A Framework for Simulating Cyclists in SUMO

Abstract

Cyclists pose an interesting challenge in the microscopic modeling and simulation of urban traffic. Like motorists, cyclists can move on roadways, tend to have one main axis of movement (longitudinal), and cannot change their velocity instantaneously. However, like pedestrians, cyclists are less bound by lane discipline and are often less rule-oriented than motorists. They flexibly adjust their lateral position within a lane, fluidly move between different types of infrastructure (bicycle lane, sidewalk, roadway), and tactically select their pathways across intersections. Their interactions with other road users are more intuitive and less defined by the lane markings. How should the behavior of such adaptable road users be modeled? In SUMO, modifications to the simulation environment enable the application of car-based models to cyclists. A driving lane is divided into multiple sub-lanes along the longitudinal axis. Lane change and car-following models can be calibrated and applied to simulate realistic bicycle and mixed traffic using this approach. However, the flexible nature of cyclists, particularly at intersections or when switching between different types of infrastructure, is difficult to simulate. A modeling framework for linking the paradigms used to simulate motor vehicle traffic (one-dimensional lane-based models) and pedestrian traffic (two-dimensional social force type models) is presented. Guidelines are used to lead each cyclist through the network while they move freely on a two-dimensional plane, their movement and interactions governed by an adapted social force model. The conceptual framework and an openly available Python package CyclistModel are introduced, and advantages and possible use cases are discussed.