Path flow estimator for planning applications in small communities

This paper presents an alternative planning framework to model and forecast network traffic for planning applications in small communities, where limited resources debilitate the development and applications of the conventional four-step travel demand forecasting model. The core idea is to use the Path Flow Estimator (PFE) to estimate current and forecast future traffic demand while taking into account of various field and planning data as modeling constraints. Specifically, two versions of PFE are developed: a base year PFE for estimating the current network traffic conditions using field data and planning data, if available, and a future year PFE for predicting future network traffic conditions using forecast planning data and the estimated base year origin–destination trip table as constraints. In the absence of travel survey data, the proposed method uses similar data (traffic counts and land use data) as a four-step model for model development and calibration. Since the Institute of Transportation Engineers (ITE) trip generation rates and Highway Capacity Manual (HCM) are both utilized in the modeling process, the analysis scope and results are consistent with those of common traffic impact studies and other short-range, localized transportation improvement programs. Solution algorithms are also developed to solve the two PFE models and integrated into a GIS-based software called Visual PFE. For proof of concept, two case studies in northern California are performed to demonstrate how the tool can be used in practice. The first case study is a small community of St. Helena, where the city’s planning department has neither an existing travel demand model nor the budget for developing a full four-step model. The second case study is in the city of Eureka, where there is a four-step model developed for the Humboldt County that can be used for comparison. The results show that the proposed approach is applicable for small communities with limited resources.     

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.     

A method to assess demand growth vulnerability of travel times on road network links

Many national governments around the world have turned their recent focus to monitoring the actual reliability of their road networks. In parallel there have been major research efforts aimed at developing modelling approaches for predicting the potential vulnerability of such networks, and in forecasting the future impact of any mitigating actions. In practice—whether monitoring the past or planning for the future—a confounding factor may arise, namely the potential for systematic growth in demand over a period of years. As this growth occurs the networks will operate in a regime closer to capacity, in which they are more sensitive to any variation in flow or capacity. Such growth will be partially an explanation for trends observed in historic data, and it will have an impact in forecasting too, where we can interpret this as implying that the networks are vulnerable to demand growth. This fact is not reflected in current vulnerability methods which focus almost exclusively on vulnerability to loss in capacity. In the paper, a simple, moment-based method is developed to separate out this effect of demand growth on the distribution of travel times on a network link, the aim being to develop a simple, tractable, analytic method for medium-term planning applications. Thus the impact of demand growth on the mean, variance and skewness in travel times may be isolated. For given critical changes in these summary measures, we are thus able to identify what (location-specific) level of demand growth would cause these critical values to be exceeded, and this level is referred to as Demand Growth Reliability Vulnerability (DGRV). Computing the DGRV index for each link of a network also allows the planner to identify the most vulnerable locations, in terms of their ability to accommodate growth in demand. Numerical examples are used to illustrate the principles and computation of the DGRV measure.     

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.     

The History of the Transport Future—Evaluating Dutch Transport Scenarios of the Past

Abstract

This paper examines 42 business‐as‐usual (BAU) scenarios for future transport and traffic development in the Netherlands from the period 1970 to 2000. An important aspect of these scenarios is that they indicate the potential future state of the transport system if the policies in place at the time continue unchanged, thus raising issues which may require a policy response. This paper shows that in most of the important BAU transport scenario results of the past in the Netherlands, such as future car use and transport emissions, the policy makers were not misled. The prognoses for traffic congestion are an important exception as they underestimated the congestion problems that would arise. This paper shows that, based on the research it examines, BAU transport forecasting is an inaccurate practice. It is recommended that the BAU scenario designer communicates this high inaccuracy, enabling the policy maker to include the inherent future uncertainty in their decision‐making.     

