韩国高速铁路列车

介绍了在韩国高速线上运行的KTX列车,以及将要运行的HSR-350X样机。     

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.

     

Exploring built environment impacts on transit use – an updated meta-analysis

ABSTRACT

The built environment (BE) is widely accepted to influence transit use (TU). Evidence to date suggests the relationship is dependent on many factors which can be difficult to account for in quantitative studies. This creates barriers to transferring research into practice. Considering many studies together can be useful for accounting for more of the factors impacting transit use. Yet, meta-analysis of research measuring these influences was last undertaken in 2010 based on 18 studies. Since then 90 new quantitative studies have been published. These recent studies use improved methodologies and are conducted in more diverse geographies. This paper reports an improved and updated meta-analysis of built environment impacts on transit use. It compares elasticity estimates from research published pre-and post-2010 and explores the impact of new methods and a more diverse geographical representation on findings. Updated meta-elasticities range from <0.01 to 0.26; a similar range to the 2010 study. However, at the individual indicator levels, more recent results are different. Elasticities for urban density, including population, employment and commercial density, have increased significantly in studies published since 2010, as did that of land use mix. However, measures of local access, design and jobs-housing balance decreased in post-2010 studies. These results confirm the small but imprecise relationship between the BE and TU. Results also suggest that while the range of elasticity impacts is relatively consistent, new study methodologies, notably those that control for regional accessibility and self-selection, and the increasing geographical diversity in study applications, is acting to change BE-TU findings at the indicator level. Research setting and context are important to consider when using empirical results to design BE strategies to promote transit use.     

Random regret minimization or random utility maximization: an exploratory analysis in the context of automobile fuel choice

Interest in alternative behavioural paradigms to random utility maximization (RUM) has existed ever since the dominance of the RUM formulation. One alternative is known as random regret minimization (RRM), which suggests that when choosing between alternatives, decision makers aim to minimize anticipated regret. Although the idea of regret is not new, its incorporation into the same discrete choice framework of RUM is very recent. This paper is the first to apply the RRM‐model framework to model choice amongst durable goods. Specifically, we estimate and compare the RRM and RUM models in a stated choice context of choosing amongst vehicles fuelled with petrol, diesel and hybrid (associated with specific levels of fuel efficiency and engine capacity). The RRM model is found to achieve a marginally better fit (using a non‐nested test of differences) than its equally parsimonious RUM counterpart. As a second contribution, we derive a formulation for regret‐based elasticities and compare utility‐based and regret‐based elasticities in the context of stated vehicle type choices. We find that in the context of our choice data, mean estimates of elasticities are different for many of the attributes and alternatives. Copyright © 2011 John Wiley & Sons, Ltd.     

Behavioural implications of preferences, risk attitudes and beliefs in modelling risky travel choice with travel time variability

The appropriate interpretation of a behavioural outcome requires allowing for risk attitude and belief of an individual, in addition to identification of preferences. This paper develops an Attribute-Specific Extended Rank-Dependent Utility Theory model to better understand choice behaviour in the presence of travel time variability, in which these three important components of choice are empirically addressed. This framework is more behaviourally appealing for travel time and travel time variability research than the traditional approach in which risk attitude and belief are overlooked. This model also reveals significant unobserved between-individual heterogeneity in preferences, risk attitudes and beliefs.     

Demand for taxi services: new elasticity evidence

This paper investigates the factors that influence the choice of, and hence demand for taxis services, a relatively neglected mode in the urban travel task. Given the importance of positioning preferences for taxi services within the broader set of modal options, we develop a modal choice model for all available modes of transport for trips undertaken by individuals or groups of individuals in a number of market segments. A sample of recent trips in Melbourne in 2012 was used to develop segment-specific mode choice models to obtain direct (and cross) elasticities of interest for cost and service level attributes. Given the nonlinear functional form of the way attributes of interest are included in the modal choice models, a simple set of mean elasticity estimates are not behaviourally meaningful; hence a decision support system is developed to enable the calculation of mean elasticity estimates under specific future service and pricing levels. Some specific direct elasticity estimates are provided as the basis of illustrating the magnitudes of elasticity estimates under likely policy settings.