Stochastic Lagrangian Traffic flow modeling and real-time traffic prediction

Chu, Kang-Ching; Saigal, Romesh

doi:10.1109/coase.2016.7743383

Cited by 12 publications

(10 citation statements)

References 14 publications

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“…An extended LWR model was proposed in [97], which takes into account the distribution of heterogeneous drivers characterized by the choice of speeds in a traffic stream. Authors of [99,100] proposed a stochastic partial differential equation (SPDE) model by adding a stochastic coercive function to the LWR model. The SPDE model can capture part of the stochastic nature of the traffic flow evolution and improve the accuracy of prediction.…”

Section: Differential Equation Model For Continuous Traffic Flowsmentioning

confidence: 99%

Vehicular mobility patterns and their applications to Internet-of-Vehicles: a comprehensive survey

Cui

et al. 2022

Sci. China Inf. Sci.

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With the growing popularity of the Internet-of-Vehicles (IoV), it is of pressing necessity to understand transportation traffic patterns and their impact on wireless network designs and operations. Vehicular mobility patterns and traffic models are the keys to assisting a wide range of analyses and simulations in these applications. This study surveys the status quo of vehicular mobility models, with a focus on recent advances in the last decade. To provide a comprehensive and systematic review, the study first puts forth a requirement-model-application framework in the IoV or general communication and transportation networks. Existing vehicular mobility models are categorized into vehicular distribution, vehicular traffic, and driving behavior models. Such categorization has a particular emphasis on the random patterns of vehicles in space, traffic flow models aligned to road maps, and individuals’ driving behaviors (e.g., lane-changing and car-following). The different categories of the models are applied to various application scenarios, including underlying network connectivity analysis, off-line network optimization, online network functionality, and real-time autonomous driving. Finally, several important research opportunities arise and deserve continuing research efforts, such as holistic designs of deep learning platforms which take the model parameters of vehicular mobility as input features, qualification of vehicular mobility models in terms of representativeness and completeness, and new hybrid models incorporating different categories of vehicular mobility models to improve the representativeness and completeness.

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Section: Differential Equation Model For Continuous Traffic Flowsmentioning

confidence: 99%

Vehicular mobility patterns and their applications to Internet-of-Vehicles: a comprehensive survey

Cui

et al. 2022

Sci. China Inf. Sci.

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“…[2] to predict imperfect traffic data, so as to adapt to more complex data. In addition to ARIMA, other data‐driven statistical models such as Kalman Filtering based model [3] and Stochastic Lagrangian traffic flow model [4], can also achieve good results in the field of traffic prediction. In ref.…”

Section: Introductionmentioning

confidence: 99%

A multi‐channel geometric algebra residual network for traffic data prediction

Zang

Chen

Jun-tao

et al. 2022

IET Intelligent Trans Sys

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Traffic data prediction offers a significant way to evaluate the future traffic congestion status; many deep learning based approaches have been widely applied in this field. Most current methods only consider short-term traffic data forecasting; however, long-term prediction, which supports the optimized distribution of traffic resources, is not well studied. Besides, multiple traffic parameters enable stronger constraints for the data estimation, but the correlation between them in both spatial and temporal domains has not been efficiently learned. Geometric algebra, as a generalization of linear algebra, provides a framework to encode multidimensional data and analyze the correlation. By combining the advantages of the deep neural network and geometric algebra, a multi-channel geometric algebra residual network (MGAResNet) is proposed to address the problem of long-term traffic data prediction. Traffic data obtained from two urban expressways are employed and experimental results demonstrate that the approach outperforms the state-of-the-art work.

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“…Traffic control, on the other hand, directly uses the state of the traffic flow as feedback. Traffic state estimation may rely on data from loop detectors or cameras at fixed locations [35,40,62] or from vehicles traveling in the traffic flow [7,11,12,16]. State estimators may involve Kalman filtering techniques [28,58,59] and data fusion [20,66,67].…”

Section: Introductionmentioning

confidence: 99%

Virtual Rings on Highways: Traffic Control by Connected Automated Vehicles

Molnár¹,

Hopka²,

Upadhyay³

et al. 2022

Preprint

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This work gives introduction to traffic control by connected automated vehicles. The influence of vehicle control on vehicular traffic and traffic control strategies are discussed and compared. It is highlighted that vehicle-to-everything connectivity allows connected automated vehicles to access the state of the traffic behind them such that feedback can be utilized to mitigate evolving congestions. Numerical simulations demonstrate that such connectivity-based traffic control is beneficial for smoothness and energy efficiency of highway traffic. The dynamics and stability of traffic flow, under the proposed controllers, are analyzed in detail to construct stability charts that guide the selection of stabilizing control gains.

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Stochastic Lagrangian Traffic flow modeling and real-time traffic prediction

Cited by 12 publications

References 14 publications

Vehicular mobility patterns and their applications to Internet-of-Vehicles: a comprehensive survey

Vehicular mobility patterns and their applications to Internet-of-Vehicles: a comprehensive survey

A multi‐channel geometric algebra residual network for traffic data prediction

Virtual Rings on Highways: Traffic Control by Connected Automated Vehicles

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