Workshop 3B: Governance,ownership and competition issues in deregulated (free market) public transport: Lessons that can be learnt from developed and developing economies

The past emphasis in this conference series has been on the best ways to deregulate regulated public transport markets. This workshop reverses this process by examining the best ways to regulate deregulated public transport markets. A hierarchy of regulatory needs is identified and three hybrid models examined, based loosely on experience from Great Britain, New Zealand and Sweden. It is argued that deregulated public transport markets are a global phenomenon but regulatory measures should reflect local requirements. The resultant process of glocalisation might result in regulatory measures that focus on the rules of law and their enforcement in emerging public transport markets (such as urban transport in Sub Saharan Africa and for the soon to be competitive inter urban market in Germany) but that focus on guidance for network integration and incentivisation for welfare maximisation in more mature public transport markets (as in Great Britain, New Zealand and Sweden).     

Sample size requirements for stated choice experiments

Stated choice (SC) experiments represent the dominant data paradigm in the study of behavioral responses of individuals, households as well as other organizations, yet in the past little has been known about the sample size requirements for models estimated from such data. Traditional orthogonal designs and existing sampling theories does not adequately address the issue and hence researchers have had to resort to simple rules of thumb or ignore the issue and collect samples of arbitrary size, hoping that the sample is sufficiently large enough to produce reliable parameter estimates, or are forced to make assumptions about the data that are unlikely to hold in practice. In this paper, we demonstrate how a recently proposed sample size computation can be used to generate so-called S-efficient designs using prior parameter values to estimate panel mixed multinomial logit models. Sample size requirements for such designs in SC studies are investigated. In a numerical case study is shown that a D-efficient and even more an S-efficient design require a (much) smaller sample size than a random orthogonal design in order to estimate all parameters at the level of statistical significance. Furthermore, it is shown that wide level range has a significant positive influence on the efficiency of the design and therefore on the reliability of the parameter estimates.     

Choosing Public Transport—Incorporating Richer Behavioural Elements in Modal Choice Models

The development of behaviourally richer representations of the role of well-established and increasingly important influences on modal choice, such as trip time reliability and accounting for risk attitude and process rules, has moved forward at a fast pace in the context of automobile travel. In the public transport setting, such contributions have, with rare exception, not been considered. In this paper, we discuss and empirically illustrate the merits of advanced modelling developments aimed at improving our understanding of public transport choice, namely the inclusion of reliability in extended expected utility theoretic forms, to recognize risk attitude and perceptual conditioning, the consideration of passenger crowding and its inclusion in linear additive models, and the role of multiple heuristics in representing attribute processing as a way of conditioning modal choice. We illustrate the mechanics of introducing these behaviourally appealing extensions using a modal choice data set collected in Sydney.     

A computationally efficient two-stage method for short-term traffic prediction on urban roads

Abstract

Short-term traffic prediction plays an important role in intelligent transport systems. This paper presents a novel two-stage prediction structure using the technique of Singular Spectrum Analysis (SSA) as a data smoothing stage to improve the prediction accuracy. Moreover, a novel prediction method named Grey System Model (GM) is introduced to reduce the dependency on method training and parameter optimisation. To demonstrate the effects of these improvements, this paper compares the prediction accuracies of SSA and non-SSA model structures using both a GM and a more conventional Seasonal Auto-Regressive Integrated Moving Average (SARIMA) prediction model. These methods were calibrated and evaluated using traffic flow data from a corridor in Central London under both normal and incident traffic conditions. The prediction accuracy comparisons show that the SSA method as a data smoothing step before the application of machine learning or statistical prediction methods can improve the final traffic prediction accuracy. In addition, the results indicate that the relatively novel GM method outperforms SARIMA under both normal and incident traffic conditions on urban roads.     

Converting mixed pedestrian flows into equivalent commuters using standard pedestrian equivalent factors

This paper develops the concept of standard pedestrian equivalent (SPE) factors for converting a mixed pedestrian flow into an equivalent commuter flow. After a comprehensive review of passenger car equivalent (PCE) methodologies, the equal total travel time method is utilised for SPE estimation. A micro-simulation approach is employed for the formulation of the total travel time–flow relationship. Field data collected on walking speed distributions for commuters and older adults in Australia are used as model inputs. An independent samples t-test confirms the significant difference between walking speeds of commuters and older adults. For this paper, a unidirectional flow on flat walkways is initially considered and evaluated across proportions of older people, different flows and different walkway widths. The introduction of older adults significantly increases total travel time especially under congested conditions. Results of this investigation can be used for evaluation or design of pedestrian facilities experiencing similar flow conditions.     

