A Dynamic Discrete Choice Activity-Based Travel Demand Model

2019 ◽  
Author(s):  
Oskar Blom Västberg ◽  
Anders Karlström ◽  
Daniel Jonsson ◽  
Marcus Sundberg
Keyword(s):  
Discrete Choice ◽  
Travel Demand ◽  
Dynamic Discrete Choice ◽  
Demand Model ◽  
Travel Demand Model

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

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

Comprehensive Modeling Framework to Integrate External Trips in a Travel Demand Model

2019 ◽  
Vol 33 (3) ◽  
pp. 04019011
Author(s):  
Syed Fazal Abbas Baqueri ◽  
Muhammad Adnan ◽  
Tom Bellemans ◽  
Davy Janssens
Keyword(s):  
Travel Demand ◽  
Demand Model ◽  
Modeling Framework ◽  
Comprehensive Modeling ◽  
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 Predicting and Visualizing the Uncertainty Propagations in Traffic Assignments Model Using Monte Carlo Simulation Method

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Mundher Seger ◽  
Lajos Kisgyörgy
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Traffic Flow ◽  
Travel Demand ◽  
Simulation Method ◽  
Transport Network ◽  
Monte Carlo Simulation Method ◽  
Traffic Flows ◽  
Demand Model ◽  
Travel Demand Model

Uncertainty can be found at all stages of travel demand model, where the error is passing from one stage to another and propagating over the whole model. Therefore, studying the uncertainty in the last stage is more important because it represents the result of uncertainty in the travel demand model. The objective of this paper is to assist transport modellers in perceiving uncertainty in traffic assignment in the transport network, by building a new methodology to predict the traffic flow and compare predicted values to the real values or values calculated in analytical methods. This methodology was built using Monte Carlo simulation method to quantify uncertainty in traffic flows on a transport network. The values of OD matrix were considered as stochastic variables following a specific probability distribution. And, the results of the simulation process represent the predicted traffic flows in each link on the transport network. Consequently, these predicted results are classified into four cases according to variability and bias. Finally, the results are drawn into figures to visualize the uncertainty in traffic assignments. This methodology was applied to a case study using different scenarios. These scenarios are varying according to inputs parameters used in MC simulation. The simulation results for the scenarios gave different bias for each link separately according to the physical feature of the transport network and original OD matrix, but in general, there is a direct relationship between the input parameter of standard deviation with the bias and variability of the predicted traffic flow for all scenarios.

Examining the Equity Impacts of Autonomous Vehicles: A Travel Demand Model Approach

2019 ◽  
Vol 2673 (5) ◽  
pp. 23-35 ◽  
Author(s):  
Jesse Cohn ◽  
Richard Ezike ◽  
Jeremy Martin ◽  
Kwasi Donkor ◽  
Matthew Ridgway ◽  
...  
Keyword(s):  
Performance Measures ◽  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Travel Demand ◽  
Autonomous Vehicle ◽  
Demand Model ◽  
The Status ◽  
Mode Share ◽  
Transit Agencies ◽  
Travel Demand Model

As investments in autonomous vehicle (AV) technology continue to grow, agencies are beginning to consider how AVs will affect travel behavior within their jurisdictions and how to respond to this new mobility technology. Different autonomous futures could reduce, perpetuate, or exacerbate existing transportation inequities. This paper presents a regional travel demand model used to quantify how transportation outcomes may differ for disadvantaged populations in the Washington, D.C. area under a variety of future scenarios. Transportation performance measures examined included job accessibility, trip duration, trip distance, mode share, and vehicle miles traveled. The model evaluated changes in these indicators for disadvantaged and non-disadvantaged communities under scenarios when AVs were primarily single-occupancy or high-occupancy, and according to whether transit agencies responded to AVs by maintaining the status quo, removing low-performing routes, or applying AV technology to transit vehicles. Across the performance measures, the high-occupancy AV and enhanced transit scenarios provided an equity benefit, either mitigating an existing gap in outcomes between demographic groups or reducing the extent to which that gap was expanded.

Scalable GEH: A Quality Measure for Comparing Observed and Modeled Single Values in a Travel Demand Model Validation

2019 ◽  
Vol 2673 (4) ◽  
pp. 722-732 ◽  
Author(s):  
Markus Friedrich ◽  
Eric Pestel ◽  
Christian Schiller ◽  
Robert Simon
Keyword(s):  
Normal Distribution ◽  
Travel Demand ◽  
Quality Measures ◽  
Quality Measure ◽  
Specific Reference ◽  
Traffic Planning ◽  
Demand Model ◽  
Trip Purpose ◽  
Traffic Volumes ◽  
Travel Demand Model

For proving the validity of a travel demand model, the deviations between observed values and modeled values must be quantified and evaluated by suitable quality measures. The comparison of observed and modeled values can refer to single values, sets of single values, and distributions of values. The paper focuses on comparing pairs of single values with specific reference to the quality measure GEH (named after Geoffrey E. Havers, who introduced it for traffic planning purposes in the 1970s), which is used in the British guidelines WebTAG. The paper describes desirable and problematic properties of the GEH and examines how it is related to normal distribution. With the objective of overcoming the limitations of the GEH an alternative quality measure was developed, which can be scaled to validate different value ranges representing hourly or daily traffic volumes, daily number of trips per person, or trip distances per trip purpose. The paper concludes with a proposal for validating single values.

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