Anticipating Welfare Impacts via Travel Demand Forecasting Models

2009 ◽  
Vol 2133 (1) ◽  
pp. 11-22 ◽  
Author(s):  
Jason D. Lemp ◽  
Kara M. Kockelman
Keyword(s):  
Travel Demand ◽  
Demand Forecasting ◽  
Welfare Impacts ◽  
Travel Demand Forecasting ◽  
Forecasting Models

scholarly journals Application of Trip Generation Models for Urban Transport Planning In a Data Scarce Developing Country City: The Case of Dar es Salaam

2014 ◽  
Vol 35 (2) ◽  
pp. 99-112
Author(s):  
Hanniabl Bwire
Keyword(s):  
Travel Demand ◽  
Demand Forecasting ◽  
Developed Countries ◽  
Dar Es Salaam ◽  
Urban Transport ◽  
Interactive Effects ◽  
Trip Generation ◽  
Transport Planning ◽  
Travel Demand Forecasting ◽  
Forecasting Models

With the increase in travel demand and traffic management problems in many developingcountries cities, travel demand forecasting models are being employed increasingly tomake informed decisions about the operational improvements to the existing transportationsystem and the design and performance of future transportation systems. The mainadvantage of using travel demand forecasting models for such purposes is that they arecapable of capturing the interactive effects of different components of the system understudy. However, for some time now there have been concerns about the application oftransport planning models in developing countries. The concerns have been mainly inrelation to the variables, coefficients and models borrowed from developed countries. Thispaper first discusses the characteristics of transport problems in developing cities andprovides a review of trip generation modelling approaches. Then, the discussion extendsfurther to cover available data for urban transport planning and trip generation modelsthat have found application in Dar es Salaam, Tanzania. The paper concludes byhighlighting how available data sources and trip generation modelling approach can beimproved to cope with the dynamic conditions in Dar es Salaam.

Measuring uncertainty in discrete choice travel demand forecasting models

2016 ◽  
Vol 39 (2) ◽  
pp. 218-237 ◽  
Author(s):  
Olga Petrik ◽  
Filipe Moura ◽  
João de Abreu e Silva
Keyword(s):  
Discrete Choice ◽  
Travel Demand ◽  
Demand Forecasting ◽  
Travel Demand Forecasting ◽  
Forecasting Models

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.

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