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 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.     

Uncertainty in the context of highway appraisal

Transport planning is based on traffic forecasts which are subject to great uncertainties. These uncertainties have generally been ignored or, at least, not explicitly included in the planning process. This paper describes the principles behind the estimation of the uncertainty (or range of error) of the forecasts of a traffic model and discusses the means by which this information can be absorbed into the decision-making in transport planning projects. These principles have been applied to the appraisal of a British highway project and throughout the paper reference is made to this project in order to illustrate the results of the practical application of an explicit treatment of uncertainty. It is believed that this approach can and should be developed for application in most areas of transport planning, leading inevitably to an improvement in the quality of the decisions taken.     

Remuneration models and revenue risk mitigation in road public–private partnership projects – a case study from Serbia

As a consequence of renewed interest in attracting private financing for infrastructure investments, public–private partnership (PPP) arrangements are mostly seen as a suitable mechanism for ensuring sound and quicker delivery of transport infrastructure projects. However, a general concern is that expectations of mobilizing private-sector funds have been overestimated in a number of cases. The purpose of this paper is to contribute to the risk analysis of transport PPP projects with substantial exogenous demand risk which could serve as a rationale for choosing the appropriate PPP model. The objective of this paper is to construct an analytical cash flow-based project model to facilitate the choice of the remuneration mechanism suitable for both private investors and public sector. The model provides an indication whether the project should be implemented as a ‘users pay’, a hybrid or an ‘annuity’ PPP model. The proposed methodology is illustrated using a case study from Serbia.     

Hubris or humility? Accuracy issues for the next 50 years of travel demand modeling

This study reviews the 50-year history of travel demand forecasting models, concentrating on their accuracy and relevance for public decision-making. Only a few studies of model accuracy have been performed, but they find that the likely inaccuracy in the 20-year forecast of major road projects is ±30 % at minimum, with some estimates as high as ±40–50 % over even shorter time horizons. There is a significant tendency to over-estimate traffic and underestimate costs, particularly for toll roads. Forecasts of transit costs and ridership are even more uncertain and also significantly optimistic. The greatest knowledge gap in US travel demand modeling is the unknown accuracy of US urban road traffic forecasts. Modeling weaknesses leading to these problems (non-behavioral content, inaccuracy of inputs and key assumptions, policy insensitivity, and excessive complexity) are identified. In addition, the institutional and political environments that encourage optimism bias and low risk assessment in forecasts are also reviewed. Major institutional factors, particularly low local funding matches and competitive grants, confound scenario modeling efforts and dampen the hope that technical modeling improvements alone can improve forecasting accuracy. The fundamental problems are not technical but institutional: high non-local funding shares for large projects warp local perceptions of project benefit versus costs, leading to both input errors and political pressure to fund projects. To deal with these issues, the paper outlines two different approaches. The first, termed ‘hubris’, proposes a multi-decade effort to substantially improve model forecasting accuracy over time by monitoring performance and improving data, methods and understanding of travel, but also by deliberately modifying the institutional arrangements that lead to optimism bias. The second, termed ‘humility’, proposes to openly quantify and recognize the inherent uncertainty in travel demand forecasts and deliberately reduce their influence on project decision-making. However to be successful either approach would require monitoring and reporting accuracy, standards for modeling and forecasting, greater model transparency, educational initiatives, coordinated research, strengthened ethics and reduction of non-local funding ratios so that localities have more at stake.     

管幕内顶进箱涵时顶部管幕力学作用的试验研究

上海市中环线虹许路北虹路下立交工程是目前世界上在饱和含水软土地层中施工的横截面最大的管幕法工程。为了充分研究箱涵顶进过程中管幕的力学作用,根据相似理论,按照实际工程状况进行了室内模型试验,得到了箱涵推进过程中地面和顶部管幕的竖向位移,以及管幕下土压力的变化特征,发现箱涵前端一定范围内的地层主要表现为竖向沉降变形,顶部管幕在箱涵推进过程中起到一定的承载作用,这对于稳定箱涵前端开挖面是有利的。试验结果对实际工程的设计与施工提供了一定的参考。     

Renegotiating PPP Contracts: Reinforcing the ‘P' in Partnership

Abstract

The issue of renegotiation is becoming increasingly important as a tool to address uncertainty of public private partnership (PPP) projects and as a mechanism to restore its economic and financial equilibrium. This paper aims to understand how and why renegotiations occur in long-lasting PPP projects and what are the pros and cons of the renegotiation. We depart from a literature review on contract incompleteness and on the determinant factors for the renegotiation of infrastructure concessions. To illustrate the discussion, the case studies of nine European PPP projects are analysed by examining the specific exogenous and endogenous determinant factors that conduce to renegotiating contracts. The implications of contractual renegotiations are explained and summarized through a comparison of our case studies with literature. Although renegotiation, per se, is not a solution for the successful implementation of PPP projects, our findings reinforce the idea of contractual flexibility as a tool that allows adapting to uncertainty. Moreover, effective communication mechanisms allow a better response to unforeseen events, reinforcing the partners’ commitment to deliver a win–win project.     

