Mining GPS trajectories of moving vehicles has led to many research directions, such as traffic modeling and driving predication. An important challenge is how to map GPS traces to a road network accurately under noisy conditions. However, to the best of our knowledge, there is no existing work that first simplifies a trajectory to improve map matching. In this paper we propose three trajectory simplification algorithms that can deal with both offline and online trajectory data. We use weighting functions to incorporate spatial knowledge, such as segment lengths and turning angles, into our simplification algorithms. In addition, we measure the noise degree of a GPS point based on its spatio-temporal relationship to its neighbors. The effectiveness of our algorithms is comprehensively evaluated on real trajectory datasets with varying the noise levels and sampling rates. Our evaluation shows that under highly noisy conditions, our proposed algorithms considerably improve map matching accuracy and reduce computational costs compared to the state-of-the-art methods.
{"title":"Spatio-temporal trajectory simplification for inferring travel paths","authors":"Hengfeng Li, L. Kulik, K. Ramamohanarao","doi":"10.1145/2666310.2666409","DOIUrl":"https://doi.org/10.1145/2666310.2666409","url":null,"abstract":"Mining GPS trajectories of moving vehicles has led to many research directions, such as traffic modeling and driving predication. An important challenge is how to map GPS traces to a road network accurately under noisy conditions. However, to the best of our knowledge, there is no existing work that first simplifies a trajectory to improve map matching. In this paper we propose three trajectory simplification algorithms that can deal with both offline and online trajectory data. We use weighting functions to incorporate spatial knowledge, such as segment lengths and turning angles, into our simplification algorithms. In addition, we measure the noise degree of a GPS point based on its spatio-temporal relationship to its neighbors. The effectiveness of our algorithms is comprehensively evaluated on real trajectory datasets with varying the noise levels and sampling rates. Our evaluation shows that under highly noisy conditions, our proposed algorithms considerably improve map matching accuracy and reduce computational costs compared to the state-of-the-art methods.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"123 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114026737","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}
Ahmed R. Mahmood, Walid G. Aref, Ahmed M. Aly, Saleh M. Basalamah
The plethora of lacation-aware devices has led to countless location-based services in which huge amounts of spatio-temporal data get created everyday. Several applications requie efficient processing of queries on the locations of moving objects over time, i.e., the moving object trajectories. This calls for efficient trajectory-based indexing methods that capture both the spatial and temporal dimensions of the data in a way that minimizes the number of disk I/Os required for both updating and querying. Motivated by applications that require only the recent history of a moving object's trajectory, this paper introduces the trails-tree; a disk-based data structure for indexing recent trajectories. The trails-tree maintains a temporal-sliding window over the trajectories and uses: (1) an in-memory memo structure that reduces the I/O cost of updates using a lazy-update mechanism, and (2) a lazy vacuum-cleaning mechanism to delete parts of the trajectories that fall out of the sliding window. Experimental evaluation illustrates that the trails-tree outperforms the state-of-the-art index structures for indexing recent trajectory data by up to a factor of two.
