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Demographic Change and Future Transport Demand: An Analysis of the British Situation 1989-2006

机译：人口变化与未来交通需求：1989 - 2006年英国形势分析

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INTRODUCTIONududThe planning of transport infrastructure requires as one ingredient sets of forecasts of transport demand. One of the factors that influences transport demand is the composition of the population in terms of person types, with considerable variation in trip making and trip distances between persons by age, sex, economic position, car availability and income (Siu et a1 1994). Normally, attention is paid to the way trip rates for each person category are changing as a result of income or car availability changes. However; the irge and sex structure of the population is also changing and may have influence on future trip making. Also of potential importance is the redistribution of the population over time, predominantly in the direction of lower density areas, which can have very different model mixes from higher density areas. ududThis paper describes work that attempts to link conventional category analysis based trip forecasting with knowledge of the changing demographic and geographic make-up of the British population. This should, in principle, be an easy task. Trip rates and trip mileages can be computed from one or more National Travel Surveys (NTS) for a highly diagnostic person classification. These trip rates are then trended or a scenario developed, and the forecast trip rates or mileages applied to the forecast population in each category to yield forecasts of the number of trips to be expected in the future, under the assumptions used in the two component forecasts. The 1985/86 NTS dataset is used in this study. udud(CONTINUES..)udud
机译：简介 ud ud运输基础设施的规划需要将运输需求的预测作为一组要素。影响运输需求的因素之一是人口的组成，按人的类型划分，其出行方式和人与人之间的出行距离随年龄，性别，经济地位，汽车可用性和收入而有很大差异（Siu等，1994）。通常，要注意由于收入或购车能力的变化，每个人类别的出行率的变化方式。然而;人口的烦恼和性别结构也在发生变化，可能会对以后的出行产生影响。具有潜在重要性的是随着时间的推移人口的重新分配，主要是在低密度区域的方向，这可能与高密度区域的模型混合非常不同。 ud ud本文介绍了一些工作，这些工作试图将基于常规类别分析的出行预测与不断变化的英国人口统计学和地理构成知识相结合。从原则上讲，这应该很容易。可以从一项或多项国家旅行调查（NTS）中计算出出行率和出行里程，从而对人员进行高度诊断。然后对这些出行率进行趋势分析或制定情景，并在两个组成部分预测中使用的假设下，将预测出行率或里程应用于每个类别的预测人口，以得出对未来预期出行次数的预测。这项研究使用了1985/86年NTS数据集。 ud ud（继续..） ud ud

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