Institutional issues in planning for more uncertain futures

Future travel demand has always been difficult to estimate. Recent trends of a slow down or stagnation in traffic growth combined with substantial demographic, economic, and technological shifts further complicate that task. This poses a significant planning challenge given that decision-making is often based on the benefits of infrastructure investments which accrue over periods as long as 60 years. In contrast to the changing ideas around what mobility in the future will look like and the types of demand it may need to service, the practice of forecasting future travel demand remains largely unchanged as do the decision-making processes which flow from this. Alternative approaches to thinking about futures such as scenario planning exist but have had more limited deployment in the transport sector. This paper explores the institutional issues surrounding the purpose, practice and barriers to changing the approaches of forecasting and decision-making through an exploration of the state of practice in the UK drawing on interviews with 23 practitioners. Drawing on Hall’s work on policy change, the research finds that there is a strong policy paradigm built around the relationships between transport investment and traffic growth. This has strengthened since the recession with an increased focus on the relationship between transport and job creation. The forecasting approaches in use today are an important part of a complex decision-making apparatus reflective of specialized policy arenas like transport. Challenges in acknowledging, representing and communicating uncertainty are identified leaving a growing tension between planning visions and planning practice. We conclude by reflecting on events which may stimulate a broader reframing of how we plan for transport futures whilst embracing key uncertainties.

     

Scenario construction for transport planning

This paper argues that the types of uncertainty having the most serious consequences for transport planning are not captured by existing modelling procedures, which typically rely on a limited amount of sensitivity testing of plans based on extrapolative forecasts. It recommends as an alternative scenario‐based planning, an approach which is finding increasing application in private sector policy formation. The paper summarises the origins of scenario planning and the gradual broadening in its range of application, before going on to suggest methods of scenario construction suitable for transport planning applications. It considers the arguments for and against associating probabilities with scenarios and also addresses the problems of formulating transport strategies and evaluating them within the context of a scenario‐based approach to strategy choice.     

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.     

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.     

Uncertainty in traffic forecasts: literature review and new results for The Netherlands

This paper provides a review of transport model applications that not only provide a central traffic forecast (or forecasts for a few scenarios), but also quantify the uncertainty in the traffic forecasts in the form of a confidence interval or related measures. Both uncertainty that results from using uncertain inputs (e.g. on income) and uncertainty in the model itself are treated. The paper goes on to describe the methods used and the results obtained for a case study in quantifying uncertainty in traffic forecasts in The Netherlands.

Does uncertainty make cost-benefit analyses pointless?

Cost-benefit analysis (CBA) is widely used in public decision making on infrastructure investments. However, the demand forecasts, cost estimates, benefit valuations and effect assessments that are conducted as part of CBAs are all subject to various degrees of uncertainty. The question is to what extent CBAs, given such uncertainties, are still useful as a way to prioritize between infrastructure investments, or put differently, how robust the policy conclusions of CBA are with respect to uncertainties. Using simulations based on real data on national infrastructure plans in Sweden and Norway, we study how investment selection and total realized benefits change when decisions are based on CBA assessments subject to several different types of uncertainty. Our results indicate that realized benefits and investment selection are surprisingly insensitive to all studied types of uncertainty, even for high levels of uncertainty. The two types of uncertainty that affect results the most are uncertainties about investment cost and transport demand. Provided that decisions are based on CBA outcomes, reducing uncertainty is still worthwhile, however, because of the huge sums at stake. Even moderate reductions of uncertainties about unit values, investment costs, future demand and project effects may increase the realized benefits infrastructure investment plans by tens or hundreds of million euros. We conclude that, despite the many types of uncertainties, CBA is able to fairly consistently separate the wheat from the chaff and hence contribute to substantially improved infrastructure decisions.     

Spatial structure and mobility

The aim of this paper is to contribute to a clearer understanding of the extent to which the spatial structure and planning of the residential environment can explain mobility, in general, and the choice of mode of transport, in particular, and what spatial planning and traffic management aspects play a significant role in this. The research showed that certain aspects of the planned environment do indeed have a clear impact on mobility. These effects are particularly apparent in trips made for shopping and social or recreational purposes. It is mainly personal characteristics that largely or almost entirely determine commuter traffic. An integral approach to the planning of residential areas is required to achieve any great changes in mobility. Only then may we expect spatial planning to have any significant impact on car use.     

