Abstract
Urban transport infrastructure is under increasing pressure from rising travel demand in many cities worldwide. It is no longer sustainable or even economically viable to cope with increased demand by continually adding capacity to transport networks. Instead, travel demand must be managed by encouraging passengers to adapt their travel behaviour. This approach necessitates a significantly deeper understanding of the seemingly random variations of passenger flows than is afforded by the current travel demand modelling techniques. This study presents a new modelling framework for predicting travel mode choice, through recreating and analysing the choice-set faced by the passenger at the time of day of their travel. A new data set has been developed by combining individual trip records from the London Travel Demand Survey (LTDS), with systematically matched trip trajectories alongside their corresponding mode alternatives from an online directions service and detailed estimates of public transport fares and car operating costs. The value of the data set is demonstrated by comparing two models of passenger mode choice based on stochastic gradient boosting trees, one using only the LTDS data and the other with our full data set. The models are then used to identify the key factors driving passenger mode choice.
Highlights
Urban transport networks are facing a number of unprecedented challenges, most notably how to deal with increased transport demand from rapid population increase
This study presents a new modelling framework for predicting travel mode choice, through recreating and analysing the choice-set faced by the passenger at the time of day of their travel
Transport models used for infrastructure investment and operations planning conventionally rely on multinomial logit random utility models (RUMs) to predict passenger mode choice (Ben-Akiva and Lerman, 1985)
Summary
Urban transport networks are facing a number of unprecedented challenges, most notably how to deal with increased transport demand from rapid population increase. Recreating passenger mode choice-sets for transport simulation: A case study of London, UK Hillel, Elshafie and Jin input predictors These models do not provide the level of insight into the seemingly random variations of passenger flows required for effective travel demand management. The adoption of several notable transportation-related technologies, such as live travel information feeds, mobile phone-based location services, contactless smart cards, vehicle tracking cameras and connected vehicles, has driven a step change in the availability of data on passenger movements of several orders of magnitude These data provide the opportunity to build much richer models of passenger behaviour, which directly infer the relationship between transport and environment conditions, and passenger travel decisions. There have been limited attempts at integrating these disparate data sources to create cohesive models
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