scholarly journals Deployment Strategy for Car-Sharing Depots by Clustering Urban Traffic Big Data Based on Affinity Propagation

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zhihan Liu ◽  
Yi Jia ◽  
Xiaolu Zhu
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
Optimization Method ◽  
Urban Traffic ◽  
Affinity Propagation ◽  
Superior Performance ◽  
Trajectory Data ◽  
Car Sharing ◽  
Gps Trajectory Data ◽  
Ap Clustering

Car sharing is a type of car rental service, by which consumers rent cars for short periods of time, often charged by hours. The analysis of urban traffic big data is full of importance and significance to determine locations of depots for car-sharing system. Taxi OD (Origin-Destination) is a typical dataset of urban traffic. The volume of the data is extremely large so that traditional data processing applications do not work well. In this paper, an optimization method to determine the depot locations by clustering taxi OD points with AP (Affinity Propagation) clustering algorithm has been presented. By analyzing the characteristics of AP clustering algorithm, AP clustering has been optimized hierarchically based on administrative region segmentation. Considering sparse similarity matrix of taxi OD points, the input parameters of AP clustering have been adapted. In the case study, we choose the OD pairs information from Beijing’s taxi GPS trajectory data. The number and locations of depots are determined by clustering the OD points based on the optimization AP clustering. We describe experimental results of our approach and compare it with standard K-means method using quantitative and stationarity index. Experiments on the real datasets show that the proposed method for determining car-sharing depots has a superior performance.

scholarly journals Extended Classification Course Improves Road Intersection Detection from Low-Frequency GPS Trajectory Data

2020 ◽  
Vol 9 (3) ◽  
pp. 181
Author(s):  
Banqiao Chen ◽  
Chibiao Ding ◽  
Wenjuan Ren ◽  
Guangluan Xu
Keyword(s):  
Clustering Algorithm ◽  
Recall Rate ◽  
Location Based Services ◽  
Trajectory Data ◽  
Intersection Detection ◽  
Map Generation ◽  
Gps Trajectory Data ◽  
Road Intersections ◽  
Road Maps ◽  
Gps Trajectory

The requirements of location-based services have generated an increasing need for up-to-date digital road maps. However, traditional methods are expensive and time-consuming, requiring many skilled operators. The feasibility of using massive GPS trajectory data provides a cheap and quick means for generating and updating road maps. The detection of road intersections, being the critical component of a road map, is a key problem in map generation. Unfortunately, low sampling rates and high disparities are ubiquitous among floating car data (FCD), making road intersection detection from such GPS trajectories very challenging. In this paper, we extend a point clustering-based road intersection detection framework to include a post-classification course, which utilizes the geometric features of road intersections. First, we propose a novel turn-point position compensation algorithm, in order to improve the concentration of selected turn-points under low sampling rates. The initial detection results given by the clustering algorithm are recall-focused. Then, we rule out false detections in an extended classification course based on an image thinning algorithm. The detection results of the proposed method are quantitatively evaluated by matching with intersections from OpenStreetMap using a variety of distance thresholds. Compared with other methods, our approach can achieve a much higher recall rate and better overall performance, thereby better supporting map generation and other similar applications.

scholarly journals A Contrastive Study on Travel Costs of Car-Sharing and Taxis Based on GPS Trajectory Data

2020 ◽  
Vol 17 (24) ◽  
pp. 9446
Author(s):  
Beibei Hu ◽  
Yue Sun ◽  
Huijun Sun ◽  
Xianlei Dong
Keyword(s):  
Market Competition ◽  
Travel Cost ◽  
Cost Calculation ◽  
Cost Advantage ◽  
Trajectory Data ◽  
Car Sharing ◽  
Travel Costs ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
The Cost

The emergence and development of car-sharing has not only satisfied people’s diverse travel needs, but also brought new solutions for improving urban traffic conditions and achieving low-carbon and green sustainable development. In recent years, car-sharing has had competition with other ways of getting around, as the acceptance of car-sharing has grown, notably taxis. Therefore, it is particularly important to explore car-sharing travel costs advantages from the perspective of consumers and discover the competitive and complementary spaces between car-sharing and other modes. Therefore, taking Beijing as an example, this paper uses GPS trajectory data based on car-sharing orders to design a travel cost framework of car-sharing and taxis. We calculate and compare the travel cost difference between these two modes under different travel characteristics. The results indicate that car-sharing is a more economical way for consumers to travel for short or medium lengths of time, while people are more inclined to take taxis for distances of long duration. Compared with on workdays, at the weekend, the cost advantage of car-sharing is greater for long-distance trips. Moreover, the cost advantage of car-sharing increases gradually with the increase in travel distance. In addition, the travel costs of car-sharing and taxis are also affected by peak and off-peak traffic periods. Compared with off-peak periods, it is more cost-effective for travelers to take taxis during peak traffic periods for various travel distances. From the perspective of the travel cost, it is of great theoretical significance to discuss the substitution (market competition) and complementary relationship (market cooperation) between car-sharing and taxis in a detailed and systematic way. It provides methods and ideas for the comparative cost calculation of car-sharing and other travel modes. This paper also provides enlightenment and guidance for the development of car-sharing. Enterprises should implement differentiated pricing, designing different charging methods for different traffic periods, travel miles, and rental times, and set up additional stations in the surrounding areas of the city. Relevant government departments should also strictly manage the market access of car-sharing, and add or open car-sharing parking lots in centralized areas and for specific periods.

