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description Publicationkeyboard_double_arrow_right Article , Other literature type 2016Publisher:Copernicus GmbH Funded by:UKRI | Integrated Spatio-Tempora...UKRI| Integrated Spatio-Temporal Data Mining for Quantitative Assessment of Road Network PerformanceAuthors: B. Anbaroglu; B. Heydecker; T. Cheng;B. Anbaroglu; B. Heydecker; T. Cheng;Abstract. Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London’s urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.
ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprsarchives-xli-b2-159-2016&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprsarchives-xli-b2-159-2016&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2018Publisher:Copernicus GmbH Authors: Peppa, M. V.; Bell, D.; Komar, T.; Xiao, W.;Peppa, M. V.; Bell, D.; Komar, T.; Xiao, W.;Abstract. Traffic flow analysis is fundamental for urban planning and management of road traffic infrastructure. Automatic number plate recognition (ANPR) systems are conventional methods for vehicle detection and travel times estimation. However, such systems are specifically focused on car plates, providing a limited extent of road users. The advance of open-source deep learning convolutional neural networks (CNN) in combination with freely-available closed-circuit television (CCTV) datasets have offered the opportunities for detection and classification of various road users. The research, presented here, aims to analyse traffic flow patterns through fine-tuning pre-trained CNN models on domain-specific low quality imagery, as captured in various weather conditions and seasons of the year 2018. Such imagery is collected from the North East Combined Authority (NECA) Travel and Transport Data, Newcastle upon Tyne, UK. Results show that the fine-tuned MobileNet model with 98.2 % precision, 58.5 % recall and 73.4 % harmonic mean could potentially be used for a real time traffic monitoring application with big data, due to its fast performance. Compared to MobileNet, the fine-tuned Faster region proposal R-CNN model, providing a better harmonic mean (80.4 %), recall (68.8 %) and more accurate estimations of car units, could be used for traffic analysis applications that demand higher accuracy than speed. This research ultimately exploits machine learning alogrithms for a wider understanding of traffic congestion and disruption under social events and extreme weather conditions.
The International Ar... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2018 . Peer-reviewedLicense: CC BYData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-4-499-2018&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 18 citations 18 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert The International Ar... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2018 . Peer-reviewedLicense: CC BYData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-4-499-2018&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type , Conference object 2017Publisher:Copernicus GmbH Funded by:UKRI | Urban Big DataUKRI| Urban Big DataAuthors: Sun, Y.;Sun, Y.;Abstract. In development of sustainable transportation and green city, policymakers encourage people to commute by cycling and walking instead of motor vehicles in cities. One the one hand, cycling and walking enables decrease in air pollution emissions. On the other hand, cycling and walking offer health benefits by increasing people’s physical activity. Earlier studies on investigating spatial patterns of active travel (cycling and walking) are limited by lacks of spatially fine-grained data. In recent years, with the development of information and communications technology, GPS-enabled devices are popular and portable. With smart phones or smart watches, people are able to record their cycling or walking GPS traces when they are moving. A large number of cyclists and pedestrians upload their GPS traces to sport social media to share their historical traces with other people. Those sport social media thus become a potential source for spatially fine-grained cycling and walking data. Very recently, Strava Metro offer aggregated cycling and walking data with high spatial granularity. Strava Metro aggregated a large amount of cycling and walking GPS traces of Strava users to streets or intersections across a city. Accordingly, as a kind of crowdsourced geographic information, the aggregated data is useful for investigating spatial patterns of cycling and walking activities, and thus is of high potential in understanding cycling or walking behavior at a large spatial scale. This study is a start of demonstrating usefulness of Strava Metro data for exploring cycling or walking patterns at a large scale.
