the trajectories of the users and use the resulting mobility traces for learning behaviour patterns. In this paper we focus on two aspects of mobility traces: noise filtering and semantic annotation. The extracted trajectories are initially noisy due to errors in geographical coordinates and time stamps. We show how such noise can be partially filtered and evaluate the performance of the filtering on a synthetic dataset. To make use of the mobility traces, an essential step is semantic annotation. Places or activities are associated with segments of the traces. This is frequently performed by integrating a database of relevant places and associating them by proximity. We demonstrate that the popularity of the places, if available, can improve the association accuracy. In our experiment, the accuracy of automatic annotation increases from 60% to 68%.
{"title":"Extracting Human Mobility Data from Geo-tagged Photos","authors":"P. Järv","doi":"10.1145/3152341.3152346","DOIUrl":"https://doi.org/10.1145/3152341.3152346","url":null,"abstract":"Photos shared by users on public websites (Flickr, Panoramio) provide a resource for mining behavioural data. When the photos are associated with locations and time stamps, we can reconstruct the trajectories of the users and use the resulting mobility traces for learning behaviour patterns. In this paper we focus on two aspects of mobility traces: noise filtering and semantic annotation. The extracted trajectories are initially noisy due to errors in geographical coordinates and time stamps. We show how such noise can be partially filtered and evaluate the performance of the filtering on a synthetic dataset. To make use of the mobility traces, an essential step is semantic annotation. Places or activities are associated with segments of the traces. This is frequently performed by integrating a database of relevant places and associating them by proximity. We demonstrate that the popularity of the places, if available, can improve the association accuracy. In our experiment, the accuracy of automatic annotation increases from 60% to 68%.","PeriodicalId":168922,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122327858","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}
Social media data which capture long-term personal travel activities as a set of space-time points (time series) become widely used for human mobility study. The space-time points representing individual activities are massive and need aggregation upon time dimension (besides space dimension) to show temporal mobility patterns. During the temporal aggregations, time series are sliced into different temporal layers, and the aggregation results could be impacted by four parameters, including layer size (time interval of each tempoal layer), start placement (the start time of the first layer), amount of overlap between two consecutive layers, and time series extent (temporal scope of the datasets for aggregation). Different parameterizations result in different mobility patterns, known as the "Modifiable Temporal Unit Problem" (MTUP; on the analogy of the "Modifiable Areal Unit Problem" or MAUP). While the general effects of MTUP are well examined in previous studies, MTUP is often ignored in trajectory reconstructions using sparse social media data. To fill this research gap, this paper will explore the impact of different temporal aggregation schemas (parameterizations) on the discovery of human mobility patterns using geo-tagged tweets within a 3D geospatial analytical system. The case study reveals that MTUP is significant during the process of detecting an individual's daily representative (regular) trajectories based on sparse online footprints. Comprehensive analysis on multiple aggregation results with different parameters could improve understanding of an individual's regular daily travel patterns. The interactive analytical system and visualization methods proposed by this study could minimize MTUP impact and help avoid false arguments.
{"title":"The impact of MTUP to explore online trajectories for human mobility studies","authors":"Xinyi Liu, Qunying Huang, Zhenlong Li, Meiliu Wu","doi":"10.1145/3152341.3152348","DOIUrl":"https://doi.org/10.1145/3152341.3152348","url":null,"abstract":"Social media data which capture long-term personal travel activities as a set of space-time points (time series) become widely used for human mobility study. The space-time points representing individual activities are massive and need aggregation upon time dimension (besides space dimension) to show temporal mobility patterns. During the temporal aggregations, time series are sliced into different temporal layers, and the aggregation results could be impacted by four parameters, including layer size (time interval of each tempoal layer), start placement (the start time of the first layer), amount of overlap between two consecutive layers, and time series extent (temporal scope of the datasets for aggregation). Different parameterizations result in different mobility patterns, known as the \"Modifiable Temporal Unit Problem\" (MTUP; on the analogy of the \"Modifiable Areal Unit Problem\" or MAUP). While the general effects of MTUP are well examined in previous studies, MTUP is often ignored in trajectory reconstructions using sparse social media data. To fill this research gap, this paper will explore the impact of different temporal aggregation schemas (parameterizations) on the discovery of human mobility patterns using geo-tagged tweets within a 3D geospatial analytical system. The case study reveals that MTUP is significant during the process of detecting an individual's daily representative (regular) trajectories based on sparse online footprints. Comprehensive analysis on multiple aggregation results with different parameters could improve understanding of an individual's regular daily travel patterns. The interactive analytical system and visualization methods proposed by this study could minimize MTUP impact and help avoid false arguments.","PeriodicalId":168922,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115433344","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}
Fine-grained real-time movement prediction is becoming increasingly important, with smartphones and vehicles constantly tracking our position and trying to guess our next location to timely provide us with recommendations, traffic forecasts, or driver assistance. Depending on the tracking accuracy, the recorded locations are first mapped to street segments, using a mobility model to choose the most likely road in case of ambiguities. The main prediction procedure uses a similar movement model (possibly incorporating additional user-specific data) to assess likely future travel choices. While the exact street topology is not essential on a very high level (e.g., when predicting the "next place" someone is going to be), it becomes more and more important if we try to predict the exact position of a person or vehicle. Similarly, different data sources (such as points of interest, land use zones, or building footprints) should be used for predictions at different levels of accuracy. In this paper, we assess current research trends concerning various types of volunteered geographical information (VGI), how this data can be used in different models to compute mobility predictions, and we present our vision for an integrated system that is able to use crowdsourced geographic data to perform mobility prediction at different levels.
