SPATIO-TEMPORAL DYNAMICS OF SHORT-TERM TRAFFIC

Short-term traffic forecasting and missing data imputation can benefit from the use of neighboring traffic information, in addition to temporal data alone. However, little attention has been given to quantifying the effect of upstream and downstream traffic on the traffic at current location. The knowledge about temporal and spatial propagation of traffic is still limited in the current literature. To fill this gap, this dissertation research focus on revealing the spatio-temporal correlations between neighboring traffic to develop reliable algorithms for short-term traffic forecasting and data imputation based on spatio-temporal dynamics of traffic. In the first part of this dissertation, spatio-temporal relationships of speed series from consecutive segments were studied for different traffic conditions. The analysis results show that traffic speeds of consecutive segments are highly correlated. While downstream traffic tends to replicate the upstream condition under light traffic conditions, it may also affect upstream condition during congestion and build up situations. These effects were statistically quantified and an algorithm for properly choosing the “best” or most correlated neighbor(s), for potential traffic prediction or imputation purposes was proposed. In the second part of the dissertation, a spatio-temporal kriging (ST-Kriging) model that determines the most desirable extent of spatial and temporal traffic data from neighboring locations was developed for short-term traffic forecasting. The new ST-Kriging model outperforms all benchmark models under various traffic conditions. In the final part of the dissertation, a spatio-temporal data imputation approach was proposed and its performance was evaluated under scenarios with different data missing rates. Compared against previous methods, better flexibility and stable imputation accuracy were reported for this new imputation technique

Shape based classification and functional forecast of traffic flow profiles

This dissertation proposes a methodology for traffic flow pattern analysis, its validation, and forecasting. The shape of the daily traffic flows are directly related to the commuter’s traffic behavior which merit analysis based on their shape characteristics. As a departure from the traditional approaches, this research proposed a methodology based on shape for traffic flow analysis. Specifically, Granulometric Size Distributions (GSDs) were used to achieve classification of daily traffic flow patterns. A mathematical morphology method was used that allows the clustering of shapes. The proposed methodology leads to discovery of interesting daily traffic phenomena such as five normal daily traffic shapes beside abnormal shapes representing accidents, congestion behavior, peak time fluctuations, and malfunctioning sensors. To ascertain the significance of shape in traffic analysis, the proposed methodology was validated through a comparative classification analysis of the original data and GSD transformed data using the Back Prorogation Neural Network (BPNN). Results demonstrated that through shape based clustering more appropriate grouping can be accomplished that can result in better estimates of model parameters. Lastly, a functional time series approach was proposed to forecast traffic flow for short and medium-term horizons. It is based on functional principal components decomposition to forecast three different traffic scenarios. Real-time forecast scenarios of partially observed traffic profiles through Penalized Least squares (PLS) technique were also demonstrated. Functional methods outperform the conventional ARIMA model in both short and medium-term forecast horizons. In addition, performance of functional methods in forecasting beyond one hour was also found to be robust and consistent. --Abstract, page iii

A Bayesian dynamic approach to modelling flow through a traffic network.

Hourly traffic flows through complicated motorway junctions form time series with a hierarchical element. Multiregression Dynamic Models provide a Bayesian framework for forecasting the time series while incorporating this hierarchical structure. Calculation of forecasts is computationally inexpensive and the model is designed in order to have readily interpretable parameters wherever possible. Expert intervention is straightforward in this system for periods of unusual activity, whether they were anticipated or not. Fundamental change to the road layout and behaviour can also be incorporated into the model without untenable complication. In this thesis work is centred on applying the methodology to one particular junction, including finding techniques to aid in using the methodology in this application. The theory of Multiregression Dynamic Models is furthered in order to do this- specifically through modelling certain parameter constraints imposed by the nature of the problem and the production of prior covariances for quantities modelled. The model was found to perform competitively in comparison to common other approaches, and the hierarchical structure offered significant advantages when expert intervention was applied

Real-time Traffic Flow Detection and Prediction Algorithm: Data-Driven Analyses on Spatio-Temporal Traffic Dynamics

