Large-scale agent-based travel demand optimization applied to Switzerland, including mode choice

Kay Werner Axhausen · Michael Balmer · Francesco Ciari · Andreas Horni · Marcel Rieser · Rashid A. Waraich

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Transportation Planning and Optimization Traffic control and management Transportation and Mobility Innovations

Abstract

This paper presents the application of the agent-based transport simulation toolkit MATSim-T to a large-scale scenario of Switzerland.The scenario is called large-scale because ca. 6 million synthetic persons, "agents", are simulated on a high-resolution network model with >1 million links.MATSim-T is able to compute a relaxed state of the simulation system within 60 iterations of the learning-based solution procedure with regard to mode choice, car route choice and choice of activity timing.This is achieved by applying improved optimization algorithms in the replanning stage.A genetic algorithm is used for times and and mode choice optimization of activity plans, together with an efficient implementation of time-dependent shortest path search for route choice.The improvements of the behavioral model reported in this paper are focused on the scoring function which can process individualized parameters for measuring the quality of all-day activity plans.Combined with disaggregate input data for population and land use, it was possible to build a heterogeneous and thus more realistic scenario.Furthermore, four modes of transport (car, public transit, bike, walk) are considered in the presented application.The generalized cost of the car option is determined by a queue simulation of traffic flow.In order to prove the concept of mode choice optimization in a multi-agent microsimulation, the other modes are modelled as abstract alternatives with static travel costs constant throughout the modeled average workday.It is shown how the model is calibrated against observed modal split data.The results are validated with average workday count data.Despite the simple cost structure of the mode alternatives, and due to a mode choice concept based on subtours, the observed spatial distribution of the modal split can be reproduced within ±10 percentage points per mode.

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References

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2021Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data 2017Open PFLOW: Creation and evaluation of an open dataset for typical people mass movement in urban areas 2015Agent-based Modeling of Traffic Behavior in Growing Metropolitan Areas 2019An improvement in MATSim computing time for large-scale travel behaviour microsimulation 2020Surveying and analysing mode and route choices in Switzerland 2010–2015

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