Negative Binomial Additive Models for Short-Term Traffic Flow Forecasting in Urban Areas

Daraghmi, Yousef‐Awwad; Yi, Chih‐Wei; Chiang, Tsun-Chieh

doi:10.1109/tits.2013.2287512

Cited by 40 publications

(33 citation statements)

References 36 publications

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“…With continuous improvement of traffic information processing, how to predict the short-term traffic flow accurately and effectively has aroused wide attention of scholars domestically and abroad [1][2][3], whereupon numerous of prominent research results have emerged. So far, relevant academic circles mainly focus on the construction and optimization of prediction models in terms of time series, linear regression, historical average model, Kalman filtering, grey theory, chaos theory, nonparametric regression, neural network, support vector machine, dynamic traffic assignment model, and so forth [4][5][6][7][8][9]. These algorithms and models mentioned above are relatively mature, and their prediction effects are acceptable under the environment of favorable traffic flow stability.…”

Section: Introductionmentioning

confidence: 99%

A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation

Huang

Deng

2018

Journal of Control Science and Engineering

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Directing against the shortcoming of low accuracy in short-term traffic flow prediction caused by strong traffic flow fluctuation, a novel method for short-term traffic forecasting based on the combination of improved grey Verhulst prediction algorithm and first-order difference exponential smoothing is proposed. Firstly, we constructed an improved grey Verhulst prediction model by introducing the Markov chain to its traditional version. Then, based on an introduced dynamic weighting factor, the improved grey Verhulst prediction method, and the first-order difference exponential smoothing technique, the new method for short-term traffic forecasting is completed in an efficient way. Finally, experiment and analysis are carried out in the light of actual data gathered from strong fluctuation environment to verify the effectiveness and rationality of our proposed scheme.

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Section: Introductionmentioning

confidence: 99%

A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation

Huang

Deng

2018

Journal of Control Science and Engineering

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“…The results of this project processed, improved, and then the Regions of Interest benefit the field of ITS in the safety domain. Further, we will integrate these results to tune the parameters of traffic flow prediction proposed in [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Accurate Real-Time Traffic Sign Recognition Based on the Connected Component Labeling and the Color Histogram Algorithms

Hasasneh¹,

Daraghmi²,

Hasaneh³

2016

International Journal of Signal Processing Systems IJSPS

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Image processing such as Traffic Sign Recognition (TSR) plays a key role in Intelligent Transportation Systems particularly in Traffic Sign Recognition (TSR) which aims at increasing driver safety. Several studies have proposed TSR systems based on different image processing and machine learning algorithms. However, the efficiency of the proposed TSR algorithms requires improvement to enable real-time alerts on onboard devices which have limited computational power. Further improvement on accuracy of TSR is also required mainly in unstable weather conditions or when multiple signs exist on one pillar. This research proposes an improved model for Automatic TSR (ATSR) consisting of improved Connected Components Labeling and color histogram. Firstly, images are captured in real-world conditions in Palestine. The region of interests is detected using the improved Connected Components Labeling algorithm combined with Vectorization to reduce computational time. Secondly, the local features of detected images are computed using the color histogram. The results of the research show that combining the Connected Components Labeling with Vectorization reduces the computational time. Also, the Connected Components Labeling algorithm followed by histogram increase the accuracy of recognition. The low computational cost and the accuracy of the model enable us to use the model on smart phones for accurately recognizing traffic signs and alerting drivers in real time. 

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“…To achieve traffic flow redistribution, it is necessary to get future traffic conditions [4,5]. The authors in [6] also emphasized that it is necessary to continuously forecast the traffic conditions for short time ahead to enable dynamic traffic control. These cause establishing efficient and accurate traffic flow forecasting models to become an important issue in traffic management.…”

Section: Introductionmentioning

confidence: 99%

“…In the new era of intelligent traffic systems, research has focus on establishing forecasting models to manage the traffic networks [10][11][12]. Many computational approaches have been commonly applied to build forecasting models, such as neural network (NN), generalized additive model (GAM), and autoregressive integrated moving average (ARIMA) [6,[13][14][15][16]. In this paper, above approaches are applied to the traffic data collected on the British freeway (M6) from 1 st to 30 th November in 2014 for evaluation.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Recent Computational Approaches in Short-Term Traffic Forecasting

Yang

Dillon

Chen

2015

IFIP Advances in Information and Communication Technology

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Part 3: Computational Intelligence and AlgorithmsInternational audienceComputational technologies under the domain of intelligent systems are expected to help the rapidly increasing traffic congestion problem in recent traffic management. Traffic management requires efficient and accurate forecasting models to assist real time traffic control systems. Researchers have proposed various computational approaches, especially in short-term traffic flow forecasting, in order to establish reliable traffic patterns models and generate timely prediction results. Forecasting models should have high accuracy and low computational time to be applied in intelligent traffic management. Therefore, this paper aims to evaluate recent computational modeling approaches utilized in short-term traffic flow forecasting. These approaches are evaluated by real-world data collected on the British freeway (M6) from 1st to 30th November in 2014. The results indicate that neural network model outperforms generalized additive model and autoregressive integrated moving average model on the accuracy of freeway traffic forecasting

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Negative Binomial Additive Models for Short-Term Traffic Flow Forecasting in Urban Areas

Cited by 40 publications

References 36 publications

A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation

A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation

Accurate Real-Time Traffic Sign Recognition Based on the Connected Component Labeling and the Color Histogram Algorithms

Evaluation of Recent Computational Approaches in Short-Term Traffic Forecasting

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