Microscopic Pedestrian Flow Characteristics: Development of an Image Processing Data Collection and Simulation Model

Microscopic pedestrian studies consider detailed interaction of pedestrians to control their movement in pedestrian traffic flow. The tools to collect the microscopic data and to analyze microscopic pedestrian flow are still very much in its infancy. The microscopic pedestrian flow characteristics need to be understood. Manual, semi manual and automatic image processing data collection systems were developed. It was found that the microscopic speed resemble a normal distribution with a mean of 1.38 m/second and standard deviation of 0.37 m/second. The acceleration distribution also bear a resemblance to the normal distribution with an average of 0.68 m/ square second. A physical based microscopic pedestrian simulation model was also developed. Both Microscopic Video Data Collection and Microscopic Pedestrian Simulation Model generate a database called NTXY database. The formulations of the flow performance or microscopic pedestrian characteristics are explained. Sensitivity of the simulation and relationship between the flow performances are described. Validation of the simulation using real world data is then explained through the comparison between average instantaneous speed distributions of the real world data with the result of the simulations. The simulation model is then applied for some experiments on a hypothetical situation to gain more understanding of pedestrian behavior in one way and two way situations, to know the behavior of the system if the number of elderly pedestrian increases and to evaluate a policy of lane-like segregation toward pedestrian crossing and inspects the performance of the crossing. It was revealed that the microscopic pedestrian studies have been successfully applied to give more understanding to the behavior of microscopic pedestrians flow, predict the theoretical and practical situation and evaluate some design policies before its implementation.

[1]  Michael Wolfe,et al.  J+ = J , 1994, ACM SIGPLAN Notices.

[2]  John J Fruin,et al.  DESIGNING FOR PEDESTRIANS: A LEVEL-OF-SERVICE CONCEPT , 1971 .

[3]  Rabab Kreidieh Ward,et al.  Motion estimation using long-term motion vector prediction , 1999, Proceedings DCC'99 Data Compression Conference (Cat. No. PR00096).

[4]  Osama Masoud,et al.  Pedestrian tracking from a stationary camera using active deformable models , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[5]  Shigeyuki Okazaki,et al.  A study of simulation model for pedestrian movement with evacuation and queuing , 1993 .

[6]  Kazunori Onoguchi,et al.  Shadow elimination method for moving object detection , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[7]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[8]  Dirk Helbing A Fluid-Dynamic Model for the Movement of Pedestrians , 1992, Complex Syst..

[9]  Akira Tomono,et al.  A moving-object extraction method robust against illumination level changes for a pedestrian counting system , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[10]  John J. Fruin,et al.  Pedestrian planning and design , 1971 .

[11]  Frank Schweitzer,et al.  Self-Organization of Complex Structures: From Individual to Collective Dynamics - Some Introductory , 1997 .

[12]  Shigeyuki Okazaki,et al.  A STUDY OF SIMULATION MODEL FOR WAYFINDING BEHAVIOR BY EXPERIMENTS IN MAZES , 1991 .

[13]  Gunnar G. Løvås,et al.  Modeling and Simulation of Pedestrian Traffic Flow , 1994 .

[14]  Joseph E. Hummer,et al.  Manual of transportation engineering studies , 1994 .

[15]  John M. Watts,et al.  Computer models for evacuation analysis , 1987 .

[16]  Tomaso A. Poggio,et al.  Pedestrian detection using wavelet templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  M. Reijnierse,et al.  G.P.4 BMD patients show relative sparing of hip flexion on muscle testing and MRI , 2014, Neuromuscular Disorders.

[18]  D. Helbing,et al.  Self-Organization Phenomena in Pedestrian Crowds , 1998, cond-mat/9806152.

[19]  Satoshi Matsushita,et al.  A STUDY OF PEDESTRIAN MOVEMENT IN ARCHITECTURAL SPACE : Part 5 A Proubing walk and a guide walk by a guideboard , 1981 .

[20]  Eric W. Marchant,et al.  Testing and application of the computer model ‘SIMULEX’ , 1995 .

[21]  Dirk Helbing,et al.  Optimal self-organization , 1999 .

[22]  Victor J. Blue,et al.  Cellular Automata Microsimulation of Bidirectional Pedestrian Flows , 1999 .

[23]  L. Henderson,et al.  Sexual Differences in Human Crowd Motion , 1972, Nature.

[24]  Tieniu Tan,et al.  Colour based object tracking , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[25]  P G Gipps,et al.  A micro simulation model for pedestrian flows , 1985 .

[26]  Dirk Helbing,et al.  A mathematical model for the behavior of pedestrians , 1991, cond-mat/9805202.

[27]  Christian Wöhler,et al.  Motion-based recognition of pedestrians , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[28]  W ReynoldsCraig Flocks, herds and schools: A distributed behavioral model , 1987 .

[29]  Shigeyuki Okazaki,et al.  建築空間における歩行のためのシミュレーションモデルの研究 : その 4 群集歩行の透視図による表現 , 1981 .