Camera location for real-time traffic state estimation in urban road network using big GPS data

Shan, Zhenyu; Zhu, Qiuping

doi:10.1016/j.neucom.2014.11.093

Cited by 21 publications

(9 citation statements)

References 50 publications

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“…Notably, the visualization model is rarely considered as the main focus of a study (Zhong et al, 2016;, but is commonly used as a complement to other advanced data-mining models (Shan and Zhu, 2015;Zhang et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

Big data analytics in supply chain management: A state-of-the-art literature review

Nguyen

Zhou

Spiegler

et al. 2018

Computers & Operations Research

403

238

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Abstract:The rapidly growing interest from both academics and practitioners in the application of big data analytics (BDA) in supply chain management (SCM) has urged the need for review of up-to-date research development in order to develop a new agenda. This review responds to the call by proposing a novel classification framework that provides a full picture of current literature on where and how BDA has been applied within the SCM context. The classification framework is structurally based on the content analysis method of Mayring (2008) techniques are employed to develop these models? The discussion tackling these four questions reveals a number of research gaps, which leads to future research directions.

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Section: Resultsmentioning

confidence: 99%

Big data analytics in supply chain management: A state-of-the-art literature review

Nguyen

Zhou

Spiegler

et al. 2018

Computers & Operations Research

403

238

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show abstract

“…These simulations are carried out to determine the maximum value of data that can be obtained by inserting N camera sensors in the network, and then compare it with the values obtained when we place the cameras in the edges indicated by the centrality lists and with the proposed location system. The definition of experiments we have carried out is based on the proposal in [ 17 ]. In this work, the authors compare their proposal with two location methods called Random Road Location Method (RRLM) and Arterial Road Location Method (ARLM).…”

Section: Resultsmentioning

confidence: 99%

“…The drawbacks of using PSO are that they require large amounts of memory and that they might not be suitable for real-time applications. Based on using historical vehicular traffic data, Shan and Zhu [ 17 ] propose a methodology to locate camera sensors to estimate traffic in real time. As a data source, they use real GPS data from more than 8000 taxis.…”

Section: Related Workmentioning

confidence: 99%

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management

Cruz-Piris

Rivera

Fernández

et al. 2018

Sensors

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One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

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“…It is often perceived as an intersection between business intelligence, geographic analysis, and data visualization [1], [2]. Up to the present, geospatial analytics has retained a vital role as solution to various domains including retail business [3], [4], [5], [6] energy conservation [7], [8], [9], [10] agriculture [11], safety planning [12], [13] and road network [14]. The targeted domain that is tackled in this paper is retail site selection.…”

Section: Introductionmentioning

confidence: 99%

Retail Site Selection using Machine Learning Algorithms

Yee*¹,

Ting²,

Ho³

2019

IJRTE

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Selecting a new site for retail business expansion has always been a challenge for decision-makers. It requires not only the sales data but the geographic data in order to decide the potential location for their respective purposes. Proper use of the data could lead to better decision-making. To date, common techniques such as geographic information system (GIS) and multi-criteria decision making (MCDM) have been applied to site selection. These methods, however, require not only extensive human effort but more importantly, difficult to validate the importance of identified variables. In this work, sales performance is proposed as a function of geospatial features to determine the suitability of a retail location. The main aim of this study was to identify features attributed to optimal site selection which in turn facilitate sales prediction for a telecommunication company in Malaysia. In this research, various feature selection techniques and machine learning models were deployed for sales prediction in order to determine the suitability of the new location. The findings show the top 3 feature selections are prediction step in VSURF, random search, and fuse learner with search strategy; the top 3 families are boosting, random forest and bagging; and the top 3 classifiers are C5.0, rf, and parRF. The crossover combination of the top feature selection-classifier can produce the AUC of more than 0.75. The highest AUC, 0.8354 was obtained through random search-parRF.

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Camera location for real-time traffic state estimation in urban road network using big GPS data

Cited by 21 publications

References 50 publications

Big data analytics in supply chain management: A state-of-the-art literature review

Big data analytics in supply chain management: A state-of-the-art literature review

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management

Retail Site Selection using Machine Learning Algorithms

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