Bi-Objective Subway Timetable Optimization Considering Changing Train Quality Based on Passenger Flow Data

2021 ◽  
pp. 149-161
Author(s):  
Hanlei Wang ◽  
Peng Wu ◽  
Yuan Yao ◽  
Xingxuan Zhuo
Keyword(s):  
Flow Data ◽  
Passenger Flow ◽  
Timetable Optimization

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.

The Managing and Analysis of Data Information in Monitoring the Passenger Flow

2013 ◽  
Vol 416-417 ◽  
pp. 2033-2037
Author(s):  
Xiao Yu Ji ◽  
Jie He ◽  
Jin Hu Peng
Keyword(s):  
Future Research ◽  
Human Beings ◽  
Flow Data ◽  
Primary Condition ◽  
Monitoring Plan ◽  
Flow Monitoring ◽  
Passenger Flow ◽  
Calculating Method ◽  
The Future ◽  
The City

Passenger flow is the circulating with purpose which is formed by human beings who want to achieve all kinds of trip activities through the help of various transportations. It is an important studying content in controlling the city traffic as well. As for the necessary monitoring of passenger flow, the primary condition is grasping the precise passenger flow data and mapping out an effective monitoring plan based on the facts and data. The paper first introduced the necessarily in monitoring and the appliance of usual calculating method of passenger flow. Based on this, the paper also put forward the managing and analysis of data information in the calculating methods of passenger flow monitoring, which is hoped to be helpful in the future research.

scholarly journals Revisiting the Demography of Disaster: Population Estimates After Hurricane Maria

2019 ◽  
Author(s):  
Alexis R Santos-Lozada
Keyword(s):  
Puerto Rico ◽  
Total Population ◽  
Vital Statistics ◽  
Population Estimates ◽  
Flow Data ◽  
Monthly Basis ◽  
Administrative Records ◽  
Passenger Flow ◽  
Migration Flows ◽  
The U.S

Hurricane María made landfall in Puerto Rico, in September 2017, causing economic damages and affecting the population by increasing temporarily increasing mortality and outgoing passenger flow. Because of the disruption in the migration flows, the volatility of this time series we must approach the production of population estimates, projections and forecasts carefully. Given that population estimates have been difficult to produce for Puerto Rico before Hurricane Maria and even more challenging following this disaster, this paper proposes an application of the demographic balancing equation using administrative records to produce population estimates on a monthly basis for Puerto Rico. A combination of data from: (1) monthly counts for deaths and births obtained from the Puerto Rico Vital Statistics Systems, (2) passenger flow data produced by the U.S. Bureau of Transportation Statistics, and (3) baseline census counts. I employ this approach to produce monthly estimates of the population for Puerto Rico, and use 2010 Census counts to assess the accuracy of the model. According to the 2010 decennial census, the population of Puerto Rico was 3,725,789 people; by employing the demographic balancing equation approach, the population was estimated to be 3,669,676 people in April 1, 2010. Using this model, I find that after Hurricane Maria, the population of Puerto Rico reached less than 3 million persons in December 2017 (2.97 million). The total population went back to over 3.0 million by January 2018 with an estimated population of 3.02 million people on September 2018.

scholarly journals Comparing passenger flow and time schedule data to analyse High-Speed Railways and urban networks in China

Urban Studies ◽  
2018 ◽  
Vol 56 (6) ◽  
pp. 1267-1287 ◽  
Author(s):  
Haoran Yang ◽  
Martin Dijst ◽  
Patrick Witte ◽  
Hans van Ginkel ◽  
Jiao’e Wang
Keyword(s):  
High Speed ◽  
Analytical Framework ◽  
Spatial Effects ◽  
Flow Data ◽  
Time Schedule ◽  
Urban Networks ◽  
Passenger Flow ◽  
Different Types ◽  
High Speed Trains ◽  
The Difference

China’s High-Speed Railways (HSR) network is the biggest in the world, transporting large numbers of passengers by high-speed trains through urban networks. Little is known about the analytical meaning of the use of two types of flow data, namely, time schedule (transportation mode flow) and passenger flow data, to characterise the configuration of urban networks regarding the potential spatial effects of HSR networks on urban networks. In this article, we compare HSR passenger flow data with time schedule data from 2013 in China within the same analytical framework. The findings show great differences in the strength of cities and links generated using the two different types of flow data. These differences can be explained largely by the socio-economic attributes of the cities involved, such as tertiary employment, GDP per capita, the cities’ topological properties (closeness centrality) in HSR networks and institutional factors (hub status), especially for the difference in link strength. The strength of first-tier cities in China with high socio-economic performance and the HSR links connecting core cites and major cities within respective regions tends to be underestimated when using time schedule flows compared with passenger flows. When analysing the spatial structure of HSR and urban networks by means of flows, it is important for urban geographers and transportation planners to consider the meaning of the different types of data with the analytical results.

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 Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations

2019 ◽  
Vol 11 (19) ◽  
pp. 5281 ◽  
Author(s):  
Peikun Li ◽  
Chaoqun Ma ◽  
Jing Ning ◽  
Yun Wang ◽  
Caihua Zhu
Keyword(s):  
Prediction Error ◽  
Prediction Accuracy ◽  
Goodness Of Fit ◽  
Pearson Correlation ◽  
Support Vector ◽  
Time Interval ◽  
Flow Data ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction

The improvement of accuracy of short-term passenger flow prediction plays a key role in the efficient and sustainable development of metro operation. The primary objective of this study is to explore the factors that influence prediction accuracy from time granularity and station class. An important aim of the study was also in presenting the proposition of change in a forecasting method. Passenger flow data from 87 Metro stations in Xi’an was collected and analyzed. A framework of short-term passenger flow based on the Empirical Mode Decomposition-Support Vector Regression (EMD-SVR) was proposed to predict passenger flow for different types of stations. Also, the relationship between the generation of passenger flow prediction error and passenger flow data was investigated. First, the metro network was classified into four categories by using eight clustering factors based on the characteristics of inbound passenger flow. Second, Pearson correlation coefficient was utilized to explore the time interval and time granularity for short-term passenger flow prediction. Third, the EMD-SVR was used to predict the passenger flow in the optimal time interval for each station. Results showed that the proposed approach has a significant improvement compared to the traditional passenger flow forecast approach. Lookback Volatility (LVB) was applied to reflect the fluctuation difference of passenger flow data, and the linear fitting of prediction error was conducted. The goodness-of-fit (R2) was found to be 0.768, indicating a good fitting of the data. Furthermore, it revealed that there are obvious differences in the prediction error of the four kinds of stations.

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