Error and optimism bias in toll road traffic forecasts

Traffic forecasts are employed in the toll road sector, inter alia, by private sector investors to gauge the bankability of candidate investment projects. Although much is written in the literature about the theory and practice of traffic forecasting, surprisingly little attention has been paid to the predictive accuracy of traffic forecasting models. This paper addresses that shortcoming by reporting the results from the largest study of toll road forecasting performance ever conducted. The author had access to commercial-in-confidence documentation released to project financiers and, over a 4-year period, compiled a database of predicted and actual traffic usage for over 100 international, privately financed toll road projects. The findings suggest that toll road traffic forecasts are characterised by large errors and considerable optimism bias. As a result, financial engineers need to ensure that transaction structuring remains flexible and retains liquidity such that material departures from traffic expectations can be accommodated.

How much is too much for tolled road users: Toll saturation and the implications for car commuting value of travel time savings?

The current practice of forecasting the demand for new tolled roads typically assumes that car users are prepared to pay a higher toll for a shorter journey, and they will keep doing so as long as the toll cost is not higher than their current value of travel time savings. Practice ignores the possibility that there could be a point when motorists stop driving on toll roads due to a toll budget constraint. The unconstrained toll budget assumption may be valid in networks where the addition of a new toll road does not result in a binding budget constraint that car users may have for using toll roads (although it could also be invoked for existing tolled routes through a reduction in use of a tolled route). In a road network like Sydney which offers a growing number of (linked) tolled roads, the binding budget constraint may be invoked, and hence including additional toll links might in turn reduce the car users’ willingness to pay for toll roads to save the same amount of travel time. When this occurs, car users are said to reach a toll saturation point (or threshold) and begin to consider avoiding one or more toll roads. Whilst toll saturation has important implications for demand forecasting and planning of toll roads, this type of behaviour has not been explored in the literature. We investigate the influence that increasing toll outlays has on preferences of car commuters to use one or more tolled roads as the number of tolled roads increases. The Sydney metropolitan area offers a unique laboratory to test this phenomenon, with nine tolled roads currently in place and another five in planning. The evidence supports the hypothesis that the value of travel time savings decreases as a consequence of toll saturation.     

A traffic analysis zone definition: a new methodology and algorithm

This paper develops a comprehensive approach to the definition of transportation analysis zones (TAZ), and therein, presents a new methodology and algorithm for the definition of TAZ embedded in geographic information systems software, improves the base algorithm with several local algorithms, and comprehensively analyses the obtained results. The results obtained are then compared to these presently used in the transportation analysis process of the Lisbon Metropolitan Area. The proposed algorithm presents a new methodology for TAZ design based on a smoothed density surface of geocoded travel demand data. The algorithm aims to minimise the loss of information when moving from a continuous representation of the origin and destination of each trip to their discrete representations through zones, and focuses on the trade-off between the statistical precision, geographical error, and the percentage of intra-zonal trips of the resulting OD matrix. The results for the Lisbon Metropolitan Area case study suggest a significant improvement in OD matrix estimates compared to current transportation analysis practises based on administrative units.

Second best toll and capacity optimisation in networks: solution algorithm and policy implications

This paper looks at the first and second best jointly optimal toll and road capacity investment problems from both policy and technical oriented perspectives. On the technical side, the paper investigates the applicability of the constraint cutting algorithm for solving the second best problem under elastic demand which is formulated as a bilevel programming problem. The approach is shown to perform well despite several problems encountered by our previous work in Shepherd and Sumalee (Netw. Spat. Econ., 4(2): 161–179, 2004). The paper then applies the algorithm to a small sized network to investigate the policy implications of the first and second best cases. This policy analysis demonstrates that the joint first best structure is to invest in the most direct routes while reducing capacities elsewhere. Whilst unrealistic this acts as a useful benchmark. The results also show that certain second best policies can achieve a high proportion of the first best benefits while in general generating a revenue surplus. We also show that unless costs of capacity are known to be low then second best tolls will be affected and so should be analysed in conjunction with investments in the network.

