A Machine Learning Method for Predicting Traffic Signal Timing from Probe Vehicle Data Article Swipe
Juliette Ugirumurera
,
Joseph Severino
,
Erik A. Bensen
,
Qichao Wang
,
Jane Macfarlane
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.02370
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.02370
Traffic signals play an important role in transportation by enabling traffic flow management, and ensuring safety at intersections. In addition, knowing the traffic signal phase and timing data can allow optimal vehicle routing for time and energy efficiency, eco-driving, and the accurate simulation of signalized road networks. In this paper, we present a machine learning (ML) method for estimating traffic signal timing information from vehicle probe data. To the authors best knowledge, very few works have presented ML techniques for determining traffic signal timing parameters from vehicle probe data. In this work, we develop an Extreme Gradient Boosting (XGBoost) model to estimate signal cycle lengths and a neural network model to determine the corresponding red times per phase from probe data. The green times are then be derived from the cycle length and red times. Our results show an error of less than 0.56 sec for cycle length, and red times predictions within 7.2 sec error on average.
Related Topics
Concepts
SIGNAL (programming language)
Computer science
Artificial neural network
Real-time computing
Traffic flow (computer networking)
Traffic signal
Gradient boosting
Signal timing
Energy (signal processing)
Floating car data
Simulation
Artificial intelligence
Engineering
Statistics
Transport engineering
Traffic congestion
Mathematics
Computer network
Random forest
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.02370
- https://arxiv.org/pdf/2308.02370
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385644448
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385644448Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2308.02370Digital Object Identifier
- Title
-
A Machine Learning Method for Predicting Traffic Signal Timing from Probe Vehicle DataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-04Full publication date if available
- Authors
-
Juliette Ugirumurera, Joseph Severino, Erik A. Bensen, Qichao Wang, Jane MacfarlaneList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.02370Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2308.02370Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2308.02370Direct OA link when available
- Concepts
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SIGNAL (programming language), Computer science, Artificial neural network, Real-time computing, Traffic flow (computer networking), Traffic signal, Gradient boosting, Signal timing, Energy (signal processing), Floating car data, Simulation, Artificial intelligence, Engineering, Statistics, Transport engineering, Traffic congestion, Mathematics, Computer network, Random forest, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.probe | 65, 87, 119 |
| abstract_inverted_index.times | 115, 123, 150 |
| abstract_inverted_index.work, | 91 |
| abstract_inverted_index.works | 74 |
| abstract_inverted_index.energy | 36 |
| abstract_inverted_index.length | 131 |
| abstract_inverted_index.method | 56 |
| abstract_inverted_index.neural | 107 |
| abstract_inverted_index.paper, | 49 |
| abstract_inverted_index.safety | 15 |
| abstract_inverted_index.signal | 23, 60, 82, 102 |
| abstract_inverted_index.times. | 134 |
| abstract_inverted_index.timing | 26, 61, 83 |
| abstract_inverted_index.within | 152 |
| abstract_inverted_index.Extreme | 95 |
| abstract_inverted_index.Traffic | 0 |
| abstract_inverted_index.authors | 69 |
| abstract_inverted_index.derived | 127 |
| abstract_inverted_index.develop | 93 |
| abstract_inverted_index.knowing | 20 |
| abstract_inverted_index.length, | 147 |
| abstract_inverted_index.lengths | 104 |
| abstract_inverted_index.machine | 53 |
| abstract_inverted_index.network | 108 |
| abstract_inverted_index.optimal | 30 |
| abstract_inverted_index.present | 51 |
| abstract_inverted_index.results | 136 |
| abstract_inverted_index.routing | 32 |
| abstract_inverted_index.signals | 1 |
| abstract_inverted_index.traffic | 10, 22, 59, 81 |
| abstract_inverted_index.vehicle | 31, 64, 86 |
| abstract_inverted_index.Boosting | 97 |
| abstract_inverted_index.Gradient | 96 |
| abstract_inverted_index.accurate | 41 |
| abstract_inverted_index.average. | 157 |
| abstract_inverted_index.enabling | 9 |
| abstract_inverted_index.ensuring | 14 |
| abstract_inverted_index.estimate | 101 |
| abstract_inverted_index.learning | 54 |
| abstract_inverted_index.(XGBoost) | 98 |
| abstract_inverted_index.addition, | 19 |
| abstract_inverted_index.determine | 111 |
| abstract_inverted_index.important | 4 |
| abstract_inverted_index.networks. | 46 |
| abstract_inverted_index.presented | 76 |
| abstract_inverted_index.estimating | 58 |
| abstract_inverted_index.knowledge, | 71 |
| abstract_inverted_index.parameters | 84 |
| abstract_inverted_index.signalized | 44 |
| abstract_inverted_index.simulation | 42 |
| abstract_inverted_index.techniques | 78 |
| abstract_inverted_index.determining | 80 |
| abstract_inverted_index.efficiency, | 37 |
| abstract_inverted_index.information | 62 |
| abstract_inverted_index.management, | 12 |
| abstract_inverted_index.predictions | 151 |
| abstract_inverted_index.eco-driving, | 38 |
| abstract_inverted_index.corresponding | 113 |
| abstract_inverted_index.intersections. | 17 |
| abstract_inverted_index.transportation | 7 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8999999761581421 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile |