A tailored machine learning approach for urban transport network flow estimation

Abstract

This study deals with urban transport network flow estimation based on Cellphone Location (CL) and License Plate Recognition (LPR) data. We first propose two methods to filter CL data and extract the spatio-temporal traffic features for a specific road segment. A tailored machine learning approach is developed, including two components: a tangible multi-grained scanning ensemble learning model and a novel two-stage zero-shot learner. The former aims to estimate traffic flow on a single link with both filtered CL data, extracted spatio-temporal traffic features, and LPR data by incorporating the unique merits thereof. The latter is capable of estimating traffic flow on those links with only CL data by considering the spatial features of these links and relevant land-use information. Finally, case studies are analysed to demonstrate the impressive performance of the tailored machine learning approach.