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

scholarly journals Applying FEATHERS for Travel Demand Analysis: Model Considerations

2018 ◽  
Vol 8 (2) ◽  
pp. 211 ◽  
Author(s):  
Qiong Bao ◽  
Bruno Kochan ◽  
Yongjun Shen ◽  
Lieve Creemers ◽  
Tom Bellemans ◽  
...  
Keyword(s):  
Travel Demand ◽  
Demand Analysis ◽  
Analysis Model

scholarly journals Intercity Travel Demand Analysis Model

2014 ◽  
Vol 6 ◽  
pp. 108180
Author(s):  
Ming Lu ◽  
Hai Zhu ◽  
Xia Luo ◽  
Lei Lei
Keyword(s):  
Logit Model ◽  
Short Distance ◽  
Travel Demand ◽  
Mode Choice ◽  
Urban Traffic ◽  
Demand Analysis ◽  
Analysis Model ◽  
Demand Forecast ◽  
Main Mode ◽  
Intercity Travel

It is well known that intercity travel is an important component of travel demand which belongs to short distance corridor travel. The conventional four-step method is no longer suitable for short distance corridor travel demand analysis for the time spent on urban traffic has a great impact on traveler's main mode choice. To solve this problem, the author studied the existing intercity travel demand analysis model, then improved it based on the study, and finally established a combined model of main mode choice and access mode choice. At last, an integrated multilevel nested logit model structure system was built. The model system includes trip generation, destination choice, and mode-route choice based on multinomial logit model, and it achieved linkage and feedback of each part through logsum variable. This model was applied in Shenzhen intercity railway passenger demand forecast in 2010 as a case study. As a result, the forecast results were consistent with the actuality. The model's correctness and feasibility were verified.

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.

Comparative Study of EMME/2 and QRS II for Modeling a Small Community

2003 ◽  
Vol 1858 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Lei Yu ◽  
Peng Yue ◽  
Hualiang Teng
Keyword(s):  
Comparative Study ◽  
Travel Demand ◽  
Demand Forecasting ◽  
Small Community ◽  
Travel Demand Forecasting ◽  
Traffic Conditions ◽  
Forecasting Models ◽  
Demand Models ◽  
Traffic Impact ◽  
Computer Based

The availability of so many computer-based travel-demand forecasting models provides transportation planners with powerful and flexible tools in the modeling phase of their planning or traffic-impact studies, but it has confused users in the selection of an appropriate model for a particular study. It is commonly recognized that none of the existing travel-demand models is perfectly suited for all network scenarios and traffic conditions. A particular model that is strong in one application scenario may be weak in a different application scenario. A comparative study is presented of two widely used travel-demand forecasting models, EMME/2 and QRS II, for applications to a small community. A structural comparison is performed, and a real-world small network is modeled by EMME/2 and QRS II to identify specific features and limitations of each model. Areas for comparison include model structure, drawing of the network, data input, network modification, parameter calibration, and modeling output. The study does not recommend either model to transportation planners for a practical application to a small community. Instead, the study identifies the major differences and common features of two models, which can help planners understand what they can expect from a certain model when they choose to use it.

scholarly journals Micm-Simulation Based Travel Demand Forecasting System for Urban Transportation Planning

2000 ◽  
Vol 17 ◽  
pp. 841-847 ◽  
Author(s):  
Yuzo Iida ◽  
Michiyoshi Iwabe ◽  
Akira Kikuchi ◽  
Ryuichi Kitamura ◽  
Kuniaki Sasaki ◽  
...  
Keyword(s):  
Transportation Planning ◽  
Travel Demand ◽  
Urban Transportation ◽  
Demand Forecasting ◽  
Travel Demand Forecasting ◽  
Simulation Based ◽  
Forecasting System
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