Forecasting freeway traffic flow for intelligent transportation systems application

Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban traffic networks, especially dynamic traffic networks, can serve as potential solutions for traffic congestion, based on the complex network theory. In this paper, we build a traffic flow network model to investigate traffic congestion problems through taxi GPS trajectories. Moreover, to verify the effectiveness of the traffic flow network, an actual case of identifying the congestion areas is considered. The results indicate that the traffic flow network is reliable. Finally, several key problems related to traffic flow networks are discussed. The proposed traffic flow network can provide a methodological reference for traffic planning, especially to solve traffic congestion problems.

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Multiple-Interval Freeway Traffic Flow Forecasting

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198196155400117 ◽

1996 ◽

Vol 1554 (1) ◽

pp. 136-141 ◽

Cited By ~ 10

Author(s):

Brian L. Smith ◽

Michael J. Demetsky

Keyword(s):

Traffic Flow ◽

Nonparametric Regression ◽

Intelligent Transportation Systems ◽

Traffic Management ◽

Past Research ◽

Transportation Systems ◽

Forecasting Model ◽

Freeway Traffic ◽

Traffic Forecasting ◽

Traffic Flow Forecasting

Freeway traffic flow forecasting will play an important role in intelligent transportation systems. The TRB Committee on Freeway Operations has included freeway flow forecasting in its 1995 research program. Much of the past research in traffic flow forecasting has addressed short-term, single-interval predictions. Such limited forecasting models will not support the development of the longer-term operational strategies needed for such events as hazardous material incidents. A multiple-interval freeway traffic flow forecasting model has been developed that predicts traffic volumes in 15-min intervals for several hours into the future. The nonparametric regression modeling technique was chosen for the multiple-interval freeway traffic flow forecasting problem. The technique possesses a number of attractive qualities for traffic forecasting. It is intuitive and uses a data base of past conditions to generate forecasts. It can also be implemented as a generic algorithm and is easily calibrated at field locations, suiting it for wide-scale deployment. The model was applied at two sites on the Capital Beltway monitored by the Northern Virginia Traffic Management System. The nonparametric regression forecasting model produced accurate short- and long-term volume estimates at both sites.

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Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems

Computer Networks ◽

10.1016/j.comnet.2020.107484 ◽

2020 ◽

Vol 182 ◽

pp. 107484 ◽

Cited By ~ 4

Author(s):

Azzedine Boukerche ◽

Yanjie Tao ◽

Peng Sun

Keyword(s):

Artificial Intelligence ◽

Traffic Flow ◽

Intelligent Transportation Systems ◽

Intelligent Transportation ◽

Transportation Systems ◽

Prediction Methods ◽

Vehicular Traffic ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Vehicular Traffic Flow

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The Application of Virtual Reality Technology on Intelligent Traffic Construction and Decision Support in Smart Cities

Wireless Communications and Mobile Computing ◽

10.1155/2021/3833562 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Gongxing Yan ◽

Yanping Chen

Keyword(s):

Big Data ◽

Traffic Flow ◽

Intelligent Transportation Systems ◽

Intelligent Transportation System ◽

Smart Cities ◽

Intelligent Transportation ◽

Transportation System ◽

Transportation Systems ◽

The Other ◽

The City

The core of smart city is to build intelligent transportation system.. An intelligent transportation system can analyze the traffic data with time and space characteristics in the city and acquire rich and valuable knowledge, and it is of great significance to realize intelligent traffic scheduling and urban planning. This article specifically introduces the extensive application of urban transportation infrastructure data in the construction and development of smart cities. This article first explains the related concepts of big data and intelligent transportation systems and uses big data to illustrate the operation of intelligent transportation systems in the construction of smart cities. Based on the machine learning and deep learning method, this paper is aimed at the passenger flow and traffic flow in the smart city transportation system. This paper deeply excavates the time, space, and other hidden features. In this paper, the traffic volume of the random sections in the city is predicted by using the graph convolutional neural network (GCNN) model, and the data are compared with the other five models (VAR, FNN, GCGRU, STGCN, and DGCNN). The experimental results show that compared with the other 4 models, the GCNN model has an increase of 8% to 10% accuracy and 15% fault tolerance. In forecasting morning and evening peak traffic flow, the accuracy of the GCNN model is higher than that of other models, and its trend is basically consistent with the actual traffic volume, the predicted results can reflect the actual traffic flow data well. Aimed at the application of intelligent transportation in an intelligent city, this paper proposes a machine learning prediction model based on big data, and this is of great significance for studying the mechanical learning of such problems. Therefore, the research of this paper has a good implementation prospect and academic value.

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