Traffic flows over time and space. This spatio-temporal dependency of traffic flow should be considered and used to enhance the performance of real-time traffic detection and prediction capabilities. This characteristic has been widely studied and various applications have been developed and enhanced. During the last decade, great attention has been paid to the increases in the number of traffic data sources, the amount of data, and the data-driven analysis methods. There is still room to improve the traffic detection and prediction capabilities through studies on the emerging resources. To this end, this dissertation presents a series of studies on real-time traffic operation for highway facilities focusing on detection and prediction.First, a spatio-temporal traffic data imputation approach was studied to exploit multi-source data. Different types of kriging methods were evaluated to utilize the spatio-temporal characteristic of traffic data with respect to two factors, including missing patterns and use of secondary data. Second, a short-term traffic speed prediction algorithm was proposed that provides accurate prediction results and is scalable for a large road network analysis in real time. The proposed algorithm consists of a data dimension reduction module and a nonparametric multivariate time-series analysis module. Third, a real-time traffic queue detection algorithm was developed based on traffic fundamentals combined with a statistical pattern recognition procedure. This algorithm was designed to detect dynamic queueing conditions in a spatio-temporal domain rather than detect a queue and congestion directly from traffic flow variables. The algorithm was evaluated by using various real congested traffic flow data. Lastly, gray areas in a decision-making process based on quantifiable measures were addressed to cope with uncertainties in modeling outputs. For intersection control type selection, the gray areas were identified and visualized

Development of dynamic recursive models for freeway travel time predictions

Traffic congestion has been a major problem in metropolitan areas, which is caused by either insufficient roadway capacity or unforesceable incidents. In order to promote the efficiency of the existing roadway networks and mitigate the impact of traffic congestion, the development of a sound prediction model for travel times is desirable. A comprehensive literature review about existing prediction models was conducted by investigating the advantages, disadvantages, and limitations of each model. Based on the features and properties of previous models, the base models including exponential smoothing model (ESM), moving average model (MAM), and Kalman filtering model (KFM) are developed to capture stochastic properties of traffic behavior for travel time prediction. By incorporating KFM into ESM and MAM, three dynamic recursive prediction models including dynamic exponential smoothing model (DESM), improved dynamic exponential smoothing model (JDESM), and dynamic moving average model (DMAM) are developed, in which the time-varying weight parameters are optimized based on the most recent observation. Model evaluation has been conducted to analyze prediction accuracy under various traffic conditions (e.g., free-flow condition, recurrent and non-recurrent congested traffic conditions). Results show that the IDESM in general outperforms other models developed in this study in prediction accuracy and stability. In addition, the feature and logic of the IDESM lead to its high transferability and adaptability, which could enable the prediction model to perform well at multiple locations and deal with complicated traffic conditions. Besides the proficient capability, the IDESM is easy to implement in the real world transportation network. Thus, the IDESM is proven an appealing approach for short-time travel time prediction under various traffic conditions. The application scope of the IDESM is identified, while the optimal prediction intervals are also suggested in this study

Design and validation of novel methods for long-term road traffic forecasting

132 p.Road traffic management is a critical aspect for the design and planning of complex urban transport networks for which vehicle flow forecasting is an essential component. As a testimony of its paramount relevance in transport planning and logistics, thousands of scientific research works have covered the traffic forecasting topic during the last 50 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. During the last two decades, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. Even in this convenient context, with abundance of open data to experiment and advanced techniques to exploit them, most predictive models reported in literature aim for shortterm forecasts, and their performance degrades when the prediction horizon is increased. Long-termforecasting strategies are more scarce, and commonly based on the detection and assignment to patterns. These approaches can perform reasonably well unless an unexpected event provokes non predictable changes, or if the allocation to a pattern is inaccurate.The main core of the work in this Thesis has revolved around datadriven traffic forecasting, ultimately pursuing long-term forecasts. This has broadly entailed a deep analysis and understanding of the state of the art, and dealing with incompleteness of data, among other lesser issues. Besides, the second part of this dissertation presents an application outlook of the developed techniques, providing methods and unexpected insights of the local impact of traffic in pollution. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowe