Travel Speed Prediction Using Fuzzy Reasoning
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
A knowledge based real-time travel time prediction system for urban network
Expert Systems with Applications: An International Journal
Automatic congestion detection and visualization using networked GPS unit data
Proceedings of the 47th Annual Southeast Regional Conference
Development of an Effective Travel Time Prediction Method Using Modified Moving Average Approach
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
New travel time prediction algorithms for intelligent transportation systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Modified K-means clustering for travel time prediction based on historical traffic data
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Travel time prediction for dynamic routing using ant based control
Winter Simulation Conference
Vehicle routing based on traffic cost at intersection
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
Flow reconstruction for data-driven traffic animation
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
| Hi-index | 0.00 |
We present a method to predict the time that will be needed to traverse a given section of a freeway when the departure is at a given time in the future. The prediction is done on the basis of the current traffic situation in combination with historical data. We argue that, for our purposes, the current traffic situation of a section of a freeway is well summarized by the current status travel time. This is the travel time that would result if one were to depart immediately and no significant changes in traffic would occur. This current status travel time can be estimated from single- or double-loop detectors, video data, probe vehicles, or any other means. Our prediction method arises from the empirical observation that there exists a linear relationship between any future travel time and the current status travel time. The slope and intercept of this relationship may change subject to the time of day and the time until departure, but linearity persists. This observation leads to a prediction scheme by means of linear regression with time-varying coefficients.