Quantifying errors in micro-scale emissions models using a case-study approach

To quantify the level of uncertainty attached to forecasts of CO₂ emissions, an analysis of errors is undertaken; looking at both errors inherent in the model structure and the uncertainties in the input data. Both error types are treated in relation to CO₂ emissions modelling using a case-study from Brisbane, Australia. To estimate input data uncertainty, an analysis of traffic conditions using Monte Carlo simulation is used. Model structure induced uncertainties are also quantified by statistical analysis for a number of traffic scenarios. To arrive at an optimal overall CO₂ prediction, the interaction between the two components is taken into account. Since a more complex model does not necessarily yield higher overall accuracy, a compromise solution is found. The results suggest that the CO₂ model used in the analysis produces low overall uncertainty under free flow traffic conditions. When average traffic speeds approach congested conditions, however, there are significant errors associated with emissions estimates.     

Sea space capacity and operation strategy analysis system

Sea space planning and congestion management is receiving more attention. However, little work on sea space capacity and strategy analyses can be found in the literature. Compared to other transportation systems, a sea space system has some special features that require consideration. The system capacity also depends on the pattern of traffic using the system. In this paper, we model a sea space as a directional network and capacity models for berthing areas, anchorage areas, fairways and their intersections, as well as the entire sea space system are developed. These models can be used to compute capacity for any given traffic pattern which can be extracted from vessel trip records or from traffic forecasts. To implement these models, a software system called Sea Space Capacity and Strategy Analysis System (SCSAS) has been developed in Visual C + + and is now being used in Singapore.     

Selection bias in build-operate-transfer transportation project appraisals

Recent empirical studies have found widespread inaccuracies in traffic forecasts despite the fact that travel demand forecasting models have been significantly improved over the past few decades. We suspect that an intrinsic selection bias may exist in the competitive project appraisal process, in addition to the many other factors that contribute to inaccurate traffic forecasts. In this paper, we examine the potential for selection bias in the governmental process of Build-Operate-Transfer (BOT) transportation project appraisals. Although the simultaneous consideration of multiple criteria is typically used in practice, traffic flow estimate is usually a key criterion in these appraisals. For the purposes of this paper, we focus on the selection bias associated with the highest flow estimate criterion. We develop two approaches to quantify the level and chance of inaccuracy caused by selection bias: the expected value approach and the probability approach. The expected value approach addresses the question “to what extent is inaccuracy caused by selection bias?”. The probability approach addresses the question “what is the chance of inaccuracy due to selection bias?”. The results of this analysis confirm the existence of selection bias when a government uses the highest traffic forecast estimate as the priority criterion for BOT project selection. In addition, we offer some insights into the relationship between the extent/chance of inaccuracy and other related factors. We do not argue that selection bias is the only reason for inaccurate traffic forecasts in BOT projects; however, it does appear that it could be an intrinsic factor worthy of further attention and investigation.     

An intermodal freight transport system for optimal supply chain logistics

Complexity in transport networks evokes the need for instant response to the changing dynamics and uncertainties in the upstream operations, where multiple modes of transport are often available, but rarely used in conjunction. This paper proposes a model for strategic transport planning involving a network wide intermodal transport system. The system determines the spatio-temporal states of road based freight networks (unimodal) and future traffic flow in definite time intervals. This information is processed to devise efficient scheduling plans by coordinating and connecting existing rail transport schedules to road based freight systems (intermodal). The traffic flow estimation is performed by kernel based support vector mechanisms while mixed integer programming (MIP) is used to optimize schedules for intermodal transport network by considering various costs and additional capacity constraints. The model has been successfully applied to an existing Fast Moving Consumer Goods (FMCG) distribution network in India with encouraging results.     

Predicting and planning airport acceptance rates in metroplex systems for improved traffic flow management decision support

Efficient planning of Airport Acceptance Rates (AARs) is key for the overall efficiency of Traffic Management Initiatives such as Ground Delay Programs (GDPs). Yet, precisely estimating future flow rates is a challenge for traffic managers during daily operations as capacity depends on a number of factors/decisions with very dynamic and uncertain profiles. This paper presents a data-driven framework for AAR prediction and planning towards improved traffic flow management decision support. A unique feature of this framework is to account for operational interdependency aspects that exist in metroplex systems and affect throughput performance. Gaussian Process regression is used to create an airport capacity prediction model capable of translating weather and metroplex configuration forecasts into probabilistic arrival capacity forecasts for strategic time horizons. To process the capacity forecasts and assist the design of traffic flow management strategies, an optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal AARs in GDPs, an overall delay reduction of up to 9.7% is achieved. The results also reveal that incorporating robustness in the design of the traffic flow management plan can contribute to decrease delay costs while increasing predictability.     

Public transport demand estimation by calibrating a trip distribution-mode choice (TDMC) model from passenger counts: A case study in Bandung,Indonesia

A problem always found in developing countries is the lack of information required for short, medium and long term planning purposes due to money and time constraints. This becomes even more valuable for problems which require ‘quick-response’ treatment. A flexible model approach allows monitoring a long term plan in order to check its short term performance at regular intervals using easily-available data. If found necessary, changes to the plan may be evaluated and eventually implemented. For this reason, the approach is deemed appropriate for long term planning and project evaluation even in the case of rapid changes in land-use, socio-economic and population parameters usually occurs in most of developing countries. A key element of the approach is a system to update the forecasting model (in particular its trip distribution and mode choice elements) using low-cost and/or easily-available information. Traffic counts are particularly attractive to be used in developing countries for planning purposes. The estimation of public transport demand, particularly important for planning purposes, is an expensive and time consuming undertaking. The need for a low-cost method to estimate the public transport demand is therefore obvious. The objective of this paper is the development of methods and techniques for modelling the public transport demand using traffic (passenger) count information and other simple zonal-planning data. We will report on a family of aggregate model combined with a family of mode choice logit models which can be calibrated from traffic (passenger) counts and other low-cost data. The model examined was the Gravity (GR) model combined with the Multi-Nominal-Logit (MNL) model. Non-Linear-Least-Squares (NLLS) estimation method was used to calibrate the parameter of the combined model. The combined TDMC model and the calibration method have been implemented into a micro-computer package capable of dealing with the study area consisting of up to 300 zones, 3000 links and 6000 nodes. The approach has been tested using the 1988 Public Transport Data Survey in Bandung (Indonesia). The model was found to provide a reasonably good fit and the calibrated parameter can then be used for forecasting purposes. General conclusion regarding the advantageous and the applicability of the approach to other environments are given.     

Incorporating Knightian uncertainty into real options analysis: Using multiple-priors in the case of rail transit investment

This paper revisits the real option investment problem from a ‘Knightian’ perspective of uncertainty. We examine the decision to invest in rail transit infrastructure (i.e., transport improvement) by treating population scale and the attitudes of decision-makers or social planners as sources of risk and ambiguity. An α-maxmin multiple-priors expected utility framework is developed to solve for the option value of rail transit investment under Knightian uncertainty. We find that the threshold for investment varies with the ambiguity attitudes (i.e., pessimism or optimism) of decision-makers regarding future population dynamics, and show that option value can in fact either increase or decrease with uncertainty subject to κ-ignorance and ambiguity. We also underline the effect of transport improvement on traffic congestion under various states of nature, and determine the specific population scale ranges for which investment is warranted under (1) risk and (2) Knightian uncertainty. These dynamics are illustrated in a numerical application adapted from a Chinese rail transit initiative specific to the Xiamen region.     

Do Road Planners Produce More ‘Honest Numbers’ than Rail Planners? An Analysis of Accuracy in Road‐traffic Forecasts in Cities versus Peripheral Regions

Abstract

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.     

