Research And Application Of Data Mining On Expressway ETC System

In recent years, with rapid development of economic society, expressway constructionin China has made remarkable achievements. As an important part of road transportationsystem，expressway is facing with contradiction between the rapid growth of traffic demandand the relatively lagging of service capability. It has become an important issue forexpressway management operations to improve traffic efficiency and alleviate traffic jameffectively, as well as solve congestion problems in toll station region. Application andgeneralization of electronic toll collection (ETC) system is an important and effectivemeasure to solve this problem. With generalization of ETC system throughout the country,expressway administrators have accumulated large amounts of raw tolling data, these detailtraffic data contain plenty of internal relation and implicit information. How to obtain validinformation from the massive data and improve management and decision level are the keytechnical problems to be solved.This paper focuses on how to effectively integrate raw ETC tolling data, utilizes datamining techniques to extract and express traffic behavior patterns and spatial-temporaltrends of traffic volume, conducts driving behavior prediction and unusual detection, andimplements traffic volume prediction, thus provides theoretical foundation and decisionsupport for expressway operations. Surrounding the problem, several aspects work havebeen studied: ETC vehicle route prediction and unusual detection, which identifies vehicledriving pattern, accurately predicts future driving route and detects abnormal route based onhistorical ETC data; Multi-dimensional prediction of ETC traffic volume, which achievesmultidimensional statistical analysis, formulates prediction model for multi-dimensionaltraffic volume, proposes a method for multi-dimensional prediction of expressway trafficvolume based on Online Analytical Mining (OLAM); Combinational ETC traffic volumeprediction model, which proposes an combination model based on single prediction models,takes full advantages of each single model to further improve accuracy and reliability ofprediction. The main research results are as follows: 1. method of vehicle route prediction and abnormal path detection are proposed inthis paper based on hybrid Markov model. Against the shortcomings of low accuracy andcoverage rate of basic Markov route prediction model, this paper introduces a new Markovmodel (a hybrid Markov route prediction model) and provides a method for classifying ETCvehicle route sequences using EM iterative clustering algorithm so that vehicles in the sameclass have the same or similar driving behavior; it also builds independent model for eachclass of vehicles to describe its driving behavior. Finally, the paper predicts the futuredriving route using historical data.2. This paper constructs a model using ETC tolling data to implementmulti-dimensional prediction of expressway traffic volume based on OLAM. Time, spaceand other dimension information are selected to formulate snowflake schema of ETC dataand get multidimensional statistics of traffic volume.3. This paper builds seasonal ARIMA(p, d, q)(P, D, Q)s model with its abnormalvalue corrected. First we selects multidimensional statistical results as sequence data sample,then conducts smoothing, model identification, outlier test, parameter estimation, modeldiagnostics and other steps on data samples respectively, thus establishes optimal seasonalARIMA(p, d, q)(P, D, Q)s model with its abnormal value corrected. Finally,multidimensional prediction of traffic volume is realized by using this predicting model, andresults show that its prediction accuracy is better than original model.4. On the basis of seasonal ARIMA model, BP neural network and support vectorregression (SVR), an optimal linear combination prediction model is proposed. This paperemploys ETC monthly traffic volume as training samples to build these three singleprediction model; then takes minimizing the sum of squared errors as objective function anduses results of single prediction model to build optimal model of calculating combinationweight coefficients, realizes the prediction of monthly traffic volume according to results ofweight coefficients calculation; Finally verifies that combination prediction model is betterthan signal prediction model by establishing evaluation index system.