V. Frías-Martínez, C. Soguero-Ruíz, E. Frías-Martínez
Commuting matrices are key for a variety of fields, including transportation engineering and urban planning. Up to now, these matrices have been typically generated from data obtained from surveys. Nevertheless, such approaches typically involve high costs which limits the frequency of the studies. Cell phones can be considered one of the main sensors of human behavior due to its ubiquity, and as a such, a pervasive source of mobility information at a large scale. In this paper we propose a new technique for the estimation of commuting matrices using the data collected from the pervasive infrastructure of a cell phone network. Our goal is to show that we can construct cell-phone generated matrices that capture the same patterns as traditional commuting matrices. In order to do so we use optimization techniques in combination with a variation of Temporal Association Rules. Our validation results show that it is possible to construct commuting matrices from call detail records with a high degree of accuracy, and as a result our technique is a cost-effective solution to complement traditional approaches.
{"title":"Estimation of urban commuting patterns using cellphone network data","authors":"V. Frías-Martínez, C. Soguero-Ruíz, E. Frías-Martínez","doi":"10.1145/2346496.2346499","DOIUrl":"https://doi.org/10.1145/2346496.2346499","url":null,"abstract":"Commuting matrices are key for a variety of fields, including transportation engineering and urban planning. Up to now, these matrices have been typically generated from data obtained from surveys. Nevertheless, such approaches typically involve high costs which limits the frequency of the studies. Cell phones can be considered one of the main sensors of human behavior due to its ubiquity, and as a such, a pervasive source of mobility information at a large scale. In this paper we propose a new technique for the estimation of commuting matrices using the data collected from the pervasive infrastructure of a cell phone network. Our goal is to show that we can construct cell-phone generated matrices that capture the same patterns as traditional commuting matrices. In order to do so we use optimization techniques in combination with a variation of Temporal Association Rules. Our validation results show that it is possible to construct commuting matrices from call detail records with a high degree of accuracy, and as a result our technique is a cost-effective solution to complement traditional approaches.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"10 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":"115074849","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}
Volumes of urban sensing data captured by consumer electronic devices are growing exponentially and current disk-resident database systems are becoming increasingly incapable of handling such large-scale data efficiently. In this paper, we report our design and implementation of U2SOD-DB, a column-oriented, Graphics Processing Unit (GPU)-accelerated, in-memory data management system targeted at large-scale ubiquitous urban sensing origin-destination data. Experiment results show that U2SOD-DB is capable of handling hundreds of millions of taxi-trip records with GPS recorded pickup and drop-off locations and times efficiently. Spatial and temporal aggregations on 150 million pickup locations and times in middle-town and downtown Manhattan areas in the New York City (NYC) can be completed in a fraction of a second. This is 10-30X faster than a serial CPU implementation due to GPU accelerations. Spatially joining the 150 million taxi pickup locations with 43 thousand polygons in identifying trip purposes has reduced the runtime from 30.5 hours to around 1,000 seconds and achieved a two orders (100X) speedup using a hybrid CPU-GPU approach.
{"title":"U2SOD-DB: a database system to manage large-scale <u>u</u>biquitous <u>u</u>rban <u>s</u>ensing <u>o</u>rigin-<u>d</u>estination data","authors":"Jianting Zhang, C. Kamga, H. Gong, L. Gruenwald","doi":"10.1145/2346496.2346522","DOIUrl":"https://doi.org/10.1145/2346496.2346522","url":null,"abstract":"Volumes of urban sensing data captured by consumer electronic devices are growing exponentially and current disk-resident database systems are becoming increasingly incapable of handling such large-scale data efficiently. In this paper, we report our design and implementation of U2SOD-DB, a column-oriented, Graphics Processing Unit (GPU)-accelerated, in-memory data management system targeted at large-scale ubiquitous urban sensing origin-destination data. Experiment results show that U2SOD-DB is capable of handling hundreds of millions of taxi-trip records with GPS recorded pickup and drop-off locations and times efficiently. Spatial and temporal aggregations on 150 million pickup locations and times in middle-town and downtown Manhattan areas in the New York City (NYC) can be completed in a fraction of a second. This is 10-30X faster than a serial CPU implementation due to GPU accelerations. Spatially joining the 150 million taxi pickup locations with 43 thousand polygons in identifying trip purposes has reduced the runtime from 30.5 hours to around 1,000 seconds and achieved a two orders (100X) speedup using a hybrid CPU-GPU approach.","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":"133128491","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}
This paper explores the smart city concept and proposes an innovative way of sensing urban places' life using aggregation of devices sensors (cameras...) and human sensors (VGI, geosocial networks) datasets. The paper also discusses the need of an enabling geospatial information platform to facilitate data discovery and access in order to support smart cities' operations. Indeed, in this context, Spatial Data Infrastructure plays an important role and acts as an enabling platform linking governments authoritative spatial information with crowd sourced, voluntary information initiatives.
