Short-Term Traffic Forecasting Using High-Resolution Traffic Data

This paper develops a data-driven toolkit for traffic forecasting using high-resolution (a.k.a. event-based) traffic data. This is the raw data obtained from fixed sensors in urban roads. Time series of such raw data exhibit heavy fluctuations from one time step to the next (typically on the order of 0.1-1 second). Short-term forecasts (10-30 seconds into the future) of traffic conditions are critical for traffic operations applications (e.g., adaptive signal control). But traffic forecasting tools in the literature deal predominantly with 3-5 minute aggregated data, where the typical signal cycle is on the order of 2 minutes. This renders such forecasts useless at the operations level. To this end, we model the traffic forecasting problem as a matrix completion problem, where the forecasting inputs are mapped to a higher dimensional space using kernels. The formulation allows us to capture both nonlinear dependencies between forecasting inputs and outputs but also allows us to capture dependencies among the inputs. These dependencies correspond to correlations between different locations in the network. We further employ adaptive boosting to enhance the training accuracy and capture historical patterns in the data. The performance of the proposed methods is verified using high-resolution data obtained from a real-world traffic network in Abu Dhabi, UAE. Our experimental results show that the proposed method outperforms other state-of-the-art algorithms

Research on economic planning and operation of electric vehicle charging stations

Appropriately planning and scheduling strategies can improve the enthusiasm of Electric vehicles (EVs), reduce charging losses, and support the power grid system. Thus, this dissertation studies the planning and operating of the EV charging station. First, an EV charging station planning strategy considering the overall social cost is proposed. Then, to reduce the charging cost and guarantee the charging demand, an optimal charging scheduling method is proposed. Additionally, by considering the uncertainty of charging demand, a data-driven intelligent EV charging scheduling algorithm is proposed. Finally, a collaborative optimal routing and scheduling method is proposed

Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies