01515625

Component

https://doi.org/10.3141/2429-01

http://scholar.google.com...lman&publication_year=2014

Travel demand forecasting models have played a critical role in transportation planning; these models support the evaluation of policies, programs, and projects that involve complex interactions between the activity system and the transportation system. Both the state of the art and the state of the practice in travel demand modeling have advanced considerably during the many decades since the original four-step model structure was conceived. However, the models are not now, and never will be, perfect representations of the systems they represent; so there are inevitably uncertainties about the forecasts that these models generate. Travel demand forecasts are important in many applications, for example, in determining whether a given alternative is financially or technically feasible or meets some benefit threshold. In these applications, uncertainties in model forecasts may translate directly into risks of not accomplishing the objectives related to the decision to implement or not implement the alternative. For projects that involve outside financing, this threshold varies greatly between equity and lender participants because of the differing risk–reward profiles. Several previous papers described the uncertainties associated with travel demand forecasting and recommended ways of improving the state of the practice. Among those recommendations is the application of formal quantitative risk analysis methods. This paper summarizes the existing literature and describes the application of one relatively straightforward but robust approach for conducting quantitative risk analysis with travel demand forecasting models. The formal risk analysis approach described here can assist by providing a more complete evaluation of a project’s likelihood of achieving specified objectives. The approach can support the development of traffic and revenue forecasts beyond a most likely scenario to consider any level of risk assumed by a project sponsor, debt provider, or rating agency.

01547309

14-1472

English

pp 1–7

2014

Transportation Research Record: Journal of the Transportation Research Board

9780309295239

Print

Figures (5) ; Maps; References (15)

Forecasting; Revenues; Risk analysis; Traffic forecasting; Traffic models; Transportation planning; Travel demand

Highways; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Jan 27 2014 2:33PM

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• Characterizing Household Vehicle Fleet Composition and Count by Type in Integrated Modeling Framework
• Choice Set Imputation: Two-Step Weighted Stratified and Hazard-Based Approach
• Effects of Uncertainty in Speed–Flow Curve Parameters on a Large-Scale Model: Case Study of the Danish National Model
• Extension of Activity-Based Modeling Approach to Incorporate Supply Side of Activities: Examples for Major Universities and Special Events
• Incorporation of Escorting Children to School in Modeling Individual Daily Activity Patterns of Household Members
• Integrated Transport Planning Framework Involving Combined Utility Regret Approach
• Is Sequential Estimation a Suitable Second Best for Estimation of Hybrid Choice Models?
• Joint Modeling of Trip Mode and Departure Time Choices Using Revealed and Stated Preference Data
• Model Complexities and Requirements for Multimodal Transport Network Design: Assessment of Classical, State-of-the-Practice, and State-of-the-Research Models
• Modeling Demand Elasticity and Route Overlapping in Stochastic User Equilibrium Through Paired Combinatorial Logit Model
• Modeling Taxi Trip Demand by Time of Day in New York City
• Multiple Discrete-Continuous Model of Activity Participation and Time Allocation for Home-Based Work Tours
• Practical Solutions for Sampling Alternatives in Large-Scale Models
• Spatial Transferability of Tour-Based Time-of-Day Choice Models: Empirical Assessment
• Synthetic Population Generation at Disaggregated Spatial Scales for Land Use and Transportation Microsimulation
• Using Survey Calibration and Statistical Matching to Reweight and Distribute Activity Schedules