Integrating an Activity-Based Travel Demand Model with Dynamic Traffic Assignment and Emission Models

2010 ◽  
Vol 2176 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Jiang Yang Hao ◽  
Marianne Hatzopoulou ◽  
Eric J. Miller
Keyword(s):  
Travel Demand ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Demand Model ◽  
Dynamic Traffic ◽  
Emission Models ◽  
Travel Demand Model

An Integrated, Validated, and Applied Activity-Based Dynamic Traffic Assignment Model for the Baltimore-Washington Region

2018 ◽  
Vol 2672 (51) ◽  
pp. 45-55 ◽  
Author(s):  
Lei Zhang ◽  
Di Yang ◽  
Sepehr Ghader ◽  
Carlos Carrion ◽  
Chenfeng Xiong ◽  
...  
Keyword(s):  
Daily Activity ◽  
Travel Demand ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Integrated Model ◽  
Time Of Day ◽  
Practical Implementation ◽  
Baseline Scenario ◽  
Demand Model ◽  
Dynamic Traffic

The paper discusses the integration process and initial applications of a new model for the Baltimore-Washington region that integrates an activity-based travel demand model (ABM) with a dynamic traffic assignment (DTA) model. Specifically, the integrated model includes InSITE, an ABM developed for the Baltimore Metropolitan Council, and DTALite, a mesoscopic DTA model. The integrated model simulates the complete daily activity choices of individuals residing in the model region, including long-term choices, such as workplace location; daily activity patterns, including joint household activities and school escorting; activity location choices; time-of-day choices; mode choices; and route choices. The paper describes the model development and integration approach, including modeling challenges, such as the need to maintain consistency between the ABM and DTA models in terms of temporal and spatial resolution, and practical implementation issues, such as managing model run time and ensuring sufficient convergence of the model. The integrated model results have been validated against observed daily traffic volumes and vehicle-miles traveled (VMT) for various functional classes. A land-use change scenario that analyzes the redevelopment of the Port Covington area in Baltimore is applied and compared with the baseline scenario. The validation and application results suggest that the integrated model outperforms a static assignment-based ABM and could capture behavioral changes at much finer time resolutions.

Accuracy and Performance of Improved Speed-Flow Curves

1998 ◽  
Vol 1646 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Richard G. Dowling ◽  
Rupinder Singh ◽  
Willis Wei-Kuo Cheng
Keyword(s):  
Travel Demand ◽  
Traffic Assignment ◽  
Demand Model ◽  
Power Functions ◽  
Flow Curves ◽  
Speed Flow ◽  
Demand Models ◽  
Higher Power ◽  
The Impact ◽  
Travel Demand Model

Skabardonis and Dowling recommended updated Bureau of Public Road speed-flow curves for freeways and signalized arterials to improve the accuracy of speed estimates used in transportation demand models. These updated curves generally involved the use of higher power functions that show relatively little sensitivity to volume changes until demand exceeds capacity, when the predicted speed drops abruptly to a very low value. Skabardonis and Dowling demonstrated that the curves provide improved estimates of vehicle speeds under both uncongested and queueing conditions; however, they did not investigate the impact of these curves on the performance of travel demand models. Practitioners have been concerned about the impacts of such abrupt speed-flow curves on the performance of their travel demand models. Spiess has stated that higher power functions are more difficult computationally for computers to evaluate and that more abrupt speed-flow curves adversely affect the rate of convergence to equilibrium solutions in the traffic assignment process. In this paper the impact of the Skabardonis and Dowling updated speed-flow curves on the performance of selected travel demand models is investigated. The updated speed-flow curves were found to significantly increase travel demand model run times. However, it is demonstrated that an alternative speed-flow equation developed by Akçelik has similar or better accuracy and provides much superior convergence properties during the traffic assignment process. The Akçelik curve significantly reduced travel demand model run times.

A demand model with departure time choice for within-day dynamic traffic assignment

2006 ◽  
Vol 175 (3) ◽  
pp. 1557-1576 ◽  
Author(s):  
Giuseppe Bellei ◽  
Guido Gentile ◽  
Lorenzo Meschini ◽  
Natale Papola
Keyword(s):  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Demand Model ◽  
Dynamic Traffic ◽  
Departure Time ◽  
Departure Time Choice

The Application of an Integrated Behavioral Activity-Travel Simulation Model for Pricing Policy Analysis

2014 ◽  
pp. 86-102 ◽  
Author(s):  
Karthik C. Konduri ◽  
Ram M. Pendyala ◽  
Daehyun You ◽  
Yi-Chang Chiu ◽  
Mark Hickman ◽  
...  
Keyword(s):  
Travel Demand ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Behavioral Activity ◽  
Dynamic Traffic ◽  
Pricing Policy ◽  
Pricing Policies ◽  
Case Study Site ◽  
Time Expenditures

This chapter demonstrates the feasibility of applying an integrated microsimulation model of activity-travel demand and dynamic traffic assignment for analyzing the impact of pricing policies on traveler activity-travel choices. The model system is based on a dynamic integration framework wherein the activity-travel simulator and the dynamic traffic assignment model communicate with one another along the continuous time axis so that trips are routed and simulated on the network as and when they are generated. This framework is applied to the analysis of a system-wide pricing policy for a small case study site to demonstrate how the model responds to various levels of pricing. Case study results show that trip lengths, travel time expenditures, and vehicle miles of travel are affected to a greater degree than activity-trip rates and activity durations as a result of pricing policies. Measures of change output by the model are found to be consistent with elasticity estimates reported in the literature.

scholarly journals Uncertainty Quantification of the Traffic Assignment Model

2020 ◽  
Author(s):  
Mundher Seger ◽  
Lajos Kisgyörgy
Keyword(s):  
Uncertainty Quantification ◽  
Traffic Flow ◽  
Travel Demand ◽  
Traffic Assignment ◽  
Posterior Distributions ◽  
Demand Model ◽  
Assignment Model ◽  
Wide Range ◽  
Least Squares Adjustment ◽  
Travel Demand Model

Forecasting of traffic flow in the traffic assignment model suffered to a wide range of uncertainties arising from different sources and exacerbating through sequential-stages of the travel demand model. Uncertainty quantification can provide insights into the level of confidence on the traffic assignment model outputs, and also identify the uncertainties of the input Origin-Destination matrix for enhancing the forecasting robustness of the travel demand model. In this paper, a systematic framework is proposed to quantify the uncertainties that lie in the Origin-Destination input matrix. Hence, this study mainly focuses on predicting the posterior distributions of uncertainty Origin-Destination pairs and correcting the biases of Origin-Destination pairs by using the inverse uncertainty quantification formulated through Least Squares Adjustment method. The posterior distributions are further used in the forward uncertainty quantification to quantify the forecast uncertainty of the traffic flow on a transport network. The results show the effectiveness of implementing the inverse uncertainty quantification framework in the traffic assignment model. And demonstrate the necessity of including uncertainty quantification of the input Origin-Destination matrix in future work of travel demand modelling.

scholarly journals A Data-Driven Quasi-Dynamic Traffic Assignment Model Integrating Multi-Source Traffic Sensor Data on the Expressway Network

2021 ◽  
Vol 10 (3) ◽  
pp. 113
Author(s):  
Xing Zeng ◽  
Xuefeng Guan ◽  
Huayi Wu ◽  
Heping Xiao
Keyword(s):  
Travel Demand ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Data Driven ◽  
Sensor Data ◽  
Hunan Province ◽  
License Plate ◽  
Dynamic Traffic ◽  
Statistical Probability ◽  
Assignment Model

Static traffic assignment (STA) models have been widely utilized in the field of strategic transport planning. However, STA models cannot fully represent the dynamic road conditions and suffer from inaccurate assignment during traffic congestion. At the same time, an increasing number of installed sensors have become an important means of detecting dynamic road conditions. To address the shortcomings of STA models, we integrate multi-source traffic sensor datasets and propose a novel data-driven quasi-dynamic traffic assignment model, named DQ-DTA. In this model, records of toll stations are used for time-varying travel demand estimation. GPS trajectory datasets of vehicles are further used to calculate the dynamic link costs of the road network, replacing the imprecise Bureau of Public Roads (BPR) function. Moreover, license plate recognition (LPR) data are used to design a statistical probability-based multipath assignment method to capture travelers’ route choices. The expressway network in the Hunan province is selected as the study area, and several classic STA models are also chosen for performance comparison. Experimental results demonstrate that the accuracy of the proposed DQ-DTA model is about 6% higher than that of the chosen STA models.

scholarly journals A Strategic Model for Dynamic Traffic Assignment

2004 ◽  
Vol 4 (3) ◽  
pp. 291-315 ◽  
Author(s):  
Younes Hamdouch ◽  
Patrice Marcotte ◽  
Sang Nguyen
Keyword(s):  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Dynamic Traffic ◽  
Strategic Model
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