Statewide and Megaregional Travel Forecasting Models: Freight and Passenger

2017 ◽  
Author(s):  
Rick Donnelly and Rolf Moeckel ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Forecasting Models ◽  
Travel Forecasting

Tests of a Family of Trip Table Refinements for Long-Range, Quick-Response Travel Forecasting

2005 ◽  
Vol 1921 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Alan J. Horowitz
Keyword(s):  
New Brunswick ◽  
Family Of Origin ◽  
Estimation Methods ◽  
Quick Response ◽  
Trip Generation ◽  
Full Matrix ◽  
Forecasting Models ◽  
Travel Forecasting ◽  
The Family ◽  
Static Planning

This paper addresses the problem of using traffic counts to ascertain zonal trip generation characteristics when performing quick-response travel forecasts. A family of origin–destination (O-D) trip table estimation methods containing three unexplored members (biproportional, uniproportional, and dynamic biproportional) is proposed to solve this problem. The family is tested on static planning networks for Tallahassee, Florida; Northfield, Minnesota; and Fredericton, New Brunswick, Canada. Results indicate that travel forecasting models can be made to match ground counts better by a simple factoring of origins, destinations, or both. The three methods that directly solve for origin or destination factors have computational and statistical advantages over full-matrix O-D trip table estimation procedures, and the results are qualitatively and quantitatively interpretable.

Market-Based Framework for Forecasting Parking Cost in Traditional and Microsimulation Modeling Applications

2005 ◽  
Vol 1921 (1) ◽  
pp. 79-88
Author(s):  
Gregory D. Erhardt ◽  
David L. Kurth ◽  
Erik E. Sabina ◽  
Smith Myung
Keyword(s):  
Travel Behavior ◽  
Mode Choice ◽  
Model Development ◽  
Important Variable ◽  
Cost Models ◽  
Forecasting Models ◽  
Travel Forecasting ◽  
Activity Modeling ◽  
Microsimulation Modeling ◽  
Parking Demand

Parking cost is an important variable in determining mode choice, yet it receives little attention in most travel forecasting models. This paper presents a framework for modeling parking supply and cost that has three advantages over most parking cost models: a market-based approach is used to equilibrate parking demand with parking supply; actual parking costs paid by groups of travelers rather than average parking costs are estimated for each transportation analysis zone; and estimates are made from longitudinal data. This framework has been applied successfully in a traditional four-step travel model and is being used in practice. It also provides additional opportunities for application in a segmented manner or in concert with a microsimulation modeling approach. Mode choice results based on aggregate and segmented applications of the framework are substantially different. Improved forecasting of parking costs should be an important consideration in any new model development. In recent years, substantial efforts have been focused on household interactions and activity modeling. Although the understanding of travel behavior has improved substantially, the improved techniques still depend on good input data for credible forecasts.

scholarly journals Towards disaggregate dynamic travel forecasting models

2007 ◽  
Vol 12 (2) ◽  
pp. 115-130 ◽  
Author(s):  
Moshe Ben-Akiva ◽  
Bottom Jon ◽  
Gao Song ◽  
Iaris N. Koutsopoulos ◽  
Wen Yang
Keyword(s):  
Forecasting Models ◽  
Travel Forecasting

scholarly journals Supernetwork Representation Formulation of a Multiclass Simultaneous Transportation Equilibrium Model as a Fixed Demand User Equilibrium Problem

2021 ◽  
Vol 12 (3) ◽  
pp. 18-33
Author(s):  
Mohamad K. Hasan ◽  
Mohammad Saoud ◽  
Raed Al-Husain
Keyword(s):  
Equilibrium Model ◽  
Supply And Demand ◽  
User Equilibrium ◽  
Trip Purpose ◽  
Forecasting Models ◽  
Travel Forecasting ◽  
Transportation Modes ◽  
Departure Time ◽  
Flexible Framework ◽  
Multimodal Network

A multiclass simultaneous transportation equilibrium model (MSTEM) explicitly distinguishes between different user classes in terms of socioeconomic attributes, trip purpose, pure and combined transportation modes, as well as departure time, all interacting over a physically unique multimodal network. It enhances the prediction process behaviorally by combining the trip generation and departure time choices to trip distribution, modal split, and trip assignment choices in a unified and flexible framework that has many advantages from both supply and demand sides. However, the development of this concept of multiple classes increases the mathematical complexity of travel forecasting models. In this research, the authors reduce this mathematical complexity by using the supernetwork representation formulation of the diagonalized MSTEM as a fixed demand user equilibrium (FDUE) problem.

Spatial transferability of travel forecasting models: a review and synthesis

2013 ◽  
Vol 5 (2-3) ◽  
pp. 104-128 ◽  
Author(s):  
Sujan Sikder ◽  
Abdul Rawoof Pinjari ◽  
Sivaramakrishnan Srinivasan ◽  
Roosbeh Nowrouzian
Keyword(s):  
Forecasting Models ◽  
Travel Forecasting ◽  
Spatial Transferability

Exploratory Modeling and Analysis for Transportation: An Approach and Support Tool - TMIP-EMAT

2019 ◽  
Vol 2673 (9) ◽  
pp. 407-418 ◽  
Author(s):  
Martin Milkovits ◽  
Rachel Copperman ◽  
Jeffrey Newman ◽  
Jason Lemp ◽  
Thomas Rossi ◽  
...  
Keyword(s):  
Travel Demand ◽  
Supply And Demand ◽  
Single Point ◽  
Transportation Systems ◽  
Analysis Tool ◽  
Demand Model ◽  
Improvement Program ◽  
Modeling And Analysis ◽  
Forecasting Models ◽  
Travel Forecasting

Traditionally, travel forecasting models have been used to provide single point predictions. That is, a single future scenario is developed and the model is applied to that scenario. This approach, however, ignores the deep uncertainty that exists in future land use, demographic, and transportation systems inputs, not to mention the uncertainty that exists in the model itself. More importantly, transportation policy decisions made on the basis of such model outputs may be misguided and ineffective. This paper demonstrates and motivates the use of travel forecasting models in an exploratory manner that accounts for the inherent uncertainties of the future. Specifically, this paper describes the user workflow for a new planning and modeling tool: the Travel Model Improvement Program Exploratory Modeling and Analysis Tool (TMIP-EMAT) that has been developed to facilitate the use of exploratory techniques with travel forecasting models. Examples from the proof of concept deployment using the Greater Buffalo-Niagara Regional Transportation Council regional travel demand model are included. The goal of the longer term study is to provide TMIP-EMAT for state and regional transportation planning agencies to assess how technological innovations will affect traffic and transit demand on major corridors 20 to 30 years down the road. The tool will illuminate interactions between transportation supply and demand on urban surface transportation systems (especially at the corridor level) through exploratory modeling and simulation, and facilitate insights into potential, possible, plausible, probable or preferred futures.

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