Automatic Ground Points Identification Method for Roadside LiDAR Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 140-152 ◽  
Author(s):  
Jianqing Wu ◽  
Hao Xu ◽  
Bin Lv ◽  
Rui Yue ◽  
Yang Li
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Transition Period ◽  
Ground Surface ◽  
Lidar Data ◽  
Connected Vehicle ◽  
Time Data ◽  
The Real ◽  
Traffic Volumes ◽  
Real Time Data Processing

Roadside light detection and ranging (LiDAR) provides a solution to fill the data gap under mixed traffic situations. The real-time high-resolution micro traffic data (HRMTD) of all road users from the roadside LiDAR sensor provides a new opportunity to serve the connected-vehicle system during the transition period from unconnected vehicles to connected vehicles. Ground surface identification is the basic data processing step for HRMTD collection. The current ground points identification algorithms based on airborne and mobile LiDAR do not work for roadside LiDAR. A novel algorithm is developed in this paper to identify and exclude ground points based on the features of LiDAR, terrain, and point density in the space. The scan feature of different beams is used to search ground points. The whole procedure can be divided into four major parts: points clustering in each beam, slope-based filtering, shape-based filtering, and ground points matrix extraction. The proposed algorithm was evaluated using the real-world LiDAR data collected at different scenarios. The results showed that this algorithm can be used for ground points exclusion under different situations (differing terrain types, weather situations, and traffic volumes) with high accuracy. This algorithm was compared with previously developed algorithms. The overall performance of the proposed algorithm is superior. The low computational load guarantees this method may be applied for real-time data processing.

Automatic Background Filtering Method for Roadside LiDAR Data

2018 ◽  
Vol 2672 (45) ◽  
pp. 106-114 ◽  
Author(s):  
Jianqing Wu ◽  
Hao Xu ◽  
Yuan Sun ◽  
Jianying Zheng ◽  
Rui Yue
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Point Clouds ◽  
Identification Accuracy ◽  
Lidar Data ◽  
Connected Vehicle ◽  
Time Data ◽  
Night Time ◽  
3D Point Clouds ◽  
Real Time Data Processing

The high-resolution micro traffic data (HRMTD) of all roadway users is important for serving the connected-vehicle system in mixed traffic situations. The roadside LiDAR sensor gives a solution to providing HRMTD from real-time 3D point clouds of its scanned objects. Background filtering is the preprocessing step to obtain the HRMTD of different roadway users from roadside LiDAR data. It can significantly reduce the data processing time and improve the vehicle/pedestrian identification accuracy. An algorithm is proposed in this paper, based on the spatial distribution of laser points, which filters both static and moving background efficiently. Various thresholds of point density are applied in this algorithm to exclude background at different distances from the roadside sensor. The case study shows that the algorithm can filter background LiDAR points in different situations (different road geometries, different traffic demands, day/night time, different speed limits). Vehicle and pedestrian shape can be retained well after background filtering. The low computational load guarantees this method can be applied for real-time data processing such as vehicle monitoring and pedestrian tracking.

Customer behavior analysis using real-time data processing

2019 ◽  
Vol 31 (1) ◽  
pp. 265-290 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Fazal Ijaz ◽  
Muhammad Syafrudin ◽  
M. Alex Syaekhoni ◽  
Norma Latif Fitriyani ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Association Rule ◽  
Customer Behavior ◽  
Time Data ◽  
Content Type ◽  
The Real ◽  
Real Time Data ◽  
Real Time Data Processing ◽  
Big Data Technology

PurposeThe purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.Design/methodology/approachIn order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.FindingsFirst, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.Research limitations/implicationsThis research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.Practical implicationsThe proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.Social implicationsThe proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.Originality/valueThe key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

Aerocraft Real-Time Measured Data Processing Method Based on Combined Filtering

2014 ◽  
Vol 716-717 ◽  
pp. 983-986
Author(s):  
Yan Li ◽  
Hua Jun Liu ◽  
Guang Lang Bian ◽  
Miao Hui Liu
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Measured Data ◽  
Control Procedure ◽  
Time Data ◽  
The Real ◽  
Data Processing Method ◽  
Real Time Data ◽  
Processing Accuracy ◽  
Real Time Data Processing

To solve the problems that resulted from using a certain filtering method alone to process the real-time data measured on aerocraft, a new method combined filter and Savitzky-Golay smoothing filter is proposed to process the real-time measuring data, which could classify and segment the measured data of aerocraft trajectory according to its priority and time domain. It could provide useful principle and control procedure of combined filters on different conditions to improve the filter efficiency, and the combined filtering results meet the needs of aerocraft real-time data processing accuracy in different measured sections.

scholarly journals The real-time big data processing method based on LSTM or GRU for the smart job shop production process

2020 ◽  
Vol 14 ◽  
pp. 174830262096239 ◽  
Author(s):  
Chuang Wang ◽  
Wenbo Du ◽  
Zhixiang Zhu ◽  
Zhifeng Yue
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Production Process ◽  
Real Time ◽  
Job Shop ◽  
Production Data ◽  
Time Data ◽  
Big Data Processing ◽  
The Real ◽  
Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

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