CORE (RIOXX-UK Aggre... arrow_drop_down CORE (RIOXX-UK Aggregator)Conference object . 2017License: CC BYData sources: CORE (RIOXX-UK Aggregator)ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2017 . Peer-reviewedLicense: CC BYData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-2-w7-1357-2017&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 8 citations 8 popularity Top 10% influence Average impulse Average Powered by BIP!download 120download downloads 120 Powered bymore_vert CORE (RIOXX-UK Aggre... arrow_drop_down CORE (RIOXX-UK Aggregator)Conference object . 2017License: CC BYData sources: CORE (RIOXX-UK Aggregator)ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2017 . Peer-reviewedLicense: CC BYData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-2-w7-1357-2017&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Article 2019 NetherlandsPublisher:Copernicus GmbH Funded by:EC | PANOPTIS, EC | INACHUS, EC | RECONASSEC| PANOPTIS ,EC| INACHUS ,EC| RECONASSKerle, N.; Nex, F.; Duarte, D.; Vetrivel, A.; Tanzi, T.; Altan, O.; Chandra, M.; Sunar, F.;Abstract. Structural disaster damage detection and characterisation is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of UAV in recent years has opened up many new opportunities for damage mapping, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. We have addressed the problem in the context of two European research projects, RECONASS and INACHUS. In this paper we synthesize and evaluate the progress of 6 years of research focused on advanced image analysis that was driven by progress in computer vision, photogrammetry and machine learning, but also by constraints imposed by the needs of first responder and other civil protection end users. The projects focused on damage to individual buildings caused by seismic activity but also explosions, and our work centred on the processing of 3D point cloud information acquired from stereo imagery. Initially focusing on the development of both supervised and unsupervised damage detection methods built on advanced texture features and basic classifiers such as Support Vector Machine and Random Forest, the work moved on to the use of deep learning. In particular the coupling of image-derived features and 3D point cloud information in a Convolutional Neural Network (CNN) proved successful in detecting also subtle damage features. In addition to the detection of standard rubble and debris, CNN-based methods were developed to detect typical façade damage indicators, such as cracks and spalling, including with a focus on multi-temporal and multi-scale feature fusion. We further developed a processing pipeline and mobile app to facilitate near-real time damage mapping. The solutions were tested in a number of pilot experiments and evaluated by a variety of stakeholders.
ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesConference object . 2019Full-Text: https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2019/conf/kerle_uav.pdfData sources: NARCISNARCISConference object . 2019Full-Text: https://ris.utwente.nl/ws/files/284380624/Kerle_2019_Uav_based_structural_damage_mapping.pdfData sources: NARCISISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2019 . Peer-reviewedLicense: CC BYData sources: CrossrefUniversity of Twente Research InformationConference object . 2019Data sources: University of Twente Research Informationadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-3-w8-187-2019&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 6 citations 6 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesConference object . 2019Full-Text: https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2019/conf/kerle_uav.pdfData sources: NARCISNARCISConference object . 2019Full-Text: https://ris.utwente.nl/ws/files/284380624/Kerle_2019_Uav_based_structural_damage_mapping.pdfData sources: NARCISISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2019 . Peer-reviewedLicense: CC BYData sources: CrossrefUniversity of Twente Research InformationConference object . 2019Data sources: University of Twente Research Informationadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-3-w8-187-2019&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Other literature type , Contribution for newspaper or weekly magazine , Article 2017 United Kingdom, PortugalPublisher:Copernicus GmbH Funded by:EC | GEO-CEC| GEO-CAuthors: Bhattacharya, D.; Painho, M.; Mishra, S.; Gupta, A.;Bhattacharya, D.; Painho, M.; Mishra, S.; Gupta, A.;Bhattacharya, D., Painho, M., Mishra, S., & Gupta, A. (2017). Mobile traffic alert and tourist route guidance system design using geospatial data. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 11-18). (ISPRS Archives; Vol. 42, No. 4W3). https://doi.org/10.5194/isprs-archives-XLII-4-W3-11-2017 The present study describes an integrated system for traffic data collection and alert warning. Geographical information based decision making related to traffic destinations and routes is proposed through the design. The system includes a geospatial database having profile relating to a user of a mobile device. The processing and understanding of scanned maps, other digital data input leads to route guidance. The system includes a server configured to receive traffic information relating to a route and location information relating to the mobile device. Server is configured to send a traffic alert to the mobile device when the traffic information and the location information indicate that the mobile device is traveling toward traffic congestion. Proposed system has geospatial and mobile data sets pertaining to Bangalore city in India. It is envisaged to be helpful for touristic purposes as a route guidance and alert relaying information system to tourists for proximity to sites worth seeing in a city they have entered into. The system is modular in architecture and the novelty lies in integration of different modules carrying different technologies for a complete traffic information system. Generic information processing and delivery system has been tested to be functional and speedy under test geospatial domains. In a restricted prototype model with geo-referenced route data required information has been delivered correctly over sustained trials to designated cell numbers, with average time frame of 27.5 seconds, maximum 50 and minimum 5 seconds. Traffic geo-data set trials testing is underway. publishersversion published
ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . Article . 2017 . Peer-reviewedLicense: CC BYRepositório da Universidade Nova de LisboaConference object . 2017Data sources: Repositório da Universidade Nova de LisboaISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsEdinburgh Research ExplorerContribution for newspaper or weekly magazine . 2017Data sources: Edinburgh Research Exploreradd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-4-w3-11-2017&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 5 citations 5 popularity Average influence Average impulse Average Powered by BIP!visibility 115visibility views 115 download downloads 81 Powered bymore_vert ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . Article . 2017 . Peer-reviewedLicense: CC BYRepositório da Universidade Nova de LisboaConference object . 2017Data sources: Repositório da Universidade Nova de LisboaISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsEdinburgh Research ExplorerContribution for newspaper or weekly magazine . 2017Data sources: Edinburgh Research Exploreradd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-4-w3-11-2017&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2016Publisher:Copernicus GmbH Authors: Haworth, J.;Haworth, J.;Abstract. Traffic congestion and its associated environmental effects pose a significant problem for large cities. Consequently, promoting and investing in green travel modes such as cycling is high on the agenda for many transport authorities. In order to target investment in cycling infrastructure and improve the experience of cyclists on the road, it is important to know where they are. Unfortunately, investment in intelligent transportation systems over the years has mainly focussed on monitoring vehicular traffic, and comparatively little is known about where cyclists are on a day to day basis. In London, for example, there are a limited number of automatic cycle counters installed on the network, which provide only part of the picture. These are supplemented by surveys that are carried out infrequently. Activity tracking apps on smart phones and GPS devices such as Strava have become very popular over recent years. Their intended use is to track physical activity and monitor training. However, many people routinely use such apps to record their daily commutes by bicycle. At the aggregate level, these data provide a potentially rich source of information about the movement and behaviour of cyclists. Before such data can be relied upon, however, it is necessary to examine their representativeness and understand their potential biases. In this study, the flows obtained from Strava Metro (SM) are compared with those obtained during the 2013 London Cycle Census (LCC). A set of linear regression models are constructed to predict LCC flows using SM flows along with a number of dummy variables including road type, hour of day, day of week and presence/absence of cycle lane. Cross-validation is used to test the fitted models on unseen LCC sites. SM flows are found to be a statistically significant (p<0.0001) predictor of total flows as measured by the LCC and the models yield R squared statistics of ~0.7 before considering spatio-temporal variation. The initial results indicate that data collected using fitness tracking apps such as Strava are a promising data source for traffic managers. Future work will incorporate the spatio-temporal structure in the data to better account for the spatial and temporal variation in the ratio of SM flows to LCC flows.
ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedLicense: CC BYData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xli-b2-515-2016&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 5 citations 5 popularity Average influence Average impulse Average Powered by BIP!more_vert ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedLicense: CC BYData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xli-b2-515-2016&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Other literature type , Article 2014Publisher:Copernicus GmbH Funded by:UKRI | Integrated Spatio-Tempora...UKRI| Integrated Spatio-Temporal Data Mining for Quantitative Assessment of Road Network PerformanceAuthors: Haworth, J.; Cheng, T.;Haworth, J.; Cheng, T.;Abstract. Spatio-temporal neighbourhood (STN) selection is an important part of the model building procedure in spatio-temporal forecasting. The STN can be defined as the set of observations at neighbouring locations and times that are relevant for forecasting the future values of a series at a particular location at a particular time. Correct specification of the STN can enable forecasting models to capture spatio-temporal dependence, greatly improving predictive performance. In recent years, deficiencies have been revealed in models with globally fixed STN structures, which arise from the problems of heterogeneity, nonstationarity and nonlinearity in spatio-temporal processes. Using the example of a large dataset of travel times collected on London’s road network, this study examines the effect of various STN selection methods drawn from the variable selection literature, varying from simple forward/backward subset selection to simultaneous shrinkage and selection operators. The results indicate that STN selection methods based on L1 penalisation are effective. In particular, the maximum concave penalty (MCP) method selects parsimonious models that produce good forecasting performance.
ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2014 . Peer-reviewedLicense: CC BYData sources: CrossrefThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2014Data sources: DOAJ-ArticlesISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticleLicense: CC BYData sources: UnpayWalladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprsarchives-xl-2-7-2014&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 3 citations 3 popularity Average influence Average impulse Average Powered by BIP!more_vert ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2014 . Peer-reviewedLicense: CC BYData sources: CrossrefThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2014Data sources: DOAJ-ArticlesISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticleLicense: CC BYData sources: UnpayWalladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprsarchives-xl-2-7-2014&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Conference object , Other literature type 2018 Austria, ItalyPublisher:Copernicus GmbH Funded by:EC | CROWDLAND, EC | LANDSENSEEC| CROWDLAND ,EC| LANDSENSEC. C. Fonte; C. C. Fonte; M. Minghini; V. Antoniou; J. Patriarca; L. See;handle: 11311/1087129
Abstract. This paper examines the feasibility of using data from OpenStreetMap (OSM), Facebook and Foursquare as a source of information on the function of buildings. Such information is rarely openly available and if available, would vary between cities by nomenclature, making comparisons between places difficult. Volunteered Geographic Information (VGI) including data from social media represents new potential sources of building function data that have not yet been exploited for this purpose. Using a part of the city of Milan as the study area, building data from OSM and points of interest (POIs) from OSM, Facebook and Foursquare were extracted to derive the building function. This resulted in the classification of building function for more than 80 % of the buildings and demonstrated that both Facebook and Foursquare can complement the building function derived from OSM, helping to fill in missing gaps. This preliminary study has demonstrated the potential of this approach for deriving building function information from open data in a simple way yet still requires independent validation with alternative sources as well as extension to other areas that have different amounts of OSM and social media coverage.
RE.PUBLIC@POLIMI Res... arrow_drop_down RE.PUBLIC@POLIMI Research Publications at Politecnico di MilanoOther literature type . Article . Conference object . 2018ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2018 . Peer-reviewedLicense: CC BYRE.PUBLIC@POLIMI Research Publications at Politecnico di MilanoConference object . 2018ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-4-209-2018&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 7 citations 7 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert RE.PUBLIC@POLIMI Res... arrow_drop_down RE.PUBLIC@POLIMI Research Publications at Politecnico di MilanoOther literature type . Article . Conference object . 2018ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2018 . Peer-reviewedLicense: CC BYRE.PUBLIC@POLIMI Research Publications at Politecnico di MilanoConference object . 2018ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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description Publicationkeyboard_double_arrow_right Article , Other literature type 2016Publisher:Copernicus GmbH Funded by:UKRI | Integrated Spatio-Tempora...UKRI| Integrated Spatio-Temporal Data Mining for Quantitative Assessment of Road Network PerformanceAuthors: B. Anbaroglu; B. Heydecker; T. Cheng;B. Anbaroglu; B. Heydecker; T. Cheng;Abstract. Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London’s urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.
ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess Routesgold 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprsarchives-xli-b2-159-2016&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2018Publisher:Copernicus GmbH Authors: Peppa, M. V.; Bell, D.; Komar, T.; Xiao, W.;Peppa, M. V.; Bell, D.; Komar, T.; Xiao, W.;Abstract. Traffic flow analysis is fundamental for urban planning and management of road traffic infrastructure. Automatic number plate recognition (ANPR) systems are conventional methods for vehicle detection and travel times estimation. However, such systems are specifically focused on car plates, providing a limited extent of road users. The advance of open-source deep learning convolutional neural networks (CNN) in combination with freely-available closed-circuit television (CCTV) datasets have offered the opportunities for detection and classification of various road users. The research, presented here, aims to analyse traffic flow patterns through fine-tuning pre-trained CNN models on domain-specific low quality imagery, as captured in various weather conditions and seasons of the year 2018. Such imagery is collected from the North East Combined Authority (NECA) Travel and Transport Data, Newcastle upon Tyne, UK. Results show that the fine-tuned MobileNet model with 98.2 % precision, 58.5 % recall and 73.4 % harmonic mean could potentially be used for a real time traffic monitoring application with big data, due to its fast performance. Compared to MobileNet, the fine-tuned Faster region proposal R-CNN model, providing a better harmonic mean (80.4 %), recall (68.8 %) and more accurate estimations of car units, could be used for traffic analysis applications that demand higher accuracy than speed. This research ultimately exploits machine learning alogrithms for a wider understanding of traffic congestion and disruption under social events and extreme weather conditions.
