Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

Datos Bibliográficos

ID22034985
AutoresJoerg Schweizer (0000-0003-2289-6111, University of Bologna, autor de correspondencia), Cristian Poliziani (0000-0002-7646-1394, University of Bologna), Federico Rupi (0000-0001-8404-5684, University of Bologna), Davide Morgano (University of Bologna), Mattia Magi (Eni (Italy))
Año2021
Volumen10
Número3
Páginas165
Fecha de publicación2021-03-14
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaISPRS International Journal of Geo-Information (JOURNAL)
Identificadores de la revistaISSN: 2220-9964 • E-ISSN: 2220-9964
EditorialMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi10030165
OpenAlexW3138785323
IdiomaEN
Citas recibidas7
Referencias citadas43

A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources

Big data · Data mining · Geography · Global Positioning System · Microsimulation · Pedestrian · Simulation · Telecommunications · Traffic simulation · Transport engineering · Computer Science · Engineering · Traffic control and management · Transportation and Mobility Innovations · Transportation Planning and Optimization · Software