A Geo-Aware Taxi Carrying Management System by Using Location Based Services and Zone Queuing Techniques on Internet of Things

Abstract

Taxi plays a crucial role in the transportation system because of the characteristic that can be hailed conveniently. Most of the taxi drivers obtain passengers by hunting on the road or waiting in a fixed taxi queuing point; however these methods have poor performance, high vacancy rate, and several critical problems such as air pollution and foul up traffic. This study proposed a taxi carrying management system by using location based services and zone queuing techniques on Internet of things. The proposed system allows drivers to both hunt on the road and wait in a queuing zone. A queuing table is used in the control center and neighbor tables are used in RSUs for zone queuing establishment. Joining and leaving mechanisms are developed for zone queuing management. To enhance service efficiency and quality, we present a scheme to prevent the ping-pong effect which is based on the location based services, a hunting rate calculation scheme, and a path planning service for taxi drivers according to the history carrying record. PRISM is used to simulate the proposed system, and the results indicated that our scheme outperforms the waiting and hunting models in terms of number of customers, vacancy rate, and profit.

A Knowledge-Based Approach to Enhance Provision of Location-Based Services in Wireless Environments

“…There are plenty of applications in the literature to provide users with specific location-based services [36]. For example, taxi searching [2], helping firefighting [3], detecting and recommending nearby friends [4], or multimedia retrieval in sport events [5], among many others. Also, there are some proposals of architectures to provide LBS.…”

“…Besides, they usually work with implicit context knowledge (e.g., possibly hardcoded within the application/service), which contributes to their non-reusability. For example, LBS for taxi searching [2], helping firefighting [3], detecting and recommending nearby friends [4], or multimedia retrieval in sport events [5] have been presented, among many others. Therefore, it is difficult to handle the information about all the LBS which could be interesting for us, and to select the most appropriate service matching our information needs.…”