{"title":"Sensing places' life to make city smarter","authors":"Stéphane Roche, A. Rajabifard","doi":"10.1145/2346496.2346503","DOIUrl":"https://doi.org/10.1145/2346496.2346503","url":null,"abstract":"This paper explores the smart city concept and proposes an innovative way of sensing urban places' life using aggregation of devices sensors (cameras...) and human sensors (VGI, geosocial networks) datasets. The paper also discusses the need of an enabling geospatial information platform to facilitate data discovery and access in order to support smart cities' operations. Indeed, in this context, Spatial Data Infrastructure plays an important role and acts as an enabling platform linking governments authoritative spatial information with crowd sourced, voluntary information initiatives.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"5 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":"115398040","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}
We introduce our vision for mining fine-grained urban traffic knowledge from mobile sensing, especially GPS location traces. Beyond characterizing human mobility patterns and measuring traffic congestion, we show how mobile sensing can also reveal details such as intersection performance statistics that are useful for optimizing the timing of a traffic signal. Realizing such applications requires co-designing privacy protection algorithms and novel traffic modeling techniques so that the needs for privacy preserving and traffic modeling can be simultaneously satisfied. We explore privacy algorithms based on the virtual trip lines (VTL) concept to regulate where and when the mobile data should be collected. The traffic modeling techniques feature an integration of traffic principles and learning/optimization techniques. The proposed methods are illustrated using two case studies for extracting traffic knowledge for urban signalized intersection.
{"title":"Towards fine-grained urban traffic knowledge extraction using mobile sensing","authors":"X. Ban, M. Gruteser","doi":"10.1145/2346496.2346514","DOIUrl":"https://doi.org/10.1145/2346496.2346514","url":null,"abstract":"We introduce our vision for mining fine-grained urban traffic knowledge from mobile sensing, especially GPS location traces. Beyond characterizing human mobility patterns and measuring traffic congestion, we show how mobile sensing can also reveal details such as intersection performance statistics that are useful for optimizing the timing of a traffic signal. Realizing such applications requires co-designing privacy protection algorithms and novel traffic modeling techniques so that the needs for privacy preserving and traffic modeling can be simultaneously satisfied. We explore privacy algorithms based on the virtual trip lines (VTL) concept to regulate where and when the mobile data should be collected. The traffic modeling techniques feature an integration of traffic principles and learning/optimization techniques. The proposed methods are illustrated using two case studies for extracting traffic knowledge for urban signalized intersection.","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":"125834512","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}
Outlier detection in large-scale taxi trip records has imposed significant technical challenges due to huge data volumes and complex semantics. In this paper, we report our preliminary work on detecting outliers from 166 millions taxi trips in the New York City (NYC) in 2009 through efficient spatial analysis and network analysis using a NAVTEQ street network with half a million edges. As a byproduct of large-scale shortest path computation in outlier detection, betweenness centralities of street network edges are computed and mapped. The techniques can be used to help better understand the connection strengths among different parts of NYC using the large-scale taxi trip records.