The International Ar... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2018 . Peer-reviewedLicense: CC BYData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess Routesgold 18 citations 18 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert The International Ar... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2018 . Peer-reviewedLicense: CC BYData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-4-499-2018&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type , Conference object 2017Publisher:Copernicus GmbH Funded by:UKRI | Urban Big DataUKRI| Urban Big DataAuthors: Sun, Y.;Sun, Y.;Abstract. In development of sustainable transportation and green city, policymakers encourage people to commute by cycling and walking instead of motor vehicles in cities. One the one hand, cycling and walking enables decrease in air pollution emissions. On the other hand, cycling and walking offer health benefits by increasing people’s physical activity. Earlier studies on investigating spatial patterns of active travel (cycling and walking) are limited by lacks of spatially fine-grained data. In recent years, with the development of information and communications technology, GPS-enabled devices are popular and portable. With smart phones or smart watches, people are able to record their cycling or walking GPS traces when they are moving. A large number of cyclists and pedestrians upload their GPS traces to sport social media to share their historical traces with other people. Those sport social media thus become a potential source for spatially fine-grained cycling and walking data. Very recently, Strava Metro offer aggregated cycling and walking data with high spatial granularity. Strava Metro aggregated a large amount of cycling and walking GPS traces of Strava users to streets or intersections across a city. Accordingly, as a kind of crowdsourced geographic information, the aggregated data is useful for investigating spatial patterns of cycling and walking activities, and thus is of high potential in understanding cycling or walking behavior at a large spatial scale. This study is a start of demonstrating usefulness of Strava Metro data for exploring cycling or walking patterns at a large scale.
CORE (RIOXX-UK Aggre... arrow_drop_down CORE (RIOXX-UK Aggregator)Conference object . 2017License: CC BYData sources: CORE (RIOXX-UK Aggregator)ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2017 . Peer-reviewedLicense: CC BYData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-2-w7-1357-2017&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 8 citations 8 popularity Top 10% influence Average impulse Average Powered by BIP!download 120download downloads 120 Powered bymore_vert CORE (RIOXX-UK Aggre... arrow_drop_down CORE (RIOXX-UK Aggregator)Conference object . 2017License: CC BYData sources: CORE (RIOXX-UK Aggregator)ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2017 . Peer-reviewedLicense: CC BYData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-2-w7-1357-2017&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Article 2019 NetherlandsPublisher:Copernicus GmbH Funded by:EC | PANOPTIS, EC | INACHUS, EC | RECONASSEC| PANOPTIS ,EC| INACHUS ,EC| RECONASSKerle, N.; Nex, F.; Duarte, D.; Vetrivel, A.; Tanzi, T.; Altan, O.; Chandra, M.; Sunar, F.;Abstract. Structural disaster damage detection and characterisation is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of UAV in recent years has opened up many new opportunities for damage mapping, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. We have addressed the problem in the context of two European research projects, RECONASS and INACHUS. In this paper we synthesize and evaluate the progress of 6 years of research focused on advanced image analysis that was driven by progress in computer vision, photogrammetry and machine learning, but also by constraints imposed by the needs of first responder and other civil protection end users. The projects focused on damage to individual buildings caused by seismic activity but also explosions, and our work centred on the processing of 3D point cloud information acquired from stereo imagery. Initially focusing on the development of both supervised and unsupervised damage detection methods built on advanced texture features and basic classifiers such as Support Vector Machine and Random Forest, the work moved on to the use of deep learning. In particular the coupling of image-derived features and 3D point cloud information in a Convolutional Neural Network (CNN) proved successful in detecting also subtle damage features. In addition to the detection of standard rubble and debris, CNN-based methods were developed to detect typical façade damage indicators, such as cracks and spalling, including with a focus on multi-temporal and multi-scale feature fusion. We further developed a processing pipeline and mobile app to facilitate near-real time damage mapping. The solutions were tested in a number of pilot experiments and evaluated by a variety of stakeholders.
ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesConference object . 2019Full-Text: https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2019/conf/kerle_uav.pdfData sources: NARCISNARCISConference object . 2019Full-Text: https://ris.utwente.nl/ws/files/284380624/Kerle_2019_Uav_based_structural_damage_mapping.pdfData sources: NARCISISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2019 . Peer-reviewedLicense: CC BYData sources: CrossrefUniversity of Twente Research InformationConference object . 2019Data sources: University of Twente Research Informationadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-3-w8-187-2019&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 6 citations 6 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesConference object . 2019Full-Text: https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2019/conf/kerle_uav.pdfData sources: NARCISNARCISConference object . 2019Full-Text: https://ris.utwente.nl/ws/files/284380624/Kerle_2019_Uav_based_structural_damage_mapping.pdfData sources: NARCISISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2019 . Peer-reviewedLicense: CC BYData sources: CrossrefUniversity of Twente Research InformationConference object . 2019Data sources: University of Twente Research Informationadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Other literature type , Contribution for newspaper or weekly magazine , Article 2017 United Kingdom, PortugalPublisher:Copernicus GmbH Funded by:EC | GEO-CEC| GEO-CAuthors: Bhattacharya, D.; Painho, M.; Mishra, S.; Gupta, A.;Bhattacharya, D.; Painho, M.; Mishra, S.; Gupta, A.;Bhattacharya, D., Painho, M., Mishra, S., & Gupta, A. (2017). Mobile traffic alert and tourist route guidance system design using geospatial data. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 11-18). (ISPRS Archives; Vol. 42, No. 4W3). https://doi.org/10.5194/isprs-archives-XLII-4-W3-11-2017 The present study describes an integrated system for traffic data collection and alert warning. Geographical information based decision making related to traffic destinations and routes is proposed through the design. The system includes a geospatial database having profile relating to a user of a mobile device. The processing and understanding of scanned maps, other digital data input leads to route guidance. The system includes a server configured to receive traffic information relating to a route and location information relating to the mobile device. Server is configured to send a traffic alert to the mobile device when the traffic information and the location information indicate that the mobile device is traveling toward traffic congestion. Proposed system has geospatial and mobile data sets pertaining to Bangalore city in India. It is envisaged to be helpful for touristic purposes as a route guidance and alert relaying information system to tourists for proximity to sites worth seeing in a city they have entered into. The system is modular in architecture and the novelty lies in integration of different modules carrying different technologies for a complete traffic information system. Generic information processing and delivery system has been tested to be functional and speedy under test geospatial domains. In a restricted prototype model with geo-referenced route data required information has been delivered correctly over sustained trials to designated cell numbers, with average time frame of 27.5 seconds, maximum 50 and minimum 5 seconds. Traffic geo-data set trials testing is underway. publishersversion published
ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . Article . 2017 . Peer-reviewedLicense: CC BYRepositório da Universidade Nova de LisboaConference object . 2017Data sources: Repositório da Universidade Nova de LisboaISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsEdinburgh Research ExplorerContribution for newspaper or weekly magazine . 2017Data sources: Edinburgh Research Exploreradd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 5 citations 5 popularity Average influence Average impulse Average Powered by BIP!visibility 115visibility views 115 download downloads 81 Powered bymore_vert ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . Article . 2017 . Peer-reviewedLicense: CC BYRepositório da Universidade Nova de LisboaConference object . 2017Data sources: Repositório da Universidade Nova de LisboaISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsEdinburgh Research ExplorerContribution for newspaper or weekly magazine . 2017Data sources: Edinburgh Research Exploreradd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-4-w3-11-2017&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2016Publisher:Copernicus GmbH Authors: Haworth, J.;Haworth, J.;Abstract. Traffic congestion and its associated environmental effects pose a significant problem for large cities. Consequently, promoting and investing in green travel modes such as cycling is high on the agenda for many transport authorities. In order to target investment in cycling infrastructure and improve the experience of cyclists on the road, it is important to know where they are. Unfortunately, investment in intelligent transportation systems over the years has mainly focussed on monitoring vehicular traffic, and comparatively little is known about where cyclists are on a day to day basis. In London, for example, there are a limited number of automatic cycle counters installed on the network, which provide only part of the picture. These are supplemented by surveys that are carried out infrequently. Activity tracking apps on smart phones and GPS devices such as Strava have become very popular over recent years. Their intended use is to track physical activity and monitor training. However, many people routinely use such apps to record their daily commutes by bicycle. At the aggregate level, these data provide a potentially rich source of information about the movement and behaviour of cyclists. Before such data can be relied upon, however, it is necessary to examine their representativeness and understand their potential biases. In this study, the flows obtained from Strava Metro (SM) are compared with those obtained during the 2013 London Cycle Census (LCC). A set of linear regression models are constructed to predict LCC flows using SM flows along with a number of dummy variables including road type, hour of day, day of week and presence/absence of cycle lane. Cross-validation is used to test the fitted models on unseen LCC sites. SM flows are found to be a statistically significant (p<0.0001) predictor of total flows as measured by the LCC and the models yield R squared statistics of ~0.7 before considering spatio-temporal variation. The initial results indicate that data collected using fitness tracking apps such as Strava are a promising data source for traffic managers. Future work will incorporate the spatio-temporal structure in the data to better account for the spatial and temporal variation in the ratio of SM flows to LCC flows.
ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedLicense: CC BYData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xli-b2-515-2016&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 5 citations 5 popularity Average influence Average impulse Average Powered by BIP!more_vert ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedLicense: CC BYData sources: CrossrefISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2016 . Peer-reviewedData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xli-b2-515-2016&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Other literature type , Article 2014Publisher:Copernicus GmbH Funded by:UKRI | Integrated Spatio-Tempora...UKRI| Integrated Spatio-Temporal Data Mining for Quantitative Assessment of Road Network PerformanceAuthors: Haworth, J.; Cheng, T.;Haworth, J.; Cheng, T.;Abstract. Spatio-temporal neighbourhood (STN) selection is an important part of the model building procedure in spatio-temporal forecasting. The STN can be defined as the set of observations at neighbouring locations and times that are relevant for forecasting the future values of a series at a particular location at a particular time. Correct specification of the STN can enable forecasting models to capture spatio-temporal dependence, greatly improving predictive performance. In recent years, deficiencies have been revealed in models with globally fixed STN structures, which arise from the problems of heterogeneity, nonstationarity and nonlinearity in spatio-temporal processes. Using the example of a large dataset of travel times collected on London’s road network, this study examines the effect of various STN selection methods drawn from the variable selection literature, varying from simple forward/backward subset selection to simultaneous shrinkage and selection operators. The results indicate that STN selection methods based on L1 penalisation are effective. In particular, the maximum concave penalty (MCP) method selects parsimonious models that produce good forecasting performance.
ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2014 . Peer-reviewedLicense: CC BYData sources: CrossrefThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2014Data sources: DOAJ-ArticlesISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticleLicense: CC BYData sources: UnpayWalladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprsarchives-xl-2-7-2014&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 3 citations 3 popularity Average influence Average impulse Average Powered by BIP!more_vert ISPRS - Internationa... arrow_drop_down ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus PublicationsISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2014 . Peer-reviewedLicense: CC BYData sources: CrossrefThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2014Data sources: DOAJ-ArticlesISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticleLicense: CC BYData sources: UnpayWalladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprsarchives-xl-2-7-2014&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Conference object , Other literature type 2018 Austria, ItalyPublisher:Copernicus GmbH Funded by:EC | CROWDLAND, EC | LANDSENSEEC| CROWDLAND ,EC| LANDSENSEC. C. Fonte; C. C. Fonte; M. Minghini; V. Antoniou; J. Patriarca; L. See;handle: 11311/1087129
Abstract. This paper examines the feasibility of using data from OpenStreetMap (OSM), Facebook and Foursquare as a source of information on the function of buildings. Such information is rarely openly available and if available, would vary between cities by nomenclature, making comparisons between places difficult. Volunteered Geographic Information (VGI) including data from social media represents new potential sources of building function data that have not yet been exploited for this purpose. Using a part of the city of Milan as the study area, building data from OSM and points of interest (POIs) from OSM, Facebook and Foursquare were extracted to derive the building function. This resulted in the classification of building function for more than 80 % of the buildings and demonstrated that both Facebook and Foursquare can complement the building function derived from OSM, helping to fill in missing gaps. This preliminary study has demonstrated the potential of this approach for deriving building function information from open data in a simple way yet still requires independent validation with alternative sources as well as extension to other areas that have different amounts of OSM and social media coverage.
RE.PUBLIC@POLIMI Res... arrow_drop_down RE.PUBLIC@POLIMI Research Publications at Politecnico di MilanoOther literature type . Article . Conference object . 2018ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2018 . Peer-reviewedLicense: CC BYRE.PUBLIC@POLIMI Research Publications at Politecnico di MilanoConference object . 2018ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-4-209-2018&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 7 citations 7 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert RE.PUBLIC@POLIMI Res... arrow_drop_down RE.PUBLIC@POLIMI Research Publications at Politecnico di MilanoOther literature type . Article . Conference object . 2018ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2018 . Peer-reviewedLicense: CC BYRE.PUBLIC@POLIMI Research Publications at Politecnico di MilanoConference object . 2018ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2018Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-4-209-2018&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu