Bus Travel Speed Prediction Using Long Short-term Memory Neural Network

被引：5
|
作者：
Jeon, Seung-Bae [1 ]
Jeong, Myeong-Hun [1 ]
Lee, Tae-Young [1 ]
Lee, Jeong-Hwan [1 ]
Cho, Jae-Myoung [2 ]
机构：
[1] Chosun Univ, Dept Civil Engn, 309 Pilmun Daero, Gwangju 61452, South Korea
[2] Songwon Univ, Dept Civil Engn, 73 Songam Ro, Gwangju 61756, South Korea
来源：
SENSORS AND MATERIALS | 2020年 / 32卷 / 12期
基金：
新加坡国家研究基金会;
关键词：
long short-term memory neural network; bus travel speed prediction; digital tachograph; autoregressive integrated moving average; ARIMA; FLOW;
D O I：
10.18494/SAM.2020.3111
中图分类号：
TH7 [仪器、仪表];
学科分类号：
0804 ; 080401 ; 081102 ;
摘要：
Improving the accuracy of public transport information has attracted attention in the development of smart cities. We aim to predict the bus travel speed on road sections using a long short-term memory (LSTM) neural network. We use digital tachograph (DTG) data combined with road link data. Motion sensors in DTG can record vehicle's operation information, such as journey distance, speed, and driving time. The experimental results show that the proposed model based on LSTM performs better than the autoregressive integrated moving average (ARIMA) model. The accuracy was improved by 20% on average.
引用
收藏
页码：4441 / 4447
页数：7
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