A Machine Learning Approach to Short-Term Traffic Flow Prediction: A Case Study of Interstate 64 in Missouri

Mohammed, Osama A.; Kianfar, Jalil

doi:10.1109/isc2.2018.8656924

Cited by 38 publications

(20 citation statements)

References 15 publications

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“…Instead, in this work we propose an approach based on Apache Spark supporting machine learning forecast models but also capable of integrating statistical forecasting models such as ARIMA through the Pandas Function API. Similarly to other works [11], we consider how the traffic volume affects the prediction. Moreover, unlike many researches [12][13][14] where the aim is rather to understand how external factors (atmospheric conditions or road indicators) affect the forecast, in this paper we consider how the concepts of data granularity and time horizon impact on the forecast accuracy.…”

Section: Related Workmentioning

confidence: 99%

Towards Multi-Model Big Data Road Traffic Forecast at Different Time Aggregations and Forecast Horizons

Savoia

2022

EAI Endorsed Trans Energy Web

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Due to its usefulness in various social contexts, from Intelligent Transportation Systems (ITSs) to the reduction of urban pollution, road traffic prediction represents an active research area in the scientific community, with strong potential impact on citizens’ well-being. Already considered a non-trivial problem, in many real applications an additional level of complexity is given by the large amount of data requiring Big Data domain technologies. In this paper, we present the first steps of a novel approach integrating both classic and machine learning models in the Spark-based big data architecture of the H2020 CLASS project, and we perform preliminary tests to see how usually little-considered variables (different data aggregation levels, time horizons and traffic density levels) influence the error of the different models.

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Section: Related Workmentioning

confidence: 99%

Towards Multi-Model Big Data Road Traffic Forecast at Different Time Aggregations and Forecast Horizons

Savoia

2022

EAI Endorsed Trans Energy Web

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“…For the free flow forecast of the expressway, Ma et al [13] proposed a forecasting method for daily traffic flow using a contextual convolutional long short-term memory recurrent neural network. Mohammed et al [14] investigated the application of four machine learning methods (the deep neural networks, distributed random forest, gradient boosting machine, and generalized linear model) for short-term traffic flow prediction on urban freeways. For urban road intersections, traffic demand forecasts are used to optimize signal timing plans.…”

Section: Related Workmentioning

confidence: 99%

Research on the Construction of a Knowledge Graph and Knowledge Reasoning Model in the Field of Urban Traffic

et al. 2021

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The integration of multi-source transportation data is complex and insufficient in most of the big cities, which made it difficult for researchers to conduct in-depth data mining to improve the policy or the management. In order to solve this problem, a top-down approach is used to construct a knowledge graph of urban traffic system in this paper. First, the model layer of the knowledge graph was used to realize the reuse and sharing of knowledge. Furthermore, the model layer then was stored in the graph database Neo4j. Second, the representation learning based knowledge reasoning model was adopted to implement knowledge completion and improve the knowledge graph. Finally, the proposed method was validated with an urban traffic data set and the results showed that the model could be used to mine the implicit relationship between traffic entities and discover traffic knowledge effectively.

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“…Models in [52][53][54] are based on ANN. There are several studies involving metering methods based on deep neural networks [55] and deep reinforcement learning [56][57][58].…”

Section: Ramp Metering Algorithmsmentioning

confidence: 99%

A Novel Ramp Metering Approach Based on Machine Learning and Historical Data

Ghanbartehrani

Sanandaji

Mokhtari³

et al. 2020

MAKE

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The random nature of traffic conditions on freeways can cause excessive congestion and irregularities in the traffic flow. Ramp metering is a proven effective method to maintain freeway efficiency under various traffic conditions. Creating a reliable and practical ramp metering algorithm that considers both critical traffic measures and historical data is still a challenging problem. In this study we use simple machine learning approaches to develop a novel real-time ramp metering algorithm. The proposed algorithm is computationally simple and has minimal data requirements, which makes it practical for real-world applications. We conduct a simulation study to evaluate and compare the proposed approach with an existing traffic-responsive ramp metering algorithm.

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A Machine Learning Approach to Short-Term Traffic Flow Prediction: A Case Study of Interstate 64 in Missouri

Cited by 38 publications

References 15 publications

Towards Multi-Model Big Data Road Traffic Forecast at Different Time Aggregations and Forecast Horizons

Towards Multi-Model Big Data Road Traffic Forecast at Different Time Aggregations and Forecast Horizons

Research on the Construction of a Knowledge Graph and Knowledge Reasoning Model in the Field of Urban Traffic

A Novel Ramp Metering Approach Based on Machine Learning and Historical Data

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