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Innovate UK Engaged Smart Transport (EST)

Funder: UK Research and InnovationProject code: NE/N007328/1
Funded under: NERC Funder Contribution: 292,229 GBP

Innovate UK Engaged Smart Transport (EST)

Description

Cities face a number of key transport challenges in the 21st Century. Congestion has a set of well-documented negative consequences, including environmental pollution, poor economic competitiveness, low levels of public satisfaction with public transport, negative impacts on personal health and wellbeing, and a broader reputational impact on urban centres that aspire to be retail and tourist destinations. Moreover, the UK's commitment to reduce carbon emissions necessitates a broader long-term shift away from private motor transport towards low carbon and mass transit modes of transport. In this way, tackling the specific issue of urban congestion relates to wider social and economic goals for cities to become better places to live and easier places to navigate. Specifically, Exeter has some of the worst air pollution and congestion statistics for a city of its size. Yet the city is also witnessing unprecedented expansion of outlying suburbs, creating greater pressure on existing arterial and city centre roads. Alongside these background factors, the city aspires to maintain its position as a key retail and tourist centre and there are plans to redevelop several areas of the city centre for retail and leisure facilities in the coming years. One of the ways in which social scientists have attempted to deal with this 'wicked' policy problem is to promote pre-formed behavioural change through the provision of information and exhortations to individual travellers to change their behaviour. However, decades of social research has illustrated that influencing behaviour is highly complex and requires a significant investment in research intelligence about what influences travel behaviours. Although recent years have witnessed a growth in social marketing approaches for influencing change, which adopt the methods of conventional marketing approaches, a set of factors that have been largely omitted from such studies and interventions is the role of what can be termed 'real time' factors in travel decision making, which are not pre-formed, but which influence practices in the moment. Research intelligence from traffic management providers suggests that factors such as weather conditions, immediate levels of traffic congestion and perceptions of the effectiveness of public transport are all important to consider when understanding both decisions to travel and also the resultant behaviour of travellers on their journeys (either as drivers or public transport users). Indeed, crucial to understand are the ways in which these conditions can be communicated to promote different practices, either as decisions to travel using different modes or to drive in a different way, and the potential for harnessing new technologies for managing travel behaviour through both the utilisation of sophisticated traffic management systems. This research therefore aims to understand and promote better 'real time' travel decision making through adopting a personalised and tailored travel behaviour approach. This will be undertaken through a two stage methodology. In stage 1, a large general survey of Exeter residents and those travelling into Exeter on a daily basis will be undertaken to explore key travel behaviours, attitudes, participants' characteristics. Using an online survey approach, the questionnaire will enable researchers to both identify key segment groups and their travel behaviours and, on the basis of these analyses, to make high level statistical links between individual behaviours and external factors, including quantitative information from other datasets. In stage 2, a panel of representative participants from the segments identified at stage 1 will be formed to explore the key relationships between behaviours and specific interventions that will be captured through a series of experiments, which will test interventions and their effectiveness.

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