Johannes Geir Kristinsson

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.

Intelligent Trip Modeling for the Prediction of an Origin–Destination Traveling Speed Profile

Accurate prediction of the traffic information in real time such as flow, density, speed, and travel time has important applications in many areas, including intelligent traffic control systems, optimizing vehicle operations, and the routing selection for individual drivers on the road. This is also a challenging problem due to dynamic changes of traffic states by many uncertain factors along a traveling route. In this paper, we present an Intelligent Trip Modeling System (ITMS) that was developed using machine learning to predict the traveling speed profile for a selected route based on the traffic information available at the trip starting time. The ITMS contains neural networks to predict short-term traffic speed based on the traveling day of the week, the traffic congestion levels at the sensor locations along the route, and the traveling time and distances to reach individual sensor locations. The ITMS was trained and evaluated by using ten months of traffic data provided by the California Freeway Performance Measurement System along a California Interstate I-405 route that is 26 mi long and contains 52 traffic sensors. The ITMS was also evaluated by the traffic data acquired from a 32-mi-long freeway section in the state of Michigan. Experimental results show that the proposed system, i.e., ITMS, has the capability of providing accurate predictions of dynamic traffic changes and traveling speed at the beginning of a trip and can generalize well to prediction of speed profiles on the freeway routes other than the routes the system was trained on.

Distance Until Charge prediction and fuel economy impact for Plug-in Hybrid Vehicles

For a Plug-in Hybrid-Electric Vehicle (PHEV) a close to linear battery depletion profile is near optimal in terms of fuel economy, given that the driven Distance Until Charge (DUC) is known. However the intended driven distance until charge is not always known. We propose a pattern recognition method to predict the DUC based on the key-on time and day observations and the on-board stored data in vehicle. We review the energy management strategy and we show a fuel economy improvement despite prediction errors.

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.

Building Efficient Probability Transition Matrix Using Machine Learning from Big Data for Personalized Route Prediction

Abstract Personalized route prediction is an important technology in many applications related to intelligent vehicles and transportation systems. Current route prediction technologies used in many general navigation systems are, by and large, based on either the shortest or the fastest route selection. Personal traveling route prediction is a very challenging big data problem, as trips getting longer and variations in routes growing. It is particularly challenging for real-time in-vehicle applications, since many embedded processors have limited memory and computational power. In this paper we present a machine learning algorithm for modeling route prediction based on a Markov chain model, and a route prediction algorithm based on a probability transition matrix. We also present two data reduction algorithms, one is developed to map large GPS based trips to a compact link-based standard route representation, and another a machine learning algorithm to significantly reduce the size of a probability transition matrix. The proposed algorithms are evaluated on real-world driving trip data collected in four months, where the data collected in the first three months are used as training and the data in the fourth month are used as testing. Our experiment results show that the proposed personal route prediction system generated more than 91% prediction accuracy in average among the test trips. The data reduction algorithm gave about 8:1 reduction in link-based standard route representation and 23:1 in reducing the size of probability transition matrix.

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.

Evaluation of the Use of Quality Attribute Scenarios in a Plug-In Hybrid Electric Vehicle Controls System - Industrial Case Study

This paper presents a study in using quality attribute scenarios to evaluate and improve the software architecture of a control system that plans and affects energy use on a plug-in hybrid electric vehicle. The study confirmed for us the value of the quality attribute workshop approach in an industrial, automotive controls setting. This experience has helped us to understand how we might improve the effectiveness of this type of architectural design method within our companys technology development environment.

Dynamic prediction of drivers' personal routes through machine learning

Personal route prediction (PRP) has attracted much research interest recently because of its technical challenges and broad applications in intelligent vehicle and transportation systems. Traditional navigation systems generate a route for a given origin and destination based on either shortest or fastest route schemes. In practice, different people may very likely take different routes from the same origin to the same destination. Personal route prediction attempts to predict a drivers route based on the knowledge of drivers preferences. In this paper we present an intelligent personal route prediction system, I_PRP, which is built based upon a knowledge base of personal route preference learned from drivers historical trips. The I_PRP contains an intelligent route prediction algorithm based on the first order Markov chain model to predict a drivers intended route for a given pair of origin and destination, and a dynamic route prediction algorithm that has the capability of predicting drivers new route after the driver departs from the predicted route.

Path-forecasting for HEV optimal energy management (POEM)

This paper studies a control strategy using path-forecasting for Hybrid Electric Vehicle (HEV) optimal energy management (POEM). In the previous work, a receding horizon control (RHC) approach was developed to solve a dynamic programming (DP) formulated optimization problem where preview information of an intended route is utilized to schedule battery state of charge (SoC) usage profile along the route for optimal fuel economy of HEVs. This paper presents our recent work that further developed POEM strategy with refined route segmentation rules and fuel consumption estimation. The work also contains the development of a simulation platform integrating POEM strategy with a production level vehicle control strategy, fuel economy evaluation of POEM under different driving cycles, and the robustness study of the POEM strategy.