Accommodating risk in the valuation of expected travel time savings

Valuation of travel time savings is a critical measure in transport infrastructure appraisal, traffic modelling and network performance. It has been recognised for some time that the travel times associated with repeated trips are subject to variation, and hence there is risk embedded in the treatment of expected travel time. In the context of the expected utility framework, we use a nonlinear probability weighting function to accommodate choice made under risk. Although the empirical findings suggest small differences between the value of expected travel time savings (VETTS) in the presence and absence of risk, the mean estimate does make a noticeable difference to time benefits when applied to real projects. By incorporating nonlinear probability weighting, our model reveals that the probabilities associated with specific travel times that are shown to respondents in the choice experiment are transformed, resulting in overweighting of outcomes with low probabilities and underweighting of outcomes with high probabilities. Copyright © 2011 John Wiley & Sons, Ltd.     

The case for space: the measurement of capacity utilisation,its relationship with reactionary delay and the calculation of the capacity charge for the British rail network

In Britain, capacity charges are levied on train operators to cover the costs imposed by increased reactionary delays – i.e. the delay that an already late train causes to a following train. These charges are based on the link between capacity utilisation and the level of reactionary delay. An established method for measuring capacity utilisation was adopted called The Capacity Utilisation Index (CUI). In this paper, we propose an alternative methodology based on the theory that the level of reactionary delay will be determined by the minimum gaps that exist between trains. We test this measure with a new data-set for the East Coast Main Line and show that it performs better than CUI. Finally, we comment on the implications of this finding for charging and for the construction of timetables.     

Exploring the role of individual attitudes and perceptions in predicting the demand for cycling: a hybrid choice modelling approach

Cycling is often promoted as a means of reducing urban congestion and improving health, social and environmental outcomes. However, the quantification of these potential benefits is not well established. This is due in part to practical difficulties in estimating cycling demand and a lack of sound methodologies to appraise cycling initiatives. In this paper we attempt to address this need by developing predictive models of cycle demand, relative to other transport modes, that capture not only the impacts of observed characteristics such as age and travel time but also the role of attitudes and perceptions. Using data from a stated preference survey, we estimate a hybrid choice model for cycle use that incorporates the role of attitudes towards cycling, perceptions of the image associated with cycling, and the stress arising from safety concerns. Model results indicate that the latent attitudes and perceptions explain an important part of the non-observable utility in a simple multinomial logit choice model. We also demonstrate policy analysis using the hybrid choice model, which allows comparisons of ‘hard’ policies such as the provision of parking facilities against ‘soft’ measures such as cycle promotion schemes.     

理解消费者对质量的定义

汽车产品的质量总是会受到巨大的关注。质量较差会导致财务成本的增加,这使得制造商把提升质量作为一项商业必要的手段。过去的数年中有无数方法和流程被用来减少浪费和消除缺陷 (比如6-Sigma法和TQM完全质量管理法)。我们并不缺少这样的衡量工具,来衡量因质量问题和返工而产生的负面经济效益。运用这些衡量工具能够理解质量对每辆车或者公司的盈利能力造成的影响,并且帮助公司取得显著的产品质     

Rising Sea Levels: Helping Decision-Makers Confront the Inevitable

Sea-level rise (SLR) is not just a future trend; it is occurring now in most coastal regions across the globe. It thus impacts not only long-range planning in coastal environments, but also emergency preparedness. Its inevitability and irreversibility on long time scales, in addition to its spatial non-uniformity, uncertain magnitude and timing, and capacity to drive non-stationarity in coastal flooding on planning and engineering timescales, create unique challenges for coastal risk-management decision processes. This review assesses past United States federal efforts to synthesize evolving SLR science in support of coastal risk management. In particular, it outlines the: (1) evolution in global SLR scenarios to those using a risk-based perspective that also considers low-probability but high-consequence outcomes, (2) regionalization of the global scenarios, and (3) use of probabilistic approaches. It also describes efforts to further contextualize regional scenarios by combining local mean sea-level changes with extreme water level projections. Finally, it offers perspectives on key issues relevant to the future uptake, interpretation, and application of sea-level change scenarios in decision-making. These perspectives have utility for efforts to craft standards and guidance for preparedness and resilience measures to reduce the risk of coastal flooding and other impacts related to SLR.