Identifying abnormal traffic congestion on non-signalised urban roads using journey time estimation
Identifying abnormal traffic congestion on non-signalised urban roads using journey time estimation
This paper describes a technique for estimating vehicle journey times on non-signalised roads using 250-ms digital loop-occupancy data produced by single inductive loop detectors. The technique was assessed to see whether abnormal periods of traffic congestion (caused by accidents and special events) could be identified using the journey time estimates produced along a key urban corridor in the city of Southampton. The technique used a neural network approach to provide historical journey time estimates every 30-seconds based on the average loop-occupancy time per vehicle (ALOTPV) data collected from the detectors during the previous 30-second period.
Results showed that using the output from 8 detectors over 1149m, journey time estimates with a mean absolute percentage deviation from the mean measured speed (MAPD) of 15% were returned. These were achieved using a neural network trained on 7 days of morning peak period data.
The journey time estimates produced were presented to the control room operator in the form of a moving graph, updating every 30-seconds. Results showed that the journey time
estimates identified 73% of the logged incidents on the test network during the analysis period.
12pp
Cherrett, Tom
e5929951-e97c-4720-96a8-3e586f2d5f95
Waterson, Ben
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Morris, Ray
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McDonald, Mike
cd5b31ba-276b-41a5-879c-82bf6014db9f
October 2002
Cherrett, Tom
e5929951-e97c-4720-96a8-3e586f2d5f95
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Morris, Ray
76babb76-e5ba-4c65-81b5-498092956cdb
McDonald, Mike
cd5b31ba-276b-41a5-879c-82bf6014db9f
Cherrett, Tom, Waterson, Ben, Morris, Ray and McDonald, Mike
(2002)
Identifying abnormal traffic congestion on non-signalised urban roads using journey time estimation.
In Proceedings of the 9th World Congress on Intelligent Transport Systems and Services.
ITS International.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
This paper describes a technique for estimating vehicle journey times on non-signalised roads using 250-ms digital loop-occupancy data produced by single inductive loop detectors. The technique was assessed to see whether abnormal periods of traffic congestion (caused by accidents and special events) could be identified using the journey time estimates produced along a key urban corridor in the city of Southampton. The technique used a neural network approach to provide historical journey time estimates every 30-seconds based on the average loop-occupancy time per vehicle (ALOTPV) data collected from the detectors during the previous 30-second period.
Results showed that using the output from 8 detectors over 1149m, journey time estimates with a mean absolute percentage deviation from the mean measured speed (MAPD) of 15% were returned. These were achieved using a neural network trained on 7 days of morning peak period data.
The journey time estimates produced were presented to the control room operator in the form of a moving graph, updating every 30-seconds. Results showed that the journey time
estimates identified 73% of the logged incidents on the test network during the analysis period.
Text
2002_5-Author_JTimes_Chicago.pdf
- Accepted Manuscript
More information
Published date: October 2002
Venue - Dates:
Proceedings of the 9th World Congress on Intelligent Transport Systems, Chicago, USA, 2002-10-14 - 2002-10-17
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Local EPrints ID: 53781
URI: http://eprints.soton.ac.uk/id/eprint/53781
PURE UUID: ef907e0b-b285-418e-8025-a3d5d7df9d30
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Date deposited: 25 Jul 2008
Last modified: 16 Mar 2024 02:59
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Author:
Ray Morris
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