DocumentCode :
735504
Title :
Investigation and prediction of traffic flow in holidays in Zhejiang section of Shenhai freeway
Author :
Hongliang Dai ; Qinglin Liu ; Fujian Wang ; Chengyu Gong
Author_Institution :
Intell. Transp. Syst., Zhejiang Sci. Res. Inst. of Transp., Hangzhou, China
fYear :
2015
fDate :
25-28 June 2015
Firstpage :
195
Lastpage :
201
Abstract :
This paper investigates the traffic data in holidays within Zhejiang province in Shenhai freeway from the spatial and temporal aspects. The results shows that Tangxia monitoring site has a much higher flow than other sites which indicates a heavy traffic jam in holidays. A further Investigation of traffic flow in Tangxia site shows a common higher traffic flow in the north direction compared with that in the south direction. The temporal distribution of traffic flow in each holiday has its own characteristics which are closely related to the characteristics of the holidays. The hourly distribution of traffic flow in each day has the similar trend with high value at daytime and lower flow at night. The artificial neural networks (ANN) model was used to predict the traffic flow in 2014 which was trained with data in the same site and holiday in 2013. The multi-layer feed-forward perceptron networks (MLP) with three-layer structure was applied to predict the traffic flow in the next 5 minutes with the past 25 minutes flow data. An example of Yandang monitoring site shows a pretty well prediction results with mean absolute relative error (MARE) and max absolute relative error (MAXARE) being 0.0214 and 0.1244, respectively. The investigation and prediction of traffic flow can provide a theoretical basis for the transportation administration to make decisions.
Keywords :
error analysis; monitoring; multilayer perceptrons; traffic information systems; transportation; ANN model; MARE; MAXARE; MLP; Shenhai freeway; Tangxia monitoring site; Zhejiang section; artificial neural networks; heavy traffic jam; holidays; max absolute relative error; mean absolute relative error; multilayer feedforward perceptron networks; traffic data; traffic flow; transportation administration; Adaptation models; Artificial neural networks; Monitoring; Neurons; Predictive models; Traffic control; Transportation; Shenhai freeway; articial neural networks; prediction; spatial and temporal distribution; traffic flow;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Transportation Information and Safety (ICTIS), 2015 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4799-8693-4
Type :
conf
DOI :
10.1109/ICTIS.2015.7232168
Filename :
7232168
Link To Document :
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