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Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction / Tianhong Zhao in Computers, Environment and Urban Systems, vol 94 (June 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101776 Titre : Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction Type de document : Article/Communication Auteurs : Tianhong Zhao, Auteur ; Zhengdong Huang, Auteur ; Wei Tu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101776 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] bati [Termes IGN] données spatiotemporelles [Termes IGN] gestion de trafic [Termes IGN] graphe [Termes IGN] logement [Termes IGN] migration pendulaire [Termes IGN] modèle de simulation [Termes IGN] régression géographiquement pondérée [Termes IGN] service public [Termes IGN] Shenzhen [Termes IGN] système de transport intelligent [Termes IGN] transport public [Termes IGN] transport urbain Résumé : (auteur) Accurate and robust short-term bus travel prediction facilitates operating the bus fleet to provide comfortable and flexible bus services. The built environment, including land use, buildings, and public facilities, has an important influence on bus travel demand prediction. However, previous studies regarded the built environment as a static feature thus even ignored its influence on bus travel in deep learning framework. To fill this gap, we propose a graph deep learning-based approach coupling with spatiotemporal influence of built environment (GDLBE) to enhance short-term bus travel demand prediction. A time-dependent geographically weighted regression method is used to resolve the dynamic influence of the built environment on bus travel demand at different times of the day. A graph deep learning module is used to capture the comprehensive spatial and temporal dependency behind massive bus travel demand. The short-term bus travel demand is predicted by fusing the dynamic built environment influences and spatiotemporal dependency. An experiment in Shenzhen is conducted to evaluate the performance of the proposed approach. Baseline methods are compared, and the results demonstrate that the proposed approach outperforms the baselines. These results will help bus fleet dispatch for smart transportation. Numéro de notice : A2022-245 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101776 Date de publication en ligne : 12/03/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100185 [article]

Towards the automated large-scale reconstruction of past road networks from historical maps / Johannes H. Uhl in Computers, Environment and Urban Systems, vol 94 (June 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101794 Titre : Towards the automated large-scale reconstruction of past road networks from historical maps Type de document : Article/Communication Auteurs : Johannes H. Uhl, Auteur ; Stefan Leyk, Auteur ; Yao-Yi Chiang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101794 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de groupement [Termes IGN] analyse de sensibilité [Termes IGN] carte ancienne [Termes IGN] carte routière [Termes IGN] carte topographique [Termes IGN] classification par nuées dynamiques [Termes IGN] données multitemporelles [Termes IGN] Etats-Unis [Termes IGN] extraction du réseau routier [Termes IGN] histoire [Termes IGN] paysage [Termes IGN] réseau routier [Termes IGN] transport routier [Termes IGN] urbanisation Résumé : (auteur) Transportation infrastructure, such as road or railroad networks, represent a fundamental component of our civilization. For sustainable planning and informed decision making, a thorough understanding of the long-term evolution of transportation infrastructure such as road networks is crucial. However, spatially explicit, multi-temporal road network data covering large spatial extents are scarce and rarely available prior to the 2000s. Herein, we propose a framework that employs increasingly available scanned and georeferenced historical map series to reconstruct past road networks, by integrating abundant, contemporary road network data and color information extracted from historical maps. Specifically, our method uses contemporary road segments as analytical units and extracts historical roads by inferring their existence in historical map series based on image processing and clustering techniques. We tested our method on over 300,000 road segments representing more than 50,000 km of the road network in the United States, extending across three study areas that cover 42 historical topographic map sheets dated between 1890 and 1950. We evaluated our approach by comparison to other historical datasets and against manually created reference data, achieving F-1 scores of up to 0.95, and showed that the extracted road network statistics are highly plausible over time, i.e., following general growth patterns. We demonstrated that contemporary geospatial data integrated with information extracted from historical map series open up new avenues for the quantitative analysis of long-term urbanization processes and landscape changes far beyond the era of operational remote sensing and digital cartography. Numéro de notice : A2022-243 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101794 Date de publication en ligne : 18/03/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101794 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100182 [article]

