Abstract
Short-term traffic flow prediction is very important and provides the basic data for traffic management and route guidance. The rules of traffic flow data during different periods in a day are different. Thus, this article proposes a membership degree-based Markov (MM) model and two period division-based Markov (PM and PW) models. The MM model introduces the membership degree to determine the state of traffic flow. The PM and PW models introduce the Fisher optimal division method to divide one day into several periods based on traffic flow data. Then, the period division-based Markov models integrate the Markov (CM) or weighted Markov (WM) model with the MM model to predict traffic volumes during different periods. The impacts of vehicle type on traffic flow prediction are also discussed. The proposed models are verified using the field data. The results show that: (1) the PM and PW models both perform better than the CM, WM, state membership degree-based Markov and weighted state membership degree-based Markov models; (2) the PW model sometimes performs better than the backward propagation (BP) neural network; (3) when traffic flow data are distinguished by vehicle type, the performance of the PM and PW models can be improved. It is suggested to adopt the proposed period division-based Markov models to predict traffic flow with the concern of vehicle type, so that more accurate traffic flow information can be provided for traffic management and route guidance.
Highlights
The accurate short-term traffic flow prediction is an important premise of which Intelligent Transportation System (ITS) provides reliable real-time road-traffic information for travelers and managers, researchers pay more and more attention to short-term traffic flow prediction [1]–[3].For precise traffic flow prediction, the rules of traffic flow data obtained from ITS should be analyzed in detail
The findings indicate that: when distinguishing the data by vehicle type, all the performance indices obtained by utilizing the PM and PW models will significantly decrease at 5% significance level, the mean absolute percentage error (MAPE) values obtained by using the calculated by the Markov (CM) and weighted Markov (WM)
The proposed membership degree-based Markov (MM), PM and PW models are verified using the field data from the four microwave detectors during the four weeks together with the CM, WM, state membership degree-based Markov (SM), WS models, and the PW model is compared with the backward propagation (BP) neural network
Summary
The accurate short-term traffic flow prediction is an important premise of which Intelligent Transportation System (ITS) provides reliable real-time road-traffic information for travelers and managers, researchers pay more and more attention to short-term traffic flow prediction [1]–[3]. Since the process of data conversion of short-term traffic flow is similar to a Markov chain, some researchers discussed the Markov model [7], [8]. Guo et al [33] proposed a weighted hybrid method by combing the neural network, the support vector machine and the random forests method These three models were individually used to predict short-term traffic flow, and the final predicted value was the weighted sum of the predicted values from these three models. The predicted value of traffic volume at time interval t calculated by the Markov (CM) model, which is denoted by xtCM, can be written as xtCM = 0.5 · (a1i + a2i ), pi (t) = max{p1(t), p2(t), p3(t), . Where a1i and a2i are the lower and upper bounds of state i , respectively
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