Air transport and economic growth: a review of the impact mechanism and causal relationships

ABSTRACT

The impacts of air transport on the economy arise both directly, via activity in the aviation sector; and indirectly, via increased spending and wider economic benefits associated with improved access to resources, markets, technology and economic mass. Economic activity, in turn, supports and generates demand for air transport. Despite its potential importance, the reciprocal nature of the causal relationship between air transport and economic performance has remained somewhat understudied. This paper provides a synthesis review of the channels the aviation sector interacts with regional economy. The review focuses on quantitative studies that contribute to the state-of-the-art understandings of the causality. We find that the reciprocal causal relationship is more likely to prevail in less developed economies. For more developed economies, only one direction of the causality is recognised, which runs from air transport to economic growth. Especially substantial is the effect of airline enplanement on service-related employment. The reverse direction of the relationship is, however, not as significant as believed in a causal sense within the developed world. Therefore, cautions need to be taken when applying income elasticities (such as the elasticity of air passenger demand with respect to GDP) in air travel demand forecasting, which implicitly assumes that economic growth causally leads to air traffic increment. Based on the fundamental links between air transport and economic growth, some typical imperfections and inefficiencies in aviation markets are discussed and promising avenues for future research are proposed.     

Activity‐based approaches to travel analysis: conceptual frameworks,models, and research problems

Recent policy discussions about information technology in transport and traffic demand management have increased interest in activity‐based approaches to the analysis of travel behaviour, in particular in the modelling of household activity scheduling which is at the core of many of the required changes in travel behaviour. This paper is a state‐of‐the‐art review of conceptualizations and models of activity scheduling with special regard to issues raised by the new policy instruments. In the course of the review, the validity of behavioural assumptions is examined critically and several needs for future research identified.     

Short-term impact analysis of fuel price policy change on travel demand in Malaysian cities

Abstract

Malaysia is one of the few countries in the world that provides a fuel subsidy to consumers. Due to the recent economic crisis, the Malaysian Government decided to revise its fuel subsidization policy from a fixed price subsidy to a floating price subsidy dependent on global oil demand. Recognizing that the change in fuel subsidization policy can have an impact on travel behavior, this article investigates the short-term impact of the policy change on private and public transportation in the Klang Valley region of Malaysia. Spectral analyses are performed to investigate if the policy change has an impact on private vehicle travel demand, measured in terms of road traffic, and short-term travel demand elasticity with respect to fuel price is estimated. To measure the impact on the public transportation system, the demand cross-elasticity values of rail transit and buses are also estimated. It was found that traffic flow reduces with an increase in fuel price, although elasticity and cross-elasticity values obtained are low. The article finds that there is a potential mode shift from private vehicles to rail transit with increasing fuel price. It is demonstrated that reducing fuel price subsidy can be an effective travel demand management strategy to alleviate congestion.     

The changing accuracy of traffic forecasts

Researchers have improved travel demand forecasting methods in recent decades but invested relatively little to understand their accuracy. A major barrier has been the lack of necessary data. We compiled the largest known database of traffic forecast accuracy, composed of forecast traffic, post-opening counts and project attributes for 1291 road projects in the United States and Europe. We compared measured versus forecast traffic and identified the factors associated with accuracy. We found measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy—traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Traffic on projects that opened from the 1980s through early 2000s was higher on average than forecast, while traffic on more recent projects was lower on average than forecast. This research provides insight into the degree of confidence that planners and policy makers can expect from traffic forecasts and suggests that we should view forecasts as a range of possible outcomes rather than a single expected outcome.

     

Future prospects for personal security in travel by public transport

This paper reports findings from a project focused on understanding the interaction between transport technologies and user needs and perceptions in supporting personal security in travel by public transport. The research engaged over 60 experts from across the UK transport sector in a combination of interviews, workshops and scenario planning activities to address a set of four application areas in relation to secure travel. These areas were information provision, travel disruption, automated transport services and flexible transport services. Four future scenario narratives (to a 2040 time horizon) were developed for each application area. A final workshop consolidated and reviewed the narrative scenarios and pulled out key themes and priority issues for policy, practice and research for the near term. Consequently a set of policy recommendations, operator and business opportunities, knowledge gaps and research priorities were identified to support and enhance provision for personal security in travel by public transport.     

Changes in transport planning policy: Changes in transport planning methodology?

The 1990s saw the emergence of influential transport legislation both in the UK and in the USA. This "watershed" period appears to indicate that a significant turning point in transport policy is underway. There is now a need to re-evaluate how transport planning is done, and to consider changes to commonly used methods. Criticisms of urban transport planning are traced in the paper. These often focus on the four-stage modelling approach, but some authors also criticise the "rational comprehensive" paradigmatical framework within which the use of four-stage computer models is situated. It is argued that the rational comprehensive model of thinking is less useful today, due to the increasing complexity of the transport planning exercise; the rejection by the public of the transport planner as "expert"; and the highly political nature of transport planning. Alternative approaches are needed in order to address the new types of problems which transport planners face. The use of one such alternative approach, the Soft Systems Methodology, is illustrated as suitable for investigating complex decision-making systems in transport planning. It is suggested that this method could be beneficial in other problematic transport planning situations where the rational comprehensive approaches prove inadequate. Finally, the paper briefly looks ahead and considers the implications of a changing policy environment to the training of transport planners in the future.     

Women,motorization and the environment

This note argues that more attention should be paid in transport forecasting and modeling to the particular nature of trips and travel behavior associate with women. Women in industrialized countries are becoming increasingly mobile but their travel patterns are different to the stereo type radial patterns often assumed as part of urban transport planning. The longer life expectancy of women and their continued desire for mobility also has policy implications. These facts, combined with potentially different attitudes towards environmental protection than men are likely to have implications for transport policy.     

Does the income elasticity of road traffic depend on the source of income?

An extensive body of literature addresses the income elasticity of road traffic, in which income is typically treated as a homogenous quantity. Here we report evidence of heterogeneity in cross-sectional estimates of the elasticity of vehicle-kilometres of travel (VKT) with respect to income, when household income is disaggregated on the basis of income source.The results are generally intuitive, and show that the cross-sectional income elasticity of road traffic is not homogeneous as is typically specified in transport planning models. We show that in a number of circumstances the cross-sectional elasticity with respect to aggregate household income is of the opposite sign in comparison to more refined estimates of elasticity disaggregated by income source. If further research confirms that the elasticities we report here are causal in nature, neglecting the elemental effects could result in misleading results affecting practical infrastructure-investment and policy decisions, particularly as the mix of income sources shifts (e.g. if, as society ages, pension income increases as a share of all income).These results are of interest to both researchers and forecasters of travel demand, as well as designers of future travel survey instruments; the latter group must decide how to generate data about respondents’ income. Current expert guidance is to collect a single estimate of aggregate income at the household level. Future travel survey design choices will bound the analyses that can be supported by the resulting survey data, and therefore methodological research to re-visit the trade-offs associated with such choices is warranted.     

Women,travel and the idea of 'sustainable transport'

Women form an increasingly large proportion of both the populations of the US and of European countries. This, together with the changing role of women in modern society, has important potential implications for transport policy. Looked at from the micro level, women have different travel patterns from men, reflecting factors such as lower labour force participation, lower income levels, their traditional role in the family, the decision-making structure within households, the current age distribution and physical vulnerability. The situation is changing and this will have implications on transport demands. The female population is also, on average, getting older. Traditional travel modelling spends much time on modal attributes, such as speed and cost, but for many women this may not be the central issue. Security is often important. Standard traffic models only implicitly allow for gender (e.g. travel time is valued different from work and non-work trips). With the increased complexity of life styles and the growth in multi-purpose trips by women, the conventional approach to travel time valuation may need to be rethought. This paper looks at some of the emerging issues regarding women and travel and puts them in the context of the emerging ideas concerning sustainable transport.     

Impacts of highly automated vehicles on travel demand: macroscopic modeling methods and some results

Automated vehicles (AV) will change transport supply and influence travel demand. To evaluate those changes, existing travel demand models need to be extended. This paper presents ways of integrating characteristics of AV into traditional macroscopic travel demand models based on the four-step algorithm. It discusses two model extensions. The first extension allows incorporating impacts of AV on traffic flow performance by assigning specific passenger car unit factors that depend on roadway type and the capabilities of the vehicles. The second extension enables travel demand models to calculate demand changes caused by a different perception of travel time as the active driving time is reduced. The presented methods are applied to a use case of a regional macroscopic travel demand model. The basic assumption is that AV are considered highly but not fully automated and still require a driver for parts of the trip. Model results indicate that first-generation AV, probably being rather cautious, may decrease traffic performance. Further developed AV will improve performance on some parts of the network. Together with a reduction in active driving time, cars will become even more attractive, resulting in a modal shift towards car. Both circumstances lead to an increase in time spent and distance traveled.

     

Some comments on activity-based approaches to the analysis and prediction of travel behavior

This note presents the author's conjectures on several symptoms raised in Kitamura's assessment of activity-based travel analysis. It focuses on some of the characteristics of the development of activity-based approaches that may help explain their limited impact to date on travel demand forecasting practice. The broader role and potential contributions of activity analysis of the demand for infrastructure systems are emphasized.     

Analysis of tourist behaviour based on the tracking data collected using a mobile communication instrument

Recently, tracking-type travel data collection methods using mobile communication instruments have developed rapidly. The detailed and accurate travel data are useful for understanding travel behaviour and evaluating the actual effects of transport policy such as traffic demand management. This paper studies a simple index of a tourist behaviour using tracking data collected with a mobile instrument. Based on the proposed index and actual data collected in Kobe, cluster analysis is applied to find the topological characteristics of tourist behaviour.     

A traffic flow simulator for short‐term travel time forecasting

This paper presents an off‐line forecasting system for short‐term travel time forecasting. These forecasts are based on the historical traffic count data provided by detectors installed on Annual Traffic Census (ATC) stations in Hong Kong. A traffic flow simulator (TFS) is developed for short‐term travel time forecasting (in terms of offline forecasting), in which the variation of perceived travel time error and the fluctuations of origin‐destination (O‐D) demand are considered explicitly. On the basis of prior O‐D demand and partial updated detector data, the TFS can estimate the link travel times and flows for the whole network together with their variances and covariances. The short‐term travel time forecasting by O‐D pair can also be assessed and the O‐D matrix can be updated simultaneously. The application of the proposed off‐line forecasting system is illustrated by a numerical example in Hong Kong.     

Probe data-driven travel time forecasting for urban expressways by matching similar spatiotemporal traffic patterns

Travel time is an effective measure of roadway traffic conditions. The provision of accurate travel time information enables travelers to make smart decisions about departure time, route choice and congestion avoidance. Based on a vast amount of probe vehicle data, this study proposes a simple but efficient pattern-matching method for travel time forecasting. Unlike previous approaches that directly employ travel time as the input variable, the proposed approach resorts to matching large-scale spatiotemporal traffic patterns for multi-step travel time forecasting. Specifically, the Gray-Level Co-occurrence Matrix (GLCM) is first employed to extract spatiotemporal traffic features. The Normalized Squared Differences (NSD) between the GLCMs of current and historical datasets serve as a basis for distance measurements of similar traffic patterns. Then, a screening process with a time constraint window is implemented for the selection of the best-matched candidates. Finally, future travel times are forecasted as a negative exponential weighted combination of each candidate’s experienced travel time for a given departure. The proposed approach is tested on Ring 2, which is a 32km urban expressway in Beijing, China. The intermediate procedures of the methodology are visualized by providing an in-depth quantitative analysis on the speed pattern matching and examples of matched speed contour plots. The prediction results confirm the desirable performance of the proposed approach and its robustness and effectiveness in various traffic conditions.     

A Review of Ex-Post Evidence for Mode Substitution and Induced Demand Following the Introduction of High-Speed Rail

ABSTRACT

To date, relatively little is known about the nature of the demand for high-speed rail (HSR) soon after inauguration of the services, despite close to 50-year experience of HSR operation and 17 166?km of HSR network around the world. This is a real lacuna given the scale of HSR construction around the world, the amount of resources committed to it, the desired accessibility, economic and environmental effects associated with HSR development and the relatively poor track record of forecasting demand for HSR services. Focusing on mode substitution and induced demand effects, this review aims to fill the gap in knowledge about the ex-post demand for HSR services in order to facilitate a learning process for the planning of the future HSR network. Although there is not much evidence on the demand for HSR services and existing evidence is largely influenced by route-specific characteristics, a methodological limitation that must be acknowledged, the evidence presented allows a better characterisation of HSR as a mode of transport. The review shows that the demand for HSR a few years after inauguration is about 10–20% induced demand and the rest is attributed to mode substitution. In terms of mode substitution, in most cases the majority of HSR passengers have used the conventional rail before. Substitution from aircraft, car and coach is generally more modest.