Changing mobility patterns in the Netherlands during COVID-19 outbreak / Sander Van Der Drift in Journal of location-based services, vol 16 n° 1 (March 2022)  

Public [article]  inJournal of location-based services > vol 16 n° 1 (March 2022) . - pp 1 - 24 Titre : Changing mobility patterns in the Netherlands during COVID-19 outbreak Type de document : Article/Communication Auteurs : Sander Van Der Drift, Auteur ; Luc Wismans, Auteur ; Marie-José Olde-Kalter, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 24 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] bicyclette [Termes IGN] comportement [Termes IGN] épidémie [Termes IGN] estimation bayesienne [Termes IGN] mobilité territoriale [Termes IGN] Pays-Bas [Termes IGN] téléphone intelligent [Termes IGN] transport [Termes IGN] transport public [Termes IGN] travail à domicile [Termes IGN] véhicule automobile Résumé : (auteur) The COVID-19 outbreak and associated measures taken had an enormous impact on society as well as a disruptive, but not necessarily negative, impact on mobility. The Ministry of Infrastructure and Water Management received the most recent insights from the Dutch Mobility Panel (DMP) on a weekly basis. These insights were used to monitor the travel behaviour and to analyse changes in the behaviour of different groups and usage of modes of transport during COVID-19. The analysis shows an enormous decrease in travel at the beginning of the implementation of the so-called ‘intelligent’ lockdown and gradual increase again towards comparable levels as before this ‘intelligent lockdown, although the distribution over time, motives and used modes has changed. It becomes clear that not everyone needs to travel during peak hours and commuter travel is also not the main reason for the increase in car usage. Furthermore, cycling has shown to be an alternative option for travellers and public transport is hardly used anymore. If it is possible to sustain the lower level of car usage and integrate public transport as an important alternative for travel again, the COVID-19 impact on mobility could have a substantial remaining positive impact on mobility. Numéro de notice : A2022-391 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17489725.2021.1876259 Date de publication en ligne : 11/03/2021 En ligne : https://doi.org/10.1080/17489725.2021.1876259 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100682 [article]

Unravelling the dynamics behind the urban morphology of port-cities using a LUTI model based on cellular automata / Aditya Tafta Nugraha in Computers, Environment and Urban Systems, vol 92 (March 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 92 (March 2022) Titre : Unravelling the dynamics behind the urban morphology of port-cities using a LUTI model based on cellular automata Type de document : Article/Communication Auteurs : Aditya Tafta Nugraha, Auteur ; Ben J. Waterson, Auteur ; Simon P. Blainey, Auteur ; et al., Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] dynamique spatiale [Termes IGN] Grande-Bretagne [Termes IGN] interaction spatiale [Termes IGN] modèle orienté agent [Termes IGN] morphologie urbaine [Termes IGN] planification urbaine [Termes IGN] port [Termes IGN] transport urbain [Termes IGN] utilisation du sol Résumé : (auteur) The urban morphology is characterised by self-organisation where interactions of multiple agents produce emerging patterns on the urban form. Port-urban relationship added to the complexity of port cities' urban form. Most urban cellular automata (CA) models simulate land-use evolution through transition rules representing multi-factored local interactions. However, calibration of CA-based urban land use and transport interaction (LUTI) models often utilise manual methods due to complexity of the process. This limits insights on urban interactions to a few explored settlements and prevents applications for planning and assessment of transport policies in other contexts. This paper, therefore, addresses three main points. The paper (i) demonstrates an improved method for the calibration of CA-based LUTI models, (ii) contributes to a better understanding of the urban dynamics in port city systems by quantifying generalizable interactions from a wide range of port-urban settlements, and (iii) illustrates how the use of these interactions in a simulation model can allow long-term impact predictions of planning interventions. These were done by formulating a model in a similar structure as a neural network model to enable automatic calibration using an application of the gradient-descent algorithm. The model was then used to quantify the dynamics between land-use, geographic, and transport factors in 46 port-based and 10 non-port settlements across Great Britain, thus enabling cross-sectional analysis. Cluster analysis of the calibrated interactions in the study areas was conducted to examine the variations of these interactions. This produced two main groups. In the first group, consisting larger settlements, connections between ports and other urban activities were weaker than in the second group which consisted of smaller port-settlements. Overall, the findings of the research are consistent with existing evidence in the port-cities literature but go further in quantifying the interaction between urban agents within port-urban systems of various sizes and types. These quantified interactions will enable planners to better predict the longer-term consequences of their interventions. Numéro de notice : A2022-084 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101733 Date de publication en ligne : 25/11/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101733 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99489 [article]

