A Joint Passenger Flow Inference and Path Recommender System for Deploying New Routes and Stations of Mass Transit Transportation

Fandel Lin, Hsun Ping Hsieh

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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% to 57%. Moreover, the proposed BDPST algorithm's performance is not far away from the optimal PF and outperforms other comparative methods from 39% to 71% in large-scale route recommendations.

Original languageEnglish
Article number3451393
JournalACM Transactions on Knowledge Discovery from Data
Volume16
Issue number1
DOIs
Publication statusPublished - 2021 Jul

All Science Journal Classification (ASJC) codes

  • General Computer Science

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