Quantifying uncertainties in a national forecasting model

Uncertainties related to demand model system outputs is an important issue in travel demand models. This paper focuses on uncertainties arisen from the fact that models are estimated on a sample of the population (and not the whole population). Forecasting systems can be quite complex, and may contain procedures that not easily permit analytically derived statistical measures of uncertainty. In this paper, the possibilities to use computer-intensive numerical methods to compute statistical measures for very complex systems, without being bound to an analytical approach, are explored. Here, the bootstrap method is used to obtain statistical measures of outputs produced by the forecasting system SAMPERS. The SAMPERS system is used by Swedish transport authorities. The bootstrap method is briefly described as well as the procedure of applying bootstrap on the SAMPERS system. Numerical results are presented for selected forecast results at different levels such as total traffic demand, origin–destination demand, train line demand and the demand on specific links. Also, the uncertainty related to the value of time estimate is analysed.     

Strategic de-confliction in the presence of a large number of 4D trajectories using a causal modeling approach

This paper presents a strategic de-confliction algorithm based on causal modeling developed under the STREAM project and launched under the umbrella of the Single European Sky ATM Research (SESAR) Program. The basic underlying concept makes use of the enriched information included in the Shared Business Trajectories (SBTs) of the flights prior to takeoff (or in the Reference Business Trajectories (RBTs) if the flight is airborne) to allocate conflict-free trajectories in a traffic planning phase that should lead to an actual conflict-free scenario in the flight execution phase in the absence of flight and/or network uncertainties. The proposed approach could decrease the workload of the air traffic controllers, thus improving the Air Traffic Management (ATM) capacity while meeting the maximum possible expectations of the Airspace Users’ requirements in terms of horizontal flight efficiency. The main modules of the implemented system are also presented in this paper; these modules are designed to enable the processing of thousands of trajectories within a few seconds or minutes and encompass a global network scope with a planning horizon of approximately 2–3 h. The causal model applied for network conflict resolution and flight routing allocation is analyzed to demonstrate how the emergent dynamics (i.e., domino effects) of local trajectory amendments can be efficiently explored to identify conflict-free Pareto-efficient network scenarios. Various performance indicators can be taken into account in the multi-criteria optimization process, thus offering to the network manager a flexible tool for fostering a collaborative planning process.     

Measuring inaccuracy in travel demand forecasting: methodological considerations regarding ramp up and sampling

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.     

Modelling car ownership in Great Britain

Over the last 50 years there has been a tenfold increase in the number of cars in Great Britain, rising from 2.6 million vehicles in 1951 to 27 million vehicles in 2001. Over the same period there has been a steady reduction in the proportion of households without access to a car and a steady increase in the proportion of households with two or more cars. If such trends continue, it is likely that there will be increased energy consumption, increased problems with traffic congestion and atmospheric pollution, and reductions to the financial viability of public transport. Given the importance of car ownership to transport and land-use planning and its relationship with energy consumption, the environment and health, it is the objective of this research to develop econometric models of household car ownership and apply the models to generate forecasts across Britain to the year 2031. To achieve this objective, the research develops discrete choice models of the household’s decision to own zero, one, two or three or more vehicles as a function of market saturation, licence holding, household income and structure, household employment, company car provision, and purchase and use costs. The models are validated to data from the 2001 Census and are used to develop a range of forecasts taking into account changes to the socio-demographic characteristics of Britain.     

Simplified transport models based on traffic counts

Having accepted the need for the development of simpler and less cumbersome transport demand models, the paper concentrates on one possible line for simplification: estimation of trip matrices from link volume counts. Traffic counts are particularly attractive as a data basis for modelling because of their availability, low cost and nondisruptive character. It is first established that in normal conditions it may be possible to find more than one trip matrix which, when loaded onto a network, reproduces the observed link volumes. The paper then identifies three approaches to reduce this underspecification problem and produce a unique trip matrix consistent with the counts. The first approach consists of assuming that trip-making behaviour can be explained by a gravity model whose parameters can be calibrated from the traffic counts. Several forms of this gravity model have been put forward and they are discussed in Section 3. The second approach uses mathematical programming techniques associated to equilibrium assignment problems to estimate a trip matrix in congested areas. This method can also be supplemented by a special distribution model developed for small areas. The third approach relies on entropy and information theory considerations to estimate the most likely trip matrix consistent with the observed flows. A particular feature of this group is that they can include prior, perhaps outdated, information about the matrix.These three approaches are then compared and their likely areas for application identified. Problems for further research are discussed and finally an assessment is made of the possible role of these models vis-a-vis recent developments in transport planning.