{"title":"Indexing recent trajectories of moving objects","authors":"Ahmed R. Mahmood, Walid G. Aref, Ahmed M. Aly, Saleh M. Basalamah","doi":"10.1145/2666310.2666427","DOIUrl":"https://doi.org/10.1145/2666310.2666427","url":null,"abstract":"The plethora of lacation-aware devices has led to countless location-based services in which huge amounts of spatio-temporal data get created everyday. Several applications requie efficient processing of queries on the locations of moving objects over time, i.e., the moving object trajectories. This calls for efficient trajectory-based indexing methods that capture both the spatial and temporal dimensions of the data in a way that minimizes the number of disk I/Os required for both updating and querying. Motivated by applications that require only the recent history of a moving object's trajectory, this paper introduces the trails-tree; a disk-based data structure for indexing recent trajectories. The trails-tree maintains a temporal-sliding window over the trajectories and uses: (1) an in-memory memo structure that reduces the I/O cost of updates using a lazy-update mechanism, and (2) a lazy vacuum-cleaning mechanism to delete parts of the trajectories that fall out of the sliding window. Experimental evaluation illustrates that the trails-tree outperforms the state-of-the-art index structures for indexing recent trajectory data by up to a factor of two.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131290070","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}
Ludovic Moncla, Walter Renteria-Agualimpia, J. Nogueras-Iso, M. Gaio
Geoparsing and geocoding are two essential middleware services to facilitate final user applications such as location-aware searching or different types of location-based services. The objective of this work is to propose a method for establishing a processing chain to support the geoparsing and geocoding of text documents describing events strongly linked with space and with a frequent use of fine-grain toponyms. The geoparsing part is a Natural Language Processing approach which combines the use of part of speech and syntactico-semantic combined patterns (cascade of transducers). However, the real novelty of this work lies in the geocoding method. The geocoding algorithm is unsupervised and takes profit of clustering techniques to provide a solution for disambiguating the toponyms found in gazetteers, and at the same time estimating the spatial footprint of those other fine-grain toponyms not found in gazetteers. The feasibility of the proposal has been tested with a corpus of hiking descriptions in French, Spanish and Italian.
{"title":"Geocoding for texts with fine-grain toponyms: an experiment on a geoparsed hiking descriptions corpus","authors":"Ludovic Moncla, Walter Renteria-Agualimpia, J. Nogueras-Iso, M. Gaio","doi":"10.1145/2666310.2666386","DOIUrl":"https://doi.org/10.1145/2666310.2666386","url":null,"abstract":"Geoparsing and geocoding are two essential middleware services to facilitate final user applications such as location-aware searching or different types of location-based services. The objective of this work is to propose a method for establishing a processing chain to support the geoparsing and geocoding of text documents describing events strongly linked with space and with a frequent use of fine-grain toponyms. The geoparsing part is a Natural Language Processing approach which combines the use of part of speech and syntactico-semantic combined patterns (cascade of transducers). However, the real novelty of this work lies in the geocoding method. The geocoding algorithm is unsupervised and takes profit of clustering techniques to provide a solution for disambiguating the toponyms found in gazetteers, and at the same time estimating the spatial footprint of those other fine-grain toponyms not found in gazetteers. The feasibility of the proposal has been tested with a corpus of hiking descriptions in French, Spanish and Italian.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131562362","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}
Tobler's First Law of Geography (TFL) is one of the key reasons why "spatial is special". The law, which states that "everything is related to everything else, but near things are more related than distant things", is central to the management, presentation, and analysis of geographic information. However, despite the importance of TFL, we have a limited general understanding of its domain-neutral properties. In this paper, we leverage recent advances in the natural language processing domain of semantic relatedness estimation to, for the first time, robustly evaluate the extent to which relatedness between spatial entities decreases over distance in a domain-neutral fashion. Our results reveal that, in general, TFL can indeed be considered a globally recognized domain-neutral property of geographic information but that there is a distance beyond which being nearer, on average, no longer means being more related.
