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Route intersection reduction with connected autonomous vehicles / Sadegh Motallebi in Geoinformatica, vol 25 n° 1 (January 2021)  

Public [article]  inGeoinformatica > vol 25 n° 1 (January 2021) . - pp 99 - 125 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 routier Résumé : (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 [article]

Hidden Markov map matching based on trajectory segmentation with heading homogeneity / Ge Cui in Geoinformatica, vol 25 n° 1 (January 2021)  

Public [article]  inGeoinformatica > vol 25 n° 1 (January 2021) . - pp 179 - 206 Titre : Hidden Markov map matching based on trajectory segmentation with heading homogeneity Type de document : Article/Communication Auteurs : Ge Cui, Auteur ; Wentao Bian, Auteur ; Xin Wang, Auteur Année de publication : 2021 Article en page(s) : pp 179 - 206 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] appariement de cartes [Termes IGN] appariement de données localisées [Termes IGN] modèle de Markov caché [Termes IGN] réseau routier [Termes IGN] segmentation [Termes IGN] trajectographie par GPS [Vedettes matières IGN] Généralisation Résumé : (Auteur) Map matching is to locate GPS trajectories onto the road networks, which is an important preprocessing step for many applications based on GPS trajectories. Currently, hidden Markov model is one of the most widely used methods for map matching. However, both effectiveness and efficiency of conventional map matching methods based on hidden Markov model will decline in the dense road network, as the number of candidate road segments enormously increases around GPS point. To overcome the deficiency, this paper proposes a segment-based hidden Markov model for map matching. The proposed method first partitions GPS trajectory into several GPS sub-trajectories based on the heading homogeneity and length constraint; next, the candidate road segment sequences are searched out for each GPS sub-trajectory; last, GPS sub-trajectories and road segment sequences are matched in hidden Markov model, and the road segment sequences with the maximum probability is identified. A case study is conducted on a real GPS trajectory dataset, and the experiment result shows that the proposed method improves the effectiveness and efficiency of the conventional HMM map matching method. Numéro de notice : A2021-094 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00429-4 Date de publication en ligne : 02/01/2021 En ligne : https://doi.org/10.1007/s10707-020-00429-4 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96934 [article]

Finding the most navigable path in road networks / Ramneek Kaur in Geoinformatica, vol 25 n° 1 (January 2021)  

Public [article]  inGeoinformatica > vol 25 n° 1 (January 2021) . - pp 207 - 240 Titre : Finding the most navigable path in road networks Type de document : Article/Communication Auteurs : Ramneek Kaur, Auteur ; Vikram Goyal, Auteur ; Venkata M. V. Gunturi, Auteur Année de publication : 2021 Article en page(s) : pp 207 - 240 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] arc [Termes IGN] calcul d'itinéraire [Termes IGN] durée de trajet [Termes IGN] programmation dynamique [Termes IGN] réseau routier Résumé : (Auteur) Input to the Most Navigable Path (MNP) problem consists of the following: (a) a road network represented as a directed graph, where each edge is associated with numeric attributes of cost and “navigability score” values; (b) a source and a destination and; (c) a budget value which denotes the maximum permissible cost of the solution. Given the input, MNP aims to determine a path between the source and the destination which maximizes the navigability score while constraining its cost to be within the given budget value. The problem can be modeled as the arc orienteering problem which is known to be NP-hard. The current state-of-the-art for this problem may generate paths having loops, and its adaptation for MNP that yields simple paths, was found to be inefficient. In this paper, we propose five novel algorithms for the MNP problem. Our algorithms first compute a seed path from the source to the destination, and then modify the seed path to improve its navigability. We explore two approaches to compute the seed path. For modification of the seed path, we explore different Dynamic Programming based approaches. We also propose an indexing structure for the MNP problem which helps in reducing the running time of some of our algorithms. Our experimental results indicate that the proposed solutions yield comparable or better solutions while being orders of magnitude faster than the current state-of-the-art for large real road networks. Numéro de notice : A2021-095 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00428-5 Date de publication en ligne : 03/01/2021 En ligne : https://doi.org/10.1007/s10707-020-00428-5 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96935 [article]