Inaccuracy in Traffic Forecasts (original) (raw)
Related papers
¿Imprecisión de las previsiones de los proyectos de obras públicas? Ámbito del transporte
Estudios de Construcción y Transportes, no. 105, pp. 195-214. Spanish translation by the publisher, 2006
D espite the enormous sums of money being spent on transportation infrastructure, surprisingly little systematic knowledge exists about the costs, benefits, and risks involved. The literature lacks statistically valid answers to the central and self-evident question of whether transportation infrastructure projects perform as forecasted. When a project underperforms, this is often explained away as an isolated instance of unfortunate circumstance; it is typically not seen as the particular expression of a general pattern of underperformance in transportation infrastructure projects. Because knowledge is wanting in this area of research, until now it has been impossible to validly refute or confirm whether underperformance is the exception or the rule.
How (In)accurate Are Demand Forecasts in Public Works Projects? The Case of Transportation
Journal of the American Planning Association, vol. 71, no. 2, pp. 131-146, 2005
This article presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth U.S.$59 billion. The study shows with very high statistical significance that forecasters generally do a poor job of estimating the demand for transportation infrastructure projects. For 9 out of 10 rail projects, passenger forecasts are overestimated; the average overestimation is 106%. For half of all road projects, the difference between actual and forecasted traffic is more than ±20%. The result is substantial financial risks, which are typically ignored or downplayed by planners and decision makers to the detriment of social and economic welfare. Our data also show that forecasts have not become more accurate over the 30-year period studied, despite claims to the contrary by forecasters. The causes of inaccuracy in forecasts are different for rail and road projects, with political causes playing a larger role for rail than for road. The cure is transparency, accountability, and new forecasting methods. The challenge is to change the governance structures for forecasting and project development. Our article shows how planners may help achieve this.
Transport Reviews, vol. 26, no. 5, pp. 537-555, 2006
Based on a review of available data from a database on large‐scale transport infrastructure projects, this paper investigates the hypothesis that traffic forecasts for road links in Europe are geographically biased with underestimated traffic volumes in metropolitan areas and overestimated traffic volumes in remote regions. The present data do not support this hypothesis. Since previous studies have shown a strong tendency to overestimated forecasts of the number of passengers on new rail projects, it could be speculated that road planners are more skilful and/or honest than rail planners. However, during the period when the investigated projects were planned (up to the late 1980s), there were hardly any strong incentives for road planners to make biased forecasts in order to place their projects in a more flattering light. Future research might uncover whether the change from the ‘predict and provide’ paradigm to ‘predict and prevent’ occurring in some European countries in the 1990s has influenced the accuracy of road traffic forecasts in metropolitan areas.
Transportation Research Part A: Policy and Practice, vol. 39, no. 6, pp. 522-530, 2005
Project promoters, forecasters, and managers sometimes object to two things in measuring inaccuracy in travel demand forecasting: (1) using the forecast made at the time of making the decision to build as the basis for measuring inaccuracy and (2) using traffic during the first year of operations as the basis for measurement. This paper presents the case against both objections. First, if one is interested in learning whether decisions about building transport infrastructure are based on reliable information, then it is exactly the traffic forecasted at the time of making the decision to build that is of interest. Second, although ideally studies should take into account so-called demand “ramp up” over a period of years, the empirical evidence and practical considerations do not support this ideal requirement, at least not for large-N studies. Finally, the paper argues that large samples of inaccuracy in travel demand forecasts are likely to be conservatively biased, i.e., accuracy in travel demand forecasts estimated from such samples would likely be higher than accuracy in travel demand forecasts in the project population. This bias must be taken into account when interpreting the results from statistical analyses of inaccuracy in travel demand forecasting.
Methods for Quantitative Risk Analysis for Travel Demand Model Forecasts
Transportation Research Record: Journal of the Transportation Research Board, 2014
Travel demand forecasting models have played a critical role in transportation planning, supporting 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 state-of-the-practice in travel demand modeling have advanced considerably over 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 around the forecasts that these models generate. There are many applications in which travel demand forecasts are important, 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 their differing risk-reward profiles. Several previous papers have 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.
IEEE Transactions on Engineering Management
Transport projects are regularly subjected to cost misperformance. The contingency set aside to cover any increases in cost due to risk and uncertainty issues is often insufficient. We review approaches that have been used to estimate a cost contingency. We show that some approaches such as reference class forecasting, which underpins the planning fallacy theory, take a biased view to formulate a contingency. Indeed, there is a perception that the risks and uncertainties that form the parts of a cost contingency cannot be accurately assessed using heuristics. The absence of an overarching theory to support the use of heuristics has resulted in them often being downplayed in a project's investment decision-making process. This article fills this void and provides the theoretical backdrop to support the use of heuristics to formulate a cost contingency. We make a clarion call to reconcile the duality of the bias and heuristic approaches, propose a balanced framework for developing a cost contingency, and suggest the use of uplifts to derisk cost estimates is redundant. We hope our advocacy for a balanced approach will stimulate debate and question the legitimacy of uplifts to solely debias cost estimates.
In this paper, evidence from the literature on the inaccuracy of forecasts from transport planning models is presented and its impact on capital infrastructure planning decision-making is demonstrated. Empirical evidence suggests that forecasts used for major planning decisions internationally have been found to be rather inaccurate (when comparing forecast flows with actually realized flows after time passed). Evidence of these irregularities in the case of a major expressway infrastructure project in Southern Greece is presented, providing a typical example of a country with an inadequate freeway network. Data from the immediate impact zone of the Attiki Odos tollway in the metropolitan area of Athens are used to demonstrate that the project resulted in a considerable change in the land use patterns and density, resulting in the generation of additional traffic flows. Having demonstrated the need for rethinking how longterm traffic is forecast, suitable recommendations for promising directions are made. Dealing with uncertainty is one key aspect that can be easily incorporated to existing forecasting tools. Furthermore, the need for more detailed and areapecific models, e.g. through the integration of activity-based modeling, specific obility patterns and demands etc. are also outlined.
Risk analysis in the evaluation of transport proposals
2004
There are uncertainties in all stages of the project development cycle including uncertainty in the costs involved in planning, designing, constructing and operating transport infrastructure. These uncertainties represent project risks that have the potential of influencing the outcomes of projects. The project evaluation process should consider project uncertainty via the use of risk analysis techniques. This paper arises from research undertaken by ARRB Transport Research in developing the Austroads Guide to Project Evaluation, and provides an overview of risk analysis for the transport practitioner and its role in the project evaluation process. A discussion of the definitions of risk and types of risks that arise within the project development process is provided, including an example illustrating key steps of the risk analysis method. A Risk ExplorerTM tool that provides a learning environment to help the user identify, assess and analyse risks related to project evaluation is ...