Application on traffic flow prediction of machine learning in intelligent transportation

Liu, Cong; Xu, Pei

doi:10.1007/s00521-020-05002-6

Cited by 87 publications

(39 citation statements)

References 20 publications

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“…Nguyen et al [33] developed a useful review and showed which deep learning applications are more efficient in transportation networks, such as traffic flow forecasting, traffic signal control, automated vehicle detection, travel demand prediction, autonomous driving, and driver behavior analysis. Moreover, Li and Xu [34] classified vehicles using different classifiers, such as Adaboost, support vector machines, reinforcement learning, support vector regression algorithms. They used the latter to improve the accuracy of short-term traffic flows.…”

Section: Optimization Simulation and Machine Learning In Itsmentioning

confidence: 99%

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

et al. 2021

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With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing.These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.

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Section: Optimization Simulation and Machine Learning In Itsmentioning

confidence: 99%

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

et al. 2021

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“…An SVM-based classifier tries to maximize the hyperplane separation between two classes by solving a linearly constrained quadratic programming problem. It is robust to overfitting while providing high generalization performance (Li and Xu [16] Mingheng et al [17]). However, the SVM models perform better in forecasting medium-duration incident cases than high-duration incident cases (Yu et al [18]).…”

Section: Support Vector Machinesmentioning

confidence: 99%

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Reza

Oliveira

Machado

et al. 2021

Sensors

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With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios.

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“…Shahrabi et al [30] used reinforcement learning to deal with dynamic job shop scheduling problem under the uncertainty of stochastic jobs and machine failures. Li and Xu [31] explored the application of machine learning in intelligent transportation. e machine learning method is applied to the traffic flow prediction to solve the problem that the traditional traffic flow prediction model cannot cope with the complex changes of traffic flow.…”

Section: Related Workmentioning

confidence: 99%

Automated Container Terminal Production Operation and Optimization via an AdaBoost-Based Digital Twin Framework

Chang

Gao

et al. 2021

Journal of Advanced Transportation

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Digital twin (DT), machine learning, and industrial Internet of things (IIoT) provide great potential for the transformation of the container terminal from automation to intelligence. The production control in the loading and unloading process of automated container terminals (ACTs) involves complex situations, which puts forward high requirements for efficiency and safety. To realize the real-time optimization and security of the ACT, a framework integrating DT with the AdaBoost algorithm is proposed in this study. The framework is mainly composed of physical space, a data service platform, and virtual space, in which the twin space and service system constitute virtual space. In the proposed framework, a multidimensional and multiscale DT model in twin space is first built through a 3D MAX and U3D technology. Second, we introduce a random forest and XGBoost to compare with AdaBoost to select the best algorithm to train and optimize the DT mechanism model. Third, the experimental results show that the AdaBoost algorithm is better than others by comparing the performance indexes of model accuracy, root mean square error, interpretable variance, and fitting error. In addition, we implement empirical experiments by different scales to further evaluate the proposed framework. The experimental results show that the mode of the DT-based terminal operation has higher loading and unloading efficiency than that of the conventional terminal operation, increasing by 23.34% and 31.46% in small-scale and large-scale problems, respectively. Moreover, the visualization service provided by the DT system can monitor the status of automation equipment in real time to ensure the safety of operation.

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Application on traffic flow prediction of machine learning in intelligent transportation

Cited by 87 publications

References 20 publications

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Automated Container Terminal Production Operation and Optimization via an AdaBoost-Based Digital Twin Framework

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