Unravelling System Optimum Structure by trajectory data analysis - Archive ouverte HAL
Conference Papers Year : 2019

Unravelling System Optimum Structure by trajectory data analysis

Abstract

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 i 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.
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Dates and versions

hal-02434567 , version 1 (27-03-2020)

Identifiers

  • HAL Id : hal-02434567 , version 1

Cite

Ruiwei Chen, Ludovic Leclercq. Unravelling System Optimum Structure by trajectory data analysis. EWGT 2019, 22nd EURO Working Group on Transportation Meeting, Sep 2019, Barcelona, Spain. 3p. ⟨hal-02434567⟩
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