Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763688
Title: A new travel demand model for outdoor recreation trips
Author: Jiao, Xihe
ISNI:       0000 0004 7652 4978
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2018
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Abstract:
Travel to outdoor recreational spaces belongs to a general class of research questions for understanding destination and travel mode choices. In travel demand modelling, discrete choice models (DCMs) have been applied to understand and predict a wide range of choices, such as how people choose among alternative destinations for jobs, homes, shopping, personal services etc. Surprisingly, DCMs have rarely been used to understand and model travel to outdoor recreational spaces. In the current literature for modelling travel to outdoor recreational spaces, the established models are Negative Binomial Regression (NBR) models, such as what was used in the UK NEA studies. However, these NBR models were developed to assess the effects of travel to outdoor recreational spaces at a national level, and they are not intended for assessing choices of individual sites. One reason for this is, as identified by previous studies, is that compared with the DCMs, the NBR models have certain limits on estimating people's choice behaviours. There is, therefore, no existing model that can represent and predict how people choose to travel to outdoor recreational spaces. Given the importance of outdoor recreational activities to urban land use planning and public health, this is a clear gap in the field. The aim of this study is to develop a new travel demand model capable of representing and predicting travel to individual outdoor recreational sites. This is achieved by answering four main research questions: First, how to build the new model for outdoor recreational travel? Secondly, is the estimation accurate enough? Thirdly, to what extent can the new model be transferred to destinations outside the case study area? And, finally, how can city planners and designers use this new method? The new model draws upon ideas from random utility theory that underlies the conventional travel demand models to represent trip generation, trip distribution and mode choice. This research follows the standard modelling procedure: data collection and preliminary analysis, model calibration, model validation and model application. The data are collated from a wide range of sources that, importantly for model transferability, cover all areas in England. The new model has been calibrated for a case study area which spanned 14 selected districts in the North-West region. Validation of the new model is based on estimating the numbers of trips to two outdoor recreational sites (Wigg Island and Wigan Flashes) and to nine English National Parks where data on visitor trips exist. In the final stage of the research, the new model is applied to estimate the changes that would arise from planning and design interventions in existing (Wigg Island and Moore Nature Reserve) and proposed (Arpley Country Park) sites. At the end of this process, it is possible to show that the new model can predict the number of trips to individual destinations and that the model can be transferred to other outdoor recreation sites. Furthermore, the new model presented here is capable of predicting the changes in the volume and catchment of visits to an existing green space after land use planning or urban ecological interventions. This is a completely new theoretical model that is focused on understanding and quantifying the travel choices to outdoor recreation sites, which can inform decision makers by forecasting changes in outdoor recreational travel demand, according to different planning scenarios.
Supervisor: Jin, Ying Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.763688  DOI:
Keywords: Travel Demand Modelling ; Discrete Choice Models ; Outdoor recreational trips ; Greenspace ; Urban Planning
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