The objective of the research described in this dissertation is to improve the travel-time prediction process using machine learning methods for the Advanced Traffic In-formation Systems (ATIS). Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. The increased demand of the traffic flow has motivated the need for development of improved applications and frameworks, which could alleviate the problems arising due to traffic flow, without the need of addition to the roadway infrastructure. In this thesis, the basic building blocks of the travel-time prediction models are discussed, with a review of the significant prior art. The problem of travel-time prediction was addressed by different perspectives in the past. Mainly the data-driven approach and the traffic flow modeling approach are the two main paths adopted viz. a viz. travel-time prediction from the methodology perspective. This dissertation, works towards the im-provement of the data-driven method. The data-driven model, presented in this dissertation, for the travel-time predic-tion on freeways was based on wavelet packet decomposition and support vector regres-sion (WPSVR), which uses the multi-resolution and equivalent frequency distribution ability of the wavelet transform to train the support vector machines. The results are compared against the classical support vector regression (SVR) method. Our results indi-cate that the wavelet reconstructed coefficients when used as an input to the support vec-tor machine for regression (WPSVR) give better performance (with selected wavelets on-ly), when compared against the support vector regression (without wavelet decomposi-tion). The data used in the model is downloaded from California Department of Trans-portation (Caltrans) of District 12 with a detector density of 2.73, experiencing daily peak hours except most weekends. The data was stored for a period of 214 days accumulated over 5 minute intervals over a distance of 9.13 miles. The results indicate an improvement in accuracy when compared against the classical SVR method. The basic criteria for selection of wavelet basis for preprocessing the inputs of support vector machines are also explored to filter the set of wavelet families for the WDSVR model. Finally, a configuration of travel-time prediction on freeways is present-ed with interchangeable prediction methods along with the details of the Matlab applica-tion used to implement the WPSVR algorithm. The initial results are computed over the set of 42 wavelets. To reduce the compu-tational cost involved in transforming the travel-time data into the set of wavelet packets using all possible mother wavelets available, a methodology of filtering the wavelets is devised, which measures the cross-correlation and redundancy properties of consecutive wavelet transformed values of same frequency band. An alternate configuration of travel-time prediction on freeways using the con-cepts of cloud computation is also presented, which has the ability to interchange the pre-diction modules with an alternate method using the same time-series data. Finally, a graphical user interface is described to connect the Matlab environment with the Caltrans data server for online travel-time prediction using both SVR and WPSVR modules and display the errors and plots of predicted values for both methods. The GUI also has the ability to compute forecast of custom travel-time data in the offline mode.

The advancement in computational intelligence and computational power and the explosionof traffic data continues to drive the development and use of Intelligent TransportSystem and smart mobility applications. As one of the fundamental components of IntelligentTransport Systems, traffic flow prediction research has been advancing from theclassical statistical and time-series based techniques to data–driven methods mainly employingdata mining and machine learning algorithms. However, significant number oftraffic flow prediction studies have overlooked the impact of road network topology ontraffic flow. Thus, the main objective of this research is to show that traffic flow predictionproblems are not only affected by temporal trends of flow history, but also by roadnetwork topology by developing prediction methods in the spatio-temporal.In this study, time–series operators and data mining techniques are used by definingfive partially overlapping relative temporal offsets to capture temporal trends in sequencesof non-overlapping history windows defined on stream of historical record of traffic flowdata. To develop prediction models, two sets of modeling approaches based on LinearRegression and Support Vector Machine for Regression are proposed. In the modelingprocess, an orthogonal linear transformation of input data using Principal ComponentAnalysis is employed to avoid any potential problem of multicollinearity and dimensionalitycurse. Moreover, to incorporate the impact of road network topology in thetraffic flow of individual road segments, shortest path network–distance based distancedecay function is used to compute weights of neighboring road segment based on theprinciple of First Law of Geography. Accordingly, (a) Linear Regression on IndividualSensors (LR-IS), (b) Joint Linear Regression on Set of Sensors (JLR), (c) Joint LinearRegression on Set of Sensors with PCA (JLR-PCA) and (d) Spatially Weighted Regressionon Set of Sensors (SWR) models are proposed. To achieve robust non-linear learning,Support Vector Machine for Regression (SVMR) based models are also proposed.Thus, (a) SVMR for Individual Sensors (SVMR-IS), (b) Joint SVMR for Set of Sensors(JSVMR), (c) Joint SVMR for Set of Sensors with PCA (JSVMR-PCA) and (d) SpatiallyWeighted SVMR (SWSVMR) models are proposed. All the models are evaluatedusing the data sets from 2010 IEEE ICDM international contest acquired from TrafficSimulation Framework (TSF) developed based on the NagelSchreckenberg model.Taking the competition’s best solutions as a benchmark, even though different setsof validation data might have been used, based on k–fold cross validation method, withthe exception of SVMR-IS, all the proposed models in this study provide higher predictionaccuracy in terms of RMSE. The models that incorporated all neighboring sensorsdata into the learning process indicate the existence of potential interdependence amonginterconnected roads segments. The spatially weighted model in SVMR (SWSVMR) revealedthat road network topology has clear impact on traffic flow shown by the varyingand improved prediction accuracy of road segments that have more neighbors in a closeproximity. However, the linear regression based models have shown slightly low coefficientof determination indicating to the use of non-linear learning methods. The resultsof this study also imply that the approaches adopted for feature construction in this studyare effective, and the spatial weighting scheme designed is realistic. Hence, road networktopology is an intrinsic characteristic of traffic flow so that prediction models should takeit into consideration.