Estimating city-wide hourly bicycle flow using a hybrid LSTM MDN

Marcus Skyum Myhrmann and Stefan Mabit

Transportation Research Part A: Policy and Practice, 2023, vol. 176, issue C

Abstract: Cycling can reduce greenhouse gas emissions and air pollution and increase public health. Hence, policymakers in cities worldwide seek to improve bicycle mode shares. Efforts to increase the bicycle’s mode share involve many measures, one of them being the improvement of cycling safety often requiring an analysis of the factors surrounding accidents. However, meaningful analysis of cycling safety requires accurate bicycle flow data that are generally sparse or only available at the aggregate level. Therefore, safety engineers often rely on aggregated variables or calibration factors that fail to account for variations in the cycling traffic relevant to policymaking.

Keywords: Bicycle flow estimation; Long Short-Term Memory; Mixture Density Network; Deep Learning; Aggregation bias (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.tra.2023.103783

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