2021/07/31 by Serio Agriesti, Agriesti, Serio, Claudio Roncoli +3
Engineering · Social Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #Transportation Planning and Optimization #Transportation and Mobility Innovations #Urban Transport and Accessibility
paper · pdf · doi:10.48550/arxiv.2108.00243
openalex publication_date 2021/07/31 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Agent based modelling has acquired the spotlight in the transportation domain\nboth in scientific literature and in real life applications, thanks to its\ncapability to deal with the ever-growing complexity of transportation systems,\nincluding future disrupting mobility technologies and services such as\nautomated driving, Mobility as a Service, and micromobility. Different software\nemerged, dedicated to the simulation of disaggregate travel demand framing\nindividual choices based on the profile of each agent. Still, the actual\nresearch work exploiting these models is scarce and the professionals with the\nknowledge to use them are few. This may be ascribed to the large amount of\nneeded input data or to a lack of commercial solutions and of research\nproduction detailing the process leading to the actual simulations. In this\npaper, a methodology to spatially assign a synthetic population by exploiting\npublicly available aggregate data is presented and implemented on a case study.\nIn doing so, the paper provides a systematic approach for a quick and efficient\ntreatment of the data needed for activity-based demand generation. Finally, the\nobtained dataset, representing a synthetic population of the city of Tallinn,\nEstonia, and its spatial assignment, is described so that it may be exploited\nby fellow researchers, since both the tools needed for spatial assignment and\nthe resulting dataset are made available as open source.\n