Automatic Vehicle Identification Technology-Based Freeway Incident Detection

2000 ◽  
Vol 1727 (1) ◽  
pp. 142-153 ◽  
Author(s):  
Bruce Hellinga ◽  
Geoff Knapp
Keyword(s):  
Travel Time ◽  
Simulated Data ◽  
Incident Detection ◽  
Time Data ◽  
Vehicle Identification ◽  
Loop Detector ◽  
Travel Time Data ◽  
Recent Emergence ◽  
Automatic Vehicle Identification ◽  
Toll Collection

The recent emergence of automatic vehicle identification technology (AVI) for use in electronic toll collection has provided an opportunity to develop automatic incident detection (AID) methods that rely on individual vehicle travel time data instead of loop detector data. The performances of three AVI-based AID algorithms are examined. Travel time data for testing of the algorithms were obtained by simulating a 12-km section of the collector facility of Highway 401 in Toronto, Ontario, Canada. The performances of the three AVI-based AID algorithms are compared with the performance of a leading loop detector–based algorithm, which was independently tested with similar simulated data. The AID performance results indicate that AVI-based AID algorithms can provide incident detection performance similar to that of existing loop detector-based AID methods.

Vehicle Re-Identification and Travel Time Measurement in Real-Time on Freeways Using Existing Loop Detector Infrastructure

1998 ◽  
Vol 1643 (1) ◽  
pp. 181-191 ◽  
Author(s):  
Benjamin Coifman
Keyword(s):  
Travel Time ◽  
Time Measurement ◽  
Identification Algorithm ◽  
Time Data ◽  
Sequence Detection ◽  
Arrival Times ◽  
Loop Detector ◽  
Travel Time Data ◽  
Upstream Station ◽  
The Difference

A new vehicle re-identification algorithm for two consecutive detector stations on a freeway, whereby a vehicle measurement made at the downstream detector station is matched with the vehicle’s corresponding measurement at the upstream station, is presented in this paper. The method is illustrated using effective vehicle length measured at dual-loop speed traps, but it is transferable to other detectors capable of extracting a vehicle signature (such as video image processing). This approach is significant because no one has attempted to use the existing detector infrastructure to match vehicle measurements between detector stations. The algorithm should improve freeway surveillance via travel time measurement, which is simply the difference between the known arrival times at the two stations for a matched vehicle. The re-identification algorithm is tolerant to noise; instead of finding the ‘best match’ for each vehicle, it finds all possible matches and then looks for sequences of vehicles from the possible matches. Even with noisy loop detector data, the sequence detection eliminates most of the possible-but-incorrect matches while the true matches remain. The new methodology will be used to examine the applications and benefits of travel-time data on real-world traffic, without the expensive costs of installing new detectors. Ordinarily, a travel-time measurement system would have to be fully deployed before the benefits can be quantified.

Depth solution from borehole and travel time data using three-variable hypersurface splines

2001 ◽  
Vol 46 (3) ◽  
pp. 201-211 ◽  
Author(s):  
P.F. Xu ◽  
Z.W. Yu ◽  
H.Q. Tan ◽  
J.X. Ji
Keyword(s):  
Travel Time ◽  
Time Data ◽  
Travel Time Data

Crustal structure in the Transvaal*

1956 ◽  
Vol 46 (4) ◽  
pp. 293-316
Author(s):  
P. G. Gane ◽  
A. R. Atkins ◽  
J. P. F. Sellschop ◽  
P. Seligman
Keyword(s):  
Travel Time ◽  
Crustal Structure ◽  
Time Data ◽  
Lower Velocity ◽  
Travel Time Data ◽  
The Mean

abstract Travel-time data are given at 25 km. intervals between 50 and 500 km. for traverses west, south, east, and north of Johannesburg. These derive from numerous seismograms of Witwatersrand earth tremors taken by means of a triggering technique. The only phases considered to be consistent are those mentioned below, and few signs of a change of velocity with depth were discovered. There were no great differences in the results for the various directions, and the mean results were: P 1 = + 0.24 + Δ / 6.18 sec . S 1 = + 0.37 + Δ / 3.66 sec . P n = + 7.61 + Δ / 8.27 sec . S n = + 11.4 + Δ / 4.73 sec . which give crustal depths of 35.1 and 33.3 km. from P and S data respectively. These depths include about 1.3 km. of superficial material of lower velocity.

Study of teleseismic P I — travel-time data

1970 ◽  
Vol 4 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Jack F. Evernden ◽  
Don M. Clark
Keyword(s):  
Travel Time ◽  
Time Data ◽  
Travel Time Data
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