Deciding whether and how to improve statewide travel demand models based on transportation planning application needs

2013 ◽  
Vol 36 (3) ◽  
pp. 244-266 ◽  
Author(s):  
Chenfeng Xiong ◽  
Lei Zhang
Keyword(s):  
Transportation Planning ◽  
Travel Demand ◽  
Demand Models

scholarly journals TRANSIMS Implementation for a Small Network and Comparison with Enhanced Four-Step Model

2014 ◽  
Author(s):  
Mansoureh Jeihani ◽  
Anam Ardeshiri
Keyword(s):  
Transportation Planning ◽  
Travel Demand ◽  
Demand Forecasting ◽  
Initial Step ◽  
Demand Analysis ◽  
Analysis Model ◽  
Step Method ◽  
Traffic Pattern ◽  
Step Model ◽  
Demand Models

Travel demand forecasting is a major tool to assist decision makers in transportation planning. While the conventional four-step trip-based approach is the dominant method to perform travel demand analysis, behavioral advances have been made in the past decade. This paper proposes and applies an enhancemnt to the four-step travel demand analysis model called Sub-TAZ. Furthermore, as an initial step toward activity-based models, a TRANSIMS Track-1 approach is implemented utilizing a detailed network developed in Sub-TAZ approach. The conventional four-step, Sub-TAZ, and TRANSIMS models were estimated in a small case study for Fort Meade, Maryland, with zonal trip tables. The models were calibrated and validated for the base year (2005), and the forecasted results for the year (2010) were compared to actual ground counts of traffic volume and speed. The study evaluated the forecasting ability of TRANSIMS versus the conventional and enhanced four-step models and provided critical observations concerning strategies for the further implementation of TRANSIMS.BACKGROUND Traffic pattern prediction is necessary for infrastructure improvement, and travel demand modeling provides tools to forecast travel patterns under various conditions. This modeling involves a series of mathematical equations that represent how people make travel choices. Traditional travel demand models use the four-step method, which was introduced in the 1950s and has been used widely in transportation planning. Although the four-step method has been practical in producing aggregate forecasts, it has some shortcomings. For example, in short-range planning networks, existing and newly constructed roads become congested much faster than forecasted (TRB 2007) and the performance of current four-step models is not always satisfactory. Additionally, these models are not behavioral in nature and as a result they are unable to represent the time chosen for travel, travelers’ responses to demand policies (e.g., toll roads, road pricing, and transit vouchers), non-motorized

Proof-of-concept for methodological framework to evaluate operational strategies with travel demand models

2015 ◽  
Vol 42 (11) ◽  
pp. 854-864
Author(s):  
Jiaqi Ma ◽  
Changju Lee ◽  
Michael J. Demetsky
Keyword(s):  
Transportation Planning ◽  
Travel Demand ◽  
Planning Process ◽  
Methodological Framework ◽  
Demand Model ◽  
Peak Period ◽  
Operational Strategies ◽  
Operational Analysis ◽  
Starting Point ◽  
Demand Models

Recently, limited available resources for physical capacity expansion have generated supports for short-term operational improvements. Yet, only a few studies have dealt with evaluating these operational strategies effectively within the traditional transportation planning process even though suitable operational strategies impact to not only specific corridors or regions but also the whole transportation network. This is because it is generally perceived that integrating travel demand models with operational analysis approaches is quite difficult due to different constraints, modeling structures, and required data sets. In this regard, the concept of methodological framework to evaluate operational strategies with travel demand models is developed and validated by the proper case study (i.e., high occupancy toll lanes deployment in the Hampton Roads area in Virginia, US) in this research. The proposed framework consists of three major components: (i) the selection of an appropriate operational analysis approach, (ii) the disaggregation of daily traffic volumes to peak period volumes, and (iii) the alignment of modeling elements between the travel demand model and operational tool. Key contributions from this research are that (i) the proposed methodology enables the evaluation of travel behavioral changes without microscopic simulation, especially in terms of capturing network flow pattern changes caused by behavioral shifts after operational strategy deployment, (ii) the proposed framework eliminates assumptions required when only operational tools are used to evaluate operational strategies, (iii) the disaggregation method of a daily trip distribution matrix into peak period matrices by using survey data are developed, (iv) specific details influencing integration in terms of data types, peak period link capacity, volume-delay functions, and link impedance are identified. Consequently, even though this research still has some limitations (e.g., inherent weakness of travel demand models), this can be a starting point to develop more detailed guidelines as well as a good reference for practitioners and researchers who wish to evaluate operation strategies within transportation planning process.

scholarly journals PENGEMBANGAN MODEL TRANSPORTASI PENUMPANG ANTAR KOTA/KABUPATEN DI PROPINSI JAWA BARAT

2015 ◽  
Vol 1 (1) ◽  
pp. 77-94
Author(s):  
I Made Suraharta
Keyword(s):  
Transportation Planning ◽  
Travel Demand ◽  
Planning Process ◽  
Transport Model ◽  
Cost Model ◽  
Trip Generation ◽  
Demand Model ◽  
Demand Models ◽  
Distribution Mode ◽  
Generalized Cost

Transport models are crucial in the transportation planning process. Transport model is made by adjusting the needs and availability of data and capability models in representing the real conditions and the future. Transportation models commonly used in transportation planning mechanism is the sequential demand models, which include the trip generation, trip distribution, mode choice, and traffic assignment. This model is suitable to be applied to various situations study areas, especially areas of the city. For intercity regional planning needs, modeling the sequential demand can be simplified into a direct demand model, the record is not much involved in modeling mode. In this study, the authors tried to develop a model of a direct demand models to represent the pattern of movement of people with other modes of road in West Java. The proposed transport model is a function of population, GDP, total number of trip generation traffic zone, the total transportation costs (generalized cost). Model results show the validity of the development of significant and can be used as a travel demand model for transportation planning.

Development of a Statewide Truck Trip Forecasting Model Based on Commodity Flows and Input-Output Coefficients

2000 ◽  
Vol 1707 (1) ◽  
pp. 49-55 ◽  
Author(s):  
Jose A. Sorratini ◽  
Robert L. Smith
Keyword(s):  
Transportation Planning ◽  
Travel Demand ◽  
State Level ◽  
Census Bureau ◽  
The State ◽  
Input Output ◽  
Commodity Flow ◽  
Demand Models ◽  
Volume Group ◽  
The U.S

This research attempts to improve the modeling of statewide truck travel demand models by using commodity flow data from the U.S. Census Bureau, a private freight database (TRANSEARCH), and input-output (I-O) coefficients. The standard urban transportation planning modeling process was applied at the state level to estimate heavy truck trips. Economic-based I-O software was used to derive the I-O direct matrix and the I-O direct coefficients at the state level for developing the trip attraction rates for 28 manufacturing sectors. The Commodity Flow Survey from the U.S. Census Bureau together with a private database developed for Wisconsin were used to develop the trip production rates. Transportation planning software (TRANPLAN) was used to distribute and assign truck trips generated at the zonal level. The selected-link function in TRANPLAN was used to adjust the initial productions and attractions in order to generate link volumes that match the actual ground counts for 40 selected links. The model only required two iterations of the selected link analysis in order to produce an acceptable match with the ground counts, compared with three iterations for two prior similar models. The rapid convergence provides clear evidence that the disaggregate trip generation models give better initial estimates of trip productions and attractions than was possible with the prior studies. A “back forecast” of 15 years to the year 1977 was found to be reasonable both in terms of the percent root mean square error by volume group and the performance measures for five screen lines.

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

scholarly journals Address-based computation of intra-cell distances for travel demand models

2021 ◽  
Vol 184 ◽  
pp. 123-130
Author(s):  
Matthias Heinrichs ◽  
Rita Cyganski ◽  
Daniel Krajzewicz
Keyword(s):  
Travel Demand ◽  
Demand Models

A copula-based continuous cross-nested logit model for tour scheduling in activity-based travel demand models

2021 ◽  
Vol 145 ◽  
pp. 324-341
Author(s):  
Sepehr Ghader ◽  
Carlos Carrion ◽  
Liang Tang ◽  
Arash Asadabadi ◽  
Lei Zhang
Keyword(s):  
Logit Model ◽  
Travel Demand ◽  
Nested Logit ◽  
Nested Logit Model ◽  
Demand Models ◽  
Tour Scheduling

Ensemble learning activity scheduler for activity based travel demand models

2021 ◽  
Vol 123 ◽  
pp. 102972
Author(s):  
Mohammad Hesam Hafezi ◽  
Naznin Sultana Daisy ◽  
Hugh Millward ◽  
Lei Liu
Keyword(s):  
Ensemble Learning ◽  
Travel Demand ◽  
Learning Activity ◽  
Demand Models

Freight Travel Demand Modeling: Synthesis of Approaches and Development of a Framework

2000 ◽  
Vol 1725 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Ram M. Pendyala ◽  
Venky N. Shankar ◽  
Robert G. McCullough
Keyword(s):  
Decision Making ◽  
Transportation Planning ◽  
Travel Demand ◽  
Freight Transportation ◽  
The Body ◽  
Travel Demand Modeling ◽  
Demand Modeling ◽  
Transportation Sector ◽  
Planning Framework ◽  
Freight Data Needs

It is increasingly being recognized at all levels of decision making that freight transportation and economic development are inextricably linked. As a result, many urban entities and states are embarking upon comprehensive freight transportation planning efforts aimed at ensuring safe, efficient, and smooth movement of freight along multimodal and intermodal networks. Over the past few decades there has been considerable published research on (1) freight transportation factors, (2) freight travel demand modeling methods, (3) freight transportation planning issues, and (4) freight data needs, deficiencies, and collection methods. A synthesis of the body of knowledge in these four areas is provided with a view to developing a comprehensive statewide freight transportation planning framework. The proposed framework consists of two interrelated components that facilitate demand estimation and decision making in the freight transportation sector.

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