车辆安全和联合国十年行动计划

预计到2030年道路交通事故将成为第五大致命原因，每年死亡人数将上升至240万人。儿童、青少年和自行车、摩托车车手以及行人这类易受碰撞的群体是最处于危险之中的。     

Not bored yet - Revisiting respondent fatigue in stated choice experiments

Stated choice surveys are used extensively in the study of choice behaviour across many different areas of research, notably in transport. One of their main characteristics in comparison with most types of revealed preference (RP) surveys is the ability to capture behaviour by the same respondent under varying choice scenarios. While this ability to capture multiple choices is generally seen as an advantage, there is a certain amount of unease about survey length. The precise definition about what constitutes a large number of choice tasks however varies across disciplines, and it is not uncommon to see surveys with up to twenty tasks per respondent in some areas. The argument against this practice has always been one of reducing respondent engagement, which could be interpreted as a result of fatigue or boredom, with frequent reference to the findings of Bradley and Daly (1994) who showed a significant drop in utility scale, i.e. an increase in error, as a respondent moved from one choice experiment to the next, an effect they related to respondent fatigue. While the work by Bradley and Daly has become a standard reference in this context, it should be recognised that not only was the fatigue part of the work based on a single dataset, but the state-of-the-art and the state-of-practice in stated choice survey design and implementation has moved on significantly since their study. In this paper, we review other literature and present a more comprehensive study investigating evidence of respondent fatigue across a larger number of different surveys. Using a comprehensive testing framework employing both Logit and mixed Logit structures, we provide strong evidence that the concerns about fatigue in the literature are possibly overstated, with no clear decreasing trend in scale across choice tasks in any of our studies. For the data sets tested, we find that accommodating any scale heterogeneity has little or no impact on substantive model results, that the role of constants generally decreases as the survey progresses, and that there is evidence of significant attribute level (as opposed to scale) heterogeneity across choice tasks.     

Network impacts of a road capacity reduction: Empirical analysis and model predictions

In spite of their widespread use in policy design and evaluation, relatively little evidence has been reported on how well traffic equilibrium models predict real network impacts. Here we present what we believe to be the first paper that together analyses the explicit impacts on observed route choice of an actual network intervention and compares this with the before-and-after predictions of a network equilibrium model. The analysis is based on the findings of an empirical study of the travel time and route choice impacts of a road capacity reduction. Time-stamped, partial licence plates were recorded across a series of locations, over a period of days both with and without the capacity reduction, and the data were ‘matched’ between locations using special-purpose statistical methods. Hypothesis tests were used to identify statistically significant changes in travel times and route choice, between the periods of days with and without the capacity reduction. A traffic network equilibrium model was then independently applied to the same scenarios, and its predictions compared with the empirical findings. From a comparison of route choice patterns, a particularly influential spatial effect was revealed of the parameter specifying the relative values of distance and travel time assumed in the generalised cost equations. When this parameter was ‘fitted’ to the data without the capacity reduction, the network model broadly predicted the route choice impacts of the capacity reduction, but with other values it was seen to perform poorly. The paper concludes by discussing the wider practical and research implications of the study’s findings.     

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.     

Examining transport futures with scenario analysis and MCA

Climate change is a global problem and across the world the transport sector is finding it difficult to break projected increases in carbon dioxide (CO₂) emissions; there are very few contexts where deep reductions in transport CO₂ emissions are being made. A number of research studies are now examining the potential for future lower CO₂ emissions in the transport sector. This paper develops this work to consider some of the wider sustainability impacts (economic, social and local environmental) as well as the lower CO₂ transport impacts of different policy trajectories. Hence the central argument made is for an integrated approach to transport policy making over the longer term - incorporating scenario analysis and multi-criteria assessment (MCA) - to help assess likely progress against a range of objectives.The analysis is based on work carried out in Oxfordshire, UK. Different packages of measures are selected and two scenarios developed which satisfy lower CO₂ aspirations, one of which also provides wider positive sustainability impacts. A simulation model has been produced to help explore the strategic policy choices and tensions evident for decision-makers involved in local transport planning. The paper argues for a ‘strategic conversation’ (Van der Heijden, 1996) at the sub-regional and city level, based upon future scenario analysis and MCA, discussing the priorities for intervention. Such an approach will help us examine the scale of change and trade-offs required in moving towards sustainable transport futures.     

Formulating and solving the network design problem by decomposition

The optimal transportation network design problem is formulated as a convex nonlinear programming problem and a solution method based on standard traffic assignment algorithms is presented. The technique can deal with network improvements which introduce new links, which increase the capacity of existing links, or which decrease the free-flow (uncongested) travel time on existing links (with or without simultaneously increasing link capacity). Preliminary computational experience with the method demonstrates that it is capable of solving very large problems with reasonable amounts of computer time.     

Book reviews

THE U.S. AIRFREIGHT INDUSTRY, by Nawal K. Taneja. Lexington Books, Lexington, Mass., 1979. 244 pp.

TRAFFIC, OPERATIONAL RESEARCH, FUTUROLOGY, by Arne Jensen. North Holland Publishing Company, Amsterdam, 1980. 321 pp. ($39.00)

PROBLEMS OF THE CARLESS, by R. E. Paaswell and W. W. Recker. Praeger Publishers, New York, 1978. 190 pp.

TRANSPORTATION AND THE ELDERLY, by Martin Wachs. University of California Press, Berkeley, 1980.

PUBLIC TRANSPORTATION: PLANNING, OPERATIONS AND MANAGEMENT, edited by George E. Gray and Lester A Hoel. Prentice Hall, Englewood Cliffs, New Jersey, 1979. 749 pp.

ETHYL ALCOHOL PRODUCTION AND USE AS A MOTOR FUEL, edited by J. K. Paul. Noyes Data Corporation, New Jersy, 1979. 354 pp ($48.00).

URBAN PLANNING AND PUBLIC TRANSPORT, edited by Roy Cresswell. The Construction Press, London, 1980. 172 pp. (£15.00)     

Maritime policy and management--30 years issue for Prof. James McConville Editorial: IMO--The economics of intimidation

Organizational Effectiveness (OE) is a rather new concept in the port business literature and not widely studied before. From the systems perspective, this concept focuses on the goals of the organization, the resources needed to achieve these goals, and the relationship between the organization and its environment. The reason behind choosing a systems approach is the significant role of seaports as open systems affected by changes and developments in world trade, supply chain and logistics trends, maritime transport, and technological developments. While seaports are multi-faceted organizations that constitute different interdependent and integrated units in their structure, they can also be regarded as the subsystems of the supply chain system. As being the social and technical systems, port organizations have unique characteristics that should be examined in detail to assess their effectiveness. To assess the effectiveness of port organizations, principally a set of effectiveness measures applicable to seaports should be determined. The main aim of this study is to assess the relative importance of the main effectiveness criteria in seaports and to determine the significance of main inputs and port subsystems which can be referred as the means to achieve effectiveness. By using the systems approach, the inputs, the processes, and the outputs of the port organizations are clearly defined with a conceptual model. Thirty-three statements have been developed under the main categories in the systems model and a two-round delphi survey is conducted, and qualitative analysis of the experts’ opinions is carried out. The results of the delphi survey show that the main port inputs are port infrastructure, equipments, technology, and port labor; the major port processes are ship and cargo operations together with the logistics services, safety and security, information technologies, and marketing; and the most important effectiveness measures for the seaports are productivity, efficiency, service quality, adaptability, information and communication management, profitability, human resource quality, and customer satisfaction.     

A cluster analysis approach for differentiating transportation modes using Bluetooth sensor data

A variety of automatic data collection technologies have been used to gather road and highway system data. The majority of these automatic data collection technologies are designed to collect vehicle-based data and either do not have the capability to collect other travel mode data (e.g., bicycles and pedestrians), or may need to be deployed differently to support this capability.

One type of wireless-based data collection system that has been deployed recently is based on Bluetooth technology. A key feature of Bluetooth-based data collection systems that makes travel mode identification feasible is that the Bluetooth-enabled devices within vehicles are also present on bicyclists and pedestrians. This research explores the effectiveness of applying cluster analysis methods when processing data collected via Bluetooth technology from vehicles, bicyclists, and pedestrians to automatically identify the associated travel modes. The results of several experiments utilizing multiple Bluetooth-based data collection units arranged linearly and in relatively close proximity on a simulated intersection demonstrate the potential of cluster analysis to accurately differentiate transportation modes from the collected data.