| Summary |
Bai-Li Tsai, 2006.06
Graduate Institute of Transportation Science, Tamkang University
In recent years, Government tries to carry out the development of Advanced Traveler Information System - among the nine service domains of Intelligent Information System. In order to provide accurate information for road users, to stand on the choices of routes and transportation, estimating the path travel time is an important issue. To estimate travel time, vehicle detectors and probe vehicles collecting information (e.g., flow, occupancy and speed, etc.) are being used. For the moment, there is quite few vehicle detectors can still be used. Under the insufficient resource and budget, it is uneasy to set up vehicle detectors widely, otherwise, to add probe vehicles in the short term to make up for the shortage of information gathering.
This study applies the concept of vehicle speed distributes in space of roadway segment and intends to investigate how many probe vehicles are enough to describe or estimate travel time for a roadway segment. The aspect of investigation is a roadway segment or a roadway segment containing intersection, according to the concept of a sample distribution which reflects population characteristics. As a result, probe vehicle can be considered as an instantaneous fixed vehicle detector by using the instantaneous speed and position of probe vehicles and it sets up a speed distribution of samples, from the inside, explores the size of probe vehicles and reflects population to estimate instantaneous travel time. Furthermore, by using the instantaneous sample method and vehicle detector data to test the data fusion, the feasibility of this method will be determined.
After conferring the size of probe vehicle, data collection through real network and establishment of the simulation network can be used when parameters are evaluated. To collect data from vehicle detectors and probe vehicles through simulation, and then carrying out data fusion to estimate travel time. Vehicle detector estimates density by using flow and occupancy rate, accords with OH and Webster model to estimate travel time, and matches up the travel time which probe vehicles drive end of the roadway segment. For this reason, this study contains: (1) Investigate and test the algorithm of probe vehicle size. (2) The comparison and suitable situation of data fusion. (3) Estimate travel time using data fusion, and hope to provide more accurate travel information for road user.
The result of this study exhibits that sizes of the probe vehicle are more than other studies by using the instantaneous distribution of speed. According to different length and flow rate of roadway segment with different probe vehicle size, it distributes about ten to sixty percent, and the average is similar to Tetsuhiro (2005) who brought up that forty percent probe vehicles can collect traffic information nonstop. Besides, the test of data fusion uses instantaneous sampling method and the result exhibits that Weighted Average is better in the one roadway segment case, Artificial Neural Network is better in the two roadway segments case, and data fusion can reduce the travel time errors from each detector has estimated.
The result of data fusion exhibits that Weighted Average is suitable for the road length under 400 meters, probe vehicle rate upon 10 percent, and update in 3 minutes (i.e., real time); Artificial Neural Network is suitable for the road length upon 400 meters, probe vehicle rate under 10 percent, and update in 5 minutes (i.e., comparatively longer time). Finally, advantages and disadvantages of two methods are provided for the related applications. |
