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Route intersection reduction with connected autonomous vehicles / Sadegh Motallebi in Geoinformatica, vol 25 n° 1 (January 2021)
[article]
Titre : Route intersection reduction with connected autonomous vehicles Type de document : Article/Communication Auteurs : Sadegh Motallebi, Auteur ; Hairuo Xie, Auteur ; Egemen Tanin, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 99 - 125 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] calcul d'itinéraire
[Termes IGN] carrefour
[Termes IGN] chemin le plus court, algorithme du
[Termes IGN] gestion de trafic
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau routier
[Termes IGN] trafic routierRésumé : (Auteur) A common cause of traffic congestions is the concentration of intersecting vehicle routes. It can be difficult to reduce the intersecting routes in existing traffic systems where the routes are decided independently from vehicle to vehicle. The development of connected autonomous vehicles provides the opportunity to address the intersecting route problem as the route of vehicles can be coordinated globally. We prototype a traffic management system for optimizing traffic with connected autonomous vehicles. The system allocates routes to the vehicles based on streaming traffic data. We develop two route assignment algorithms for the system. The algorithms can help to mitigate traffic congestions by reducing intersecting routes. Extensive experiments are conducted to compare the proposed algorithms and two state-of-the-art route assignment algorithms with both synthetic and real road networks in a simulated traffic management system. The experimental results show that the proposed algorithms outperform the competitors in terms of the travel time of the vehicles. Numéro de notice : A2021-093 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00420-z Date de publication en ligne : 23/08/2020 En ligne : https://doi.org/10.1007/s10707-020-00420-z Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96933
in Geoinformatica > vol 25 n° 1 (January 2021) . - pp 99 - 125[article]Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data / Yi Qiang in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)
[article]
Titre : Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data Type de document : Article/Communication Auteurs : Yi Qiang, Auteur ; Jinwen Xu, Auteur Année de publication : 2020 Article en page(s) : pp 2434 - 2450 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse de groupement
[Termes IGN] données localisées des bénévoles
[Termes IGN] étude empirique
[Termes IGN] Google Maps
[Termes IGN] Ohio (Etats-Unis)
[Termes IGN] participation du public
[Termes IGN] réseau routier
[Termes IGN] résilience écologique
[Termes IGN] risque naturel
[Termes IGN] trafic routierRésumé : (auteur) Climate change and natural hazards pose great threats to road transport systems which are ‘lifelines’ of human society. However, there is generally a lack of empirical data and approaches for assessing resilience of road networks in real hazard events. This study introduces an empirical approach to evaluate road network resilience using crowdsourced traffic data in Google Maps. Based on the conceptualization of resilience and the Hansen accessibility index, resilience of road network is measured from accumulated accessibility reduction over time during a hazard. The utility of this approach is demonstrated in a case study of the Cleveland metropolitan area (Ohio) in Winter Storm Harper. The results reveal strong spatial variations of the disturbance and recovery rate of road network performance during the hazard. The major findings of the case study are: (1) longer distance travels have higher increasing ratios of travel time during the hazard; (2) communities with low accessibility at the normal condition have lower road network resilience; (3) spatial clusters of low resilience are identified, including communities with low socio-economic capacities. The introduced approach provides ground-truth validation for existing quantitative models and supports disaster management and transportation planning to reduce hazard impacts on road network. Numéro de notice : A2020-691 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1694681 Date de publication en ligne : 25/11/2020 En ligne : https://doi.org/10.1080/13658816.2019.1694681 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96229
in International journal of geographical information science IJGIS > vol 34 n° 12 (December 2020) . - pp 2434 - 2450[article]Unfolding spatial-temporal patterns of taxi trip based on an improved network kernel density estimation / Boxi Shen in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)
[article]
Titre : Unfolding spatial-temporal patterns of taxi trip based on an improved network kernel density estimation Type de document : Article/Communication Auteurs : Boxi Shen, Auteur ; Xiang Xu, Auteur ; Jun Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 683 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] appariement de cartes
[Termes IGN] estimation par noyau
[Termes IGN] mobilité urbaine
