Implementation of free-floating and station-based carsharing in an agent-based travel demand model

2018 ◽  
Vol 12 ◽  
pp. 151-158 ◽  
Author(s):  
Michael Heilig ◽  
Nicolai Mallig ◽  
Ole Schröder ◽  
Martin Kagerbauer ◽  
Peter Vortisch
Keyword(s):  
Travel Demand ◽  
Demand Model ◽  
Agent Based ◽  
Travel Demand Model

scholarly journals Creating an integrated agent-based travel demand model by combining mobiTopp and MATSim

2019 ◽  
Vol 151 ◽  
pp. 776-781 ◽  
Author(s):  
Lars Briem ◽  
Nicolai Mallig ◽  
Peter Vortisch
Keyword(s):  
Travel Demand ◽  
Demand Model ◽  
Agent Based ◽  
Travel Demand Model

scholarly journals Validation of an agent-based travel demand model with floating car data

2019 ◽  
Vol 37 ◽  
pp. 242-249
Author(s):  
Carlos Llorca ◽  
Sasan Amini ◽  
Ana Tsui Moreno ◽  
Rolf Moeckel
Keyword(s):  
Travel Demand ◽  
Demand Model ◽  
Agent Based ◽  
Floating Car Data ◽  
Travel Demand Model

scholarly journals Modeling intermodal travel behavior in an agent-based travel demand model

2021 ◽  
Vol 184 ◽  
pp. 202-209
Author(s):  
Tim Wörle ◽  
Lars Briem ◽  
Michael Heilig ◽  
Martin Kagerbauer ◽  
Peter Vortisch
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Demand Model ◽  
Agent Based ◽  
Travel Demand Model

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

2021 ◽  
Vol 6 (2) ◽  
pp. 271-284 ◽  
Author(s):  
Alona Pukhova ◽  
Ana Tsui Moreno ◽  
Carlos Llorca ◽  
Wei-Chieh Huang ◽  
Rolf Moeckel
Keyword(s):  
Travel Demand ◽  
Ghg Emissions ◽  
Air Travel ◽  
Demand Model ◽  
Long Distance ◽  
Agent Based ◽  
Distance Travel ◽  
Transport Emissions ◽  
Long Distance Travel ◽  
Travel Demand Model

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 CO<sub>2</sub> emissions. The agent-based long-distance travel demand model is composed of trip generation, destination choice, mode choice and CO<sub>2</sub> 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 CO<sub>2</sub> emissions. Potential strategies and policies to reduce air travel demand and its CO<sub>2</sub> 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 CO<sub>2</sub> emissions from transport by 7.5%.

scholarly journals Integrating Urban Last-Mile Package Deliveries into an Agent-Based Travel Demand Model

2021 ◽  
Vol 184 ◽  
pp. 178-185
Author(s):  
Anna Reiffer ◽  
Jelle Kübler ◽  
Lars Briem ◽  
Martin Kagerbauer ◽  
Peter Vortisch
Keyword(s):  
Travel Demand ◽  
Demand Model ◽  
Agent Based ◽  
Last Mile ◽  
Travel Demand Model

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

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Gabriel Wilkes ◽  
Lars Briem ◽  
Michael Heilig ◽  
Tim Hilgert ◽  
Martin Kagerbauer ◽  
...  
Keyword(s):  
Transport System ◽  
Travel Demand ◽  
Waiting Times ◽  
Demand Model ◽  
Agent Based ◽  
Demand Models ◽  
Decision Making Processes ◽  
The City ◽  
As Effects ◽  
Travel Demand Model

Abstract 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.

Model Calibration and Forecasts of Air Travel Demand with Categorized Household Socioeconomic Attributes

2020 ◽  
Vol 2674 (6) ◽  
pp. 363-371
Author(s):  
Jungin Kim ◽  
Ikki Kim ◽  
Jaeyeob Shim ◽  
Hansol Yoo ◽  
Sangjun Park
Keyword(s):  
Travel Behavior ◽  
Household Income ◽  
Travel Demand ◽  
Behavioral Model ◽  
Income Group ◽  
Air Travel ◽  
Jeju Island ◽  
Demand Model ◽  
Air Travel Demand ◽  
Travel Demand Model

The objectives of this study were to (1) construct an air demand model based on household data and (2) forecast future air demand to explain the relationship between air demand and individual travel behavior. To this end, domestic passenger air travel demand at Jeju Island in South Korea was examined. A multiple regression model with numerous explanatory variables was established by examining categorized household socioeconomic data that affected air demand. The air travel demand model was calibrated for 2009–2015 based on the annual average number of visits to Jeju Island by households in certain income groups. The explanatory variable was set using a dummy variable for each household income group and the proportion of airfare to GDP per capita. Higher household income meant more frequent visits to Jeju Island, which was well-represented in the model. However, the value of the coefficient for the highest income was lower than the value for the second-highest income group. This suggested that the highest income group preferred overseas travel destinations to domestic ones. The future air demand for Jeju airport was predicted as 26,587,407 passengers in 2026, with a subsequent gradual increase to approximately 33,000,000 passengers by 2045 in this study. This study proposed an air travel demand model incorporating household socioeconomic attributes to reflect individual travel behavior, which contrasts with previous studies that used aggregate data. By constructing an air travel model that incorporated socioeconomic factors as a behavioral model, more accurate and consistent projections could be obtained.

A Complex Network Methodology for Travel Demand Model Evaluation and Validation

2018 ◽  
Vol 18 (4) ◽  
pp. 1051-1073 ◽  
Author(s):  
Meead Saberi ◽  
Taha H. Rashidi ◽  
Milad Ghasri ◽  
Kenneth Ewe
Keyword(s):  
Complex Network ◽  
Model Evaluation ◽  
Travel Demand ◽  
Demand Model ◽  
Travel Demand Model
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