Emerging technologies for smart cities’ transportation: Geo-information, data analytics and machine learning approaches / Li-Minn Ang in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 2 (February 2022) . - n° 85 Titre : Emerging technologies for smart cities’ transportation: Geo-information, data analytics and machine learning approaches Type de document : Article/Communication Auteurs : Li-Minn Ang, Auteur ; Jasmine Kah Phooi Seng, Auteur ; Ericmoore Ngharamike, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 85 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] données massives [Termes IGN] planification urbaine [Termes IGN] système de transport intelligent [Termes IGN] trafic routier [Termes IGN] transport collectif [Termes IGN] transport urbain [Termes IGN] ville intelligente [Termes IGN] zone urbaine Résumé : (auteur) With the recent increase in urban drift, which has led to an unprecedented surge in urban population, the smart city (SC) transportation industry faces a myriad of challenges, including the development of efficient strategies to utilize available infrastructures and minimize traffic. There is, therefore, the need to devise efficient transportation strategies to tackle the issues affecting the SC transportation industry. This paper reviews the state-of-the-art for SC transportation techniques and approaches. The paper gives a comprehensive review and discussion with a focus on emerging technologies from several information and data-driven perspectives including (1) geoinformation approaches; (2) data analytics approaches; (3) machine learning approaches; (4) integrated deep learning approaches; (5) artificial intelligence (AI) approaches. The paper contains core discussions on the impacts of geo-information on SC transportation, data-driven transportation and big data technology, machine learning approaches for SC transportation, innovative artificial intelligence (AI) approaches for SC transportation, and recent trends revealed by using integrated deep learning towards SC transportation. This survey paper aimed to give useful insights to researchers regarding the roles that data-driven approaches can be utilized for in smart cities (SCs) and transportation. An objective of this paper was to acquaint researchers with the recent trends and emerging technologies for SC transportation applications, and to give useful insights to researchers on how these technologies can be exploited for SC transportation strategies. To the best of our knowledge, this is the first comprehensive review that examines the impacts of the various five driving technological forces—geoinformation, data-driven and big data technology, machine learning, integrated deep learning, and AI—in the context of SC transportation applications. Numéro de notice : A2022-118 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11020085 Date de publication en ligne : 24/01/2022 En ligne : https://doi.org/10.3390/ijgi11020085 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99649 [article]

Access to urban parks: Comparing spatial accessibility measures using three GIS-based approaches / Siqin Wang in Computers, Environment and Urban Systems, vol 90 (November 2021)  

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Cellular automata based land-use change simulation considering spatio-temporal influence heterogeneity of light rail transit construction: A case in Nanjing, China / Jiaming Na in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)  

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A method of hydrographic survey technology selection based on the decision tree supervised learning / Ivana Golub Medvešek (2021)  

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Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London / Nilufer Sari Aslam in Annals of GIS, vol 27 n° 1 (January 2021)  

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Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC / Andreas Keler in Geo-spatial Information Science, vol 23 n° 2 (June 2020)  

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Spatio-temporal evaluation of transport accessibility of the Istanbul metrobus line / Wasim Shoman in Geocarto international, vol 35 n° 6 ([01/05/2020])  

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Geographies of maritime transport, Ch. 4. Geography versus topology in the evolution of the global container shipping network (1977-2016) / César Ducruet (2020)    

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Sustainable built environment and urban growth management / Wann-Ming Wey (2020)  

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L’accessibilité ferroviaire à Paris des grandes aires urbaines françaises : approche par la time geography / Laurent Chapelon in Mappemonde, n° 127 (juillet 2019)  

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Human mobility, spatiotemporal context, and environmental health / Mei-Po Kwan (2019)  

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