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"author", "firstName": "James", "lastName": "Haworth" } ], "abstractNote": "Understanding travel behaviour and travel demand is of constant importance to transportation communities and agencies in every country. Nowadays, attempts have been made to automatically infer transportation modes from positional data, such as the data collected by using GPS devices so that the cost in time and budget of conventional travel diary survey could be significantly reduced. Some limitations, however, exist in the literature, in aspects of data collection (sample size selected, duration of study, granularity of data), selection of variables (or combination of variables), and method of inference (the number of transportation modes to be used in the learning). This paper therefore, attempts to fully understand these aspects in the process of inference. We aim to solve a classification problem of GPS data into different transportation modes (car, walk, cycle, underground, train and bus). We first study the variables that could contribute positively to this classification, and statistically quantify their discriminatory power. We then introduce a novel approach to carry out this inference using a framework based on Support Vector Machines (SVMs) classification. The framework was tested using coarse-grained GPS data, which has been avoided in previous studies, achieving a promising accuracy of 88% with a Kappa statistic reflecting almost perfect agreement.", "publicationTitle": "Computers, Environment and Urban Systems", "publisher": "", "place": "", "date": "November 2012", "volume": "36", "issue": "6", "section": "", "partNumber": "", "partTitle": "", "pages": "526-537", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Computers, Environment and Urban Systems", "DOI": "10.1016/j.compenvurbsys.2012.06.001", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0198971512000543", "accessDate": "2014-01-14T20:45:35Z", "PMID": "", "PMCID": "", "ISSN": "0198-9715", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Classification", "type": 1 }, { "tag": "SVM", "type": 1 }, { "tag": "Transportation mode", "type": 1 }, { "tag": "Travel behaviour", "type": 1 }, { "tag": "Variable selection", "type": 1 } ], "collections": [ "35JB83QW" ], "relations": {}, "dateAdded": "2014-01-14T20:45:35Z", "dateModified": "2014-01-14T20:45:35Z" } }, { "key": "MHER6IDR", "version": 33, "library": { "type": "group", "id": 183050, "name": "Ped_Bike_PacTrans", "links": { "alternate": { "href": "https://www.zotero.org/groups/ped_bike_pactrans", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/183050/items/MHER6IDR", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ped_bike_pactrans/items/MHER6IDR", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/183050/items/JSF6H73S", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1403081, "username": "mskiles17", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/mskiles17", "type": "text/html" } } } }, "data": { "key": "MHER6IDR", "version": 33, "parentItem": "JSF6H73S", "itemType": "attachment", "linkMode": "imported_url", "title": "Snapshot", "accessDate": "2014-01-13T20:08:56Z", "url": "http://amonline.trb.org/2014-1.405766/t-1166-1.412873/380-1.412938/14-4637-1.412948/14-4637-1.412949?qr=1", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "14-4637-1.html", "md5": "160295a5f902b187f9d252d2a4533518", "mtime": 1389643737301, "tags": [], "relations": {}, "dateAdded": "2014-01-13T20:08:56Z", "dateModified": "2014-01-13T20:08:56Z" } }, { "key": "JSF6H73S", "version": 32, "library": { "type": "group", "id": 183050, "name": "Ped_Bike_PacTrans", "links": { "alternate": { "href": "https://www.zotero.org/groups/ped_bike_pactrans", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/183050/items/JSF6H73S", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ped_bike_pactrans/items/JSF6H73S", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1403081, "username": "mskiles17", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/mskiles17", "type": "text/html" } } }, "numChildren": 1 }, "data": { "key": "JSF6H73S", "version": 32, "itemType": "webpage", "title": "14-4637", "creators": [], "abstractNote": "", "websiteTitle": "", "websiteType": "", "date": "", "publisher": "", "place": "", "DOI": "", "citationKey": "", "url": "http://amonline.trb.org/2014-1.405766/t-1166-1.412873/380-1.412938/14-4637-1.412948/14-4637-1.412949?qr=1", "accessDate": "2014-01-13T20:08:56Z", "shortTitle": "", "language": "", "rights": "", "extra": "", "tags": [ { "tag": "Paper", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2014-01-13T20:08:56Z", "dateModified": "2014-01-13T20:08:56Z" } }, { "key": "6TDSPJW9", "version": 31, "library": { "type": "group", "id": 183050, "name": "Ped_Bike_PacTrans", "links": { "alternate": { "href": "https://www.zotero.org/groups/ped_bike_pactrans", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/183050/items/6TDSPJW9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ped_bike_pactrans/items/6TDSPJW9", "type": "text/html" } }, "meta": { "createdByUser": { "id": 147574, "username": "mikelowry", "name": "Mike Lowry", "links": { "alternate": { "href": "https://www.zotero.org/mikelowry", "type": "text/html" } } }, "creatorSummary": "Raford and Ragland", "parsedDate": "2004-01-01", "numChildren": 0 }, "data": { "key": "6TDSPJW9", "version": 31, "itemType": "journalArticle", "title": "Space Syntax: Innovative Pedestrian Volume Modeling Tool for Pedestrian Safety", "creators": [ { "creatorType": "author", "firstName": "Noah", "lastName": "Raford" }, { "creatorType": "author", "firstName": "David", "lastName": "Ragland" } ], "abstractNote": "An innovative pedestrian modeling technique