篇名：Travel Time Prediction System Based on Data Clustering for
Waste Collection Vehicles
作者：Chi-Hua Chen*(陈志华),Feng-Jang Hwang,Hsu-Yang Kung
来源：IEICE Transactions on Information and Systems
年份：2019
DOI: 10.1587/transinf.2018EDP7299
文章摘要：
In recent years, intelligent transportation system (ITS) techniques have been widely exploited to enhance the quality of public services. As one of the worldwide leaders in recycling, Taiwan adopts the waste collection and disposal policy named “trash doesn't touch the ground”, which requires the public to deliver garbage directly to the collection points for awaiting garbage collection. This study develops a travel time prediction system based on data clustering for providing real-time information on the arrival time of waste collection vehicle (WCV). The developed system consists of mobile devices (MDs), on-board units (OBUs), a fleet management server (FMS), and a data analysis server (DAS). A travel time prediction model utilizing the adaptive-based clustering technique coupled with a data feature selection procedure is devised and embedded in the DAS. While receiving inquiries from users' MDs and relevant data from WCVs' OBUs through the FMS, the DAS performs the devised model to yield the predicted arrival time of WCV. Our experiment result demonstrates that the proposed prediction model achieves an accuracy rate of 75.0% and outperforms the reference linear regression method and neural network technique, the accuracy rates of which are 14.7% and 27.6%, respectively. The developed system is effective as well as efficient and has gone online.
近年来，智能交通系统（ITS）技术被广泛应用于提高公共服务质量。台湾实行“垃圾不落地”的垃圾收集和处理政策，要求公众将垃圾直接送到垃圾收集点等待垃圾收集。本研究开发了一个基于数据聚类的行程时间预测系统，用于提供垃圾收集车（WCV）到达时间的实时信息。开发的系统由移动设备（MDs）、车载设备（OBU）、车队管理服务器（FMS）和数据分析服务器（DAS）组成。设计了一种基于自适应聚类技术的行程时间预测模型，并将其嵌入DAS中。当通过FMS接收来自用户MDs的查询和来自WCV OBU的相关数据时，DAS执行设计的模型以产生WCV的预计到达时间。实验结果表明，该预测模型的准确率为75.0%，优于参考线性回归方法和神经网络技术，其准确率分别为14.7%和27.6%。开发的系统不仅高效，而且已经上线。