{"title":"Smarter outlier detection and deeper understanding of large-scale taxi trip records: a case study of NYC","authors":"Jianting Zhang","doi":"10.1145/2346496.2346521","DOIUrl":"https://doi.org/10.1145/2346496.2346521","url":null,"abstract":"Outlier detection in large-scale taxi trip records has imposed significant technical challenges due to huge data volumes and complex semantics. In this paper, we report our preliminary work on detecting outliers from 166 millions taxi trips in the New York City (NYC) in 2009 through efficient spatial analysis and network analysis using a NAVTEQ street network with half a million edges. As a byproduct of large-scale shortest path computation in outlier detection, betweenness centralities of street network edges are computed and mapped. The techniques can be used to help better understand the connection strengths among different parts of NYC using the large-scale taxi trip records.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"45 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":"127401825","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. Momtazpour, P. Butler, M. S. Hossain, M. C. Bozchalui, Naren Ramakrishnan, Ratnesh K. Sharma
The confluence of several developments has created an opportune moment for energy system modernization. In the past decade, smart grids have attracted many research activities in different domains. To realize the next generation of smart grids, we must have a comprehensive understanding of interdependent networks and processes. Next-generation energy systems networks cannot be effectively designed, analyzed, and controlled in isolation from the social, economic, sensing, and control contexts in which they operate. In this paper, we develop coordinated clustering techniques to work with network models of urban environments to aid in placement of charging stations for an electrical vehicle deployment scenario. We demonstrate the multiple factors that can be simultaneously leveraged in our framework in order to achieve practical urban deployment. Our ultimate goal is to help realize sustainable energy system management in urban electrical infrastructure by modeling and analyzing networks of interactions between electric systems and urban populations.
{"title":"Coordinated clustering algorithms to support charging infrastructure design for electric vehicles","authors":"M. Momtazpour, P. Butler, M. S. Hossain, M. C. Bozchalui, Naren Ramakrishnan, Ratnesh K. Sharma","doi":"10.1145/2346496.2346517","DOIUrl":"https://doi.org/10.1145/2346496.2346517","url":null,"abstract":"The confluence of several developments has created an opportune moment for energy system modernization. In the past decade, smart grids have attracted many research activities in different domains. To realize the next generation of smart grids, we must have a comprehensive understanding of interdependent networks and processes. Next-generation energy systems networks cannot be effectively designed, analyzed, and controlled in isolation from the social, economic, sensing, and control contexts in which they operate. In this paper, we develop coordinated clustering techniques to work with network models of urban environments to aid in placement of charging stations for an electrical vehicle deployment scenario. We demonstrate the multiple factors that can be simultaneously leveraged in our framework in order to achieve practical urban deployment. Our ultimate goal is to help realize sustainable energy system management in urban electrical infrastructure by modeling and analyzing networks of interactions between electric systems and urban populations.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"49 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":"121717511","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}
Huajian Mao, Wuman Luo, Haoyu Tan, L. Ni, Nong Xiao
To enable smart transportation, a large volume of vehicular GPS trajectory data has been collected in the metropolitan-scale Shanghai Grid project. The collected raw GPS data, however, suffers from various errors. Thus, it is inappropriate to use the raw GPS dataset directly for many potential smart transportation applications. Map matching, a process to align the raw GPS data onto the corresponding road network, is a commonly used technique to calibrate the raw GPS data. In practice, however, there is no ground truth data to validate the calibrated GPS data. It is necessary and desirable to have ground truth data to evaluate the effectiveness of various map matching algorithms, especially in complex environments. In this paper, we propose truthFinder, an interactive map matching system for ground truth data exploration. It incorporates traditional map matching algorithms and human intelligence in a unified manner. The accuracy of truthFinder is guaranteed by the observation that a vehicular trajectory can be correctly identified by human-labeling with the help of a period of historical GPS dataset. To the best of our knowledge, truthFinder is the first interactive map matching system trying to explore the ground truth from historical GPS trajectory data. To measure the cost of human interactions, we design a cost model that classifies and quantifies user operations. Having the guaranteed accuracy, truthFinder is evaluated in terms of operation cost. The results show that truthFinder makes the cost of map matching process up to two orders of magnitude less than the pure human-labeling approach.
