Over the past decade, the popularity of regional bus services has grown in large North American cities owing to more people living in suburban areas and commuting to the Central Business District to work every day. Estimating journey time for regional buses is challenging because of the low frequencies and long commuting distances that typically characterize such services. This research project developed a mathematical model to estimate regional bus travel time using artificial neural networks (ANN). ANN outperformed other forecasting methods, namely historical average and linear regression, by an average of 35 and 26 seconds respectively. The ANN results showed, however, overestimation by 40% to 60%, which can lead to travellers missing the bus. An operational strategy is integrated into the model to minimize stakeholders’ costs when the model’s forecast time is later than the scheduled bus departure time. This operational strategy should be varied as the commuting distance decreases.

Over the past decade, the popularity of regional bus services has grown in large North American cities owing to more people living in suburban areas and commuting to the Central Business District to work every day. Estimating journey time for regional buses is challenging because of the low frequencies and long commuting distances that typically characterize such services. This research project developed a mathematical model to estimate regional bus travel time using artificial neural networks (ANN). ANN outperformed other forecasting methods, namely historical average and linear regression, by an average of 35 and 26 seconds respectively. The ANN results showed, however, overestimation by 40% to 60%, which can lead to travellers missing the bus. An operational strategy is integrated into the model to minimize stakeholders’ costs when the model’s forecast time is later than the scheduled bus departure time. This operational strategy should be varied as the commuting distance decreases.

In this dissertation, new algorithms that address three traffic problems of major importance are developed. First automatic identification and prediction algorithms are developed to identify and predict the occurrence of traffic congestion. The identification algorithms concoct a model to identify speed thresholds by exploiting historical spatiotemporal speed matrices. We employ the speed model to define a cutoff speed separating free-flow from congested traffic. We further enhance our algorithm by utilizing weather and visibility data. To our knowledge, we are the first to include weather and visibility variables in formulating an automatic congestion identification model. We also approach the congestion prediction problem by adopting an algorithm which employs Adaptive Boosting machine learning classifiers again something novel that has not been done previously. The algorithm is promising where it resulted in a true positive rate slightly higher than 0.99 and false positive rate less than 0.001. We next address the issue of travel time modeling. We propose algorithms to model travel time using various machine learning and statistical learning techniques. We obtain travel time models by employing the historical spatiotemporal speed matrices in conjunction with our algorithms. The algorithms yield pertinent information regarding travel time reliability and prediction of travel times. Our proposed algorithms give better predictions compared to the state of practice algorithms. Finally we consider driver safety at signalized intersections and uncontrolled intersections in a connected vehicles environment. For signalized intersections, we exploit datasets collected from four controlled experiments to model the stop-run behavior of the driver at the onset of the yellow indicator for various roadway surface conditions and multiple vehicle types. We further propose a new variable (predictor) related to driver aggressiveness which we estimate by monitoring how drivers respond to yellow indications. The performance of the stop-run models shows improvements after adding the new aggressiveness predictor. The proposed models are practical and easy to implement in advanced driver assistance systems. For uncontrolled intersections, we present a game theory based algorithm that models the intersection as a chicken game to solve the conflicts between vehicles crossing the intersection. The simulation results show a 49% saving in travel time on average relative to a stop control when the vehicles obey the Nash equilibrium of the game. / Ph. D.

This thesis focuses on two important problems related to DDDAS: interest management (data distribution) and prediction models. In order to reduce communication overhead, we propose a new interest management mechanism for mobile peer-to-peer systems. This approach involves dividing the entire space into cells and using an efficient sorting algorithm to sort the regions in each cell. A mobile landmarking scheme is introduced to implement this sort-based scheme in mobile peer-to-peer systems. The design does not require a centralized server, but rather, every peer can become a mobile landmark node to take a server-like role to sort and match the regions. Experimental results show that the scheme has better computational efficiency for both static and dynamic matching. In order to improve communication efficiency, we present a travel time prediction model based on boosting, an important machine learning technique, and combine boosting and neural network models to increase prediction accuracy. We also explore the relationship between the accuracy of travel time prediction and the frequency of traffic data collection with the long term goal of minimizing bandwidth consumption. Several different sets of experiments are used to evaluate the effectiveness of this model. The results show that the boosting neural network model outperforms other predictors.

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.

