Traffic forecasts under uncertainty and capacity constraints

Traffic forecasts provide essential input for the appraisal of transport investment projects. However, according to recent empirical evidence, long-term predictions are subject to high levels of uncertainty. This article quantifies uncertainty in traffic forecasts for the tolled motorway network in...

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Published inTransportation (Dordrecht) Vol. 39; no. 1; pp. 1 - 17
Main Authors Matas, Anna, Raymond, Josep-Lluis, Ruiz, Adriana
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.01.2012
Springer
Springer Nature B.V
SeriesTransportation
Subjects
Online AccessGet full text
ISSN0049-4488
1572-9435
DOI10.1007/s11116-011-9325-1

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Abstract Traffic forecasts provide essential input for the appraisal of transport investment projects. However, according to recent empirical evidence, long-term predictions are subject to high levels of uncertainty. This article quantifies uncertainty in traffic forecasts for the tolled motorway network in Spain. Uncertainty is quantified in the form of a confidence interval for the traffic forecast that includes both model uncertainty and input uncertainty. We apply a stochastic simulation process based on bootstrapping techniques. Furthermore, the article proposes a new methodology to account for capacity constraints in long-term traffic forecasts. Specifically, we suggest a dynamic model in which the speed of adjustment is related to the ratio between the actual traffic flow and the maximum capacity of the motorway. As an illustrative example, this methodology is applied to a specific public policy that consists of suppressing the toll on a certain motorway section before the concession expires.
AbstractList Traffic forecasts provide essential input for the appraisal of transport investment projects. However, according to recent empirical evidence, long-term predictions are subject to high levels of uncertainty. This article quantifies uncertainty in traffic forecasts for the tolled motorway network in Spain. Uncertainty is quantified in the form of a confidence interval for the traffic forecast that includes both model uncertainty and input uncertainty. We apply a stochastic simulation process based on bootstrapping techniques. Furthermore, the article proposes a new methodology to account for capacity constraints in long-term traffic forecasts. Specifically, we suggest a dynamic model in which the speed of adjustment is related to the ratio between the actual traffic flow and the maximum capacity of the motorway. As an illustrative example, this methodology is applied to a specific public policy that consists of suppressing the toll on a certain motorway section before the concession expires.[PUBLICATION ABSTRACT]
Traffic forecasts provide essential input for the appraisal of transport investment projects. However, according to recent empirical evidence, long-term predictions are subject to high levels of uncertainty. This article quantifies uncertainty in traffic forecasts for the tolled motorway network in Spain. Uncertainty is quantified in the form of a confidence interval for the traffic forecast that includes both model uncertainty and input uncertainty. We apply a stochastic simulation process based on bootstrapping techniques. Furthermore, the article proposes a new methodology to account for capacity constraints in long-term traffic forecasts. Specifically, we suggest a dynamic model in which the speed of adjustment is related to the ratio between the actual traffic flow and the maximum capacity of the motorway. As an illustrative example, this methodology is applied to a specific public policy that consists of suppressing the toll on a certain motorway section before the concession expires.
Traffic forecasts provide essential input for the appraisal of transport investment projects. However, according to recent empirical evidence, long-term predictions are subject to high levels of uncertainty. This article quantifies uncertainty in traffic forecasts for the tolled motorway network in Spain. Uncertainty is quantified in the form of a confidence interval for the traffic forecast that includes both model uncertainty and input uncertainty. We apply a stochastic simulation process based on bootstrapping techniques. Furthermore, the article proposes a new methodology to account for capacity constraints in long-term traffic forecasts. Specifically, we suggest a dynamic model in which the speed of adjustment is related to the ratio between the actual traffic flow and the maximum capacity of the motorway. As an illustrative example, this methodology is applied to a specific public policy that consists of suppressing the toll on a certain motorway section before the concession expires. Reprinted by permission of Springer
Author Ruiz, Adriana
Matas, Anna
Raymond, Josep-Lluis
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10.1080/01441640500124779
10.1007/s11116-006-9110-8
10.1007/s11116-009-9199-7
10.1515/9780691218632
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Keywords Traffic forecast
Toll motorways
Uncertainty
Data analysis
Transportation
Freeway
Road traffic
Forecast model
Toll
Result
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Snippet Traffic forecasts provide essential input for the appraisal of transport investment projects. However, according to recent empirical evidence, long-term...
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SubjectTerms Applied sciences
Bias
Bootstrap mechanism
Bootstrap method
Computer simulation
Economic Geography
Economics
Economics and Finance
Engineering Economics
Exact sciences and technology
Forecasting
Forecasting techniques
Ground, air and sea transportation, marine construction
Innovation/Technology Management
Logistics
Long term
Marketing
Mathematical models
Methodology
Motorways
Networks
Optimism
Organization
Project evaluation
Public policy
Regional/Spatial Science
Road network
Road traffic
Road transportation and traffic
Roads & highways
Simulation
Spain
Stochastic processes
Studies
Toll motorways
Toll roads
Traffic
Traffic engineering
Traffic flow
Traffic forecast
Transport economics
Transportation planning
Transportation planning, management and economics
Uncertainty
Variables
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Title Traffic forecasts under uncertainty and capacity constraints
URI https://link.springer.com/article/10.1007/s11116-011-9325-1
http://econpapers.repec.org/article/kaptransp/v_3a39_3ay_3a2012_3ai_3a1_3ap_3a1-17.htm
https://www.proquest.com/docview/906064858
https://www.proquest.com/docview/1019628308
https://www.proquest.com/docview/918749378
Volume 39
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