Determining service provider and transport system related effects of ridesourcing services by simulation within the travel demand model mobiTopp

Purpose Ridesourcing services have become popular recently and play a crucial role in Mobility as a Service (MaaS) offers. With their increasing importance, the need arises to integrate them into travel demand models to investigate transport system-related effects. As strong interdependencies between different people’s choices exist, microscopic and agent-based model approaches are especially suitable for their simulation. Method This paper presents the integration of shared and non-shared ridesourcing services (i.e., ride-hailing and ride-pooling) into the agent-based travel demand model mobiTopp. We include a simple vehicle allocation and fleet control component and extend the mode choice by the ridesourcing service. Thus, ridesourcing is integrated into the decision-making processes on an agent’s level, based on the system’s specific current performance, considering current waiting times and detours, among other data. Results and Discussion In this paper, we analyze the results concerning provider-related figures such as the number of bookings, trip times, and occupation rates, as well as effects on other travel modes. We performed simulation runs in an exemplary scenario with several variations with up to 1600 vehicles for the city of Stuttgart, Germany. This extension for mobiTopp provides insights into interdependencies between ridesourcing services and other travel modes and may help design and regulate ridesourcing services.

Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviation

Every sector needs to minimize GHG emissions to limit climate change. Emissions from transport, however, have remained mostly unchanged over the past thirty years. In particular, air travel for short-haul flights is a significant contributor to transport emissions. This article identifies factors that influence the demand for domestic air travel. An agent-based model was implemented for domestic travel in Germany to test policies that could be implemented to reduce air travel and CO₂ emissions. The agent-based long-distance travel demand model is composed of trip generation, destination choice, mode choice and CO₂ emission modules. The travel demand model was estimated and calibrated with the German Household Travel Survey, including socio-demographic characteristics and area type. Long-distance trips were differentiated by trip type (daytrip, overnight trip), trip purpose (business, leisure, private) and mode (auto, air, long-distance rail and long-distance bus). Emission factors by mode were used to calculate CO₂ emissions. Potential strategies and policies to reduce air travel demand and its CO₂ emissions are tested using this model. An increase in airfares reduced the number of air trips and reduced transport emissions. Even stronger effects were found with a policy that restricts air travel to trips that are longer than a certain threshold distance. While such policies might be difficult to implement politically, restricting air travel has the potential to reduce total CO₂ emissions from transport by 7.5%.

Development of an urban travel demand model of the Town of Oakville

This dissertation documents the development of a travel demand model of the Town of Oakville. The purpose of the model was to predict traffic demand on study area roadways during the morning peak hour. The model focused on the prediction of auto driver trips. The model was developed based on the 1996 Transportation Tomorrow Survey data, available from the Data Management Group at the University of Toronto. The model was developed in accordance with the Urban Transportation Modelling System (UTMS) of models. Activities included: Trip Generation, Trip Distribution, and Trip Assignment. Accounting for Mode Split was unnecessary as the model focused only on auto driver trips. The final product of the model is a set of traffic flows over links in the transportation network. The model was developed using TransCAD transportation GIS (Geographic Information System) software. TransCAD is a microcomputer software specifically designed for transportation planning and data management.

Development of an urban travel demand model of the Town of Oakville

This dissertation documents the development of a travel demand model of the Town of Oakville. The purpose of the model was to predict traffic demand on study area roadways during the morning peak hour. The model focused on the prediction of auto driver trips. The model was developed based on the 1996 Transportation Tomorrow Survey data, available from the Data Management Group at the University of Toronto. The model was developed in accordance with the Urban Transportation Modelling System (UTMS) of models. Activities included: Trip Generation, Trip Distribution, and Trip Assignment. Accounting for Mode Split was unnecessary as the model focused only on auto driver trips. The final product of the model is a set of traffic flows over links in the transportation network. The model was developed using TransCAD transportation GIS (Geographic Information System) software. TransCAD is a microcomputer software specifically designed for transportation planning and data management.