Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data
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 compl...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/10/3/165 |
| _version_ | 1827697672627159040 |
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| author | Joerg Schweizer Cristian Poliziani Federico Rupi Davide Morgano Mattia Magi |
| author_facet | Joerg Schweizer Cristian Poliziani Federico Rupi Davide Morgano Mattia Magi |
| author_sort | Joerg Schweizer |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:15:08Z |
| format | Article |
| id | doaj.art-0f9f5c53aaf2478c8e8f4482b59208ac |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T13:15:08Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-0f9f5c53aaf2478c8e8f4482b59208ac2023-11-21T10:27:37ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-03-0110316510.3390/ijgi10030165Building a Large-Scale Micro-Simulation Transport Scenario Using Big DataJoerg Schweizer0Cristian Poliziani1Federico Rupi2Davide Morgano3Mattia Magi4Department of Civil, Chemical, Environmental and Materials Engineering, University of Bologna, 40126 Bologna, ItalyDepartment of Civil, Chemical, Environmental and Materials Engineering, University of Bologna, 40126 Bologna, ItalyDepartment of Civil, Chemical, Environmental and Materials Engineering, University of Bologna, 40126 Bologna, ItalyDepartment of Civil, Chemical, Environmental and Materials Engineering, University of Bologna, 40126 Bologna, ItalyRighetti & Monte—Ingegneri e Architetti Associati, 40126 Bologna, ItalyA 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.https://www.mdpi.com/2220-9964/10/3/165large scaleagent-basedmicro-simulationmode choice modelbig dataGPS traces |
| spellingShingle | Joerg Schweizer Cristian Poliziani Federico Rupi Davide Morgano Mattia Magi Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data ISPRS International Journal of Geo-Information large scale agent-based micro-simulation mode choice model big data GPS traces |
| title | Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data |
| title_full | Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data |
| title_fullStr | Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data |
| title_full_unstemmed | Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data |
| title_short | Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data |
| title_sort | building a large scale micro simulation transport scenario using big data |
| topic | large scale agent-based micro-simulation mode choice model big data GPS traces |
| url | https://www.mdpi.com/2220-9964/10/3/165 |
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