Agent-Based Simulation Versus Econometrics – from Macro- to Microscopic Approaches in Route Choice Simulation

2006 ◽  
pp. 61-72 ◽  
Author(s):  
Gustavo Kuhn Andriotti ◽  
Franziska Klügl
Keyword(s):  
Route Choice ◽  
Agent Based Simulation ◽  
Agent Based

A Multi Agent Based Simulation Framework for the Study of Transit Passenger’s Route Choice Behavior

2010 ◽  
Vol 108-111 ◽  
pp. 525-529
Author(s):  
Yi Kui Mo ◽  
Xiang Rong Qiao ◽  
Yong Yun Su
Keyword(s):  
Choice Behavior ◽  
Route Choice ◽  
Simulation System ◽  
Simulation Software ◽  
Massachusetts Institute Of Technology ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Route Choice Behavior ◽  
Multi Agent ◽  
Institute Of Technology

In light of the characteristics of transit passenger’s route choice behavior, this paper introduces a multi-agent based simulation approach into the study of this behavior.At first, the paper analyzes factors affecting transit passenger’s route choice behavior and then studies the rules of behavior for transit passengers when making route choices. The paper further proposes a utility function for selected routes and examines ways to investigate and analyze corresponding data so as to provide a basis for the modeling of passenger agent’s route choice behavior. Following this, the paper builds up a simulation system for transit passenger’s route choice behavior based on the multi-agent simulation software Starlogo developed by Massachusetts Institute of Technology (MIT), and explains the process that will actually take place when using the simulation system. Finally, inadequacies of the study are analyzed and the focus of further research is indicated.

scholarly journals Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Lennart Adenaw ◽  
Markus Lienkamp
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
User Behavior ◽  
Transport Sector ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Charging Station ◽  
Spatio Temporal

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

Sign in / Sign up

Export Citation Format

Share Document