known as Space Syntax is described. The technique was used to create estimates of pedestrian volumes for the city of Oakland, California. Pedestrian exposure rates were calculated, and a relative risk index for the city's first pedestrian master plan was created with those estimates. A major challenge facing planners, transportation engineers, and pedestrian-safety advocates is the lack of detailed, high-quality pedestrian-exposure data. Exposure is defined as the rate of contact with a potentially harmful agent or event. Pedestrian exposure is therefore defined as the rate of pedestrian contact with potentially harmful situations involving moving vehicles (e.g., crossing an intersection). Pedestrian risk is defined as the probability that a pedestrian-vehicle collision will occur, based on the rate of exposure. To estimate exposure, pedestrian volume measurements must be made, but such measurements are not easily obtainable. In the absence of accurate exposure data, pedestrian-safety decisions are often made by estimation, rule of thumb, or political influence and result in mixed and potentially less effective outcomes. The value of the Space Syntax volume-modeling approach for generating estimates of pedestrian exposure is explored with the city of Oakland as a case study. The method's theoretical background, data requirements, implementation, and results are discussed. It is suggested that the output of the model—citywide pedestrian volume estimates—is useful to pedestrians, planners, and transportation engineers. The value of the pedestrian-exposure concept for the planning professional is discussed.", "publicationTitle": "Transportation Research Record: Journal of the Transportation Research Board", "publisher": "", "place": "", "date": "January 1, 2004", "volume": "1878", "issue": "-1", "section": "", "partNumber": "", "partTitle": "", "pages": "66-74", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.3141/1878-09", "citationKey": "", "url": "http://dx.doi.org/10.3141/1878-09", "accessDate": "2013-09-20T21:15:57Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "Space Syntax", "language": "", "libraryCatalog": "MetaPress", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "PWHJWUW7" ], "relations": {}, "dateAdded": "2013-09-20T21:15:57Z", "dateModified": "2013-09-20T21:15:57Z" } }, { "key": "GWSJVXKW", "version": 31, "library": { "type": "group", "id": 183050, "name": "Ped_Bike_PacTrans", "links": { "alternate": { "href": "https://www.zotero.org/groups/ped_bike_pactrans", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/183050/items/GWSJVXKW", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ped_bike_pactrans/items/GWSJVXKW", "type": "text/html" } }, "meta": { "createdByUser": { "id": 147574, "username": "mikelowry", "name": "Mike Lowry", "links": { "alternate": { "href": "https://www.zotero.org/mikelowry", "type": "text/html" } } }, "creatorSummary": "Raford and Ragland", "parsedDate": "2005-12-01", "numChildren": 2 }, "data": { "key": "GWSJVXKW", "version": 31, "itemType": "journalArticle", "title": "Pedestrian Volume Modeling for Traffic Safety and Exposure Analysis:", "creators": [ { "creatorType": "author", "firstName": "Noah", "lastName": "Raford" }, { "creatorType": "author", "firstName": "David R.", "lastName": "Ragland" } ], "abstractNote": "This paper examines three types of pedestrian volume models in light of their usefulness for estimating pedestrian exposure for pedestrian safety research. The need for pedestrian flow data as part of pedestrian exposure and safety analysis is outlined, and the background of each type of model is discussed. It then selects the space syntax network analysis model to estimate pedestrian volumes for the city of Boston, Massachusetts. It was found that the model was able to accurately predict pedestrian flows (r-squared 0.81, p-value < 0.0001) after incorporating distance to transit stops and major tourist attractions. These findings suggest that in addition to estimating pedestrian volumes in geographic locations where data is not available, pedestrian volume modeling can also be useful for estimating pedestrian volumes in future conditions. 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As a kind of user behavior, the transportation modes, such as walking, driving, etc., that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information. In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs. The contribution of this work lies in the following two aspects. On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used. On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance. This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement.", "proceedingsTitle": "Proceedings of the 10th international conference on Ubiquitous computing", "conferenceName": "", "publisher": "ACM", "place": "New York, NY, USA", "date": "2008", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "312–321", "series": "UbiComp '08", "seriesNumber": "", "DOI": "10.1145/1409635.1409677", "ISBN": "978-1-60558-136-1", "citationKey": "", "url": "http://doi.acm.org/10.1145/1409635.1409677", "accessDate": "2013-07-16T17:50:45Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ACM Digital Library", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "GPS", "type": 1 }, { "tag": "GeoLife", "type": 1 }, { "tag": "infer transportation mode", "type": 1 }, { "tag": "machine learning", "type": 1 }, { "tag": "recognize human behavior", "type": 1 } ], "collections": [ "35JB83QW" ], "relations": {}, "dateAdded": "2013-07-16T17:50:45Z", "dateModified": "2013-07-16T17:50:45Z" } }, { "key": "7EBV67QI", "version": 19, "library": { "type": "group", "id": 183050, "name": "Ped_Bike_PacTrans", "links": { "alternate": { "href": "https://www.zotero.org/groups/ped_bike_pactrans", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/183050/items/7EBV67QI", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ped_bike_pactrans/items/7EBV67QI", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/183050/items/EG74A2VZ", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1403081, "username": "mskiles17", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/mskiles17", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "7EBV67QI", "version": 19, "parentItem": "EG74A2VZ", "itemType": "attachment", "linkMode": "imported_url", "title": "ScienceDirect Full Text PDF", "accessDate": "2013-07-16T17:08:16Z", "url": "http://www.sciencedirect.com/science?