Real-Time Piecewise Regression

2017 ◽  
Vol 2643 (1) ◽  
pp. 9-18
Author(s):  
Yang Zhang ◽  
Yuandong Liu ◽  
Lee D. Han
Keyword(s):  
Big Data ◽  
Numerical Algorithms ◽  
Streaming Data ◽  
Data Sets ◽  
Trajectory Data ◽  
Fixed Rate ◽  
Piecewise Regression ◽  
Ubiquitous Sensing ◽  
Gps Trajectory Data ◽  
Value Decomposition

Ubiquitous sensing technologies make big data a trendy topic and a favored approach in transportation studies and applications, but the increasing volumes of data sets present remarkable challenges to data collection, storage, transfer, visualization, and processing. Fundamental aspects of big data in transportation are discussed, including how many data to collect and how to collect data effectively and economically. The focus is GPS trajectory data, which are used widely in this domain. An incremental piecewise regression algorithm is used to evaluate and compress GPS locations as they are produced. Row-wise QR decomposition and singular value decomposition are shown to be valid numerical algorithms for incremental regression. Sliding window–based piecewise regression can subsample the GPS streaming data instantaneously to preserve only the points of interest. Algorithm performance is evaluated completely as accuracy and compression power. A procedure is presented for users to choose the best parameter value for their GPS devices. Results of experiments with real-world trajectory data indicate that when the proper parameter value is selected, the proposed method achieves significant compression power (more than 10 times), maintains acceptable accuracy (less than 5 m), and always outperforms the fixed-rate sampling approach.

scholarly journals The Effects of Munsell Neutral Grey Backgrounds on the Colour of Chrysoprase and the Application of AP Clustering to Chrysoprase Colour Grading

Minerals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1092
Author(s):  
Yuansheng Jiang ◽  
Ying Guo ◽  
Yufei Zhou ◽  
Xiang Li ◽  
Simin Liu
Keyword(s):  
Clustering Algorithm ◽  
Affinity Propagation ◽  
Fluorescent Lamp ◽  
Colour Difference ◽  
Green Colour ◽  
Difference Formula ◽  
First Time ◽  
Ap Clustering ◽  
Scientific Classification

Chrysoprase is a popular gemstone with consumers because of its charming apple green colour but a scientific classification of its colour has not yet been achieved. In this research, we determined the most effective background of the Munsell Chart for chrysoprase colour grading under a 6504 K fluorescent lamp and applied an affinity propagation (AP) clustering algorithm to the colour grading of coloured gems for the first time. Forty gem-quality chrysoprase samples from Australia were studied using a UV-VIS spectrophotometer and Munsell neutral grey backgrounds. The results determined the effects of a Munsell neutral grey background on the observed colour. It was found that the Munsell N9.5 background was the most effective for colour grading in this case. The observed chrysoprase colours were classified into five groups: Fancy Light, Fancy, Fancy Intense, Fancy Deep and Fancy Dark. The feasibility of the colour grading scheme was verified using the colour difference formula DE2000.

scholarly journals Clustering Mining of Urban Traffic Flow Based on CVAE

2020 ◽  
pp. 34-44
Author(s):  
Min-Tong Su ◽  
◽  
Jin Zheng ◽  
Zu-Ping Zhang
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Clustering Algorithm ◽  
Control Strategies ◽  
Urban Traffic ◽  
Living Standards ◽  
Temporal Data ◽  
Low Dimensional ◽  
The City ◽  
Ap Clustering

Understanding the urban traffic flow at intersections is helpful to formulate traffic control strategies, so as to ease traffic pressure and improve people's living standards. There are many related researches on traffic flow, and similarity research is one of them. Different from the traditional way, this paper studies the traffic flow from the perspective of image similarity. The Convolutional Variational Auto-Encoder (CVAE) is introduced to extract the low-dimensional features of traffic flow during a day, and Affinity Propagation (AP) clustering algorithm is used to cluster the features without real labels. Combining the clustering results with geographic coordinates reveals the distribution pattern of traffic flow. The experimental data includes about 10 million vehicle records at 650 intersections in Changsha on a certain day. The clustering results show that the traffic flow at the intersection of Changsha City can be divided into three categories according to the time-variant trends, and the distribution of each category basically conforms to the daily traffic laws of the city. Furthermore, the effectiveness of the clustering process is further verified by clustering the open source temporal data of different lengths.

scholarly journals Urban Hotspot Area Detection Using Nearest-Neighborhood-Related Quality Clustering on Taxi Trajectory Data

2021 ◽  
Vol 10 (7) ◽  
pp. 473
Author(s):  
Qingying Yu ◽  
Chuanming Chen ◽  
Liping Sun ◽  
Xiaoyao Zheng
Keyword(s):  
Clustering Algorithm ◽  
Local Density ◽  
Real Life ◽  
Urban Traffic ◽  
Cluster Center ◽  
Trajectory Data ◽  
Related Quality ◽  
Nearest Neighborhood ◽  
The Impact ◽  
Taxi Trajectory

Urban hotspot area detection is an important issue that needs to be explored for urban planning and traffic management. It is of great significance to mine hotspots from taxi trajectory data, which reflect residents’ travel characteristics and the operational status of urban traffic. The existing clustering methods mainly concentrate on the number of objects contained in an area within a specified size, neglecting the impact of the local density and the tightness between objects. Hence, a novel algorithm is proposed for detecting urban hotspots from taxi trajectory data based on nearest neighborhood-related quality clustering techniques. The proposed spatial clustering algorithm not only considers the maximum clustering in a limited range but also considers the relationship between each cluster center and its nearest neighborhood, effectively addressing the clustering issue of unevenly distributed datasets. As a result, the proposed algorithm obtains high-quality clustering results. The visual representation and simulated experimental results on a real-life cab trajectory dataset show that the proposed algorithm is suitable for inferring urban hotspot areas, and that it obtains better accuracy than traditional density-based methods.

scholarly journals Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Shuoben Bi ◽  
Ruizhuang Xu ◽  
Aili Liu ◽  
Luye Wang ◽  
Lei Wan
Keyword(s):  
Information Entropy ◽  
Input Data ◽  
Clustering Algorithm ◽  
Scientific Basis ◽  
Urban Traffic ◽  
Massive Data ◽  
Trajectory Data ◽  
Research Areas ◽  
Density Based Clustering ◽  
Traffic Guidance

In view of the fact that the density-based clustering algorithm is sensitive to the input data, which results in the limitation of computing space and poor timeliness, a new method is proposed based on grid information entropy clustering algorithm for mining hotspots of taxi passengers. This paper selects representative geographical areas of Nanjing and Beijing as the research areas and uses information entropy and aggregation degree to analyze the distribution of passenger-carrying points. This algorithm uses a grid instead of original trajectory data to calculate and excavate taxi passenger hotspots. Through the comparison and analysis of the data of taxi loading points in Nanjing and Beijing, it is found that the experimental results are consistent with the actual urban passenger hotspots, which verifies the effectiveness of the algorithm. It overcomes the shortcomings of a density-based clustering algorithm that is limited by computing space and poor timeliness, reduces the size of data needed to be processed, and has greater flexibility to process and analyze massive data. The research results can provide an important scientific basis for urban traffic guidance and urban management.

scholarly journals K-affinity propagation clustering algorithm for the classification of part-time workers using the internet

2021 ◽  
Vol 24 (1) ◽  
pp. 464
Author(s):  
Novendri Isra Asriny ◽  
Muhammad Muhajir ◽  
Devi Andrian
Keyword(s):  
Clustering Algorithm ◽  
Optimal Number ◽  
Affinity Propagation ◽  
The Internet ◽  
Affinity Propagation Clustering ◽  
Part Time ◽  
Second Period ◽  
Ap Clustering ◽  
Cluster Points

There has been a significant increase in the number of part-time workers in the last 3 years. Data collected from sakernas BPS showed that the number of part-time workers was 125,443,748 in the second period of 2016. This number rapidly increased in 2017, 2018 and 2019 in the same period, by 128,062,746, 131,005,641, and 133,560,880 workers. Based on the increase in the last 3 years, East Java province has the highest number of part-time workers that use the internet. This research aims to determine the number of part-time workers that use the internet by using the k-affinity propagation (K-AP) clustering. This method is used to produce the optimal number of cluster points (exemplar) is the affinity propagation (AP). Three clusters were used to determine the sum of the smallest value ratio. The result showed that clusters 1, 2, and 3 have 3, 23, and 5 members in Bondowoso, Jombang, and Surabaya districts.

scholarly journals Identifying the Key Nodes and Sections of Urban Roadway Network Based on GPS Trajectory Data

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jing Wang ◽  
Chunjiao Dong ◽  
Chunfu Shao ◽  
Shichen Huang ◽  
Shuang Wang
Keyword(s):  
Network Model ◽  
Evaluation System ◽  
Large Scale ◽  
Urban Traffic ◽  
Identification Algorithm ◽  
Trajectory Data ◽  
Dynamic Traffic ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
Key Nodes

This paper proposes a novel approach to identify the key nodes and sections of the roadway network. The taxi-GPS trajectory data are regarded as mobile sensor to probe a large scale of urban traffic flows in real time. First, the urban primary roadway network model and dual roadway network model are developed, respectively, based on the weighted complex network. Second, an evaluation system of the key nodes and sections is developed from the aspects of dynamic traffic attributes and static topology. At the end, the taxi-GPS data collected in Xicheng District of Beijing, China, are analyzed. A comprehensive analysis of the spatial-temporal changes of the key nodes and sections is performed. Moreover, the repetition rate is used to evaluate the performance of the identification algorithm of key nodes and sections. The results show that the proposed method realizes the expression of topological structure and dynamic traffic attributes of the roadway network simultaneously, which is more practicable and effective in a large scale.

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