scholarly journals Selection and analysis of input parameters influencing pedestrian micro-simulated crossing time

2021 ◽  
Author(s):  
Chiara Gruden ◽  
Irena Ištoka Otković ◽  
Matjaž Šraml
Keyword(s):  
Statistical Tests ◽  
Mathematical Formulation ◽  
Force Model ◽  
Social Force ◽  
Simulation Tools ◽  
Micro Simulation ◽  
Crossing Time ◽  
Input Parameters ◽  
Side Preference ◽  
Simulation Input

Pedestrian unrestrained behaviour, sudden movements and vulnerability are elements, which can highly affect road safety, especially when interacting with motorized vehicles. Therefore, it is important to have a deep insight in pedestrian behaviour. A way to tackle this issue is micro-simulation. Modern micro-simulation tools, indeed, allow, thanks to the implemented mathematical formulation of the problem, to model and repeat a real situation in a virtual environment. Nevertheless, they need to well-fit the real observed behaviour: the calibration step allows to make the model reliable, by adapting selected, influential model input parameters. By dealing with pedestrian issues, software Vissim/Viswalk has been selected for micro-simulation, which implements Helbing's Social Force model. This model is based on several parameters, like relaxation time, side preference, strength and range of pedestrian interactions, amount of anisotropy, parameters governing the forces among pedestrians, noise, number of reacting pedestrians, queue order and straightness, which need to be set by the user when creating the model, but they can be hardly measured. This paper presents a selection of the recalled input parameters, on which statistical tests are carried out to understand their influence on the behavioural output – crossing time - that is supposed to describe pedestrian crossing behaviour. This is the first step towards the development of a new calibration methodology, which will keep advantage of artificial intelligence tools to fine-tune micro-simulation input parameters.

scholarly journals Optimization of Metro Passenger Organizing of Alighting and Boarding Processes: Simulated Evidence from the Metro Station in Nanjing, China

2019 ◽  
Vol 11 (13) ◽  
pp. 3682 ◽  
Author(s):  
Jiajie Yu ◽  
Yanjie Ji ◽  
Liangpeng Gao ◽  
Qi Gao
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Optimal Level ◽  
Force Model ◽  
Social Force ◽  
Metro Station ◽  
Physical Energy ◽  
The Social ◽  
Micro Simulation ◽  
The Status

Since the long dwell time and chaotic crowds make metro trips inefficient and dissatisfying, the importance of optimizing alighting and boarding processes has become more prominent. This paper focuses on the adjustment of passenger organizing modes. Using field data from the metro station in Nanjing, China, a micro-simulation model of alighting and boarding processes based on an improved social force paradigm was built to simulate the movement of passengers under different passenger organizing modes. Unit flow rate, delay, and social force work (SFW) jointly reflect the efficiency and, especially, the physical energy consumption of passengers under each mode. It was found that when passengers alighted and boarded by different doors, efficiency reached its optimal level which was 76.92% higher than the status quo of Nanjing, and the physical energy consumption was reduced by 16.30%. Both the findings and the model can provide support for passenger organizing in metro stations, and the concept of SFW can be applied to other scenes simulated by the social force model, such as evacuations of large-scale activities, to evaluate the physical energy consumption of people.

scholarly journals A re-examination of the role of friction in the original Social Force Model

2020 ◽  
Vol 121 ◽  
pp. 42-53 ◽  
Author(s):  
I.M. Sticco ◽  
G.A. Frank ◽  
F.E. Cornes ◽  
C.O. Dorso
Keyword(s):  
Force Model ◽  
Social Force ◽  
Social Force Model

scholarly journals Crowd Evacuation Guidance Based on Combined Action Reinforcement Learning

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 26
Author(s):  
Yiran Xue ◽  
Rui Wu ◽  
Jiafeng Liu ◽  
Xianglong Tang
Keyword(s):  
Reinforcement Learning ◽  
Guidance System ◽  
Force Model ◽  
Interactive Simulation ◽  
Social Force ◽  
Novel Approach ◽  
Learning Agent ◽  
Network Output ◽  
Combined Action ◽  
Crowd Evacuation

Existing crowd evacuation guidance systems require the manual design of models and input parameters, incurring a significant workload and a potential for errors. This paper proposed an end-to-end intelligent evacuation guidance method based on deep reinforcement learning, and designed an interactive simulation environment based on the social force model. The agent could automatically learn a scene model and path planning strategy with only scene images as input, and directly output dynamic signage information. Aiming to solve the “dimension disaster” phenomenon of the deep Q network (DQN) algorithm in crowd evacuation, this paper proposed a combined action-space DQN (CA-DQN) algorithm that grouped Q network output layer nodes according to action dimensions, which significantly reduced the network complexity and improved system practicality in complex scenes. In this paper, the evacuation guidance system is defined as a reinforcement learning agent and implemented by the CA-DQN method, which provides a novel approach for the evacuation guidance problem. The experiments demonstrate that the proposed method is superior to the static guidance method, and on par with the manually designed model method.

scholarly journals A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 314-335
Author(s):  
Hafiz Usman Ahmed ◽  
Ying Huang ◽  
Pan Lu
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Modeling Tools ◽  
Microscopic Traffic Simulation ◽  
Simulation Tools ◽  
Car Following ◽  
Driving Behaviors ◽  
Micro Simulation ◽  
Models And Modeling

The platform of a microscopic traffic simulation provides an opportunity to study the driving behavior of vehicles on a roadway system. Compared to traditional conventional cars with human drivers, the car-following behaviors of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) would be quite different and hence require additional modeling efforts. This paper presents a thorough review of the literature on the car-following models used in prevalent micro-simulation tools for vehicles with both human and robot drivers. Specifically, the car-following logics such as the Wiedemann model and adaptive cruise control technology were reviewed based on the vehicle’s dynamic behavior and driving environments. In addition, some of the more recent “AV-ready (autonomous vehicles ready) tools” in micro-simulation platforms are also discussed in this paper.

scholarly journals Simulating Interactions between Pedestrians, Segway Riders and Cyclists in Shared Spaces Using Social Force Model

2018 ◽  
Vol 34 ◽  
pp. 91-98 ◽  
Author(s):  
Charitha Dias ◽  
Hiroaki Nishiuchi ◽  
Satoshi Hyoudo ◽  
Tomoyuki Todoroki
Keyword(s):  
Force Model ◽  
Social Force ◽  
Social Force Model ◽  
Shared Spaces

Social Force Model Describing Pedestrian and Cyclist Behaviour in Shared Spaces

2019 ◽  
pp. 477-486
Author(s):  
Yufei Yuan ◽  
Bernat Goñi-Ros ◽  
Tim P. van Oijen ◽  
Winnie Daamen ◽  
Serge P. Hoogendoorn
Keyword(s):  
Force Model ◽  
Social Force ◽  
Social Force Model ◽  
Shared Spaces
Sign in / Sign up

Export Citation Format

Share Document