Data Mining Algorithms for Traffic Sampling, Estimation and Forecasting
Committee memberObradovic, Zoran
DepartmentComputer and Information Science
Permanent link to this recordhttp://hdl.handle.net/20.500.12613/2720
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AbstractDespite the significant investments over the last few decades to enhance and improve road infrastructure worldwide, the capacity of road networks has not kept pace with the ever increasing growth in demand. As a result, congestion has become endemic to many highways and city streets. As an alternative to costly and sometimes infeasible construction of new roads, transportation departments are increasingly looking at ways to improve traffic flow over the existing infrastructure. The biggest challenge in accomplishing this goal is the ability to sample traffic data, estimate traffic current state, and forecast its future behavior. In this thesis, we first address the problem of traffic sampling where we propose strategies for frugal sensing where we collect a fraction of the observed traffic information to reduce costs while achieving high accuracy. Next we demonstrate how traffic estimation using deterministic traffic models can be improved using proposed data reconstruction techniques. Finally, we propose how mixture of experts algorithm which consists of two regime-specific linear predictors and a decision tree gating function can improve short-term and long-term traffic forecasting. As mobile devices become more pervasive, participatory sensing is becoming an attractive way of collecting large quantities of valuable location-based data. An important participatory sensing application is traffic monitoring, where GPS-enabled smartphones can provide invaluable information about traffic conditions. We propose a strategy for frugal sensing in which the participants send only a fraction of the observed traffic information to reduce costs while achieving high accuracy. The strategy is based on autonomous sensing, in which participants make decisions to send traffic information without guidance from the central server, thus reducing the communication overhead and improving privacy. To provide accurate and computationally efficient estimation of the current traffic, we propose to use a budgeted version of the Gaussian Process model on the server side. The experiments on real-life traffic data sets indicate that the proposed approach can use up to two orders of magnitude less samples than a baseline approach with only a negligible loss in accuracy. The estimation of the state of traffic provides a detailed picture of the conditions of a traffic network based on limited traffic measurements and, as such, plays a key role in intelligent transportation systems. Most often, traffic measurements are aggregated over multiple time steps, and this procedure raises the question of how to best use this information for state estimation. Reconstructing the high-resolution measurements from the aggregated ones and using them to correct the state estimates at every time step are proposed. Several reconstruction techniques from signal processing, including kernel regression and a reconstruction approach based on convex optimization, were considered. Experimental results show that signal reconstruction leads to more accurate traffic state estimation as compared with the standard approach for dealing with aggregated measurements. Accurate traffic speed forecasting can help in trip planning by allowing travelers to avoid congested routes, either by choosing alternative routes or by changing the departure time. An important feature of traffic is that it consists of free flow and congested regimes, which have significantly different properties. Training a single traffic speed predictor for both regimes typically results in suboptimal accuracy. To address this problem, a mixture of experts algorithm which consists of two regime-specific linear predictors and a decision tree gating function was developed. Experimental results showed that mixture of experts approach outperforms several popular benchmark approaches.
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Showing items related by title, author, creator and subject.
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