Including traffic jam avoidance in an agent-based network model

Computational Social Networks
Christian HoferManfred Füllsack

Abstract

Understanding traffic is an important challenge in different scientific fields. While there are many approaches to constructing traffic models, most of them rely on origin-destination data and have difficulties when phenomena should be investigated that have an effect on the origin-destination matrix. A macroscopic traffic model is introduced that is novel in the sense that no origin-destination data are required as an input. This information is generated from mobility behavior data using a hybrid approach between agent-based modeling to find the origin and destination points of each vehicle and network techniques to find efficiently the routes most likely used to connect those points. The simulated road utilization and resulting congestion is compared to traffic data to quantitatively evaluate the results. Traffic jam avoidance behavior is included in the model in several variants, which are then all evaluated quantitatively. The described model is applied to the City of Graz, a typical European city with about 320,000 inhabitants. Calculated results correspond well with reality. The introduced traffic model, which uses mobility data instead of origin-destination data as input, was successfully applied and offers unique advant...Continue Reading

Software Mentioned

Google Maps
SUMO
OpenStreetMap
VISSIM
MatSIM
OSMnx
Google Traffic
Google Maps Distance Matrix API

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