Calculating regression and prediction models of bicycling in Paris : biking determinants analysis (1997-2015) and projecting the feasibility of municipal objectives for cycling by 2020

Abstract

Parisian cycling increased importantly in the past twenty years. Reviewing fifty years of Parisian transport planning and details of the bike-sharing programme Vélib’, I argue in the background research that municipal biking planning and the public bicycles Vélib’ can explain this development of urban biking (1997-2015). The city also has high ambitions for the biking modal share, aiming for fifteen per cent of all Parisian transport by 2020. I want to discover what the determinants of Parisian biking are, and if the latter can be modelled and predicted; thereby, I can verify if predictions match municipal objectives for 2020. I calculate correlations between the Parisian cycling index and its possible determinants with annual values on biking and other variables from 1997 to 2015 in the first part of the analysis (chapter V). This analysis shows that cycling infrastructure, Vélib’ memberships and gasoline price are the strongest positive biking determinants, while car traffic is the strongest negative determinant. In the second part of the analysis, knowing these determinants, I can find multiple linear regression models with high R-squared values (around 0,97 and 0,98) and low standard errors. The best regression model combines linear infrastructure, car traffic volume and Vélib’ memberships. The predictions in the last part of the analysis chapter reveal that in the current tendencies, the Parisian biking modal share will reach about 7 per cent by 2020, instead of the 15 per cent aimed. But I illustrate how the objective can be accomplished, by either improving drastically one of the determinants or the three of them simultaneously to reach a modal share of 15 per cent. The results and the models found appear to be more satisfactory and accurate than the ones of previous researches, presented in the literature review. The findings may be useful for public authorities and decision-makers during processes of biking planning, and it might contribute to future research in this topic.