A Peak Traffic Congestion Prediction Method Based on Bus Driving Time
Road traffic congestion has a large impact on travel. The accurate prediction of traffic congestion has become a hot topic in intelligent transportation systems (ITS). Recently, a variety of traffic congestion prediction methods have been proposed. However, most approaches focus on floating car data...
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MDPI AG
2019-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/7/709 |
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| author | Zhao Huang Jizhe Xia Fan Li Zhen Li Qingquan Li |
| author_facet | Zhao Huang Jizhe Xia Fan Li Zhen Li Qingquan Li |
| author_sort | Zhao Huang |
| collection | DOAJ |
| description | Road traffic congestion has a large impact on travel. The accurate prediction of traffic congestion has become a hot topic in intelligent transportation systems (ITS). Recently, a variety of traffic congestion prediction methods have been proposed. However, most approaches focus on floating car data, and the prediction accuracy is often unstable due to large fluctuations in floating speed. Targeting these challenges, we propose a method of traffic congestion prediction based on bus driving time (TCP-DT) using long short-term memory (LSTM) technology. Firstly, we collected a total of 66,228 bus driving records from 50 buses for 66 working days in Guangzhou, China. Secondly, the actual and standard bus driving times were calculated by processing the buses’ GPS trajectories and bus station data. Congestion time is defined as the interval between actual and standard driving time. Thirdly, congestion time prediction based on LSTM (T-LSTM) was adopted to predict future bus congestion times. Finally, the congestion index and classification (CI-C) model was used to calculate the congestion indices and classify the level of congestion into five categories according to three classification methods. Our experimental results show that the T-LSTM model can effectively predict the congestion time of six road sections at different time periods, and the average mean absolute percentage error (<inline-formula> <math display="inline"> <semantics> <mrow> <mover accent="true"> <mrow> <mi>M</mi> <mi>A</mi> <mi>P</mi> <mi>E</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics> </math> </inline-formula>) and root mean square error (<inline-formula> <math display="inline"> <semantics> <mrow> <mover accent="true"> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics> </math> </inline-formula>) of prediction are 11.25% and 14.91 in the morning peak, and 12.3% and 14.57 in the evening peak, respectively. The TCP-DT method can effectively predict traffic congestion status and provide a driving route with the least congestion time for vehicles. |
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| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T20:56:25Z |
| publishDate | 2019-07-01 |
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| spelling | doaj.art-45e85cfdc02543e6bccb92f88c4d94752022-12-22T04:03:40ZengMDPI AGEntropy1099-43002019-07-0121770910.3390/e21070709e21070709A Peak Traffic Congestion Prediction Method Based on Bus Driving TimeZhao Huang0Jizhe Xia1Fan Li2Zhen Li3Qingquan Li4Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, ChinaShenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, ChinaShenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, ChinaShenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, ChinaShenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, ChinaRoad traffic congestion has a large impact on travel. The accurate prediction of traffic congestion has become a hot topic in intelligent transportation systems (ITS). Recently, a variety of traffic congestion prediction methods have been proposed. However, most approaches focus on floating car data, and the prediction accuracy is often unstable due to large fluctuations in floating speed. Targeting these challenges, we propose a method of traffic congestion prediction based on bus driving time (TCP-DT) using long short-term memory (LSTM) technology. Firstly, we collected a total of 66,228 bus driving records from 50 buses for 66 working days in Guangzhou, China. Secondly, the actual and standard bus driving times were calculated by processing the buses’ GPS trajectories and bus station data. Congestion time is defined as the interval between actual and standard driving time. Thirdly, congestion time prediction based on LSTM (T-LSTM) was adopted to predict future bus congestion times. Finally, the congestion index and classification (CI-C) model was used to calculate the congestion indices and classify the level of congestion into five categories according to three classification methods. Our experimental results show that the T-LSTM model can effectively predict the congestion time of six road sections at different time periods, and the average mean absolute percentage error (<inline-formula> <math display="inline"> <semantics> <mrow> <mover accent="true"> <mrow> <mi>M</mi> <mi>A</mi> <mi>P</mi> <mi>E</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics> </math> </inline-formula>) and root mean square error (<inline-formula> <math display="inline"> <semantics> <mrow> <mover accent="true"> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics> </math> </inline-formula>) of prediction are 11.25% and 14.91 in the morning peak, and 12.3% and 14.57 in the evening peak, respectively. The TCP-DT method can effectively predict traffic congestion status and provide a driving route with the least congestion time for vehicles.https://www.mdpi.com/1099-4300/21/7/709intelligent transportation systemsLSTMroad congestion predictionGPS trajectorydriving time |
| spellingShingle | Zhao Huang Jizhe Xia Fan Li Zhen Li Qingquan Li A Peak Traffic Congestion Prediction Method Based on Bus Driving Time Entropy intelligent transportation systems LSTM road congestion prediction GPS trajectory driving time |
| title | A Peak Traffic Congestion Prediction Method Based on Bus Driving Time |
| title_full | A Peak Traffic Congestion Prediction Method Based on Bus Driving Time |
| title_fullStr | A Peak Traffic Congestion Prediction Method Based on Bus Driving Time |
| title_full_unstemmed | A Peak Traffic Congestion Prediction Method Based on Bus Driving Time |
| title_short | A Peak Traffic Congestion Prediction Method Based on Bus Driving Time |
| title_sort | peak traffic congestion prediction method based on bus driving time |
| topic | intelligent transportation systems LSTM road congestion prediction GPS trajectory driving time |
| url | https://www.mdpi.com/1099-4300/21/7/709 |
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