Travel time forecasting on a freeway corridor: a dynamic information fusion model based on the random forests approach Article Swipe
Purpose Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways. Design/methodology/approach As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways. Findings The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions. Originality/value This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.
Related Topics
Concepts
Overfitting
Random forest
Interpretability
Computer science
Variance (accounting)
Machine learning
Decision tree
Set (abstract data type)
Data mining
Artificial intelligence
Sample (material)
Predictive modelling
Process (computing)
Artificial neural network
Programming language
Accounting
Operating system
Chromatography
Business
Chemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1108/srt-11-2020-0027
- https://www.emerald.com/insight/content/doi/10.1108/SRT-11-2020-0027/full/pdf?title=travel-time-forecasting-on-a-freeway-corridor-a-dynamic-information-fusion-model-based-on-the-random-forests-approach
- OA Status
- diamond
- Cited By
- 6
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3195260954
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3195260954Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1108/srt-11-2020-0027Digital Object Identifier
- Title
-
Travel time forecasting on a freeway corridor: a dynamic information fusion model based on the random forests approachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-16Full publication date if available
- Authors
-
Bo Qiu, Wei FanList of authors in order
- Landing page
-
https://doi.org/10.1108/srt-11-2020-0027Publisher landing page
- PDF URL
-
https://www.emerald.com/insight/content/doi/10.1108/SRT-11-2020-0027/full/pdf?title=travel-time-forecasting-on-a-freeway-corridor-a-dynamic-information-fusion-model-based-on-the-random-forests-approachDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.emerald.com/insight/content/doi/10.1108/SRT-11-2020-0027/full/pdf?title=travel-time-forecasting-on-a-freeway-corridor-a-dynamic-information-fusion-model-based-on-the-random-forests-approachDirect OA link when available
- Concepts
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Overfitting, Random forest, Interpretability, Computer science, Variance (accounting), Machine learning, Decision tree, Set (abstract data type), Data mining, Artificial intelligence, Sample (material), Predictive modelling, Process (computing), Artificial neural network, Programming language, Accounting, Operating system, Chromatography, Business, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2024: 3, 2023: 1, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Smart and Resilient Transportation |
| primary_location.landing_page_url | https://doi.org/10.1108/srt-11-2020-0027 |
| publication_date | 2021-08-16 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2911964244, https://openalex.org/W3121099426, https://openalex.org/W2954326879, https://openalex.org/W2021914902, https://openalex.org/W2023255777, https://openalex.org/W2610052998, https://openalex.org/W2052533453, https://openalex.org/W2795399733, https://openalex.org/W2116588188, https://openalex.org/W2552841680, https://openalex.org/W2748559863, https://openalex.org/W2115629999, https://openalex.org/W3044154912, https://openalex.org/W2163413094, https://openalex.org/W1989491491, https://openalex.org/W2028336648, https://openalex.org/W2016078139, https://openalex.org/W2199682317, https://openalex.org/W2120054128, https://openalex.org/W3139110786, https://openalex.org/W2137394815, https://openalex.org/W2135410644, https://openalex.org/W2106100548, https://openalex.org/W2127644823, https://openalex.org/W2115090475, https://openalex.org/W2051503055, https://openalex.org/W1999832866, https://openalex.org/W2061358509, https://openalex.org/W2146399611, https://openalex.org/W1983074193, https://openalex.org/W2115868680, https://openalex.org/W2126545577, https://openalex.org/W2111991989, https://openalex.org/W2394843433, https://openalex.org/W2765137096 |
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