Incorporating Stability of Mode Choice into an Agent-Based Travel Demand Model

2017 ◽  
pp. 28-39
Author(s):  
Nicolai Mallig ◽  
Peter Vortisch
Keyword(s):  
Travel Demand ◽  
Mode Choice ◽  
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

Travel Demand Modeling and Conformity Determination: Atlanta Regional Commission Case Study

2002 ◽  
Vol 1817 (1) ◽  
pp. 172-176 ◽  
Author(s):  
Guy Rousseau ◽  
Tracy Clymer
Keyword(s):  
Land Use ◽  
Travel Demand ◽  
Mode Choice ◽  
Time Of Day ◽  
Traffic Model ◽  
Demand Model ◽  
Primary Input ◽  
Land Use Model ◽  
Home Based ◽  
Travel Demand Model

The Atlanta Regional Commission (ARC) regional travel demand model is described as it relates to its link-based emissions postprocessor. In addition to conformity determination, an overview of other elements is given. The transit networks include the walk and highway access links. Trip generation addresses trip production, trip attraction, reconciliation of productions and attractions, and special adjustments made for Hartsfield Atlanta International Airport. Trip distribution includes the application of the composite impedance variable. In the mode choice model, home-based work uses a logit function, whereas nonwork uses information from the home-based work to estimate modal shares. Traffic assignment includes preparation of time-of-day assignments. The model assigns single-occupancy vehicles, high-occupancy vehicles, and trucks by using separate trip tables. The procedures can accept or prohibit each of the three types of vehicles from each highway lane. Feedback between the land use model and the traffic model is accounted for via composite impedances generated by the traffic model and is a primary input to the land use model DRAM/EMPAL. The land use model is based on census tract geography, whereas the travel demand model is based on traffic analysis zones that are subareas within census tracts. The ARC model has extended the state of the practice by using the log sum variable from mode choice as the impedance measure rather than the standard highway time. This change means that the model is sensitive not only to highway travel time but also to highway and transit costs.

Parsimonious Modeling and Uncertainty Quantification for Transportation Systems Planning Applied to California High-Speed Rail

2012 ◽  
Vol 2266 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Quentin Noreiga ◽  
Mark McDonald
Keyword(s):  
Uncertainty Quantification ◽  
High Speed ◽  
Travel Demand ◽  
Mode Choice ◽  
Demand Model ◽  
Parameter Uncertainties ◽  
Main Mode ◽  
High Speed Rail ◽  
The Impact ◽  
Travel Demand Model

This paper presents a parsimonious travel demand model (PTDM) derived from a proprietary parent travel demand model developed by Cambridge Systematics (CS) for the California high-speed rail system. The purpose of the PTDM is to reduce computational expense for model simulations, optimization and sensitivity analyses, and other repetitive analyses. The PTDM is used to quantify the significance of parameter uncertainties with the use of mean value first-order second moment methods for uncertainty quantification and sensitivity analysis. The PTDM changes the model resolution of the parent travel demand model from a traffic analysis zone to a county-level analysis. The three-step model contains trip frequency, destination choice, and main mode choice models and is calibrated to match the results of the CS model. The main mode choice model predicts primary mode choice results for car, commercial air, conventional rail, and high-speed rail. The PTDM uses data and models similar to parent models to show how uncertainty in travel demand model predictions can be quantified. This paper does not attempt to assess the reliability of parent model forecasts, and the results should not be used to evaluate uncertainty in the California High-Speed Rail Authority's rider ship and revenue forecasts. However, the uncertainty quantification methodology presented here, when applied to the CS model, can be used to quantify the impact of parameter uncertainty on the forecast results.

