Sen Gupta, Rajorshi and Vadali, Sharada R (2007): Stochastic Dominance Approach to Evaluate Optimism Bias in Truck Toll Forecasts. Published in: Transportation Research Record: Journal of the Transportation Research Board , Vol. 2066, (December 2008): pp. 98-105.
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Abstract
Optimism bias is a consistent feature associated with truck toll forecasts, à la Standard & Poor’s and the NCHRP synthesis reports. Given the persistent problem, two major sources of this bias are explored. In particular, the ignorance of operating cost as a demand-side factor and lack of attention to user heterogeneity are found to contribute to this bias. To address it, stochastic dominance analysis is used to assess the risk associated with toll revenue forecasts. For a hypothetical corridor, it is shown that ignorance of operating cost savings can lead to upward bias in the threshold value of time distribution. Furthermore, dominance analysis demonstrates that there is greater risk associated with the revenue forecast when demand heterogeneity is factored in. The approach presented can be generally applied to all toll forecasts and is not restricted to trucks.
Item Type: | MPRA Paper |
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Original Title: | Stochastic Dominance Approach to Evaluate Optimism Bias in Truck Toll Forecasts |
Language: | English |
Keywords: | Forecast Bias; Operating costs; Risk assessment; Savings; Stochastic Dominance; Tolls;Trucks |
Subjects: | D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D81 - Criteria for Decision-Making under Risk and Uncertainty C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R4 - Transportation Economics > R41 - Transportation: Demand, Supply, and Congestion ; Travel Time ; Safety and Accidents ; Transportation Noise |
Item ID: | 12891 |
Depositing User: | Rajorshi Sen Gupta |
Date Deposited: | 22 Jan 2009 05:49 |
Last Modified: | 01 Oct 2019 07:26 |
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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/12891 |