[Termes IGN] modèle conceptuel de données localisées
[Termes IGN] modèle conceptuel de flux
[Termes IGN] Shenzhen
[Termes IGN] taxi
[Termes IGN] trafic routier
[Termes IGN] trafic urbain
[Termes IGN] trajet (mobilité)Résumé : (auteur) Taxi mobility data plays an important role in understanding urban mobility in the context of urban traffic. Specifically, the taxi is an important part of urban transportation, and taxi trips reflect human behaviors and mobility patterns, allowing us to identify the spatial variety of such patterns. Although taxi trips are generated in the form of network flows, previous works have rarely considered network flow patterns in the analysis of taxi mobility data; Instead, most works focused on point patterns or trip patterns, which may provide an incomplete snapshot. In this work, we propose a novel approach to explore the spatial-temporal patterns of taxi travel by considering point, trip and network flow patterns in a simultaneous fashion. Within this approach, an improved network kernel density estimation (imNKDE) method is first developed to estimate the density of taxi trip pick-up and drop-off points (ODs). Next, the correlation between taxi service activities (i.e., ODs) and land-use is examined. Then, the trip patterns of taxi trips and its corresponding routes are analyzed to reveal the correlation between trips and road structure. Finally, network flow analysis for taxi trip among areas of varying land-use types at different times are performed to discover spatial and temporal taxi trip ODs from a new perspective. A case study in the city of Shenzhen, China, is thoroughly presented and discussed for illustrative purposes. Numéro de notice : A2020-730 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9110683 Date de publication en ligne : 15/11/2020 En ligne : https://doi.org/10.3390/ijgi9110683 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96337
in ISPRS International journal of geo-information > vol 9 n° 11 (November 2020) . - n° 683[article]A graph convolutional network model for evaluating potential congestion spots based on local urban built environments / Kun Qin in Transactions in GIS, Vol 24 n° 5 (October 2020)
[article]
Titre : A graph convolutional network model for evaluating potential congestion spots based on local urban built environments Type de document : Article/Communication Auteurs : Kun Qin, Auteur ; Yuanquan Xu, Auteur ; Chaogui Kang, Auteur ; Mei-Po Kwan, Auteur Année de publication : 2020 Article en page(s) : pp 1382-1401 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du bâti
[Termes IGN] données GPS
[Termes IGN] graphe
[Termes IGN] image Streetview
[Termes IGN] planification urbaine
[Termes IGN] point d'intérêt
[Termes IGN] taxi
[Termes IGN] trafic routier
[Termes IGN] Wuhan (Chine)
[Termes IGN] zone urbaine denseRésumé : (Auteur) Automatically identifying potential congestion spots in cities has significant practical implications for efficient urban development and management. It requires the ability to examine the relationships between urban built environment features and traffic congestion situations. This article presents a novel and effective approach for achieving the task based on a machine‐learning technique and publicly available street‐view imagery and point‐of‐interest (POI) data. The proposed multiple‐graph‐based convolutional network architecture can: (a) extract essential urban built environment features from street‐view imagery and neighboring POIs; (b) model the spatial dependencies between traffic congestion on road networks via graph convolution; and (c) evaluate the risk level of road intersections to emerging congestion situations based on local built environment features. We apply the model to Wuhan in China, and predict the potential congestion spots across the city. The results confirm that the model prediction is highly consistent (about 85.5%) when compared to the ground‐truth data based on traffic indices derived from a big taxi GPS trajectory dataset. This research enhances the understanding of traffic congestion situations under various geographic, societal, and economic contexts based on easily accessible road, street‐view, and POI datasets at large spatiotemporal scales. Numéro de notice : A2020-702 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12641 Date de publication en ligne : 04/06/2020 En ligne : https://doi.org/10.1111/tgis.12641 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96225
in Transactions in GIS > Vol 24 n° 5 (October 2020) . - pp 1382-1401[article]Machine‐learning prediction models for pedestrian traffic flow levels: Towards optimizing walking routes for blind pedestrians / Achituv Cohen in Transactions in GIS, Vol 24 n° 5 (October 2020)
[article]
Titre : Machine‐learning prediction models for pedestrian traffic flow levels: Towards optimizing walking routes for blind pedestrians Type de document : Article/Communication Auteurs : Achituv Cohen, Auteur ; Sagi Dalyot, Auteur Année de publication : 2020 Article en page(s) : pp 1264-1279 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données localisées des bénévoles
