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• Passenger's non-compliance behavior is defined and observed. • Critical time indicators are defined to measure the alighting and boarding efficiency. • A simulation model is proposed to simulate passengers' alighting and boarding process at metro platform. • Effects of Passenger's non-compliance behaviors under different passenger volume conditions are analyzed. • Different alighting and boarding rules could be applied to increase alighting and boarding efficiency. Passengers' alighting and boarding process in metro stations has attracted increasing research attention since it has significant influence on the platform passenger distribution as well as the train dwell time. In this study, a field survey was firstly performed in a densely populated metro station at downtown area in Hong Kong to observe passengers' alighting and boarding characteristics and collect sample data. One commonly existing phenomenon is found that boarding passengers start to get aboard even when there are alighting passengers still inside the metro carriage. This is defined as passengers' non-compliance behaviors in this paper. In addition, time indicators are defined to measure the alighting and boarding efficiency. Then a microscopic pedestrian simulation model based on the Social Force Model is proposed to simulate the passengers' alighting and boarding patterns at metro platform. The verification result shows the good applicability of the proposed model to simulate the actual situation. Finally, several simulation tests are conducted to explore the impacts that passengers' non-compliance behaviors have on the alighting and boarding efficiency in different passenger volume conditions. The simulation result shows that higher level of passengers' non-compliance behaviors leads to longer passenger's alighting duration and boarding duration, but the influence on the overall transaction time is related to different passenger volume conditions. Thus, metro station facility could apply different alighting and boarding rules in different passenger volume conditions to increase alighting and boarding efficiency. [ABSTRACT FROM AUTHOR]

In highly motorised countries, some sectors of the population own and use cars despite struggling to afford their running costs, and so may be particularly vulnerable to motor fuel prices increases, whether market-led or policy-driven. This paper proposes a novel, disaggregated approach to investigating vulnerability to such increases at the household level. We propose a set of indicators of ‘car-related economic stress’ (CRES), based on individual household level expenditure data for the UK, to identify which low-income households spend disproportionately on running motor vehicles, and to assess the depth of their economic stress. By subsequently linking the dataset to local fuel price data, we are able to model the disaggregated price elasticities of car fuel demand. This provides us with an indicator of each household’s adaptive capacity to fuel price increases. The findings show that ‘Low Income, High Cost’ households (LIHC) account for 9% of UK households and have distinct socio-demographic characteristics. Interestingly, they are characterised by very low responses to fuel price increases, which may cause them to compromise on other important areas of their household expenditures. Simulations suggest that a 20% increase in fuel prices would substantially increase the depth, but not the incidence of CRES. Overall, the study sheds light on a sector of the population with high levels of vulnerability to fuel price increases, owing to high exposure, high sensitivity and low adaptive capacity. This raises challenges for social, environmental and resilience policy in the transport sector. [ABSTRACT FROM AUTHOR]