Valuation of road projects with uncertain outcomes

This is a practical implementation of a multicriteria methodology developed to assess the risk involved in investment or policy projects. It is based on the specification of a non-linear, but piecewise linear, additive utility function, where the partial utility functions are specified as recursive exponential functions of only one curvature parameter. This allows for a reduction of the quantity of information necessary to build the utility function in a context of uncertainty. It is introduced in an aggregation model whereby the partial utility functions are built separately through a specific questioning process. The methodology is applied to the selection of road infrastructure projects of the Walloon Region in Belgium with several experts and civil servants from the Transport Ministry whose individual utility functions are derived. Software called MUSTARD is used, which stepwise and interactively helps the decision-makers to formulate the problem, build the criteria's distributions and state their preferences. The projects are ranked according to their rate of return computed on the basis of the projects' certain equivalent money values.     

Prioritizing road extension projects with interdependent benefits under time constraint

Since transportation projects are costly and resources are limited, prioritizing or sequencing the projects is imperative. This study was inspired by a client who asked: “I have tens of approved road extension projects, but my financial resources are limited. I cannot construct all the projects simultaneously, so can you help me prioritize my projects?” To address this question, the benefits and costs of all the possible scenarios must be known. However, the impacts (or benefit) of road extension projects are highly interdependent, and in sizable cases cannot be specified thoroughly. We demonstrate that the problem is analogous to the Traveling Salesman Problem (TSP). Dynamic change in travel demand during construction is another aspect of the complexity of the problem. The literature is yet to provide efficient methods for large cases. To this end, we developed a heuristic methodology in which the variation of travel demand during the construction period is considered. We introduce a geometrical objective function for which a solution-finding policy based on “gradient maximization” is developed. To address the projects’ interdependency, a special neural network (NN) model was devised. We developed a search engine hybridized of Ant Colony and Genetic Algorithm to seek a solution to the TSP-like problem on the NN based on gradient maximization. The algorithm was calibrated and applied to real data from the city of Winnipeg, Canada, as well as two cases based on Sioux-Falls. The results were reliable and identification of the optimum solution was achievable within acceptable computational time.     

A technique for the investigation of scheme benefits in large-scale transport networks

Existing methods of evaluating large-scale transport networks involve the use of mathematical models of traffic flow which are generally both large and complex. However, the time and cost involved in the use of these models normally restricts their use for the detailed forecasting of traffic flows and costs to the assessment of a relatively small number of alternative patterns of overall investment. In order to evaluate the individual projects and groups of projects which go to make up an overall investment plan, it is, therefore, usually necessary to make simplifying assumptions about the influence of any one project on the overall traffic pattern, so as to isolate it from the influence of neighbouring projects. These assumptions generally result in the loss of a certain amount of the detail normally available from a standard model, and the task of assessing the relative value of different projects and the amount of interaction between them is made more difficult.This paper describes a new technique, designed to permit the evaluation of individual projects whilst still retaining the level of detail available from a full-scale mathematical model. The aim has been to produce a cheap and easy-to-use technique, capable of producing substantially the same results as a standard model. The technique uses newly developed computer algorithms which short-cut the full-scale model by forecasting the changes in an existing travel pattern resulting from the influence of a particular project. Initial tests suggest that approaching the problem in this way can save up to 70% of the computing time and costs involved in the use of a standard model for the evaluation of individual projects.The technique as described here is envisaged as a tool for aiding strategic investment decisions. It can, however, provide data for more detailed investigations, and could, with modifications, carry out these investigations on smaller problems than those for which it was originally designed.Crown copyright reserved, 1973     

Prioritizing new bicycle facilities to improve low-stress network connectivity

This paper introduces a new method to prioritize bicycle improvement projects based on accessibility to important destinations, such as grocery stores, banks, and restaurants. Central to the method is a new way to classify “bicycling stress” using marginal rates of substitution which are commonly developed through empirical behavioral research on bicyclist route choice. MRS values are input parameters representing bicycling stress associated with the number of lanes and speed limit of a street. The method was programmed as a geographic information system tool and requires commonly available data. The tool is demonstrated on three improvement scenarios that were recently proposed for Seattle, Washington. The full build-out scenario consists of 771 projects that include various new bike lanes, protected bike lanes, and multi-use trails. The tool produces priority rankings based on a project’s ability to improve low-stress connectivity between homes and important destinations. The analysis identifies specific areas and neighborhoods that can be expected to exhibit better bikeability. Transportation planners can use the tool to help communicate anticipated project impacts to decision-makers and the public.     

