ACE-INPUTS: A Cost-Effective Intelligent Public Transportation System

Jongchan LEE
Sanghyun PARK
Minkoo SEO
Sang-Wook KIM

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E90-D    No.8    pp.1251-1261
Publication Date: 2007/08/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e90-d.8.1251
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Distributed Cooperation and Agents
Keyword: 
mobile public transportation system,  heuristic routing algorithm,  location-based services,  

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Summary: 
With the rapid adoption of mobile devices and location based services (LBS), applications provide with nearby information like recommending sightseeing resort are becoming more and more popular. In the mean time, traffic congestion in cities led to the development of mobile public transportation systems. In such applications, mobile devices need to communicate with servers via wireless communications and servers should process queries from tons of devices. However, because users can not neglect the payment for the wireless communications and server capacities are limited, decreasing the communications made between central servers and devices and reducing the burden on servers are quite demanding. Therefore, in this paper, we propose a cost-effective intelligent public transportation system, ACE-INPUTS, which utilizes a mobile device to retrieve the bus routes to reach a destination from the current location at the lowest wireless communication cost. To accomplish this task, ACE-INPUTS maintains a small amount of information on bus stops and bus routes in a mobile device and runs a heuristic routing algorithm based on such information. Only when a user asks more accurate route information or calls for a "leave later query", ACE-INPUTS entrusts the task to a server into which real-time traffic and bus location information is being collected. By separating the roles into mobile devices and servers, ACE-INPUTS is able to provide bus routes at the lowest wireless communication cost and reduces burden on servers. Experimental results have revealed that ACE-INPUTS is effective and scalable in most experimental settings.

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