Applying Support Vector Regression in Interpolating Missing Traffic Data: A Case Study of Fixed Vehicle Detectors

• Main Authors: Kuei-Cheng Ye, 葉蕢誠
• Other Authors: Chee-Chung Tong
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/mxb87v

碩士 === 淡江大學 === 運輸管理學系碩士班 === 104 === 　　Real-time traffic data serve as fundamental necessity for traffic authorities or planners to monitor vehicular flows over the road networks in order to develop management strategies or provide travel information to road users. However, these crucial data can be disrupted and missing due to problems between among collecting, transmitting, or handling processes and therefore poses serious consequence. As such, how to deal with traffic missing data becomes an important issue. Various interpolating methods have been proposed with limited success due to the facts that none is perfect without shortcomings; some inherit theoretical presumptions and conditions, some may impose complex computational processes, some require proprietary toolboxes (black boxes) or special- purpose programs, some need to employ massive historical data for pattern matching. Most of all, suffered with limited validations, none can be declared as the best and applicable to all situations due to lacking of stability and transferability in model parameters. 　　The Support Vector Regression (SVR) derived from the mechanical learning methods of Support Vector Machine (SVM) bears the characteristics of high error tolerance, and can handle non-linear problems through transferring data into hyper linear spaces by Kernel function. Applications of SVR to transportation related problems have been successfully demonstrated in various issues such as traffic volume forecasting, travel time prediction. The purpose of this thesis is to develop SVR based model for traffic data interpolation and to investigate the model performance including accuracy of prediction and stability of parameters. 　　This study chose to explore the missing data problem of fixed-type vehicle detectors most commonly used at present time in Taiwan. The travel speed data is of particular interest to be studied. SVR models were specified with a primary form of “difference” model where an attribute variable was defined as the difference between the referenced upper stream and the downstream speeds; and several extension forms for adjustments of roadway geometry conditions. Models were calibrated at several sites with two different road classes and with different traffic flow conditions. Parameter calibrations were performed by two major stages, including three parameters of kernel function and then two parameters for linear regression. Model validations were performed to include self-validation (at the same site) and cross-validation (across different sites). Results show that prediction accuracy was mostly very good with MAPE less than 10%, while the parameter stability/transferability was less satisfactory. 　　Finally, a comparative study between SVR-based models and Transfer Function-based models was implemented. The result showed that both models could generate high accurate predictions with relative quick operations using generally accessible computer software respectively. However, transfer function technique seemed to be with higher parameter stability.