Passenger flow forecast for customized bus based on time series fuzzy clustering algorithm

2019 ◽  
Vol 20 (1) ◽  
pp. 42-60 ◽  
Author(s):  
Ming Li ◽  
Linlin Wang ◽  
Jingfeng Yang ◽  
Zhenkun Zhang ◽  
Nanfeng Zhang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Clustering Algorithm ◽  
Scheduling Algorithm ◽  
Flow Analysis ◽  
Urban Traffic ◽  
Flow Data ◽  
Line Planning ◽  
Passenger Flow ◽  
Bus Services ◽  
Operation And Management

Abstract Customized bus services are conducive to improving urban traffic and environment, and have attracted widespread attention. However, the problems encountered in the new customized bus mode include the large difference between the basis of customized bus passenger flow data analysis and the basis of the traditional bus passenger flow data analysis, and the difficulty in different vehicle scheduling caused by the combination of traditional and customized bus modes. We propose a customized bus passenger flow analysis algorithm and multi-destination customized bus line capacity scheduling algorithm, and display them in an intuitive way. The experimental results show that the algorithm model established in this paper can basically meet the data requirements of operation and management, and can provide decision support for customized bus line planning.

scholarly journals Data Analysis of Pile-free bike-sharing from Market Status to Operation Management Mode

2021 ◽  
Vol 251 ◽  
pp. 01052
Author(s):  
Jingyu Liang ◽  
Yiqing Zhu ◽  
Peixue Lin
Keyword(s):  
Data Analysis ◽  
Active Role ◽  
Urban Traffic ◽  
Management Mode ◽  
Future Research ◽  
Bike Sharing ◽  
Market Status ◽  
Subject Areas ◽  
The Individual ◽  
Operation And Management

Shared travel plays a more and more active role in the emergence of urban traffic, but there is no systematic and perfect combing and analysis on the study of bike-sharing without piles, especially on its operation and management mode. This paper will conduct a comprehensive data analysis on the research of bike-sharing without piles from the individual micro level and the enterprise government level, and explore the research focus and possible subject areas of future research.

Large Sections Peak Traffic Flow Subway Station Simulation Modeling

2014 ◽  
Vol 1065-1069 ◽  
pp. 3325-3328
Author(s):  
Xin Hua Zhang ◽  
Shu Hao Xu ◽  
Li Li Wu ◽  
Yin Hua Du ◽  
Zhi Jun Duan ◽  
...  
Keyword(s):  
Simulation Modeling ◽  
Flow Analysis ◽  
Local Area ◽  
Subway Station ◽  
Peak Time ◽  
Mathematical Software ◽  
Flow Data ◽  
Traffic Dynamics ◽  
Passenger Flow ◽  
Large Sections

This paper, three subway lines converge site meet a change to the peak time for passenger flow analysis, with the analysis of passenger flow field physical statistics, image processing technology to guide passenger flow data, analysis of local area biggest traffic speed and density, traffic dynamics theory, the application of mathematical software Matlab, establish mathematical model.

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

DESIGN OF AN INTERACTIVE VIRTUAL FACTORY USING CELL FORMATION METHODOLOGIES

2003 ◽  
Vol 02 (02) ◽  
pp. 229-246 ◽  
Author(s):  
T. KESAVADAS ◽  
M. ERNZER
Keyword(s):  
Virtual Environment ◽  
Clustering Algorithm ◽  
Flow Analysis ◽  
Cell Formation ◽  
Open Architecture ◽  
Successful Implementation ◽  
Design Environment ◽  
Virtual Factory ◽  
Software Modules ◽  
And Performance

This paper describes an interactive virtual environment for modeling and designing factories and shop floors. The factory building tool is developed as an open architecture in which various modules can be utilized to quickly implement factory design algorithms ranging from plant layout to factory flow analysis. Software modules and utilities have been implemented to allow easy set-up of the visual interface. In this paper, this virtual factory is used to implement cellular manufacturing (CM) system. CM has traditionally been a very complicated system to implement in practice. However successful implementation of the system has improved productivity immersely. Several issues involved in implementing CM within our virtual factory machine modeling and interface designs for defining the cells, are discussed. The mathematical clustering algorithm called Modified Boolean Method was implemented to automatically generate complex virtual environments. The virtual factory makes the process of CM-based factory design a very easy and intuitive process. Though the cell formation problem is NP-complete in 2D space, issues related to human factors and ergonomics can be better perceived in a 3D virtual environment. It also leads to further optimization with respect to maintainability and performance, and thus help get better solutions, which are not visible unless the factory is built. Our virtual factory interface also allows easy reassignment of machines and parts, subcontracting of bottleneck parts and rearranging of machines within the same design environment, making this a productive industrial tool. 3D virtual factory can also be automatically generated from the Part Machine interface called the Virtual Matrix Interface.

Collaborative passenger flow control of urban rail transit network considering balanced distribution of passengers

2021 ◽  
pp. 2150461
Author(s):  
Xiang Li ◽  
Yan Bai ◽  
Kaixiong Su
Keyword(s):  
Flow Control ◽  
Programming Model ◽  
Flow Characteristics ◽  
Service Level ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Control Measures ◽  
Rail Transit ◽  
Passenger Flow ◽  
Urban Rail

The increase of urban traffic demands has directly affected some large cities that are now dealing with more serious urban rail transit congestion. In order to ensure the travel efficiency of passengers and improve the service level of urban rail transit, we proposed a multi-line collaborative passenger flow control model for urban rail transit networks. The model constructed here is based on passenger flow characteristics and congestion propagation rules. Considering the passenger demand constraints, as well as section transport and station capacity constraints, a linear programming model is established with the aim of minimizing total delayed time of passengers and minimizing control intensities at each station. The network constructed by Line 2, Line 6 and Line 8 of the Beijing metro is the study case used in this research to analyze control stations, control durations and control intensities. The results show that the number of delayed passengers is significantly reduced and the average flow control ratio is relatively balanced at each station, which indicates that the model can effectively relieve congestion and provide quantitative references for urban rail transit operators to come up with new and more effective passenger flow control measures.

New Intelligent Transport Systems Applications in Europe to Improve Bus Services

2002 ◽  
Vol 1791 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Nick Hounsell ◽  
Graham Wall
Keyword(s):  
Public Transport ◽  
Traffic Control ◽  
Urban Traffic ◽  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Bus Priority ◽  
Intelligent Transport ◽  
Message Signs ◽  
Investment Process ◽  
Bus Services

Applications of information technology are expanding rapidly across all modes of transport, under the general heading of intelligent transport systems (ITS). For bus-based public transport, a cluster of applications has been developed that can help improve the efficiency and performance of buses on the street, thus helping to provide a real transport alternative to the private car. An initial summary of a range of such ITS examples in Europe is provided, including automatic vehicle location (AVL), bus priority in traffic control systems, automatic ticketing systems, automatic camera enforcement systems, and variable message signs. Then the focus shifts to one area where activity is most pronounced—the implementation of AVL systems and their integration with urban traffic control (UTC) systems. A review of typical AVL/UTC systems operational in Europe is then presented through the identification of some eight alternative architectures and associated system characteristics, such as the technologies used and the location of bus priority “intelligence.” This is followed by a summary of examples from cities in Europe that have implemented these architectures, together with typical results illustrating the effectiveness of these systems. The diversity of architectures, technologies, and systems is recognized as beneficial in providing customer choice, but can generate a significant difficulty for decision makers in local authorities wishing to invest in public transport ITS technologies. A concluding discussion lists some of the key issues involved in this investment process.

scholarly journals eHMCOKE: an enhanced overlapping clustering algorithm for data analysis

2021 ◽  
Vol 10 (4) ◽  
pp. 2212-2222
Author(s):  
Alvincent E. Danganan ◽  
Edjie Malonzo De Los Reyes
Keyword(s):  
Data Analysis ◽  
Clustering Algorithm ◽  
Median Absolute Deviation ◽  
Accuracy Rate ◽  
Absolute Deviation ◽  
Improvement Rate ◽  
Overlapping Clustering ◽  
Overlapping Clusters ◽  
Runtime Performance

Improved multi-cluster overlapping k-means extension (IMCOKE) uses median absolute deviation (MAD) in detecting outliers in datasets makes the algorithm more effective with regards to overlapping clustering. Nevertheless, analysis of the applied MAD positioning was not considered. In this paper, the incorporation of MAD used to detect outliers in the datasets was analyzed to determine the appropriate position in identifying the outlier before applying it in the clustering application. And the assumption of the study was the size of the cluster and cluster that are close to each other can led to a higher runtime performance in terms of overlapping clusters. Therefore, additional parameters such as radius of clusters and distance between clusters are added measurements in the algorithm procedures. Evaluation was done through experimentations using synthetic and real datasets. The performance of the eHMCOKE was evaluated via F1-measure criterion, speed and percentage of improvement. Evaluation results revealed that the eHMCOKE takes less time to discover overlap clusters with an improvement rate of 22% and achieved the best performance of 91.5% accuracy rate via F1-measure in identifying overlapping clusters over the IMCOKE algorithm. These results proved that the eHMCOKE significantly outruns the IMCOKE algorithm on mosts of the test conducted.

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