Effect of group behavior on pedestrian choice for vertical walking facilities

2020 ◽  
Vol 34 (07) ◽  
pp. 2050056
Author(s):  
Yongxing Li ◽  
Wenjing Wu ◽  
Xin Guo ◽  
Yu Lin ◽  
Shiguang Wang
Keyword(s):  
Support Vector Machine ◽  
Cellular Automata ◽  
Simulation Model ◽  
Choice Model ◽  
Group Behavior ◽  
Support Vector ◽  
Floor Field ◽  
The Mean ◽  
Simulation Results ◽  
Pedestrian Simulation

Analyzing the characteristics of group behavior, leader-follower model which adopts dynamic group floor field to represent the attraction in a group is used to model pedestrian group behavior. Pedestrian choice model of vertical walking facilities based on support vector machine (SVM) with the effect of group behavior is established. Fusing pedestrian choice model of vertical walking facilities and leader-follower model into a cellular automata (CA)-based pedestrian simulation model, we simulate the pedestrian choice process for vertical walking facilities with the effect of group behavior. The simulation results indicate that with the effect of group behavior, the choice results of some pedestrians are changed, and the efficiency of pedestrians passing is reduced. To some extent, the efficiency of pedestrians passing is improved with the mean distribution of luggage in each group.

scholarly journals Subdivided Cellular Automata Model Considering Anticipation Floor Field and Analysis of Pedestrian Detour Behavior

2021 ◽  
Vol 13 (19) ◽  
pp. 10621
Author(s):  
Jinrui Liu ◽  
Maosheng Li ◽  
Panpan Shu
Keyword(s):  
Cellular Automata ◽  
Simulation Model ◽  
Field Potential ◽  
Distribution Characteristics ◽  
Cellular Automata Model ◽  
Floor Field ◽  
Spatial Indicators ◽  
Temporal And Spatial ◽  
Pedestrian Simulation ◽  
Ks Test

The micro-pedestrian simulation model represented by the cellular automata model is an important simulation model. Improvements in various aspects enable a better description of the various behaviors of pedestrians, such as pedestrian avoidance behavior, companion behavior, as well as transcendence behavior, waiting behavior and detour behavior. This paper takes the pedestrian detour behavior in the circle antipode experiment as the main entry point. The subdivision cellular automaton model is integrated into the prediction field to model and simulate the detour behavior. At the same time, it explores the degree of subdivision of the cell. Pedestrian heterogeneity and the influence of predicted field potential energy on the simulated pedestrian trajectory. Finally, based on the temporal and spatial indicators of pedestrian trajectory characteristics, the KS test and DTW method are used to evaluate the similarity of the trajectory distribution characteristics and time series characteristics with experimental samples, and evaluate and compare models with or without heterogeneity. The results show that the trajectory characteristics of heterogeneous pedestrians are closer to the experiment than homogeneous pedestrians.

NUMERICAL SIMULATION AND VALIDITY OF A NOVEL METHOD FOR THE PREDICTION OF ARTERY STENOSIS VIA INPUT IMPEDANCE AND SUPPORT VECTOR MACHINE

2014 ◽  
Vol 26 (01) ◽  
pp. 1450002 ◽  
Author(s):  
Hanguang Xiao
Keyword(s):  
Support Vector Machine ◽  
Input Impedance ◽  
Recursive Algorithm ◽  
Artery Stenosis ◽  
Support Vector ◽  
Arterial Tree ◽  
Novel Method ◽  
The Mean ◽  
Fold Cross Validation ◽  
Systemic Arterial Tree

The early detection and intervention of artery stenosis is very important to reduce the mortality of cardiovascular disease. A novel method for predicting artery stenosis was proposed by using the input impedance of the systemic arterial tree and support vector machine (SVM). Based on the built transmission line model of a 55-segment systemic arterial tree, the input impedance of the arterial tree was calculated by using a recursive algorithm. A sample database of the input impedance was established by specifying the different positions and degrees of artery stenosis. A SVM prediction model was trained by using the sample database. 10-fold cross-validation was used to evaluate the performance of the SVM. The effects of stenosis position and degree on the accuracy of the prediction were discussed. The results showed that the mean specificity, sensitivity and overall accuracy of the SVM are 80.2%, 98.2% and 89.2%, respectively, for the 50% threshold of stenosis degree. Increasing the threshold of the stenosis degree from 10% to 90% increases the overall accuracy from 82.2% to 97.4%. Increasing the distance of the stenosis artery from the heart gradually decreases the overall accuracy from 97.1% to 58%. The deterioration of the stenosis degree to 90% increases the prediction accuracy of the SVM to more than 90% for the stenosis of peripheral artery. The simulation demonstrated theoretically the feasibility of the proposed method for predicting artery stenosis via the input impedance of the systemic arterial tree and SVM.