{"title":"Leveraging advances in natural language processing to better understand Tobler's first law of geography","authors":"Toby Jia-Jun Li, Shilad Sen, Brent J. Hecht","doi":"10.1145/2666310.2666493","DOIUrl":"https://doi.org/10.1145/2666310.2666493","url":null,"abstract":"Tobler's First Law of Geography (TFL) is one of the key reasons why \"spatial is special\". The law, which states that \"everything is related to everything else, but near things are more related than distant things\", is central to the management, presentation, and analysis of geographic information. However, despite the importance of TFL, we have a limited general understanding of its domain-neutral properties. In this paper, we leverage recent advances in the natural language processing domain of semantic relatedness estimation to, for the first time, robustly evaluate the extent to which relatedness between spatial entities decreases over distance in a domain-neutral fashion. Our results reveal that, in general, TFL can indeed be considered a globally recognized domain-neutral property of geographic information but that there is a distance beyond which being nearer, on average, no longer means being more related.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115536758","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 a framework to create a world-wide live map of public transit, i.e. the real-time movement of all buses, subways, trains and ferries. Our system is based on freely available General Transit Feed Specification (GTFS) timetable data and also features real-time delay information (where available). The main problem of such a live tracker is the enormous amount of data that has to be handled (millions of vehicle movements). We present a highly efficient back-end that accepts temporal and spatial boundaries and returns all relevant trajectories and vehicles in a format that allows for easy rendering by the client. The real-time movement visualization of complete transit networks allows to observe the current state of the system, to estimate the transit coverage of certain areas, to display delays in a neat manner, and to inform a mobile user about near-by vehicles. Our system can be accessed via http://tracker.geops.ch/. The current implementation features over 80 transit networks, including the complete Netherlands (with real-time delay data), and various metropolitan areas in the US, Europe, Australia and New Zealand. We continuously integrate new data. Especially for Europe and North America we expect to achieve almost full coverage soon.
{"title":"Real-time movement visualization of public transit data","authors":"H. Bast, P. Brosi, Sabine Storandt","doi":"10.1145/2666310.2666404","DOIUrl":"https://doi.org/10.1145/2666310.2666404","url":null,"abstract":"We introduce a framework to create a world-wide live map of public transit, i.e. the real-time movement of all buses, subways, trains and ferries. Our system is based on freely available General Transit Feed Specification (GTFS) timetable data and also features real-time delay information (where available). The main problem of such a live tracker is the enormous amount of data that has to be handled (millions of vehicle movements). We present a highly efficient back-end that accepts temporal and spatial boundaries and returns all relevant trajectories and vehicles in a format that allows for easy rendering by the client. The real-time movement visualization of complete transit networks allows to observe the current state of the system, to estimate the transit coverage of certain areas, to display delays in a neat manner, and to inform a mobile user about near-by vehicles. Our system can be accessed via http://tracker.geops.ch/. The current implementation features over 80 transit networks, including the complete Netherlands (with real-time delay data), and various metropolitan areas in the US, Europe, Australia and New Zealand. We continuously integrate new data. Especially for Europe and North America we expect to achieve almost full coverage soon.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130129807","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}
Historical traffic information is valuable for transportation analysis and planning, as well as for route search services. In view of these applications, we propose the k traffic-tolerant paths problem (TTP) on road networks, which takes a source-destination pair and historical traffic information as input, and returns k paths that minimize the aggregate (historical) travel time. Unlike the shortest path problem, the TTP problem has a combinatorial search space that renders the optimal solution expensive to compute. We propose an exact algorithm and a heuristic algorithm for this problem. Experiments on real traffic data demonstrate the effectiveness of TTP paths and the efficiency of our proposed algorithms.
{"title":"Historical traffic-tolerant paths in road networks","authors":"Pui Hang Li, Man Lung Yiu, K. Mouratidis","doi":"10.1145/2666310.2666483","DOIUrl":"https://doi.org/10.1145/2666310.2666483","url":null,"abstract":"Historical traffic information is valuable for transportation analysis and planning, as well as for route search services. In view of these applications, we propose the k traffic-tolerant paths problem (TTP) on road networks, which takes a source-destination pair and historical traffic information as input, and returns k paths that minimize the aggregate (historical) travel time. Unlike the shortest path problem, the TTP problem has a combinatorial search space that renders the optimal solution expensive to compute. We propose an exact algorithm and a heuristic algorithm for this problem. Experiments on real traffic data demonstrate the effectiveness of TTP paths and the efficiency of our proposed algorithms.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130830445","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}
The problem of finding an electric vehicle route that optimizes both driving time and energy consumption can be modeled as a bicriterion path problem. Unfortunately, the problem of finding optimal bicriterion paths is NP-complete. This paper studies such problems restricted to two-phase paths, which correspond to a common way people drive electric vehicles, where a driver uses one driving style (say, minimizing driving time) at the beginning of a route and another driving style (say, minimizing energy consumption) at the end. We provide efficient polynomial-time algorithms for finding optimal two-phase paths in bicriterion networks, and we empirically verify the effectiveness of these algorithms for finding good electric vehicle driving routes in the road networks of various U.S. states. In addition, we show how to incorporate charging stations into these algorithms.