A copula-based joint multinomial discrete–continuous model of vehicle type choice and miles of travel

In this paper, a joint model of vehicle type choice and utilization is formulated and estimated on a data set of vehicles drawn from the 2000 San Francisco Bay Area Travel Survey. The joint discrete–continuous model system formulated in this study explicitly accounts for common unobserved factors that may affect the choice and utilization of a certain vehicle type (i.e., self-selection effects). A new copula-based methodology is adopted to facilitate model estimation without imposing restrictive distribution assumptions on the dependency structures between the errors in the discrete and continuous choice components. The copula-based methodology is found to provide statistically superior goodness-of-fit when compared with previous estimation approaches for joint discrete–continuous model systems. The model system, when applied to simulate the impacts of a doubling in fuel price, shows that individuals are more likely to shift vehicle type choices than vehicle usage patterns.

An analysis of weekly out-of-home discretionary activity participation and time-use behavior

Activity-travel behavior research has hitherto focused on the modeling and understanding of daily time use and activity patterns and resulting travel demand. In this particular paper, an analysis and modeling of weekly activity-travel behavior is presented using a unique multi-week activity-travel behavior data set collected in and around Zurich, Switzerland. The paper focuses on six categories of discretionary activity participation to understand the determinants of, and the inter-personal and intra-personal variability in, weekly activity engagement at a detailed level. A panel version of the Mixed Multiple Discrete Continuous Extreme Value model (MMDCEV) that explicitly accounts for the panel (or repeated-observations) nature of the multi-week activity-travel behavior data set is developed and estimated on the data set. The model also controls for individual-level unobserved factors that lead to correlations in activity engagement preferences across different activity types. To our knowledge, this is the first formulation and application of a panel MMDCEV structure in the econometric literature. The analysis suggests the high prevalence of intra-personal variability in discretionary activity engagement over a multi-week period along with inter-personal variability that is typically considered in activity-travel modeling. In addition, the panel MMDCEV model helped identify the observed socio-economic factors and unobserved individual specific factors that contribute to variability in multi-week discretionary activity participation.

How do people respond to congestion mitigation policies? A multivariate probit model of the individual consideration of three travel-related strategy bundles

This study explores the relationships between adoption and consideration of three travel-related strategy bundles (travel maintaining/increasing, travel reducing, and major location/lifestyle change), linking them to a variety of explanatory variables. The data for this study are the responses to a fourteen-page survey returned by nearly 1,300 commuting workers living in three distinct San Francisco Bay area neighborhoods in May 1998. We first identified patterns of adoption and consideration among the bundles, using pairwise correlation tests. The test results indicate that those who have adopted coping strategies continue to seek for improvements across the spectrum of generalized cost, but perhaps most often repeating the consideration of a previously-adopted bundle. Furthermore, we developed a multivariate probit model for individuals’ simultaneous consideration of the three bundles. It is found that in addition to the previous adoption of the bundles, qualitative and quantitative Mobility-related variables, Travel Attitudes, Personality, Lifestyle, Travel Liking, and Sociodemographics significantly affect individual consideration of the strategy bundles. Overall, the results of this study give policy makers and planners insight into understanding the dynamic nature of individuals’ responses to travel-related strategies, as well as differences between the responses to congestion that are assumed by policy makers and those that are actually adopted by individuals.

Operating costs in Norwegian toll companies: a panel data analysis

The objective of this paper is to ease the planning of new toll projects by providing estimates of operating costs, and to help us make better informed decisions about the design of toll collection systems. To do so we use panel data for Norwegian toll companies to estimate average cost functions. The main results can be summarised as follows. We provide evidence of very important unexploited economies of scale. The estimated cost curves are very steep for traffic levels below the sample mean, and become almost entirely flat over a wide range above the sample mean. A higher share of vehicles using on board units will significantly reduce average costs. Competitive tendering will significantly reduce average operating costs by as much as 25%. Our results also suggest that increased number of lanes, higher debt and passenger charging will increase average operating costs whereas average operating costs are lower for toll cordons compared with other projects.

Do changes in neighborhood characteristics lead to changes in travel behavior? A structural equations modeling approach

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that residents in suburban neighborhoods drive more and walk less than their counterparts in traditional environments. However, most studies confirm only an association between the built environment and travel behavior, and have yet to establish the predominant underlying causal link: whether neighborhood design independently influences travel behavior or whether preferences for travel options affect residential choice. That is, residential self-selection may be at work. A few studies have recently addressed the influence of self-selection. However, our understanding of the causality issue is still immature. To address this issue, this study took into account individuals’ self-selection by employing a quasi-longitudinal design and by controlling for residential preferences and travel attitudes. In particular, using data collected from 547 movers currently living in four traditional neighborhoods and four suburban neighborhoods in Northern California, we developed a structural equations model to investigate the relationships among changes in the built environment, changes in auto ownership, and changes in travel behavior. The results provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking.

An analysis of bicycle route choice preferences in Texas,US

In the US, the rise in motorized vehicle travel has contributed to serious societal, environmental, economic, and public health problems. These problems have increased the interest in encouraging non-motorized modes of travel (walking and bicycling). The current study contributes toward this objective by identifying and evaluating the importance of attributes influencing bicyclists’ route choice preferences. Specifically, the paper examines a comprehensive set of attributes that influence bicycle route choice, including: (1) bicyclists’ characteristics, (2) on-street parking, (3) bicycle facility type and amenities, (4) roadway physical characteristics, (5) roadway functional characteristics, and (6) roadway operational characteristics. The data used in the analysis is drawn from a web-based stated preference survey of Texas bicyclists. The results of the study emphasize the importance of a comprehensive evaluation of both route-related attributes and bicyclists’ demographics in bicycle route choice decisions. The empirical results indicate that travel time (for commuters) and motorized traffic volume are the most important attributes in bicycle route choice. Other route attributes with a high impact include number of stop signs, red light, and cross-streets, speed limits, on-street parking characteristics, and whether there exists a continuous bicycle facility on the route.

Toll Roads in Australia: An Overview of Characteristics and Accuracy of Demand Forecasts

Abstract

A primary motivation of this paper is to draw together, in one source, information on the nature, extent and performance of Australia's evolving toll road network which is currently spread across many disparate published and unpublished sources. This paper provides key information (e.g. length, toll rates, year opened, operator(s) and payment alternatives) on all of the fully interoperable toll roads in Australia that are present in Sydney (e.g. the M2, M4, M5, etc.), Melbourne (CityLink and EastLink) and Brisbane (the Gateway Bridge, the Logan Motorway and the Gateway Extension). Where available, we compare and discuss actual traffic levels and forecasts, revealing the sizeable gap or ‘error’ in forecasts, especially during the first year of operation. Ordinary least squares regression and panel random effects regression models are developed to identify potential sources of explanation of differences in error forecasts between the Australian toll roads at various points post the opening date. A separate analysis of a sample of toll roads in the USA was also undertaken that supports the main findings from the Australian toll road network. Key influences on errors in forecasts are the capacity of a toll road, the elapsed time that the toll road has been in place, the specific period of time in which a tolled road is introduced into the network (which influences the complexity of route options including multiple tolled routes and hence toll saturation), the length of the tolled route, the presence of cash payment and the charging regime (i.e. fixed vs. distance‐based or variable user tolls).     

