Trend Drift Discovery for Individual Highway Drivers through Ensemble Learning
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Ding, Weilong
Wang, Zhe
Wang, Jianwu
Han, Yanbo
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Abstract
Inter-city transportation plays an important role in
modern smart cities, and has accumulated massive
spatio-temporal data from various sensors in IoT
(Internet of things). Current travel characteristics and
future trends of highway traffic are valuable for traffic
guidance and personalized service. As a routine
domain analysis, trend drift discovery for highway
drivers faces challenges in processing efficiency and
predictive accuracy. Sensitive privacy of business data
has to be considered, executive latency on huge data is
hard to guarantee, and correlation among spatiotemporal characteristics cannot be fully employed. In
this paper, a travel-characteristic based method is
proposed to discover the potential drift of payment
identity for individual highway drivers. Considering
time, space, subjective preference and objective
property, monthly travel characteristics are modeled
on toll data from highway toll stations, and predictive
error for those trends can be reduced dramatically
through gradient boosting classification technology.
With real-world data of one Chinese provincial
highway, extensive experiments show that our method
has second-level in executive latency with more than
85% F1-score for predictive accuracy.