Title
A New Framework of Intelligent Public Transportation System Based on the Internet of Things
Abstract
As a new paradigm of information technology, the Internet of Things (IoT) is attracting increasing attention from various industrial fields. It is foreseeable that the applications of IoT will be prevalent in the public transportation system and bring changes to the system in the near future. In this paper, we analyze the impact of IoT environment on the public transportation system, propose a new framework of the intelligent public transportation system based on IoT, and present the deployment of the elements, the communication network, and the three-tier architecture of the system in detail. We also present the information flow, technical scheme, optimization model, and algorithm of the main modules of dynamic optimization of the system. The innovative points of this paper lie in: (1) a new framework for public transport system based on IoT, which integrates the scheduling problems of subway, bus, and shared taxi, is proposed for better-coordinated transfer solutions; (2) transport flow prediction methods based on periodic patterns mining is proposed for road flow analysis and passenger flow analysis, and; (3) mathematical model and DSS-based evolutionary computation algorithm are proposed for solving the dynamic bus scheduling and controlling problems. The proposed intelligent transport system based on IoT can assist the decision makers to increase the utilization rate of the transport resources, improve the efficiency of scheduling, and reduce passengers' traveling time.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2913288
IEEE ACCESS
Keywords
Field
DocType
Public transportation system,dynamic scheduling,traffic flow,the Internet of Things
Information flow (information theory),Software deployment,Telecommunications network,Scheduling (computing),Information technology,Computer science,Evolutionary computation,Public transport,Utilization rate,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
1
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Xing-Gang Luo113814.85
Hong-bo Zhang241.47
Zhongliang Zhang3142.02
Yang Yu4105.28
Ke Li55026.41