Unravelling System Optimum Structure by trajectory data analysis
| Autor: | Chen, Ruiwei, Leclercq, Ludovic |
|---|---|
| Přispěvatelé: | Laboratoire d'Ingénierie Circulation Transport (LICIT UMR TE), Université de Lyon-École Nationale des Travaux Publics de l'État (ENTPE)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), EC/H2020/646592/EU/A Multiscale and Multimodal Modelling Approach for Green Urban Traffic Management/MAGnUM_ERC, Cadic, Ifsttar |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
NETWORK-RELATED TRAJECTORY FEATURES
TRAITEMENT DES DONNEES SYSTEM OPTIMUM DUREE DU TRAJET CIRCULATION ROUTIERE RESEAU ROUTIER MODELISATION [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation TRAJECTORY DATA ANALYSIS ITINERAIRE ROUTIER FEUX DE CIRCULATION GESTION DU TRAFIC SUPERVISED LEARNING TRAJECTOIRE SIMULATION [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation SYSTEM OPTIMUM(SO) |
| Zdroj: | hEART 2019, 8th Symposium of the European Association for Research in Transportation hEART 2019, 8th Symposium of the European Association for Research in Transportation, Sep 2019, Budapest, Hungary. 9p EWGT 2019, 22nd EURO Working Group on Transportation Meeting EWGT 2019, 22nd EURO Working Group on Transportation Meeting, Sep 2019, Barcelona, Spain. 3p |
| Popis: | hEART 2019, 8th Symposium of the European Association for Research in Transportation, Budapest, HONGRIE, 04-/09/2019 - 06/09/2019; This work investigates network-related trajectory features to unravel trips that the most contribute to the system under-performance. When such trips are identified, features analysis also permits to identify the best alternatives in terms of routes to make the system to its optimum. First, data mining is carried out on trajectories obtained from reference dynamic traffic assignment (DTA) simulations in a real-world network, based on User-Equilibrium (UE) and System-Optimum (SO). This helps us (i) to target the trajectories to be changed, and (ii) to identify their main features (trip lengths, experienced travel time, path marginal costs, and network-related features such as betweenness centrality and traffic light parameters, etc.). Similarity analysis based on Longest Common Subsequence, Principle Component Analysis are the main methods that are performed to carry out descriptive analysis of trajectories. Supported Vector Machine is then used to determinate the features with regards to their contribution to better network performance. |
| Databáze: | OpenAIRE |
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