Pedestrian evacuation behavior analysis and simulation in multi-exits case

2017 ◽  
Vol 28 (10) ◽  
pp. 1750128 ◽  
Author(s):  
Yongxing Li ◽  
Hongfei Jia ◽  
Jun Li ◽  
Jian Gong ◽  
Kechao Sun
Keyword(s):  
Cellular Automata ◽  
Choice Model ◽  
Current Position ◽  
Time Model ◽  
Evacuation Time ◽  
Pedestrian Evacuation ◽  
Shortest Distance ◽  
Evacuation Behavior ◽  
Exit Choice ◽  
Pedestrian Simulation

Considering the process of pedestrian evacuation as pedestrian walking freely from current position to exit and queuing at the exit, estimated evacuation time model for single pedestrian is established. Based on estimated evacuation time and shortest distance, pedestrian exit choice model is established considering pedestrian preference. Pedestrian exit choice model is added into pedestrian simulation model which is built based on cellular automata. Pedestrian evacuation behavior in multi-exits case is simulated. The simulations indicate that pedestrian evacuation model built in our work describes the pedestrian evacuation behavior well.

Parameters Selection and Application of LS-SVM Based on Chaotic Ant Swarm Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 423-426
Author(s):  
Chun Li Xie ◽  
Dan Dan Zhao ◽  
Juan Wang ◽  
Cheng Shao
Keyword(s):  
Support Vector Machines ◽  
Nonlinear Systems ◽  
Fitness Function ◽  
Selection Method ◽  
Support Vector ◽  
Mean Square ◽  
Parameters Selection ◽  
Vector Machines ◽  
The Mean ◽  
Simulation Results

Parameters selection plays an important role for the performance of least squares support vector machines (LS-SVM). In this paper, a novel parameters selection method for LS-SVM is presented based on chaotic ant swarm (CAS) algorithm. Using this method, the optimization model is established, within which the fitness function is the mean square error (MSE) index, and the constraints are the ranges of the designing parameters. The proposed method is used in the identification for inverse model of the nonlinear systems, and simulation results are given to show the efficiency.

scholarly journals Urban space change and future prediction of Kangpur Nagar, Uttar Pradesh using EO data

2021 ◽  
Vol 8 (1-2) ◽  
pp. 72-86
Author(s):  
Shubham Sharma ◽  
Suraj Kumar Singh ◽  
Shruti Kanga ◽  
Nikola Kranjčić ◽  
Bojan Đurin
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Population Growth ◽  
Simulation Model ◽  
Support Vector Machine Model ◽  
Uttar Pradesh ◽  
Urban Change ◽  
Support Vector ◽  
Machine Model ◽  
Change Dynamics

Urban Land use changes, measurements, and the analysis of rate trends of growth would help in resources management and planning, etc. In this study, we analyze the urban change dynamics using a support vector machine model. This method derives the urban and rural land-use change and various components, such as population growth, built-up areas, and other utilities. Urban growth increases rapidly due to exponential growth of population, industrial growth, etc. The population growth also affects the availability of various purposes in its spatial distribution. In this present study, we carried out using multi-temporal satellite remote sensing data Landsat MSS (Multispectral scanner), ETM+ (Enhanced thematic mapper), OLI (Operational land imager) for the analysis of urban change dynamics between years 1980-1990, 1990-2003, 2012-2020 in Kanpur Nagar city in the state of Uttar Pradesh in India. In our study, we used SVM (Support Vector Machine) Model to analyze the urban change dynamics. A support vector machine classification technique was applied to generate the LULC maps using Landsat images of the years 1980, 1990, 2003, and 2020. Envi and ArcGIS software had used to identify the land cover changes and the applying urban simulation model (CA- Markov model) in Idrisi selva edition 17.0 software. The LULC maps of 2003 and 2020 were used to simulate the LULC projected map for 2050 using (Cellular automata) CA- Markov based simulation model.

Pedestrian choice behavior analysis and simulation of ticket gate machine in rail transit station

2019 ◽  
Vol 30 (04) ◽  
pp. 1950027 ◽  
Author(s):  
Yongxing Li ◽  
David Z.W. Wang ◽  
Yanyan Chen ◽  
Chengcheng Song ◽  
Hongfei Jia ◽  
...  
Keyword(s):  
Simulation Model ◽  
Minimum Distance ◽  
Choice Behavior ◽  
Simulation Software ◽  
Rail Transit ◽  
Matlab Simulation ◽  
Behavior Simulation ◽  
Minimum Number ◽  
Simulation Results ◽  
Pedestrian Simulation

Considering pedestrian preferences for the minimum distance, the minimum number of queuing pedestrians and the shortest estimated time, three pedestrian choice strategies of ticket gate machine are proposed. Pedestrian choice strategies of ticket gate machine are added into pedestrian simulation model which is based on cellular automata, and pedestrian choice behavior simulation model of ticket gate machine is obtained. On the platform of MATLAB simulation software, pedestrian choice behavior is simulated. Simulation results indicate that choice strategies of ticket gate machine proposed in this paper describe pedestrian choice behavior well, and it needs to consider the ratio of bidirectional pedestrian generation rate in the process of setting ticket gate machines in the bidirectional passage.

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