Jason Meyer

Crowd sourced energy estimation in connected vehicles

Accurately forecasting the energy consumption profile of a vehicle is a key requirement of many growing research areas such as horizon based energy management and eco-routing. However, the energy consumption rate of a vehicle depends on many factors making it very difficult to estimate. Many of these factors such as traffic light timing, traffic congestion and weather, change from day to day and trip to trip. While real time traffic information and traffic light timing schedules can be used to help predict the effect of the first two factors, the impact of weather cannot be as easily predicted based on a weather report. Depending on the topology of the route including other vehicles on the road, the local wind speed relative to a vehicle can differ greatly from a predicted bulk wind speed. The effect of precipitation is also difficult to predict because it depends on the amount falling and the amount accumulated on the road. In this paper it is first shown that energy consumption prediction errors due to un-modeled effects, including most notably weather, exhibit a high amount of trip-to-trip variation and a smaller amount of variation within a trip. Next, it is demonstrated that moderate wind speeds have an observable effect on energy consumption and this effect varies based on the direction of travel and wind direction. This analysis also illustrates the challenges in predicting the effect of wind speed and precipitation on energy consumption based on a weather forecast. Finally, a case is made for future research involving the use of current and recent data from a large population of vehicles to provide a more accurate energy consumption profile by reducing the prediction errors due to un-modeled effects.

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.

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.