Personal Mobility In Mixed Traffic

We construct an effective procedure to predict traffic patterns of personal mobility in real time under heterogeneous traffic conditions. The primary challenges involved in this study are not only to devise stochastic behavioural model of a personal mobility to cope with users’ stochastic characteristics, but also to install an integrated deep learning approach, including a Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) methods, to measure the inclination towards route choice manoeuvres under mixed traffic conditions.

The major contributions of the proposed model are to entail easily converged estimates of the behavioural dynamics of personal mobility using the concept of additive white noise. In addition, we integrate the continuous stochastic behaviour model with the route-choice model predicted by the deep learning approach to consider a possibility of the route-choice manoeuvre in diffusion in travel speed. We construct a microsimulation platform to calibrate and validate the proposed model using a field data set. Moreover, we expect the results of simulations will demonstrate that the proposed method is robust and accurate for explaining behavioural dynamics of personal mobility under mixed traffic conditions.