Abstract
Recent years have seen renewed policy interest in urban cycling due to the negative impacts of motorized traffic, obesity and emissions. Simulating bicycle mode share and flows can help decide where to build new infrastructure for maximum impact, though modelling budgets are limited. The four step model used for vehicles is not typically used for this task as, aside from the expense of use, it is designed around too-large zone sizes and a simplified network. Alternative approaches are based on aggregate statistics or spatial network analysis, the latter being necessary to create a model sufficiently sensitive to infrastructure location, although still requiring considerable modelling effort due to the need to simulate motor vehicle flows in order to account for the effect of motorized traffic in disincentivising cycling. The model presented uses an existing spatial network analysis methodology on an unsimplified network, but simplifies the analysis by substituting explicit prediction of motorized traffic flow with an alternative based on road classification. The method offers a large reduction in modelling effort, but nonetheless gives model correlation with actual cycling flows (R2 = 0.85) broadly comparable to a previous model with motorized traffic fully simulated (R2 = 0.78).
Highlights
Recent years have seen renewed policy interest in urban cycling due to the negative impacts of motorized traffic, obesity and emissions
The modelling areas, for example, differ only by a factor of 7 in this study; and the number of network links differ by a factor of 3 as it is the less dense areas which have been excluded from the simpler model
This paper has attempted to improve the transferability of spatial network analysis based cycling transport models by eliminating dependence on a detailed motor vehicle model
Summary
Recent years have seen renewed policy interest in urban cycling due to the negative impacts of motorized traffic, obesity and emissions. Later work[18] managed to discard these assumptions, in their place incorporating agglomeration effects, multiple trip purposes, heterogeneous preferences of different classes of cyclist, and the deterring effects of traffic and slope on mode share, to obtain a cross-validated fit with coefficient of determination R2 = 0.78 between modelled and measured cyclist flows. In the latter model, both mode and route choice are based on “cyclist-adjusted distance” i.e. distance with penalties applied for slope, turns, and level of predicted motorized traffic flow on each individual link within the network. Similar models of the pedestrian mode have been produced[19]
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