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

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

Large-Scale Application of a Combined Destination and Mode Choice Model Estimated with Mixed Stated and Revealed Preference Data

2017 ◽  
Vol 2669 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Michael Heilig ◽  
Nicolai Mallig ◽  
Tim Hilgert ◽  
Martin Kagerbauer ◽  
Peter Vortisch
Keyword(s):  
Large Scale ◽  
Travel Demand ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Revealed Preference ◽  
Demand Model ◽  
Agent Based ◽  
Preference Data ◽  
Mode Choice Model

The diffusion of new modes of transportation, such as carsharing and electric vehicles, makes it necessary to consider them along with traditional modes in travel demand modeling. However, there are two main challenges for transportation modelers. First, the new modes’ low share of usage leads to a lack of reliable revealed preference data for model estimation. Stated preference survey data are a promising and well-established approach to close this gap. Second, the state-of-the-art model approaches are sometimes stretched to their limits in large-scale applications. This research developed a combined destination and mode choice model to consider these new modes in the agent-based travel demand model mobiTopp. Mixed revealed and stated preference data were used, and new modes (carsharing, bikesharing, and electric bicycles) were added to the mode choice set. This paper presents both challenges of the modeling process, mainly caused by large-scale application, and the results of the new combined model, which are as good as those of the former sequential model although it also takes the new modes into consideration.

scholarly journals Development of mode choice models of a trip maker for Hyderabad metropolitan city

2018 ◽  
Vol 7 (1.6) ◽  
pp. 1
Author(s):  
Bollini Prasad ◽  
Kumar Molugaram
Keyword(s):  
Travel Demand ◽  
Mode Choice ◽  
Rapid Development ◽  
Motor Vehicles ◽  
Demand Model ◽  
Trade Offs ◽  
Modern Cities ◽  
Choice Analysis ◽  
Distribution Mode ◽  
Travel Demand Model

The rapid development of urbanization, population growth and the rapid development of economy resulted in the rapid increase in the total number of motor vehicles in the modern cities of India. Consequently, the importance of forecasting of the travel demand model has been increased in the recent years. Forecasting of the travel demand model involves various stages of trip generation and distribution, mode choice and traffic assignment. Among these stages, the mode choice analysis is a prominent stage as it considers the travelers mode to reach their destination. Further, study of mode choice criteria has become a vital area of research as individual and household socio-demographics exert a strong influence on travel mode choice decisions. There is a huge literature on travel model choice modeling to predict the range of trade-offs of transportation of commuters considering travel time and travel cost. In such literature intercity mode choice behavior has gained significant attention by several authors. In this study an attempt has made in order to calculate the model share of the different modes between the circle to the circle, and it is found that the modal share of 2-wheeler is 70 %, bus is about 23 % and car is about 7% of the total trips.

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%.

Estimation of a Long-Distance Travel Demand Model using Trip Surveys, Location-Based Big Data, and Trip Planning Services

2018 ◽  
Vol 2672 (47) ◽  
pp. 103-113 ◽  
Author(s):  
Carlos Llorca ◽  
Joseph Molloy ◽  
Joanna Ji ◽  
Rolf Moeckel
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Mode Choice ◽  
Demand Model ◽  
Long Distance ◽  
Trip Planning ◽  
Combining Data ◽  
Distance Travel ◽  
Long Distance Travel ◽  
Travel Demand Model

Long-distance trips are less frequent than short-distance urban trips, but contribute significantly to the total distance traveled, and thus to congestion and transport-related emissions. This paper develops a long-distance travel demand model for the province of Ontario, Canada. In this paper, long-distance demand includes non-recurrent overnight trips and daytrips longer than 40 km, as defined by the Travel Survey for Residents in Canada (TSRC). We developed a microscopic discrete choice model including trip generation, destination choice, and mode choice. The model was estimated using travel surveys, which did not provide data about destination attractiveness and modal level of service. Therefore, a data collection method was designed to obtain publicly available data from the location-based social network Foursquare and from the online trip planning service Rome2rio. In the first case, Foursquare data characterized land uses and predominant activities of the destination alternatives, by the number of user check-ins at different venue types (i.e., ski areas, outdoor or medical activities, etc.). In the second case, the use of Rome2rio data described the modal alternatives for each observed trip. Combining data from travel surveys, Foursquare, and Rome2rio, coefficients of the model were estimated econometrically. It was found that the Foursquare data on number of check-ins at destinations was statistically significant, especially for leisure trips, and improved the goodness of fit compared with models that only used population and employment. Additionally, Rome2rio mode-specific variables were found to be significant for mode choice selection, making the resulting model sensitive to changes in travel time, transit fares, or service frequencies.

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