Refining trip starting and ending locations when estimating travel-demand at large urban scale
| Autor: | Jean Krug, Cécile Becarie, Ludovic Leclercq, Arthur Burianne |
|---|---|
| Přispěvatelé: | Laboratoire d'Ingénierie Circulation Transport (LICIT UMR TE ), École Nationale des Travaux Publics de l'État (ENTPE)-Université de Lyon-Université Gustave Eiffel, EC/H2020/646592/EU/A Multiscale and Multimodal Modelling Approach for Green Urban Traffic Management/MAGnUM_ERC |
| Rok vydání: | 2021 |
| Předmět: |
LYON
Mathematical problem Operations research Computer science DEPLACEMENT URBAIN ACQUISITION DES DONNEES Geography Planning and Development DUREE DU TRAJET 0211 other engineering and technologies Urban studies Microsimulation Transportation 02 engineering and technology DEPLACEMENT CIRCULATION ROUTIERE 11. Sustainability 0502 economics and business TRAFIC ROUTIER DEMAND DOWNSCALING General Environmental Science MICROSCOPIC SIMULATION Estimation 050210 logistics & transportation TRAITEMENT DES DONNEES 05 social sciences 021107 urban & regional planning MODELISATION [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation MODELE MICROSCOPIQUE GESTION DU TRAFIC VOYAGE URBAN TRANSPORTATION Work (electrical) TRANSPORT URBAIN SIMULATION Path (graph theory) TRIPS architecture Scale (map) |
| Zdroj: | Journal of Transport Geography Journal of Transport Geography, 2021, 93, 35p. ⟨10.1016/j.jtrangeo.2021.103041⟩ |
| ISSN: | 0966-6923 |
| Popis: | Demand estimation is an important step for multiple applications in urban studies. However, the level of accuracy required depends on the objective of the study. In dynamic traffic microsimulation, the estimation of demand needs to be accurate, as it is intended to describe individuals' trips on a very small-scale. In this case, poor estimation of trip initiation, path, and endings could result in the wrong estimation of the city-block scale's traffic state. Estimating demand at a large-scale with high-resolution is not only very challenging because it requires a large volume of data from multiple sources, but the underlying mathematical problem is considerable and thus hard to solve. In this paper, we address the issue of trip starts and ends when modeling large perimeters. We propose to enhance the location of trip initiation and termination by merging heterogeneous and large public datasets. To do so, we develop a series of algorithms that identify fine-mesh areas where trips could reliably start or end and we share the estimated demand within these sub-areas, following the distribution of trip purposes (Home, Work, Shop, etc.). The method is deployed in Lyon city, France, and validated on an extraction of it. Micro-simulation results show that the demand, once accurately distributed, changes the overall network's performance, confirming the significant influence of trip endings and starts on the overall traffic dynamics. |
| Databáze: | OpenAIRE |
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