A bottom-up solution for the multi-facility optimal pavement resurfacing problem

Transportation infrastructure management has been a subject of growing economic importance in recent years due to the magnitude of agency expenditures. Increasingly sophisticated methods have been developed to model pavement deterioration and solve for optimal management strategies. However, it is unclear whether these more complex methods are providing more useful results. This paper presents a simple approach for optimizing the frequency and intensity of resurfacing for multiple highway facilities. It builds upon existing optimization methods for the single-facility, continuous-state, continuous-time problem and corresponding results, which include a threshold structure for optimal solutions. This threshold structure allows for mathematical simplifications and for a straightforward optimization approach to be applied to the multi-facility case. The approach is bottom-up rather than top-down, preserving facility-specific features to develop informative budget allocation results. Application of the approach in a case study indicates that solutions are likely to be robust to deterioration model uncertainty, which is consistent with previous facility-level findings. In addition, the methodology is shown to be robust to the form of the deterioration model.     

基于多层次体系的交通科技项目模糊综合评价

交通科技项目具有明显的多因素性和不确定性,导致对其进行的评价也具有多目标性和模糊性。文章根据交通科技项目的特点提出基于多层次体系的模糊综合评价方法,介绍以多层次模糊综合评价模型为框架评价交通科技项目的步骤,为西部交通科技项目对社会经济的影响评价提供依据。     

Costs of travel time uncertainty and benefits of travel time information: Conceptual model and numerical examples

A negative effect of congestion that tends to be overlooked is travel time uncertainty. Travel time uncertainty causes scheduling costs due to early or late arrival. The negative effects of travel time uncertainty can be reduced by providing travellers with travel time information, which improves their estimate of the expected travel time, thereby reducing scheduling costs. In order to assess the negative effects of uncertainty and the benefits of travel time information, this paper proposes a conceptual model of departure time choice under travel time uncertainty and information. The model is based on expected utility theory, and includes the variation in travel time, the quality of travel time information and travellers’ perception of the travel time. The model is illustrated by an application to the case of the A2 motorway between Beesd and Utrecht in the Netherlands.     

Uncertain benefits: Application of Bayesian melding to the Alaskan Way Viaduct in Seattle

Uncertainty is inherent in major infrastructure projects, but public decision-making for such projects ignores it. We investigate the uncertainty about the future effects of tearing down the Alaskan Way Viaduct in downtown Seattle, using an integrated model of housing, jobs, land use and transportation, on outcomes including average commute times. Our methodology combines the urban simulation model UrbanSim with the regional transportation model. We assess uncertainty using Bayesian melding, yielding a full predictive distribution of average commute times on 22 different routes in 2020. Of these routes, 14 do not include the viaduct and eight do. For the 14 base routes that do not include the viaduct, the predictive distributions overlap substantially, and so there is no indication that removing the viaduct would increase commute times for these routes. For each of the eight routes that do include the viaduct, the 95% predictive interval for the difference in average travel times between the two scenarios includes zero, so there is not strong statistical support for the conclusion that removing the viaduct would lead to any increase in travel times. However, the median predicted increase is positive for each of these routes, with an average of 6 min, suggesting that there may be some measurable increase in travel time for drivers that use the viaduct as a core component of their commute.     

A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach

The objective of this paper is to introduce a multi-year pavement maintenance programming methodology that can explicitly account for uncertainty in pavement deterioration. This is accomplished with the development of a simulation-based genetic algorithm (GA) approach that is capable of planning the maintenance activities over a multi-year planning period. A stochastic simulation is used to simulate the uncertainty of future pavement conditions based on the calibrated deterioration model while GA is used to handle the combinatorial nature of the network-level pavement maintenance programming. The effects of the uncertainty of pavement deterioration on the maintenance program are investigated using a case study. The results show that programming the maintenance activities using only the expected pavement conditions is likely to underestimate the required maintenance budget and overestimate the performance of pavement network.     

