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

Development of a Statewide Truck-Travel Demand Model with Limited Origin-Destination Survey Data

1997 ◽  
Vol 1602 (1) ◽  
pp. 14-21 ◽  
Author(s):  
Man-Bae Park ◽  
Robert L. Smith
Keyword(s):  
Survey Data ◽  
Count Data ◽  
Travel Demand ◽  
Functional Class ◽  
Gravity Models ◽  
Generation Model ◽  
Demand Model ◽  
Modeling Framework ◽  
Actual Volume ◽  
Travel Demand Model

A simple statewide truck-travel demand model for Wisconsin was developed using only readily available data, including a small amount of data from origin-destination travel surveys (O-D) and fairly extensive truck-classification count data. A simple trip-rate trip-generation model based on zonal population was used to estimate the initial internal trip productions and attractions. Despite the small amount of O-D survey data that were available, gravity-model friction-factor curves were developed for three trip types. The calibrated gravity models produced trip tables that were good matches for the O-D survey data. The calibrated gravity models were then used as the modeling framework for the adjustment of zonal productions and attractions by selected-link-based (SELINK) analysis. SELINK adjustment factors were computed for all zones with trips on one or more of the selected links. The adjustments were made so that the ratio of the actual volume for the selected link (ground count) to the total assigned volume approached 1 for all selected links. The SELINK analysis produced a calibrated model after only three iterations when either 16 or 32 selected links were used statewide for the calibration. The overall accuracy of the calibrated model was measured by using classification count data for 154 locations around the state. The model provided a good fit based on a wide variety of measures, including percent root-mean-square error and comparisons for screenlines, functional class, areas, and vehicle kilometers traveled.

scholarly journals Challenges and Opportunities in Applying High-Fidelity Travel Demand Model for Improved Network-Wide Traffic Estimation: A Review and Discussion

2014 ◽  
Vol 8 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Riad Mustafa ◽  
Ming Zhong
Keyword(s):  
Travel Demand ◽  
Traffic Volume ◽  
Volume Estimation ◽  
High Fidelity ◽  
Demand Model ◽  
Modeling Framework ◽  
Demand Models ◽  
Challenges And Opportunities ◽  
Traffic Estimation ◽  
Travel Demand Model

Abstract: Estimating traffic volume at a link level is important to transportation planners, traffic engineers, and policy makers. More specifically, this vital parameter has been used in transportation planning, traffic operations, highway geometric design, pavement design, and resource allocation. However, traditional factor approach, regression-­‐based models, and artificial neural network models failed to present network-­‐wide traffic volume estimates because they rely on traffic counts for model development, and they all have inherent weaknesses. A review to previous research work and the state-­‐of-­‐practice clearly indicates that the Traditional Four-step Travel Demand Model (TFTDM) was generally based on large traffic analysis zones (TAZs) and networks consisting of high functional-class roads only. Consequently, this conventional modeling framework yielded a limited number of link traffic assignments with fairly high estimation errors. In the light of these facts and the obvious need of accurate network-wide traffic estimates, this review is conducted. In particular, this paper provides an extensive review of using traditional travel demand models for improved network-­‐wide traffic volume estimation. The paper then focuses on the challenges and opportunities in achieving high-fidelity travel demand model (HFTDM). This review has revealed that, opportunities in relation to both technological advances and intelligent data present a substantial potential in developing the proposed HFTDM for a much more accurate traffic estimation at a network-­‐wide level. Finally, the paper concludes with key findings from the review and provides a few recommendations for future research related to the topic.

scholarly journals Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

2021 ◽  
pp. 036119812199714
Author(s):  
Gabriel Wilkes ◽  
Roman Engelhardt ◽  
Lars Briem ◽  
Florian Dandl ◽  
Peter Vortisch ◽  
...  
Keyword(s):  
Real Time ◽  
Travel Demand ◽  
State Of The Art ◽  
Choice Model ◽  
Mode Choice ◽  
Attractive Alternative ◽  
Full Potential ◽  
Demand Model ◽  
Modeling Framework ◽  
Demand And Supply

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

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

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.

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