Understanding, modelling and predicting transport mobility in urban environments

摘要: In the last three decades global population has been growing at an essentially constant rate, around 1.5 per cent year, to about 6.026 billion in 2000 when it was estimated that 47% of live urban environment. Further, a United Nations' projection indicates 60% total may be living settlement by year 2025. This increasing urbanisation brings with increased employment, delivers affluence, which then continues cycle migration and movement these metropolitan areas both developed developing countries. As cities increase expand their area, there is consequential expansion transportation accompanying service infrastructure. People travel daily, irrespective vast differences culture, economic conditions means transportation. daily mobility sought for its own sake as well bridge spatial distance separates homes from work place, accomplish household's domestic needs undertake social journeys, such visiting friends taking holidays. As world's undertakes variety modes, individual's behaviour mode-choice governed complex matrix physical human, management indicators, measures and/or drivers. A literature review describes current understanding this concludes identifying defining set fundamental underlying drive private motorised, public transport non-motorised (walking bicycling) national, city household levels. As practical instruments, models play important role providing decision-makers analytical tools help them understand city's different future scenarios face. While not necessarily producing foolproof information or predictions, are still best methods available test likely implications alternative policy decisions rapidly changing Urban generally based on notion traffic can modelled aggregate through statistical data predictive modelling techniques. In research, dimensional analysis used derive sketch-plan any environment four-decades detailed land-use pattern large international sample cities. These basis deemed level. Importantly, also embody key attributes. They are: * easy use, minimising user requirements inputs * policy-sensitive, capable assessing sufficient range options * reliable robust over time, so results consistently believed. The capacity predict personal statistically validated against independent 83 located five continents. Despite simplicity maintaining consistent functional form time-series across all geographic cultural regions, motorised model explain up 92% variance mobility. The less consistent, particular part model. Results wealthier much better. Reasons inadequacies discussed. modes least successful work. attributed combination inadequate and, very likely, more micro-level determinants usage modes. The equation thesis able present trends automobile use individual high degree reliability. offers planners focused direction have potential control deliver restraint policies strategies. series case studies shows wide applications past environmental strategy ability calculate assess motor vehicle emissions inventories makes suggestions area level modelling, particular, how improve better understood modelled.