Conjoining congestion speed-cycle patterns and deep learning neural network for short-term traffic speed forecasting

Tang, Weike; Yiu, Ka Fai Cedric; Chan, Kit Yan; Zhang, K.

doi:10.1016/j.asoc.2023.110154

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…In 28 , the authors tackle the problem of urban traffic congestion level prediction using a fusion-based graph convolutional network. The result of 29 combines congestion speed-cycle patterns and a deep-learning neural network for short-term traffic speed predicting. In 30 , for traffic congestion prediction, the authors implement and evaluate four machine learning techniques: feed-forward neural networks, radial basis function neural networks, simple linear regression model, and polynomial linear regression model.…”

Section: Related Workmentioning

confidence: 99%

Predicting vehicle travel time on city streets for trip preplanning and predicting heavy traffic for proactive control of street congestion

Nofal

2024

Sci Rep

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We investigate if the vehicle travel time after 6 h on a given street can be predicted, provided the hourly vehicle travel time on the street in the last 19 h. Likewise, we examine if the traffic status (i.e., low, mild, or high) after 6 h on a given street can be predicted, provided the hourly traffic status of the street in the last 19 h. To pursue our objectives, we exploited historical hourly traffic data from Google Maps for a main street in the capital city of Jordan, Amman. We employ several machine learning algorithms to construct our predictive models: neural networks, gradient boosting, support vector machines, AdaBoost, and nearest neighbors. Our experimental results confirm our investigations positively, such that our models have an accuracy of around 98–99% in predicting vehicle travel time and traffic status on our study’s street for the target hour (i.e., after 6 h from a specific point in time). Moreover, given our time series traffic data and our constructed predictive models, we inspect the most critical indicators of street traffic status and vehicle travel time after 6 h on our study’s street. However, as we elaborate in the article, our predictive models do not agree on the degree of importance of our data features.

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Section: Related Workmentioning

confidence: 99%

Predicting vehicle travel time on city streets for trip preplanning and predicting heavy traffic for proactive control of street congestion

Nofal

2024

Sci Rep

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“…Furthermore, since ANN has strong self-learning and self-adaptation abilities, many scholars proposed short-term traffic flow prediction models based on ANN [20]. Tang et al [21] raised Neighbor Subset Deep Neutral Network (NSDNN) to forecast spatio-temporal data, which can extract useful inputs from nearby roads by conjoining a deep neutral network and the subset selection method. Considering the spatial correlation of traffic flow, the paper [22] proposed a method to predict the spatio-temporal characteristics of short-term traffic flow by combing the k-nearest neighbor algorithm and bidirectional long-short-term memory network model.…”

Section: Introductionmentioning

confidence: 99%

Cloud Model-Based Fuzzy Inference System for Short-Term Traffic Flow Prediction

Liu,

Wang,

Wang

et al. 2023

Mathematics

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Since traffic congestion during peak hours has become the norm in daily life, research on short-term traffic flow forecasting has attracted widespread attention that can alleviate urban traffic congestion. However, the existing research ignores the uncertainty of short-term traffic flow forecasting, which will affect the accuracy and robustness of traffic flow forecasting models. Therefore, this paper proposes a short-term traffic flow forecasting algorithm combining the cloud model and the fuzzy inference system in an uncertain environment, which uses the idea of the cloud model to process the traffic flow data and describe its randomness and fuzziness at the same time. First, the fuzzy c-means algorithm is selected to carry out cluster analysis on the original traffic flow data, and the number and parameter values of the initial membership function of the system are obtained. Based on the cloud reasoning algorithm and the cloud rule generator, an improved fuzzy reasoning system is proposed for short-term traffic flow predictions. The reasoning system cannot only capture the uncertainty of traffic flow data, but it also can describe temporal dependencies well. Finally, experimental results indicate that the proposed model has a better prediction accuracy and better stability, which reduces 0.6106 in RMSE, reduces 0.281 in MAE, and reduces 0.0022 in MRE compared with the suboptimal comparative methods.

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“…Much research work has been conducted on different aspects to avoid road congestion, such as traffic flow propagation [3][4][5], traffic congestion prediction [6][7][8][9], and the correlation of traffic congestion [10,11]. But it is inevitable that road congestion can propagate to adjacent nodes in an urban road network [12].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Recovery Model and Maintenance Optimization for Urban Road Networks with Congestion

Dui

Zhang

et al. 2023

Mathematics

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Urban road networks have promoted high-quality travel for residents by increasing connectivity and intelligence. But road congestion has not been effectively alleviated, causing a loss of time and energy. At present, the recovery of urban road networks mainly considers removing the failed edges. Considering the recovery cost and time, it is important to take active maintenance behavior to restore these networks. One of the key problems is dispatching traffic workers reasonably to achieve timely maintenance. In this paper, a flow-distribution-based process and execution (FD-PE) model is established for solving congestion. The maintenance centers (MC) study the reasons for and spread of congestion by edge flow. Based on the genetic algorithm (GA), two models of maintenance for urban road networks are developed, which include a single MC-centered dispatching plan and the co-scheduling of MCs. Both models aim at minimizing recovery time and allocating maintenance resources. The road network in Zhengzhou is borrowed as a case to explain the feasibility of the proposed models. The results show that on the premise of dividing network regions, it is reasonable to take a single MC to recover congestion. Compared with a single MC, the co-scheduling of MCs may save more time.

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Conjoining congestion speed-cycle patterns and deep learning neural network for short-term traffic speed forecasting

Cited by 9 publications

References 28 publications

Predicting vehicle travel time on city streets for trip preplanning and predicting heavy traffic for proactive control of street congestion

Predicting vehicle travel time on city streets for trip preplanning and predicting heavy traffic for proactive control of street congestion

Cloud Model-Based Fuzzy Inference System for Short-Term Traffic Flow Prediction

Recovery Model and Maintenance Optimization for Urban Road Networks with Congestion

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