{"title":"Vision Paper: Using Volunteered Geographic Information to Improve Mobility Prediction","authors":"D. Bucher","doi":"10.1145/3152341.3152344","DOIUrl":"https://doi.org/10.1145/3152341.3152344","url":null,"abstract":"Fine-grained real-time movement prediction is becoming increasingly important, with smartphones and vehicles constantly tracking our position and trying to guess our next location to timely provide us with recommendations, traffic forecasts, or driver assistance. Depending on the tracking accuracy, the recorded locations are first mapped to street segments, using a mobility model to choose the most likely road in case of ambiguities. The main prediction procedure uses a similar movement model (possibly incorporating additional user-specific data) to assess likely future travel choices. While the exact street topology is not essential on a very high level (e.g., when predicting the \"next place\" someone is going to be), it becomes more and more important if we try to predict the exact position of a person or vehicle. Similarly, different data sources (such as points of interest, land use zones, or building footprints) should be used for predictions at different levels of accuracy. In this paper, we assess current research trends concerning various types of volunteered geographical information (VGI), how this data can be used in different models to compute mobility predictions, and we present our vision for an integrated system that is able to use crowdsourced geographic data to perform mobility prediction at different levels.","PeriodicalId":168922,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126632310","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}
Xiliang Liu, Kang Liu, Mingxiao Li, F. Lu, Mengdi Liao, Ren Yang
Sensored traffic data in modern cities have been collected and applied for various purposes in the domain of intelligent transportation systems (ITS). However, analyzing these traffic data often lacks in priori knowledge due to the dynamics of transportation systems, making it hard to cope with diverse scenarios with specific models. In view of the limitations of traditional approaches, in this paper, we propose the Stepwise Heterogeneous Ensemble (SHE) for citywide traffic analysis based on stacked generalization. We first prove SHE's effectiveness using error-ambiguity decomposition technique. Secondly we analyze the optimal linear combination of SHE and present the stepwise iterating strategy. We also demonstrate its validity based on Kullback-Leibler divergence analysis. Thirdly we integrate six classical approaches into SHE framework, including linear least squares regression (LLSR), autoregressive moving average (ARMA), historical mean (HM), artificial neural network (ANN), radical basis function neural network (RBFNN), support vector machine (SVM). We further compare SHE's performance with other four linear combination models, namely equal weights method (EW), optimal weights method (OW), minimum error method (ME) and minimum variance method (MV). A series of experiments are conducted with a real city traffic dataset in Beijing city. The results show that the proposed SHE method behaves more robust and precise than other six single methods. Moreover, this method also outperforms other four different combination strategies both in variance and bias. In addition, the SHE method provides an open-ending framework for citywide traffic analysis, which means any new promising models can be easily incorporated into it in the future.
{"title":"SHE: Stepwise Heterogeneous Ensemble Method for Citywide Traffic Analysis","authors":"Xiliang Liu, Kang Liu, Mingxiao Li, F. Lu, Mengdi Liao, Ren Yang","doi":"10.1145/3152341.3152345","DOIUrl":"https://doi.org/10.1145/3152341.3152345","url":null,"abstract":"Sensored traffic data in modern cities have been collected and applied for various purposes in the domain of intelligent transportation systems (ITS). However, analyzing these traffic data often lacks in priori knowledge due to the dynamics of transportation systems, making it hard to cope with diverse scenarios with specific models. In view of the limitations of traditional approaches, in this paper, we propose the Stepwise Heterogeneous Ensemble (SHE) for citywide traffic analysis based on stacked generalization. We first prove SHE's effectiveness using error-ambiguity decomposition technique. Secondly we analyze the optimal linear combination of SHE and present the stepwise iterating strategy. We also demonstrate its validity based on Kullback-Leibler divergence analysis. Thirdly we integrate six classical approaches into SHE framework, including linear least squares regression (LLSR), autoregressive moving average (ARMA), historical mean (HM), artificial neural network (ANN), radical basis function neural network (RBFNN), support vector machine (SVM). We further compare SHE's performance with other four linear combination models, namely equal weights method (EW), optimal weights method (OW), minimum error method (ME) and minimum variance method (MV). A series of experiments are conducted with a real city traffic dataset in Beijing city. The results show that the proposed SHE method behaves more robust and precise than other six single methods. Moreover, this method also outperforms other four different combination strategies both in variance and bias. In addition, the SHE method provides an open-ending framework for citywide traffic analysis, which means any new promising models can be easily incorporated into it in the future.","PeriodicalId":168922,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115381587","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}
{"title":"Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility","authors":"","doi":"10.1145/3152341","DOIUrl":"https://doi.org/10.1145/3152341","url":null,"abstract":"","PeriodicalId":168922,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123973843","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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