Momentum integration: the syntax of cycling

Cycling has long been known to have significant physical and mental health benefits for its participants. It has the potential to significantly reduce the carbon footprint of a city, increase personal mobility, improve transportation equality, improve air quality, and reduce congestion. While cycling has seen a major increase over the recent past, it is still a relatively small proportion of overall transportation. One significant factor in inhibiting the growth of cycling in many UK cities has been the lack of sufficient dedicated cycle routes. This deficiency is partly due to the lack of any recognized method of forecasting the practicability of future dedicated cycle lane provision. Historically, the prediction of movement rates for cyclists, using space syntax methods, has been weaker than that achieved for pedestrian rates. This paper theorizes that cyclists? route choice is primarily dominated by the momentum of the cyclist rather than route complexity. In this paper we introduce momentum integration as an alternative mechanism to understand cyclist movement. Momentum integration unifies multiple aspects of movement (specifically angular complexity, elevation change, traffic lights position) into a singular system, which can be computed using traditional syntax methods. This paper describes the methods of momentum integration and introduces new software known as ?Momentum Mercury?, which uses open source, centre-line data to compute momentum integration maps. The paper then continues to produce a movement rate analysis comparison between traditional space syntax methods and momentum integration using a survey of cycle usage in a major UK city. Analysis of this data shows that they are momentum method improves upon previous pedestrian correlation.