Fuzzy deep learning based urban traffic incident detection

Hatri, Chaimae El; Boumhidi, Jaouad

doi:10.1016/j.cogsys.2017.12.002

Cited by 62 publications

(23 citation statements)

References 11 publications

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“…The following notable techniques have been applied to traffic accident detection: a dynamic Bayesian network by Sun and Sun [21]; a data mining technique for injury severity prediction by Chong et el. [22]; Bayesian logistic regression for crash risk analysis by Wang et al [23]; and fuzzy deep learning for urban traffic incidence detection by Hatri and Boumhidi [24].…”

Section: Previous Researchesmentioning

confidence: 99%

Temporal Impulse of Traffic Accidents in South Korea

Shin

Lee

2020

IEEE Access

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With the emergence of urban computing technology, the development of smart cities has gained much attention as a means to improve citizens' quality of life. As traffic accidents constitute a major problem that affects the quality of life, an effective solution to address this problem can significantly increase the level of intelligence of smart cities. This paper presents the development of a mathematical model for accurate analysis of big data to promote the effectiveness of policy decisions, thereby largely advancing the intelligent transportation systems (ITS) of smart cities. Temporal impulse was designed as a novel and measurable quantity to analyze traffic accidents by identifying the hidden patterns, such as varying causes and diverging impacts of traffic accidents. Based on the big data produced by the South Korean National Police Agency, we analyzed traffic accidents over three years by applying the temporal impulse. The research results suggested that the temporal impulse not only helped in identifying the varying influence of weather and driver conditions but also facilitated the establishment of sophisticated policies in the implementation of smart cities with the use of urban computing technology. As presented in the section VII, our simulation outputs indicated that our temporal model was predictive within the parameter space comprising driver's dynamic behaviors, day of the week, and environmental factors including weather, road surface condition, and road type. INDEX TERMS Big data, traffic accident data, intelligent transportation systems, smart city technology, stochastic process, time series, and temporal impulse.

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Section: Previous Researchesmentioning

confidence: 99%

Temporal Impulse of Traffic Accidents in South Korea

Shin

Lee

2020

IEEE Access

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“…At the same time, it can effectively solve the problems of high data dimension, difficult sample labeling, difficult feature construction, and difficult training in the era of big data. e literature in [10] introduces the combination of extreme learning machine and self-encoder for the first time. It is believed that the feature expression ability of ELM-AE can provide a good solution for multilayer feedforward neural network.…”

Section: Introductionmentioning

confidence: 99%

A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection

Fang

Yang

Zheng

et al. 2020

Mathematical Problems in Engineering

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In Beijing, Shanghai, Hangzhou, and other cities in China, traffic congestion caused by traffic incidents also accounts for 50% to 75% of the total traffic congestion on expressways. Therefore, it is of great significance to study an accurate and timely automatic traffic incident detection algorithm for ensuring the operation efficiency of expressways and improving the level of road safety. At present, many effective automatic event detection algorithms have been proposed, but the existing algorithms usually take the original traffic flow parameters as input variables, ignoring the construction of feature variable sets and the screening of important feature variables. This paper presents an automatic event detection algorithm based on deep cycle limit learning machine. The traffic flow, speed, and occupancy of downstream urban expressway are extracted as input values of the deep-loop neural network. The initial connection weights and output thresholds of the deep-loop neural network are optimized by using the improved particle swarm optimization (PSO) algorithm for global search. The higher classification accuracy of the extreme learning machine is trained, and the generalization performance of the extreme learning machine is improved. In addition, the extreme learning machine is used as a learning unit for unsupervised learning layer by layer. Finally, the microwave detector data of Tangqiao viaduct in Hangzhou are used to verify the experiment and compared with LSTM, CNN, gradient-enhanced regression tree, SVM, BPNN, and other methods. The results show that the algorithm can transfer low-level features layer by layer to form a more complete feature representation, retaining more original input information. It can save expensive computing resources and reduce the complexity of the model. Moreover, the detection accuracy of the algorithm is high, the detection rate is higher than 98%, and the false alarm rate is lower than 3%. It is better than LSTM, CNN, gradient-enhanced regression tree, and other algorithms. It is suitable for urban expressway traffic incident detection.

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“…Overall, this technique has shown better problem-solving efficiency compared to other methods (33)(34)(35)(36)(37)(38). Some studies have also supported the uncertainty and fuzzy states, incorporating the latter into other techniques (39)(40)(41). Such solutions have been proposed for AT problems and are based on the Internet of Things (IoT) (42)(43)(44), optimization methods (45)(46)(47)(48), multi-objective optimization techniques (49)(50)(51), and intelligent agent-based methods (52).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Air Traffic Management Efficiency Based on Deep Learning Enriched by the Long Short-term Memory(LSTM) and Extreme Learning Machine(ELM)

Aghdam

Tabbakh

Chabok

et al. 2020

Preprint

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Air traffic management refers to the activities required for the efficient and safe management of the national air system (NAS) for each country. This concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. The present study aimed to increase ATM efficiency using the deep learning approach. The main research objective was to propose a deep learning model with the application of a long short-term memory-based deep learning model in order to increase the predictive accuracy in short daily and long-term annual windows by enhancing deep learning (two-dimensional). In addition, the deep model output was transferred to the extreme learning machine fast learning deep neural machine in order to calculate the estimated time of arrival real-time based on other similar input data, including the NAS data, bureau of transportation statistics system, and automatic dependent surveillance-broadcast system. The final results indicated the increased accuracy of ATM compared to other studies.

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Fuzzy deep learning based urban traffic incident detection

Cited by 62 publications

References 11 publications

Temporal Impulse of Traffic Accidents in South Korea

Temporal Impulse of Traffic Accidents in South Korea

A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection

Optimization of Air Traffic Management Efficiency Based on Deep Learning Enriched by the Long Short-term Memory(LSTM) and Extreme Learning Machine(ELM)

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