Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms

Weerasekera, Rivindu; Sridharan, Mohan; Ranjitkar, Prakash

doi:10.1155/2020/7057519

Cited by 7 publications

(4 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results show that aggregation levels from 3 min to 6 min gave the best prediction results in the context of machine learning and deep Learning models. The obtained results are compatible with several experimentation studies, such as in [31].…”

Section: A Data Preprocessingsupporting

confidence: 92%

A generic framework for forecasting short-term traffic conditions on urban highways

Attoui

Meddeb

2021

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)

View full text Add to dashboard Cite

With the emergence of Connected and Smart Cities, the need to predict traffic conditions has led to the development of a large variety of forecasting algorithms. In spite of various research efforts, the choice of models and techniques strongly depends on the use case, the highway infrastructure as well as the provided dataset. This study is launched as part of a project which aims to design an Intelligent Transport System (ITS) dedicated to highway supervisors to regulate traffic. This system needs to be supplied by continuous, real-time forecasting of shortterm traffic congestions in order to make decisions accordingly. In this paper, we propose a general framework that, first, performs different data preprocessing techniques to improve data quality, and second, provides real-time multiple horizons predictions. Our framework uses different models combining Machine learning and Deep learning algorithms. Experiments results confirmed the necessity of the data preprocessing step, especially with highly dynamic data and heterogeneous mobility contexts. In addition, our methodology is tested in a real case study and shows very encouraging results.

show abstract

Section: A Data Preprocessingsupporting

confidence: 92%

A generic framework for forecasting short-term traffic conditions on urban highways

Attoui

Meddeb

2021

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)

View full text Add to dashboard Cite

show abstract

“…There are some preliminary, albeit undocumented, efforts to aggregate, mine and process multiple sources of connected (e.g., smartphone, vehicle, infrastructure, and environment). Such efforts include use of multi-sensor fusion techniques ( 73 ), aggregation of spatiotemporal data using machine learning algorithms, and use of artificial intelligence for block-chain enabled intelligent internet of things (loT) architecture to reach inference by minimizing and/or eliminating the limitations of individual data sources ( 74 – 76 ).…”

Section: Resultsmentioning

confidence: 99%

Use of Connected Technologies to Assess Barriers and Stressors for Age and Disability-Friendly Communities

Zanwar

Kim

et al. 2021

Front. Public Health

View full text Add to dashboard Cite

Background: The benefits of engaging in outdoor physical activity are numerous for older adults. However, previous work on outdoor monitoring of physical activities did not sufficiently identify how older adults characterize and respond to diverse elements of urban built environments, including structural characteristics, safety attributes, and aesthetics.Objective: To synthesize emerging multidisciplinary trends on the use of connected technologies to assess environmental barriers and stressors among older adults and for persons with disability.Methods: A multidisciplinary overview and literature synthesis.Results: First, we review measurement and monitoring of outdoor physical activity in community environments and during transport using wearable sensing technologies, their contextualization and using smartphone-based applications. We describe physiological responses (e.g., gait patterns, electrodermal activity, brain activity, and heart rate), stressors and physical barriers during outdoor physical activity. Second, we review the use of visual data (e.g., Google street images, Street score) and machine learning algorithms to assess physical (e.g., walkability) and emotional stressors (e.g., stress) in community environments and their impact on human perception. Third, we synthesize the challenges and limitations of using real-time smartphone-based data on driving behavior, incompatibility with software data platforms, and the potential for such data to be confounded by environmental signals in older adults. Lastly, we summarize alternative modes of transport for older adults and for persons with disability.Conclusion: Environmental design for connected technologies, interventions to promote independence and mobility, and to reduce barriers and stressors, likely requires smart connected age and disability-friendly communities and cities.

show abstract

“…Yu [15], based on the wavelet decomposition method, obtained the best integration degree of the data through hierarchical and similarity analysis of ITS data, and completed the data integration. Weerasekera [16] investigated the ability of several algorithms to reliably model traffic flow at different data resolutions.…”

Section: Introductionmentioning

confidence: 99%

Urban Traffic Flow Prediction Based on Bayesian Deep Learning Considering Optimal Aggregation Time Interval

Fu,

Wang,

Sun

et al. 2024

Sustainability

View full text Add to dashboard Cite

Predicting short-term urban traffic flow is a fundamental and cost-effective strategy in traffic signal control systems. However, due to the interrupted, periodic, and stochastic characteristics of urban traffic flow influenced by signal control, there are still unresolved issues related to the selection of the optimal aggregation time interval and the quantifiable uncertainties in prediction. To tackle these challenges, this research introduces a method for predicting urban interrupted traffic flow, which is based on Bayesian deep learning and considers the optimal aggregation time interval. Specifically, this method utilizes the cross-validation mean square error (CVMSE) method to obtain the optimal aggregation time interval and to establish the relationship between the optimal aggregation time interval and the signal cycle. A Bayesian LSTM-CNN prediction model, which extends the LSTM-CNN model under the Bayesian framework to a probabilistic model to better capture the stochasticity and variation in the data, is proposed. Experimental results derived from real-world data demonstrate gathering traffic flow data based on the optimal aggregation time interval significantly enhances the prediction accuracy of the urban interrupted traffic flow model. The optimal aggregation time interval for urban interrupted traffic flow data corresponds to a multiple of the traffic signal control cycle. Comparative experiments indicate that the Bayesian LSTM-CNN prediction model outperforms the state-of-the-art prediction models.

show abstract

Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms

Cited by 7 publications

References 49 publications

A generic framework for forecasting short-term traffic conditions on urban highways

A generic framework for forecasting short-term traffic conditions on urban highways

Use of Connected Technologies to Assess Barriers and Stressors for Age and Disability-Friendly Communities

Urban Traffic Flow Prediction Based on Bayesian Deep Learning Considering Optimal Aggregation Time Interval

Contact Info

Product

Resources

About