Travel-time information is essential in Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of the spatiotemporal evolution of roadway traffic state and travel time. From the perspective of travelers, such information can result in better traveler route choice and departure time decisions. From the transportation agency perspective, such data provide enhanced information with which to better manage and control the transportation system to reduce congestion, enhance safety, and reduce the carbon footprint of the transportation system. The objective of the research presented in this dissertation is to develop a framework that includes three major categories of methodologies to predict the spatiotemporal evolution of the traffic state. The proposed methodologies include macroscopic traffic modeling, computer vision and recursive probabilistic algorithms. Each developed method attempts to predict traffic state, including roadway travel times, for different prediction horizons. In total, the developed multi-tool framework produces traffic state prediction algorithms ranging from short – (0~5 minutes) to medium-term (1~4 hours) considering departure times up to an hour into the future. The dissertation first develops a particle filter approach for use in short-term traffic state prediction. The flow continuity equation is combined with the Van Aerde fundamental diagram to derive a time series model that can accurately describe the spatiotemporal evolution of traffic state. The developed model is applied within a particle filter approach to provide multi-step traffic state prediction. The testing of the algorithm on a simulated section of I-66 demonstrates that the proposed algorithm can accurately predict the propagation of shockwaves up to five minutes into the future. The developed algorithm is further improved by incorporating on- and off-ramp effects and more realistic boundary conditions. Furthermore, the case study demonstrates that the improved algorithm produces a 50 percent reduction in the prediction error compared to the classic LWR density formulation. Considering the fact that the prediction accuracy deteriorates significantly for longer prediction horizons, historical data are integrated and considered in the measurement update in the developed particle filter approach to extend the prediction horizon up to half an hour into the future. The dissertation then develops a travel time prediction framework using pattern recognition techniques to match historical data with real-time traffic conditions. The Euclidean distance is initially used as the measure of similarity between current and historical traffic patterns. This method is further improved using a dynamic template matching technique developed as part of this research effort. Unlike previous approaches, which use fixed template sizes, the proposed method uses a dynamic template size that is updated each time interval based on the spatiotemporal shape of the congestion upstream of a bottleneck. In addition, the computational cost is reduced using a Fast Fourier Transform instead of a Euclidean distance measure. Subsequently, the historical candidates that are similar to the current conditions are used to predict the experienced travel times. Test results demonstrate that the proposed dynamic template matching method produces significantly better and more stable prediction results for prediction horizons up to 30 minutes into the future for a two hour trip (prediction horizon of two and a half hours) compared to other state-of-the-practice and state-of-the-art methods. Finally, the dissertation develops recursive probabilistic approaches including particle filtering and agent-based modeling methods to predict travel times further into the future. Given the challenges in defining the particle filter time update process, the proposed particle filtering algorithm selects particles from a historical dataset and propagates particles using data trends of past experiences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem in the particle filtering process. INRIX probe data along I-64 and I-264 from Richmond to Virginia Beach are used to test the proposed algorithm. The results demonstrate that the particle filtering approach produces less than a 10 percent prediction error for trip departures up to one hour into the future for a two hour trip. Furthermore, the dissertation develops an agent-based modeling approach to predict travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision making system, which predicts the travel time for each time interval according to past experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent's recommendation (predicted travel time with associated weight) provides a macroscopic level of output – predicted travel time distribution. The case study demonstrated that the agent-based model produces less than a 9 percent prediction error for prediction horizons up to one hour into the future. / Ph. D.

An iterative travel time forecasting scheme, named the Advanced Multilane Prediction based Real-time Fastest Path (AMPRFP) algorithm, is presented in this dissertation. This scheme is derived from the conventional kernel estimator based prediction model by the association of real-time nonlinear impacts that caused by neighboring arcs’ traffic patterns with the historical traffic behaviors. The AMPRFP algorithm is evaluated by prediction of the travel time of congested arcs in the urban area of Jacksonville City. Experiment results illustrate that the proposed scheme is able to significantly reduce both the relative mean error (RME) and the root-mean-squared error (RMSE) of the predicted travel time. To obtain high quality real-time traffic information, which is essential to the performance of the AMPRFP algorithm, a data clean scheme enhanced empirical learning (DCSEEL) algorithm is also introduced. This novel method investigates the correlation between distance and direction in the geometrical map, which is not considered in existing fingerprint localization methods. Specifically, empirical learning methods are applied to minimize the error that exists in the estimated distance. A direction filter is developed to clean joints that have negative influence to the localization accuracy. Synthetic experiments in urban, suburban and rural environments are designed to evaluate the performance of DCSEEL algorithm in determining the cellular probe’s position. The results show that the cellular probe’s localization accuracy can be notably improved by the DCSEEL algorithm. Additionally, a new fast correlation technique for overcoming the time efficiency problem of the existing correlation algorithm based floating car data (FCD) technique is developed. The matching process is transformed into a 1-dimensional (1-D) curve matching problem and the Fast Normalized Cross-Correlation (FNCC) algorithm is introduced to supersede the Pearson product Moment Correlation Co-efficient (PMCC) algorithm in order to achieve the real-time requirement of the FCD method. The fast correlation technique shows a significant improvement in reducing the computational cost without affecting the accuracy of the matching process.