_ob=MiamiImageURL&_cid=271803&_user=854313&_pii=S0198971509000556&_check=y&_origin=article&_zone=toolbar&_coverDate=30-Nov-2009&view=c&originContentFamily=serial&wchp=dGLbVlB-zSkWz&md5=e2dccbab1d5101ea643daabb6c5e9938&pid=1-s2.0-S0198971509000556-main.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Dodge et al. - 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The methodology consists of three stages: trajectory data preparation; global descriptors computation; and local feature extraction. The key element is an algorithm that decomposes the profiles generated for different movement parameters (velocity, acceleration, etc.) using variations in sinuosity and deviation from the median line. Hence, the methodology enables the extraction of local movement features in addition to global ones that are essential for modeling and analyzing moving objects in applications such as trajectory classification, simulation and extraction of movement patterns. As a case study, we show how the method can be employed in classifying trajectory data generated by unknown moving objects and assigning them to known types of moving objects, whose movement characteristics have been previously learned. We have conducted a series of experiments that provide evidence about the similarities and differences that exist among different types of moving objects. The experiments show that the methodology can be successfully applied in automatic transport mode detection. It is also shown that eye-movement data cannot be successfully used as a proxy of full-body movement of humans, or vehicles.", "publicationTitle": "Computers, Environment and Urban Systems", "publisher": "", "place": "", "date": "November 2009", "volume": "33", "issue": "6", "section": "", "partNumber": "", "partTitle": "", "pages": "419-434", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Computers, Environment and Urban Systems", "DOI": "10.1016/j.compenvurbsys.2009.07.008", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0198971509000556", "accessDate": "2013-07-16T17:08:16Z", "PMID": "", "PMCID": "", "ISSN": "0198-9715", "archive": "", "archiveLocation": "", "shortTitle": "Spatial Data Mining–Methods and Applications", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Movement behavior", "type": 1 }, { "tag": "Movement parameters", "type": 1 }, { "tag": "Moving object", "type": 1 }, { "tag": "Moving point data mining", "type": 1 }, { "tag": "Trajectory classification", "type": 1 }, { "tag": "Trajectory decomposition", "type": 1 } ], "collections": [ "35JB83QW" ], "relations": {}, "dateAdded": "2013-07-16T17:08:16Z", "dateModified": "2013-07-16T17:08:16Z" } }, { "key": "5CBZW2MJ", "version": 19, "library": { "type": "group", "id": 183050, "name": "Ped_Bike_PacTrans", "links": { "alternate": { "href": "https://www.zotero.org/groups/ped_bike_pactrans", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/183050/items/5CBZW2MJ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ped_bike_pactrans/items/5CBZW2MJ", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/183050/items/PITW9M4W", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1403081, "username": "mskiles17", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/mskiles17", "type": "text/html" } } } }, "data": { "key": "5CBZW2MJ", "version": 19, "parentItem": "PITW9M4W", "itemType": "attachment", "linkMode": "imported_url", "title": "IEEE Xplore Abstract Record", "accessDate": "2013-07-16T15:53:25Z", "url": "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4911579", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "abs_all.html", "md5": "772356089d780b8496482b39ec0948fc", "mtime": 1373990004879, "tags": [], "relations": {}, "dateAdded": "2013-07-16T15:53:25Z", "dateModified": "2013-07-16T15:53:25Z" } }, { "key": "9WGS998G", "version": 18, "library": { "type": "group", "id": 183050, "name": "Ped_Bike_PacTrans", "links": { "alternate": { "href": "https://www.zotero.org/groups/ped_bike_pactrans", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/183050/items/9WGS998G", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ped_bike_pactrans/items/9WGS998G", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/183050/items/PITW9M4W", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1403081, "username": "mskiles17", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/mskiles17", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "9WGS998G", "version": 18, "parentItem": "PITW9M4W", "itemType": "attachment", "linkMode": "imported_url", "title": "IEEE Xplore Full Text PDF", "accessDate": "2013-07-16T15:53:22Z", "url": "http://ieeexplore.ieee.org/ielx5/4840596/4911563/04911579.pdf?tp=&arnumber=4911579&isnumber=4911563", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Reddy et al. - 2008 - Determining transportation mode on mobile phones.pdf", "md5": "32f442b531d2d2c2ef7dae4c7fff01ec", "mtime": 1373990003661, "tags": [], "relations": {}, "dateAdded": "2013-07-16T15:53:22Z", "dateModified": "2013-07-16T15:53:24Z" } } ]