A hybrid model of fuzzy and AHP for handling public assessments on transportation projects

Having an effective public participation in transportation planning and project development processes has been a major concern for developed countries. In the United States, for instance, all state Departments of Transportation are subject to the Transportation Equity Act (TEA-21) that formally requires public involvement in transportation planning. Since transportation planning involves public resources and values, judgments by the public should play a key role in determining final decisions. Therefore, all these agencies are required not only to disseminate information to the public, but also to solicit and consider public opinion in forming transportation policy. This work presents a decision support model, with public involvement and public oversight, to help policy makers select appropriate transportation projects for implementation. Since focus groups will face multiple objectives and inexact information in the process, a hybrid model of fuzzy logic and analytical hierarchy process (AHP) is proposed. A set of ‘if–then’ rules based on Weber’s psycho-physical law of 1834 is presented to reason from fuzzy numbers to capture essential subjective preferences, pairwise, among the alternatives. The AHP is then incorporated to estimate preference allotments among alternatives. An example application of the suggested method is provided seeking public approval of an appropriate public bus transportation system choosing between one run by municipal authorities and one run by private agencies to show how this procedure works.

Estimating the robustness of questionnaire results: lessons from a mixed-mode survey of expectations for tele-working and road-based business travel

The robustness of questionnaire results to various forms of bias are explored in the context of a dual-mode (web and hardcopy) survey of employers’ anticipations of levels of employee commuting and business travel activity under a range of future ICT scenarios. The questionnaire incorporated several innovative features which, together with the dual-mode format, allowed an unusually wide range of analyses. For example: the robustness of respondents’ opinions was tested by examining the effect of incorporating alternative versions of a briefing text, one being very positive and one very negative, about the role of ICT; instrument bias was identified via detailed comparison of the results from the two versions of the questionnaire; and the impact of exogenous factors which are often ignored or taken as constant was assessed via special supplementary questions. Analysis showed that the robustness of opinions and expectations varied and was influenced by respondent characteristics, and that results from the two versions of the questionnaire differed significantly. It is concluded that opinions and expectations are less robust, and questionnaire results are more subject to bias and myopic interpretation, than is generally recognised and that web-based surveys seem particularly vulnerable to sampling bias. Methods are suggested for measuring robustness, for reducing bias and for validating and contextualising results. The use of contrasting briefing texts is recommended as a means of establishing the robustness of opinions and expectations while supplementary questions are recommended for validating and contextualising SP and SE exercises.

The effect of complex models of externalities on estimated optimal tolls

Transport externalities such as costs of emissions and accidents are increasingly being used within appraisal and optimisation frameworks alongside the more traditional congestion analysis to set optimal transport policies. Models of externalities and costs of externalities may be implemented by a simple constant cost per vehicle-km approach or by more complex flow and speed dependent approaches. This paper investigates the impact of using both simple and more complex models of CO₂ emissions and cost of accidents on the optimal toll for car use and upon resulting welfare levels. The approach adopted is to use a single link model with a technical approach to the representation of the speed-flow relationship as this reflects common modelling practice. It is shown that using a more complex model of CO₂ emitted increases the optimal toll significantly compared to using a fixed cost approach while reducing CO₂ emitted only marginally. A number of accident models are used and the impact on tolls is shown to depend upon the assumptions made. Where speed effects are included in the accident model, accident costs can increase compared to the no toll equilibrium and so tolls should in this case be reduced compared to the congestion optimal toll. Finally it is shown that the effect of adding variable CO₂ emission models along with a fixed cost per vehicle-km for accidents can increase the optimal toll by 44% while increasing the true welfare gained by only 8%. The results clearly demonstrate that model assumptions for externalities can have a significant impact on the resulting policies and in the case of accidents the policies can be reversed.

Just pricing: the distributional effects of congestion pricing and sales taxes

Those who oppose tolls and other forms of road pricing argue that low-income, urban residents will suffer if they must pay to use congested freeways. This contention, however, fails to consider (1) how much low-income residents already pay for transportation in taxes and fees, or (2) how much residents would pay for highway infrastructure under an alternative revenue-generating scheme, such as a sales tax. This paper compares the cost burden of a value-priced road, State Route 91 (SR91) in Orange County, California with the cost burden under Orange County’s local option transportation sales tax, Measure M. We find that although the sales tax spreads the costs of transportation facilities across a large number of people inside and outside Orange County, it redistributes about $3 million (USD) in revenues from less affluent residents to those with higher incomes. The entire Measure M program redistributes an estimated $26 million from low-income residents to the more affluent. Low-income drivers as individuals save substantially if they do not have to pay tolls, but as a group low-income residents, on average, pay more out-of-pocket with sales taxes.

The potential demand for a urban freight consolidation centre

This paper reports the results of a stated-preference study aimed at investigating how transport decisions are made by receivers or by transport operators about the potential use of an urban freight consolidation centre in the city of Fano, Italy. Because there are no revealed preference data, a stated-choice methodology is used. The stated-choice experiments present two alternatives—one using a private vehicle subject to various traffic regulations and one using the urban freight consolidation centre with varying cost and efficiency levels. Conventional discrete choice data modelling shows that the potential demand is influenced mainly by the distance of the parking bay from the shop, by access permit cost, by the service cost of the urban freight consolidation centre, and by the delay in delivery time. Simulations are then performed to assess how the potential demand is affected by various incentives and regulations affecting urban goods distribution.

The principles of calibrating traffic microsimulation models

Traffic microsimulation models normally include a large number of parameters that must be calibrated before the model can be used as a tool for prediction. A wave of methodologies for calibrating such models has been recently proposed in the literature, but there have been no attempts to identify general calibration principles based on their collective experience. The current paper attempts to guide traffic analysts through the basic requirements of the calibration of microsimulation models. Among the issues discussed here are underlying assumptions of the calibration process, the scope of the calibration problem, formulation and automation, measuring goodness-of-fit, and the need for repeated model runs.

Residential location,vehicle ownership and travel in Asia: a comparative analysis of Kei-Han-Shin and Kuala Lumpur metropolitan areas

The study examines the relationships between residential location, vehicle ownership and mobility in two metropolitan areas of Asia, Kei-Han-Shin area of Japan and Kuala Lumpur area of Malaysia. It shows that, behind apparent similarities of household auto ownership and travel time expenditure per household member, there are many causal relationships that are distinct between the areas. The similarities and differences between the two areas point to the conjecture that the evolution of a metropolitan area may be unique and path dependent, being heavily influenced by the history and culture of the locale, spatial and geographical constraints, and historical progression in infrastructure development.

Route choice preferences: insights from Portuguese freight forwarders and truck drivers

ABSTRACT

Exploring route choice in the context of tolled alternatives can support road operators to achieve better utilization of the infrastructure, as well as maximizing revenue collection. The research presented in this paper is conducted in the context of OPTIMUM, a European Union-funded project. The research objectives include a two-component system of models that proactively calculates commercial vehicles’ toll prices. The component presented in this paper rests on the development of a route choice model that estimates the probabilities of using two alternative routes (toll road vs. national road), based on route attributes and user characteristics. To explore the usefulness of the proposed methodology a case study involving 50 truck drivers and 25 freight operators was conducted in Portugal between January 2016 and November 2017. Results from the route choice model reveal interesting insights about the role of incentives in the choice of toll roads, the perspectives of the different decision-makers and produce Values of Time for the study area.     

Urban form,travel time,and cost relationships with tour complexity and mode choice

The primary purpose of this study was to investigate how relative associations between travel time, costs, and land use patterns where people live and work impact modal choice and trip chaining patterns in the Central Puget Sound (Seattle) region. By using a tour-based modeling framework and highly detailed land use and travel data, this study attempts to add detail on the specific land use changes necessary to address different types of travel, and to develop a comparative framework by which the relative impact of travel time and urban form changes can be assessed. A discrete choice modeling framework adjusted for demographic factors and assessed the relative effect of travel time, costs, and urban form on mode choice and trip chaining characteristics for the three tour types. The tour based modeling approach increased the ability to understand the relative contribution of urban form, time, and costs in explaining mode choice and tour complexity for home and work related travel. Urban form at residential and employment locations, and travel time and cost were significant predictors of travel choice. Travel time was the strongest predictor of mode choice while urban form the strongest predictor of the number of stops within a tour. Results show that reductions in highway travel time are associated with less transit use and walking. Land use patterns where respondents work predicted mode choice for mid day and journey to work travel.