Tony Phillips

Real time vehicle speed prediction using a Neural Network Traffic Model

Prediction of the traffic information such as flow, density, speed, and travel time is important for traffic control systems, optimizing vehicle operations, and the individual driver. Prediction of future traffic information is a challenging problem due to many dynamic contributing factors. In this paper, various methodologies for traffic information prediction are investigated. We present a speed prediction algorithm, NNTM-SP (Neural Network Traffic Modeling-Speed Prediction) that trained with the historical traffic data and is capable of predicting the vehicle speed profile with the current traffic information. Experimental results show that the proposed algorithm gave good prediction results on real traffic data and the predicted speed profile shows that NNTM-SP correctly predicts the dynamic traffic changes.

Constrained flow control in storage networks: Capacity maximization and balancing

This paper studies the control of distributed storage networks with guarantees of constraints satisfaction and asymptotic stability. We consider two problems: network capacity maximization and network balancing. In the first part of the paper we describe the two problems, highlight their importance in a wide number of engineering applications, and compare them by analyzing the properties of their solutions. In the second part we present algorithms for solving both problems by using a convex one-step model predictive controller (MPC) which guarantees persistent state and flow constraints satisfaction. We present simple conditions which link the network topology, the MPC weights and the asymptotic stability of the closed-loop system. A numerical example illustrates the effectiveness of the proposed approach.

Balancing of battery networks via constrained optimal control

This paper defines two control problems: the capacity maximization and the battery balancing problems. In the first part, we compare the two problems by analyzing properties of their solutions. In the second part, two algorithms are presented for solving the capacity maximization and battery balancing problems respectively. These algorithms are based on constrained optimization techniques. We prove asymptotic convergence of both algorithms and present a numerical example.

Intelligent speed profile prediction on urban traffic networks with machine learning

Accurate prediction of traffic information such as flow, density, speed, and travel time is an important component for traffic control systems and optimizing vehicle operation. Prediction of an individual speed profile on an urban network is a challenging problem because traffic flow on urban routes is frequently interrupted and delayed by traffic lights, stop signs, and intersections. In this paper, we present an Intelligent Speed Profile Prediction on Urban Traffic Network (ISPP_UTN) that can predict a speed profile of a selected urban route with available traffic information at the trip starting time. ISPP_UTN consists of four speed prediction Neural Networks (NNs) that can predict speed in different traffic areas. ISPP_UTN takes inputs from three different categories of traffic information such as the historical individual driving data, geographical information, and traffic pattern data. Experimental results show that the proposed algorithm gave good prediction results on real traffic data and the predicted speed profiles are close to the real recorded speed profiles.

Real time vehicle speed predition using gas-kinetic traffic modeling

Prediction of the traffic information such as flow, density, speed, and travel time is important for traffic control systems, optimizing vehicle operations, and the individual driver. Prediction of future traffic information is a challenging problem due to many dynamic contributing factors. In this paper, macroscopic and kinetic traffic modeling approaches are investigated. We present a speed prediction algorithm, KTM-SP, based on gas-kinetic traffic modeling. Experimental results show that the proposed algorithm gave good prediction results on real traffic data.

Intelligent Trip Modeling on Ramps using ramp classification and knowledge base

Speed profile prediction on ramps is a challenging problem because speed changes on ramps involve complicated lane maneuvering and frequent acceleration or deceleration depending on geometry of the ramp and traffic volumes. Ramps can be categorized into three groups based on their interconnection of freeway: freeway entering ramps, freeway exit ramps, and inter freeway ramps. However, different geographical shapes of ramps within the same category cause different speed profile distributions. To predict speed profile on any ramp types, we proposed an Intelligent Trip Modeling on Ramp (ITMR) System that consists of a ramp classification method based on the decision tree and speed profile prediction neural networks. The proposed ITMR takes inputs from geographical data on the route and also the personal driving pattern extracted from the knowledge base built with the individual historical driving data. Experimental results show that the proposed system learned dynamic ramp speed changes very well to provide accurate prediction results on multiple freeway entering ramps, exit ramps and inter freeway ramps.