Graham Currie

Causes of youth licensing decline: a synthesis of evidence

In recent decades, young adults in many developed nations have become increasingly less likely to acquire a driving license. If this trend continues it could have significant impacts on transport futures. Licensing reductions have only recently been identified and causes are only just being explored. This paper presents a first synthesis of available evidence including an assessment of more influential causal factors. It begins by documenting the declining trend evident in 9 of 14 documented countries; the average rate of decline is 0.6% per annum, with highest declines documented in Australia. A range of causal factors are documented from cross-sectional and longitudinal studies. Changes in life stage and living arrangements, changes in motoring affordability, location and transport, graduated driver licensing schemes, attitudinal influences and the role of e-communication are all explored. Evidence is in general weak and preliminary but suggests multiple causes rather than any single influence. However, of the evidence available life stage factors and affordability influences have stronger links to license decline but are only likely to have a low affect size.

Using GPS Data to Gain Insight into Public Transport Travel Time Variability

Transit service reliability is an important determinant of service quality, which has been mainly studied from the perspective of passengers waiting at stops. Day-to-day variability of travel time also deteriorates service reliability, but is not a well-researched area in the literature partly due to the lack of comprehensive data sets on bus travel times. While this problem is now being addressed through the uptake of global positioning system (GPS)-based tracking systems, methodologies to analyze these data sets are limited. This paper addresses this issue by investigating day-to-day variability in public transport travel time using a GPS data set for a bus route in Melbourne, Australia. It explores the nature and shape of travel time distributions for different departure time windows at different times of the day. Factors causing travel time variability of public transport are also explored using a linear regression analysis. The results show that in narrower departure time windows, travel time distributions are best characterized by normal distributions. For wider departure time windows, peak-hour travel times follow normal distributions, while off-peak travel times follow lognormal distributions. The factors contributing to the variability of travel times are found to be land use, route length, number of traffic signals, number of bus stops, and departure delay relative to the scheduled departure time. Travel time variability is higher in the AM peak and lower in the off-peak. The impact of rainfall on travel time variability is only found significant in the AM peak. While the paper presents new methods for analyzing GPS-based data, there is much scope for expanding knowledge through wider applications to new data sets and using a wider range of explanatory variables.

The demand performance of bus rapid transit

This article uses a trip attribute approach to examine the relative passenger attractiveness of bus rapid transit (BRT) systems compared to other transit modes. It examines how passengers value trip attributes for on street bus, BRT, and light rail and heavy rail systems in passenger behavior research. Empirical data is presented which suggests that passengers value trip attributes for BRT and rail modes in a broadly similar manner. All of these transit modes are favored relative to on street bus. These findings suggest that BRT systems should be as effective as rail in generating patronage when developed to replace on street bus services. This conclusion, in association with research demonstrating lower costs for BRT systems compared to rail, may be used to claim cost effectiveness advantages for BRT. However, a number of limitations in the evidence are identified and additional research suggested. Conclusions of the research are also used to suggest ways to improve BRT system design to enhance demand performance.

Gap Analysis of Public Transport Needs:Measuring Spatial Distribution of Public Transport Needs and Identifying Gaps in the Quality of Public Transport Provision

A new approach to assessing the performance of public transport in meeting the needs of transport-disadvantaged people in the community is described. It reviews previous and current research in this area and describes how a new approach has been developed and applied with Hobart, Australia, as a study area. The approach aims to identify geographical gaps in public transport provision where travel needs are high but services are poor or nonexistent. It involves the use of readily available socioeconomic statistics to quantify the distribution of needs in the community with a single transport needs index. A public transport network model measures the public transport accessibility to these groups and a geographical information systems approach is used to display the distribution of the identified gaps between service and needs. The technique is highly relevant for smaller urban centers where the justification of public transport subsidies is largely social-needs-based—that is, where congestion and environmental benefits of transit are less critical. It is also relevant to recent work in transport accessibility audits and in the assessment of community impacts of alternative transit development strategies.

Investigating Links between Social Capital and Public Transport

Abstract Social capital (SC) describes the advantage individuals and communities can gain from social participation, mutual assistance and trust. The provision of travel options for those who are socially disadvantaged is a major rationale for providing public transport. While there has been recent work on how transport can address social exclusion, SC has been overlooked. This article describes the theoretical basis of SC and suggests ways in which the concept might relate to public transport planning, research and practice. Disadvantaged groups can lack SC, mobility and accessibility. Public transport can provide mobility for this group and, in doing so, provide a greater opportunity to create social networks, trust and reciprocity. Although these concepts are abstract, plausible links are identified between the concepts of enhanced positive social interaction associated with the ‘livable city’ concept and those engendered in SC theory. Public transport by definition involves travelling with others and hence provides opportunities for social interaction while travelling. While a series of possible links between SC and public transport are theorized, it is unlikely these are necessarily strong since other mobility options are available and a range of land‐use and non‐transport alternatives can address social needs. Aspects of the SC concept are already covered by the social mobility and accessibility literature, although it does offer a wider insight into the potential advantages of improving access and mobility for disadvantaged communities. The concept of SC is complex and suffers from a ‘fuzzy’ definition. There is a lack of quantitative primary research associated with measuring SC. Measuring the influence of improved mobility options on SC in disadvantaged communities would be a worthwhile research area. Despite the challenges associated with researching SC, the links between SC and travel present an opportunity to understand how public transport acts to address social disadvantage through the provision of mobility to disadvantaged communities.

Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction

Neural networks have been employed in a multitude of transportation engineering applications because of their powerful capabilities to replicate patterns in field data. Predictions are always subject to uncertainty arising from two sources: model structure and training data. For each prediction point, the former can be quantified by a confidence interval, whereas total prediction uncertainty can be represented by constructing a prediction interval. While confidence intervals are well known in the transportation engineering context, very little attention has been paid to construction of prediction intervals for neural networks. The proposed methodology in this paper provides a foundation for constructing prediction intervals for neural networks and quantifying the extent that each source of uncertainty contributes to total prediction uncertainty. The application of the proposed methodology to predict bus travel time over four bus route sections in Melbourne, Australia, leads to quantitative decomposition of total prediction uncertainty into the component sources. Overall, the results demonstrate the capability of the proposed method to provide robust prediction intervals.

Bus Transit Oriented Development—Strengths and Challenges Relative to Rail

While rail has been the focus of most planning for Transit Oriented Development (TOD), there has been much interest in bus-related TOD with an emphasis on new bus rapid transit (BRT) systems in North and South America and Australia. This paper takes a critical look at the strengths and challenges of bus-based transit systems compared to rail in relation to TOD. It includes a literature review and an assessment of TOD-related developments. The focus is on performance of BRT systems in relation to TOD with specific reference to BRT systems in Australia. In addition, TOD related to local suburban bus service is examined. The paper describes the general concept of TOD and how it relates to features of transit modes, outlines the literature relevant to bus-based TOD, and identifies the strengths and challenges of bus-based transit systems in relation to TOD. It concludes by summarizing the relative strengths and challenges of BRT and local bus services compared to rail. The findings of the review are used to identify ways in which bus-based TOD might be better planned and implemented.

An Integrated Framework to Predict Bus Travel Time and Its Variability Using Traffic Flow Data

Information about bus travel time and its variability is a key indicator of service performance, and it is valued by passengers and operators. Despite the important effect of traffic flow on bus travel time, previous predictive approaches have not fully considered a traffic measure making their predictions unresponsive to the dynamic changes in traffic congestion. In addition, existing methodologies have primarily concerned predicting average travel time given a certain set of input values. However, predicting travel-time variability has not received sufficient attention in previous research. This article proposes an integrated framework to predict bus average travel time and its variability on the basis of a range of input variables including traffic flow data. The framework is applied using GPS-based travel-time data for a bus route in Melbourne, Australia, in conjunction with dynamic traffic flow data collected by the Sydney Coordinated Adaptive Traffic Systems loop detectors and a measure of schedule adherence. Central to the framework are two artificial neural networks that are used to predict the average and variance of travel times for a certain set of input values. The outcomes are then used to construct a prediction interval corresponding to each input value set. The article demonstrates the ability of the proposed framework to provide robust prediction intervals. The article also explores the value that traffic flow data can provide to the accuracy of travel-time predictions compared with when either temporal variables or scheduled travel times are the base for prediction. While the use of scheduled travel times results in the poorest prediction performance, incorporating traffic flow data yields minor improvements in prediction accuracy compared with when temporal variables are used.

New Methodology for Optimizing Transit Priority at the Network Level

A new methodology for optimizing transit road space priority at the network level is proposed. Transit vehicles carry large numbers of passengers within congested road space efficiently. This aids justification of transit priority. Almost all studies that have investigated transit priority lanes focus at a link or an arterial road level, and no study has investigated road space allocation for priority from a network perspective. The aim of the proposed approach is to find the optimum combination of exclusive lanes in an existing operational transport network. Mode share is assumed variable, and an assignment is performed for both private and transit traffic. The problem is formulated by using bilevel programming, which minimizes the total travel time. The approach is applied to an example network and the results are discussed. The approach can identify the optimal combination of transit priority lanes and achieve the global optimum of the objective function. Areas for further development are discussed.

Evaluating the Congestion Relief Impacts of Public Transport in Monetary Terms

Traffic congestion is a major urban transport problem. Efficient public transport (PT) can be one of the potential solutions to the problem of urban road traffic congestion. Public transport systems can carry a significant amount of trips during congested hours, improving overall transportation capacity, and can release the burden of excess demand on congested road networks. This paper presents a comparative assessment of international research valuing the congestion relief impacts of PT. It explores previous research valuing congestion relief impacts and examines secondary evidence demonstrating changes in mode split associated with changes in public transport. The research establishes a framework for estimating the monetary value of the congestion reduction impacts of public transport. Congestion relief impacts are valued at between 4.4 and 151.4 cents (Aus