{"title":"Exploration of ground truth from raw GPS data","authors":"Huajian Mao, Wuman Luo, Haoyu Tan, L. Ni, Nong Xiao","doi":"10.1145/2346496.2346515","DOIUrl":"https://doi.org/10.1145/2346496.2346515","url":null,"abstract":"To enable smart transportation, a large volume of vehicular GPS trajectory data has been collected in the metropolitan-scale Shanghai Grid project. The collected raw GPS data, however, suffers from various errors. Thus, it is inappropriate to use the raw GPS dataset directly for many potential smart transportation applications. Map matching, a process to align the raw GPS data onto the corresponding road network, is a commonly used technique to calibrate the raw GPS data. In practice, however, there is no ground truth data to validate the calibrated GPS data. It is necessary and desirable to have ground truth data to evaluate the effectiveness of various map matching algorithms, especially in complex environments. In this paper, we propose truthFinder, an interactive map matching system for ground truth data exploration. It incorporates traditional map matching algorithms and human intelligence in a unified manner. The accuracy of truthFinder is guaranteed by the observation that a vehicular trajectory can be correctly identified by human-labeling with the help of a period of historical GPS dataset. To the best of our knowledge, truthFinder is the first interactive map matching system trying to explore the ground truth from historical GPS trajectory data. To measure the cost of human interactions, we design a cost model that classifies and quantifies user operations. Having the guaranteed accuracy, truthFinder is evaluated in terms of operation cost. The results show that truthFinder makes the cost of map matching process up to two orders of magnitude less than the pure human-labeling approach.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"122 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":"117315325","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}
Using sensor data from fixed highway traffic detectors, as well as data from highway patrol logs and local weather stations, we aim to answer the domain problem: "A traffic incident just occurred. How severe will its impact be?" In this paper we show a practical system for predicting the cost and impact of highway incidents using classification models trained on sensor data and police reports. Our models are built on an understanding of the spatial and temporal patterns of the expected state of traffic at different times of day and locations and past incidents. With high accuracy, our model can predict false reports of incidents that are made to the highway patrol and classify the duration of the incident-induced delays and the magnitude of the incident impact, measured as a function of vehicles delayed, the spatial and temporal extent of the incident. Equipped with our predictions of traffic incident costs and relative impacts, highway operators and first responders will be able to more effectively respond to reports of highway incidents, ultimately improving drivers' welfare and reducing urban congestion.
{"title":"Mining traffic incidents to forecast impact","authors":"Mahalia Miller, Chetan Gupta","doi":"10.1145/2346496.2346502","DOIUrl":"https://doi.org/10.1145/2346496.2346502","url":null,"abstract":"Using sensor data from fixed highway traffic detectors, as well as data from highway patrol logs and local weather stations, we aim to answer the domain problem: \"A traffic incident just occurred. How severe will its impact be?\" In this paper we show a practical system for predicting the cost and impact of highway incidents using classification models trained on sensor data and police reports. Our models are built on an understanding of the spatial and temporal patterns of the expected state of traffic at different times of day and locations and past incidents. With high accuracy, our model can predict false reports of incidents that are made to the highway patrol and classify the duration of the incident-induced delays and the magnitude of the incident impact, measured as a function of vehicles delayed, the spatial and temporal extent of the incident. Equipped with our predictions of traffic incident costs and relative impacts, highway operators and first responders will be able to more effectively respond to reports of highway incidents, ultimately improving drivers' welfare and reducing urban congestion.","PeriodicalId":350527,"journal":{"name":"UrbComp '12","volume":"17 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":"117316236","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}
Compared with traditional itinerary planning, intention oriented itinerary recommendation can provide more flexible activity planning without the user pre-determined destinations and is specially helpful for those strangers in unfamiliar environment. Rank and classification of points of interest (POI) from location based social networks (LBSN) are used to indicate different user intentions. Mining on physical trajectories of vehicles can provide exact civil traffic information for path planning. In this paper, a POI category-based itinerary recommendation framework combining physical trajectories with LBSN is proposed. Specifically, a Voronoi graph based GPS trajectory analysis method is proposed to build traffic information networks, and an ant colony algorithm for multi-object optimization is also implemented to find the most appropriate itineraries. We conduct experiments on datasets from FourSquare and Geo-Life project. A test on satisfaction of recommended items is also performed. Results show that the satisfaction reaches 80% in average.
