Real-Time Traffic Prediction and Probing Strategy for Lagrangian Traffic Data

Chu, Kang-Ching; Saigal, Romesh

doi:10.1109/tits.2018.2818686

Cited by 21 publications

(11 citation statements)

References 39 publications

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“…In [ 36 ] are used Generalized Beta-Gaussian Bayesian Networks on less than 250 map links while in [ 37 ] is used SUMO [ 38 ] to simulate traffic in Cologne, Germany for two models that predicts traffic on time intervals that are less than 1 min and greater than 1 min, respectively. A macroscopic traffic flow model is used in [ 3 ] to real-time traffic prediction and congestion on highways. The work in [ 3 ] is able to warn the driver in less than 7 s before entering traffic jam.…”

Section: Related Workmentioning

confidence: 99%

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Scalable Data Model for Traffic Congestion Avoidance in a Vehicle to Cloud Infrastructure

Stan

Suciu

Potolea

2021

Sensors

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Traffic congestion experience in urban areas has negative impact on our daily lives by consuming our time and resources. Intelligent Transportation Systems can provide the necessary infrastructure to mitigate such challenges. In this paper, we propose a novel and scalable solution to model, store and control traffic data based on range query data structures (K-ary Interval Tree and K-ary Entry Point Tree) which allows data representation and handling in a way that better predicts and avoids traffic congestion in urban areas. Our experiments, validation scenarios, performance measurements and solution assessment were done on Brooklyn, New York traffic congestion simulation scenario and shown the validity, reliability, performance and scalability of the proposed solution in terms of time spent in traffic, run-time and memory usage. The experiments on the proposed data structures simulated up to 10,000 vehicles having microseconds time to access traffic information and below 1.5 s for congestion free route generation in complex scenarios. To the best of our knowledge, this is the first scalable approach that can be used to predict urban traffic and avoid congestion through range query data structure traffic modelling.

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Section: Related Workmentioning

confidence: 99%

“…Because of this, drivers spend a lot of their time in traffic: billions of hours of extra time sitting in traffic which results in hundreds of billions of USD congestion cost [ 1 , 2 ]. In the major US urban areas 32% of the daily travel time occurred under congested traffic [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Scalable Data Model for Traffic Congestion Avoidance in a Vehicle to Cloud Infrastructure

Stan

Suciu

Potolea

2021

Sensors

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“…Plenty of machine learning (ML) models are used to predict the traffic flow. Existing ML methods are still full of challenges for how to deal with big data [9]. It is still worth discussing and studying how to further improve the prediction accuracy of highway and capture spatio-temporal information and data analysis [10,11].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Segmented Traffic Flow Prediction with ANPRS Data Based on Improved XGBoost

Sun

Jiao

2021

Journal of Advanced Transportation

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Traffic prediction is highly significant for intelligent traffic systems and traffic management. eXtreme Gradient Boosting (XGBoost), a scalable tree lifting algorithm, is proposed and improved to predict more high-resolution traffic state by utilizing origin-destination (OD) relationship of segment flow data between upstream and downstream on the highway. In order to achieve fine prediction, a generalized extended-segment data acquirement mode is added by incorporating information of Automatic Number Plate Recognition System (ANPRS) from exits and entrances of toll stations and acquired by mathematical OD calculation indirectly without cameras. Abnormal data preprocessing and spatio-temporal relationship matching are conducted to ensure the effectiveness of prediction. Pearson analysis of spatial correlation is performed to find the relevance between adjacent roads, and the relative importance of input modes can be verified by spatial lag input and ordinary input. Two improved models, independent XGBoost (XGBoost-I) with individual adjustment parameters of different sections and static XGBoost (XGBoost-S) with overall adjustment of parameters, are conducted and combined with temporal relevant intervals and spatial staggered sectional lag. The early_stopping_rounds adjustment mechanism (EAM) is introduced to improve the effect of the XGBoost model. The prediction accuracy of XGBoost-I-lag is generally higher than XGBoost-I, XGBoost-S-lag, XGBoost-S, and other baseline methods for short-term and long-term multistep ahead. Additionally, the accuracy of the XGBoost-I-lag is evaluated well in nonrecurrent conditions and missing cases with considerable running time. The experiment results indicate that the proposed framework is convincing, satisfactory, and computationally reasonable.

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“…There are many researchers focusing on all kinds of problems related to urban traffic, such as traffic flow prediction [5–10], traffic modelling [11–13], traffic pattern analysis [14], and so on. However, most of those works extract the spatio‐temporal relation, which is unexplainable as a hidden feature for final purpose.…”

Section: Introductionmentioning

confidence: 99%

Mining the spatio‐temporal pattern using matrix factorisation: a case study of traffic flow

Truong

2020

IET intell. transp. syst

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Real-Time Traffic Prediction and Probing Strategy for Lagrangian Traffic Data

Cited by 21 publications

References 39 publications

Scalable Data Model for Traffic Congestion Avoidance in a Vehicle to Cloud Infrastructure

Scalable Data Model for Traffic Congestion Avoidance in a Vehicle to Cloud Infrastructure

Spatio-Temporal Segmented Traffic Flow Prediction with ANPRS Data Based on Improved XGBoost

Mining the spatio‐temporal pattern using matrix factorisation: a case study of traffic flow

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