{"title":"Two-phase bicriterion search for finding fast and efficient electric vehicle routes","authors":"M. Goodrich, Pawel Pszona","doi":"10.1145/2666310.2666382","DOIUrl":"https://doi.org/10.1145/2666310.2666382","url":null,"abstract":"The problem of finding an electric vehicle route that optimizes both driving time and energy consumption can be modeled as a bicriterion path problem. Unfortunately, the problem of finding optimal bicriterion paths is NP-complete. This paper studies such problems restricted to two-phase paths, which correspond to a common way people drive electric vehicles, where a driver uses one driving style (say, minimizing driving time) at the beginning of a route and another driving style (say, minimizing energy consumption) at the end. We provide efficient polynomial-time algorithms for finding optimal two-phase paths in bicriterion networks, and we empirically verify the effectiveness of these algorithms for finding good electric vehicle driving routes in the road networks of various U.S. states. In addition, we show how to incorporate charging stations into these algorithms.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121762256","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}
Georgios Skoumas, Klaus Arthur Schmid, Gregor Jossé, Andreas Züfle, M. Nascimento, M. Renz, D. Pfoser
Directions and paths, as commonly provided by navigation systems, are usually derived considering absolute metrics, e.g., finding the shortest path within an underlying road network. With the aid of crowdsourced geospatial data we aim at obtaining paths that do not only minimize distance but also lead through more popular areas using knowledge generated by users. We extract spatial relations such as "nearby" or "next to" from geo-textual travel blogs, that define closeness between pairs of points of interest (POIs) and quantify each of these relations using a probabilistic model. Using Bayesian inference, we obtain a probabilistic measure of spatial closeness according to the crowd. Applying this measure to the corresponding road network, we derive an altered cost function taking crowdsourced spatial relations into account. We propose two routing algorithms on the enriched road networks. To evaluate our approach, we use Flickr photo data as a ground truth for popularity. Our experimental results -- based on real world datasets -- show that the computed paths yield competitive solutions in terms of path length while also providing more "popular" paths, making routing easier and more informative for the user.
{"title":"Towards knowledge-enriched path computation","authors":"Georgios Skoumas, Klaus Arthur Schmid, Gregor Jossé, Andreas Züfle, M. Nascimento, M. Renz, D. Pfoser","doi":"10.1145/2666310.2666485","DOIUrl":"https://doi.org/10.1145/2666310.2666485","url":null,"abstract":"Directions and paths, as commonly provided by navigation systems, are usually derived considering absolute metrics, e.g., finding the shortest path within an underlying road network. With the aid of crowdsourced geospatial data we aim at obtaining paths that do not only minimize distance but also lead through more popular areas using knowledge generated by users. We extract spatial relations such as \"nearby\" or \"next to\" from geo-textual travel blogs, that define closeness between pairs of points of interest (POIs) and quantify each of these relations using a probabilistic model. Using Bayesian inference, we obtain a probabilistic measure of spatial closeness according to the crowd. Applying this measure to the corresponding road network, we derive an altered cost function taking crowdsourced spatial relations into account. We propose two routing algorithms on the enriched road networks. To evaluate our approach, we use Flickr photo data as a ground truth for popularity. Our experimental results -- based on real world datasets -- show that the computed paths yield competitive solutions in terms of path length while also providing more \"popular\" paths, making routing easier and more informative for the user.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116380488","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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