Dealing with latency effects in travel time prediction on motorways
Real-time traffic information is now a crucial part of operating a road network. The quality, accuracy and reliability of such information is critical to the road operators and users. Real-time travel time prediction methods using Automatic Number Plate Recognition cameras or Bluetooth/Wi-Fi readers...
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doaj-09ecb709e1104579a45fd51adefd11ca2021-03-18T04:43:07ZengElsevierTransportation Engineering2666-691X2020-12-012100009Dealing with latency effects in travel time prediction on motorwaysDavid Laoide-Kemp0Margaret O'Mahony1Trinity Centre for Transport Research, Department of Civil, Structural & Environmental, Engineering, Trinity College Dublin, Dublin 2, Ireland; Transport Infrastructure Ireland, Parkgate Business Centre, Parkgate St, Dublin 8, IrelandTrinity Centre for Transport Research, Department of Civil, Structural & Environmental, Engineering, Trinity College Dublin, Dublin 2, Ireland; Corresponding author.Real-time traffic information is now a crucial part of operating a road network. The quality, accuracy and reliability of such information is critical to the road operators and users. Real-time travel time prediction methods using Automatic Number Plate Recognition cameras or Bluetooth/Wi-Fi readers that use matching algorithms to generate travel times in real-time can be vulnerable to an inherent latency issue. Measured travel times are based on vehicles that have already completed the journey and may not be representative for users about to commence that same journey. The aim of this research was to identify the latency in travel time prediction, quantify its effect, and develop a model to remove it. Datasets for the M50 motorway in Dublin, Ireland, were used to conduct the analysis. The results show that real-time travel times can be more accurately predicted when combined with historical travel time information. The approach was found to be valid and achievable and the developed tool can predict and inform both road operators and users during regular periods of congestion. The project also identified other data sources, such as real-time Automated Incident Detection (AID) loop data, incident and weather data, that can further enhance the predicted travel time calculation.http://www.sciencedirect.com/science/article/pii/S2666691X20300105TrafficReal-timeTravel timeLatencyMotorway |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
David Laoide-Kemp Margaret O'Mahony |
spellingShingle |
David Laoide-Kemp Margaret O'Mahony Dealing with latency effects in travel time prediction on motorways Transportation Engineering Traffic Real-time Travel time Latency Motorway |
author_facet |
David Laoide-Kemp Margaret O'Mahony |
author_sort |
David Laoide-Kemp |
title |
Dealing with latency effects in travel time prediction on motorways |
title_short |
Dealing with latency effects in travel time prediction on motorways |
title_full |
Dealing with latency effects in travel time prediction on motorways |
title_fullStr |
Dealing with latency effects in travel time prediction on motorways |
title_full_unstemmed |
Dealing with latency effects in travel time prediction on motorways |
title_sort |
dealing with latency effects in travel time prediction on motorways |
publisher |
Elsevier |
series |
Transportation Engineering |
issn |
2666-691X |
publishDate |
2020-12-01 |
description |
Real-time traffic information is now a crucial part of operating a road network. The quality, accuracy and reliability of such information is critical to the road operators and users. Real-time travel time prediction methods using Automatic Number Plate Recognition cameras or Bluetooth/Wi-Fi readers that use matching algorithms to generate travel times in real-time can be vulnerable to an inherent latency issue. Measured travel times are based on vehicles that have already completed the journey and may not be representative for users about to commence that same journey. The aim of this research was to identify the latency in travel time prediction, quantify its effect, and develop a model to remove it. Datasets for the M50 motorway in Dublin, Ireland, were used to conduct the analysis. The results show that real-time travel times can be more accurately predicted when combined with historical travel time information. The approach was found to be valid and achievable and the developed tool can predict and inform both road operators and users during regular periods of congestion. The project also identified other data sources, such as real-time Automated Incident Detection (AID) loop data, incident and weather data, that can further enhance the predicted travel time calculation. |
topic |
Traffic Real-time Travel time Latency Motorway |
url |
http://www.sciencedirect.com/science/article/pii/S2666691X20300105 |
work_keys_str_mv |
AT davidlaoidekemp dealingwithlatencyeffectsintraveltimepredictiononmotorways AT margaretomahony dealingwithlatencyeffectsintraveltimepredictiononmotorways |
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