Cost-effectiveness of reductions in greenhouse gas emissions from High-Speed Rail and urban transportation projects in California

A rising trend in state and federal transportation finance is to invest capital dollars into projects which reduce greenhouse gas (GHG) emissions. However, a key metric for comparing projects, the cost-effectiveness of GHG emissions reductions, is highly dependent on the cost-benefit methodology employed in the analysis. Our analysis comparing California High-Speed Rail and three urban transportation projects shows how four different accounting framings bring wide variations in cost per metric tonne of GHG emissions reduced. In our analysis, life-cycle GHG emissions are joined with full cost accounting to better understand the benefits of cap-and-trade investments. Considering only public subsidy for capital, none of the projects appear to be a cost-effective means to reduce GHG emissions (i.e., relative to the current price of GHG emissions in California’s cap-and-trade program at $12.21 per tonne). However, after adjusting for the change in private costs users incur when switching from the counterfactual mode (automobile or aircraft) to the mode enabled by the project, all investments appear to reduce GHG emissions at a net savings to the public. Policy and decision-makers who consider only the capital cost of new transportation projects can be expected to incorrectly assess alternatives and indirect benefits (i.e., how travelers adapt to the new mass transit alternative) should be included in decision-making processes.     

Flexibility in planning and development of a container terminal: an application of an American-style call option

The prosperity and social progress of developed and developing economies is highly dependent on the existence of efficient transport infrastructure. Nevertheless, current budgetary constraints are jeopardizing the necessary investments in new or existing infrastructure. New models for planning and managing infrastructure are now necessary to overcome the lack of public economic resources available. Port infrastructure is no exception and, due to the vast number of uncertainties involving these projects, it is relevant to maximize the capture of the latent value of flexible options. Incorporating flexibility in these projects, prior to the implementation phase, can be a solution that allows port managers to address future uncertainties and mitigate risk exposure. This paper analyzes the incorporation of flexibility in port planning through the use of an American call option to the physical capacity expansion problem. The rationale is to implement a flexible expansion plan, through options that can be exercised at any given time, that are able to deal with uncertainty in demand. The paper uses a case study – Terminal Container of Ferrol, in Spain – and the results support the hypothesis that imbedded flexibility will robustly increase the net present value of the project.     

整体式转向梯形机构的优化设计

转向梯形机构是使汽车转向时实现内、外轮理想转角关系的核心部件。建立了整体式转向梯形机构的数学模型,介绍了基于MATLAB优化工具箱的整体式转向梯形机构的优化设计计算程序。利用该程序,用户可以交互式输入结构基本参数,即可得到优化计算结果,并自动绘制出实际输出角和输出角期望值随输入角的变化曲线,以便用户分析、比较和选择。为转向梯形设计提供了高效、精确的实用方法。     

Optimal subwork zone operational strategy for short‐term work zone projects in four‐lane two‐way freeways

Roadside trees in Singapore are regularly trimmed for the purpose of traffic safety and roadside tree‐trimming project is one typical type of short‐term work zone projects. To implement such a short‐term work zone project, contractors usually divide an entire work zone into multiple subwork zones with the uniform length. This paper aims to determine an optimal subwork zone strategy for the short‐term work zone projects in four‐lane two‐way freeways with time window and uniform subwork zone length constraints. The deterministic queuing model is employed to estimate total user delay caused by the work zone project by taking into account variable traffic speeds. Based on the user delay estimations, this paper proceeds to build a minimization model subject to time window and uniform length constraints for the optimal subwork zone strategy problem. This paper also presents a variation of the minimization model to examine the impact of unequal subwork zone length constraint. Since these minimization models belong to the mixed‐integer non‐differentiable optimization problems, an iterative algorithm embedding with the genetic simulated annealing method is thus proposed to solve these models. Finally, a numerical example is carried out to investigate the effectiveness of the proposed models. Copyright © 2010 John Wiley & Sons, Ltd.     

Short/medium-term prediction for the aviation emissions in the en route airspace considering the fluctuation in air traffic demand

This paper proposes a novel short/medium-term prediction method for aviation emissions distribution in en route airspace. An en route traffic demand model characterizing both the dynamics and the fluctuation of the actual traffic demand is developed, based on which the variation and the uncertainty of the short/medium-term traffic growth are predicted. Building on the demand forecast the Boeing Fuel Flow Method 2 is applied to estimate the fuel consumption and the resulting aviation emissions in the en route airspace. Based on the traffic demand prediction and the en route emissions estimation, an aviation emissions prediction model is built, which can be used to forecast the generation of en route emissions with uncertainty limits. The developed method is applied to a real data set from Hefei Area Control Center for the en route emission prediction in the next 5 years, with time granularities of both months and years. To validate the uncertainty limits associated with the emission prediction, this paper also presents the prediction results based on future traffic demand derived from the regression model widely adopted by FAA and Eurocontrol. The analysis of the case study shows that the proposed method can characterize well the dynamics and the fluctuation of the en route emissions, thereby providing satisfactory prediction results with appropriate uncertainty limits. The prediction results show a gradual growth at an average annual rate of 7.74%, and the monthly prediction results reveal distinct fluctuation patterns in the growth.