Abstract

In this work, a novel decision assistant system for urban transportation, called Route Scheme Assistant (RSA), is proposed to address two crucial issues that few former researches have focused on: route-based passenger flow (PF) inference and multivariant high-PF route recommendation. First, RSA can estimate the PF of arbitrary user-designated routes effectively by utilizing Deep Neural Network (DNN) for regression based on geographical information and spatial-temporal urban informatics. Second, our proposed Bidirectional Prioritized Spanning Tree (BDPST) intelligently combines the parallel computing concept and Gaussian mixture model (GMM) for route recommendation under users’ constraints running in a timely manner. We did experiments on bus-ticket data of Tainan and Chicago and the experimental results show that the PF inference model outperforms baseline and comparative methods from 41 …

Date

July 20, 2021

Authors

Fandel Lin, Hsun-Ping Hsieh

Journal

ACM Transactions on Knowledge Discovery from Data

Volume

16

Issue

1

Pages

1-36

Publisher

ACM