[Termes IGN] données spatiotemporelles
[Termes IGN] gestion des itinéraires
[Termes IGN] handicap
[Termes IGN] itinéraire piétionnier
[Termes IGN] modèle de simulation
[Termes IGN] navigation pédestre
[Termes IGN] OpenStreetMap
[Termes IGN] personne non-voyante
[Termes IGN] point d'intérêt
[Termes IGN] trafic routierRésumé : (Auteur) Navigation and orientation while walking in urban spaces pose serious challenges for blind pedestrians, sometimes even on a daily basis. Research shows the practicability of computerized weighted network route planning algorithms based on OpenStreetMap mapping data for calculating customized routes for blind pedestrians. While data about pedestrians and vehicle traffic flow at different times throughout the day influence the route choices of blind pedestrians, such data do not exist in OpenStreetMap. Quantifying the correlation between spatial structure and traffic flow could be used to fill this gap. As such, we investigated machine‐learning methods to develop a computerized model for predicting pedestrian traffic flow levels, with the objective of enriching the OpenStreetMap database. This article presents prediction results by implementing six machine‐learning algorithms based on parameters relating to the geometrical and topological configuration of streets in OpenStreetMap, as well as points‐of‐interest such as public transportation and shops. The Random Forest algorithm produced the best results, whereby 95% of the testing data were successfully predicted. These results indicate that machine‐learning algorithms can accurately generate necessary temporal data, which when combined with the available crowdsourced open mapping data could augment the reliability of route planning algorithms for blind pedestrians. Numéro de notice : A2020-700 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12674 Date de publication en ligne : 04/08/2020 En ligne : https://doi.org/10.1111/tgis.12674 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96210
in Transactions in GIS > Vol 24 n° 5 (October 2020) . - pp 1264-1279[article]Network-constrained bivariate clustering method for detecting urban black holes and volcanoes / Qiliang Liu in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)PermalinkMeasuring accessibility of bus system based on multi-source traffic data / Yufan Zuo in Geo-spatial Information Science, vol 23 n° 3 (September 2020)PermalinkCyclists' exposure to air pollution and noise in Mexico City : contribution of real-time traffic density indicators integrated into GIS / Philippe Apparicio in Revue internationale de géomatique, vol 30 n° 3-4 (juillet - décembre 2020)PermalinkAn empirical study on the intra-urban goods movement patterns using logistics big data / Pengxiang Zhao in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)PermalinkA hybrid deep learning–based model for automatic car extraction from high-resolution airborne imagery / Mehdi Khoshboresh Masouleh in Applied geomatics, vol 12 n° 2 (June 2020)PermalinkA multi-factor spatial optimization approach for emergency medical facilities in Beijing / Liang Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkTraffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning / Yann Méneroux in International Journal of Data Science and Analytics JDSA, vol 10 n° 1 (June 2020)PermalinkDynamic floating stations model for emergency medical services with a consideration of traffic data / Chih-Hong Sun in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)PermalinkTechniques for efficient detection of rapid weather changes and analysis of their impacts on a highway network / Adil Alim in Geoinformatica, vol 24 n° 2 (April 2020)PermalinkAn OD flow clustering method based on vector constraints: a case study for Beijing taxi origin-destination data / Xiaogang Guo in ISPRS International journal of geo-information, vol 9 n° 2 (February 2020)PermalinkAssessing public transit performance using real-time data: spatiotemporal patterns of bus operation delays in Columbus, Ohio, USA / Yongha Park in International journal of geographical information science IJGIS, vol 34 n° 2 (February 2020)PermalinkPermalinkPermalinkPermalinkA reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy / Yan Zhou in Transactions in GIS, Vol 23 n° 5 (October 2019)PermalinkDetecting and mapping traffic signs from Google Street View images using deep learning and GIS / Andrew Campbell in Computers, Environment and Urban Systems, vol 77 (september 2019)PermalinkAnalyse spatiotemporelle des tournées de livraison d’une entreprise de livraison à domicile / Khaled Belhassine in Revue internationale de géomatique, vol 29 n° 2 (avril - juin 2019)PermalinkEmbedding road networks and travel time into distance metrics for urban modelling / Henry Crosby in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)PermalinkA methodology with a distributed algorithm for large-scale trajectory distribution prediction / QiuLei Guo in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)PermalinkAnalyse d’images par méthode de Deep Learning appliquée au contexte routier en conditions météorologiques dégradées / Khouloud Dahmane (2019)Permalink