Coupled microscopic modelling of traffic demand and traffic flow on a city level

Abstract

Microscopic models of traffic demand and traffic flow have reached a maturity which allows their usage for investigating large-scale effects of measures and/or developments. In microscopic models, every participant is described individually by a set of attributes. Demand models use attributes such as the age, employment state, sex, home location, and the availability of mobility options for computing the person’s mobility in a region defined by activity locations and mode-dependent travel times. Traffic flow models replicate the progress of individual users – may they be vehicles or individual persons as well – through a given transportation network. As in demand models, every participant is defined using a set of attributes, mainly ones that describe its physical attributes, such as the maximum acceleration/deceleration capabilities, the maximum speed or the emission class. The microscopic representation of user and vehicle groups is intrinsically sensitive to a large number of influences, because the used parameters directly determine the individuals’ behavior. The usage of disaggregated populations and vehicle fleets additionally enables to investigate the effects of measures that influence only a part of the population or the vehicle fleet, such as environmental zones or parking fees. At first, the benefits of using microscopic models are outlined. Then, the fundamentals of microscopic models for traffic flow simulation and demand modelling are given respectively, using the microscopic traffic flow simulation “SUMO” (Krajzewicz et al., 2012) and the agent-based demand model “TAPAS” (Heinrichs et al., 2016; Hertkorn, 2005) as examples. Afterwards, an example use cases that demonstrates why it is necessary to employ both simulation types is given. Then, a new system that couples both simulations for diminishing the missing interaction between traffic performance and mode choice is outlined. The report ends with a summary.