{"title":"Intention oriented itinerary recommendation by bridging physical trajectories and online social networks","authors":"Xiangxu Meng, Xinye Lin, Xiaodong Wang","doi":"10.1145/2346496.2346508","DOIUrl":"https://doi.org/10.1145/2346496.2346508","url":null,"abstract":"Compared with traditional itinerary planning, intention oriented itinerary recommendation can provide more flexible activity planning without the user pre-determined destinations and is specially helpful for those strangers in unfamiliar environment. Rank and classification of points of interest (POI) from location based social networks (LBSN) are used to indicate different user intentions. Mining on physical trajectories of vehicles can provide exact civil traffic information for path planning. In this paper, a POI category-based itinerary recommendation framework combining physical trajectories with LBSN is proposed. Specifically, a Voronoi graph based GPS trajectory analysis method is proposed to build traffic information networks, and an ant colony algorithm for multi-object optimization is also implemented to find the most appropriate itineraries. We conduct experiments on datasets from FourSquare and Geo-Life project. A test on satisfaction of recommended items is also performed. Results show that the satisfaction reaches 80% in average.","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":"129073379","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}
For people travelling using public transport, overcrowding is one of the major causes of discomfort. However, most Advanced Traveller Information Systems (ATIS) do not take crowdedness into account, suggesting routes either based on number of interchanges or overall travel time, regardless of how comfortable (in terms of crowdedness) the trip might be. Identifying times when public transport is overcrowded could help travellers change their travel patterns, by either travelling slightly earlier or later, or by travelling from/to a different but geographically close station. In this paper, we illustrate how historical automated fare collection systems data can be mined in order to reveal station crowding patterns. In particular, we study one such dataset of travel history on the London underground (known colloquially as the "Tube"). Our spatio-temporal analysis demonstrates that crowdedness is a highly regular phenomenon during the working week, with large spikes occurring in short time intervals. We then illustrate how crowding levels can be accurately predicted, even with simple techniques based on historic averages. These results demonstrate that information regarding crowding levels can be incorporated within ATIS, so as to provide travellers with more personalised travel plans.
{"title":"Avoiding the crowds: understanding Tube station congestion patterns from trip data","authors":"Irina Ceapa, Chris Smith, L. Capra","doi":"10.1145/2346496.2346518","DOIUrl":"https://doi.org/10.1145/2346496.2346518","url":null,"abstract":"For people travelling using public transport, overcrowding is one of the major causes of discomfort. However, most Advanced Traveller Information Systems (ATIS) do not take crowdedness into account, suggesting routes either based on number of interchanges or overall travel time, regardless of how comfortable (in terms of crowdedness) the trip might be. Identifying times when public transport is overcrowded could help travellers change their travel patterns, by either travelling slightly earlier or later, or by travelling from/to a different but geographically close station. In this paper, we illustrate how historical automated fare collection systems data can be mined in order to reveal station crowding patterns. In particular, we study one such dataset of travel history on the London underground (known colloquially as the \"Tube\"). Our spatio-temporal analysis demonstrates that crowdedness is a highly regular phenomenon during the working week, with large spikes occurring in short time intervals. We then illustrate how crowding levels can be accurately predicted, even with simple techniques based on historic averages. These results demonstrate that information regarding crowding levels can be incorporated within ATIS, so as to provide travellers with more personalised travel plans.","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":"125803282","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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