Paper Info
Title
Potential Trend Discovery For Highway Drivers On Spatio-Temporal Data
Abstract
Inter-city transportation plays an important role in modern cities, and has accumulated massive spatio-temporal data from various sensors by IoT (Internet of things) technologies. Travel characteristics and future trends of highway behind data are valuable for traffic guidance and personalized service. As a routine domain analysis, trend discovery for highway drivers faces challenges in processing efficiency and predictive accuracy. Insufficient profiles of those drivers are available directly, sensible executive latency on huge data is hard to guarantee, and inadequate features among spatio-temporal correlations hinder the analytical accuracy. In this paper, a travel-characteristic based method is proposed to discover the potential trend of payment identity for highway drivers. Considering time, space, subjective preference and objective property, travel characteristics are modeled on monthly data from highway toll stations, through which predictive errors can be reduced by gradient boosting classification. With real-world data of one Chinese provincial highway network, extensive experiments and case studies show that our method has second-level executive latency with more than 85% F1-score for trend discovery.
Year
DOI
Venue
2021
10.1007/s11276-020-02536-4
WIRELESS NETWORKS
Keywords
DocType
Volume
Spatio&#8208, temporal data, Travel characteristics, Potential trend, Ensemble learning, Highway, Big data
Journal
27
Issue
ISSN
Citations 
5
1022-0038
1
PageRank 
References 
Authors
0.36
0
6
Name
Order
Citations
PageRank
Weilong Ding195.09
Zhe Wang232925.19
Jun Chen310.36
Yanqing Xia410.36
Jianwu Wang521526.72
Zhuofeng Zhao621.06