Optimization of real-time traffic network assignment based on IoT data using DBN and clustering model in smart city

Yang, Jiachen; Han, Yurong; Wang, Yafang; Jiang, Bin; Lv, Zhihan; Song, Houbing

doi:10.1016/j.future.2017.12.012

Cited by 78 publications

(38 citation statements)

References 33 publications

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“…One of the most significant areas of interest in smart transportation is navigation or route optimization. Using data from the users' mobile devices [12], or with side units placed in specified locations on the road [13], applications try to estimate traffic congestion and propose optimal route options to minimize traveling times, and therefore reduce car emissions and energy consumption. Furthermore, to support the energy consumption reduction, street lights are proposed that can detect traffic conditions and operate accordingly, instead of being constantly on with a time schedule.…”

Section: Smart Transportationmentioning

confidence: 99%

“…In [12], the purpose is to optimize the traffic network configuration by estimating the best number and location of the processing centers for a sum of suppliers. The authors use K-Means to calculate the distance of the suppliers and the centers, so as to minimize the cost.…”

Section: Clusteringmentioning

confidence: 99%

“…As mentioned earlier, the purpose of [12] is to perform real-time traffic network assignments. This study uses a Deep Belief Network (DBN) to pre-process input data and initialize the clustering centers that are fed in K-Means to perform the needed clustering.…”

Section: Deep Learningmentioning

confidence: 99%

“…Namely, in [36], an MDP algorithm and V2V communication have been used for a group routing to minimize traffic congestion. In [12], a combination of k-Means and DBN is preferred to optimize the traffic network configuration. Short-term traffic forecasting is addressed in [18], and it is faced with multiple approaches such as a BN-SARIMA, an FF-NN, and a NARX model.…”

Section: Route Optimizationmentioning

confidence: 99%

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A Review of Machine Learning and IoT in Smart Transportation

et al. 2019

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With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

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Section: Smart Transportationmentioning

confidence: 99%

Section: Clusteringmentioning

confidence: 99%

Section: Deep Learningmentioning

confidence: 99%

Section: Route Optimizationmentioning

confidence: 99%

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A Review of Machine Learning and IoT in Smart Transportation

et al. 2019

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“…The objective of this algorithm is to group data items into a K number of clusters. Example of IoT use-case [77] Predictive analytics Q-learning To forward a query to a proper query processor -that implemented K-Means clustering is optimization of real-time traffic network assignment [73]. Apriori is an extensively used algorithm for association rule mining.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Data Analytics in the Internet of Things: A Survey

Saleem

Chishti

2019

SCPE

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The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented volumes of data, thereby creating a data deluge. Data collected from these sensors can be used to comprehend, examine and control intricate environments around us, facilitating greater intelligence, smarter decision-making, and better performance. The key challenge here is how to mine out proﬁcient information from such immense data. Copious solutions have been put forth to obtain valuable inferences and insights, however, these solutions are still in their developing stages. Moreover, conventional procedures do not address the surging analytical demands of IoT systems. Motivated to resolve this concern, this work investigates the key enablers for performing desired data analytics in IoT applications. A comprehensive survey on the identiﬁed key enablers including their role in IoT data analytics, use cases in which they have been applied and the corresponding IoT applications for the use cases is presented. Furthermore, open research challenges and future research opportunities are also discussed. This article can be used as a basis to foster advanced research in the arena of IoT data analytics.

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