Performance monitoring is an issue of growing concern both nationally and in Washington State. Travel-times and speeds are a key measure in performance. In this project, the authors use vehicles as probes and develop a framework for modeling the time series that arise from sampling transit vehicle locations as a function of time. These samples of vehicles' location are obtained from the King County Metro Automatic Vehicle Location (AVL) system. An optimal filter method is developed that estimates speed as a function of space and time. In this work, an optimal solution for the state vector, containing the variables' speed and position, is possible at each step using the Kalman filter result. This type of filter solution requires the creation of a model for the process; in this case, a relationship between location and time for the vehicles and the creation of a measurement model to account for measurement errors. The use of such formalism depends upon the assumption that the deviations of the actual system from the idealized model are normally distributed. The model was applied against data from both freeways and arterials to test this assumption. In most ranges of travel, the resulting probability of distribution membership is on the order of 0.9, indicating that the assumption of normally distributed errors is indeed a good one.
Irregularly Sampled Transit Vehicles Used as a Probe Vehicle Traffic Sensor
1999
36 pages
Report
No indication
English
Irregularly Sampled Transit Vehicles Used as Traffic Sensors
Transportation Research Record | 2000
|Irregularly Sampled Transit Vehicles Used as Traffic Sensors
Online Contents | 2000
|Transit Vehicles as Traffic Probe Sensors
Transportation Research Record | 2002
|Transit vehicles as traffic probe sensors
IEEE | 2001
|Transit Vehicles as Traffic Probe Sensors
Online Contents | 2002
|