An Agent Based Meta-Model For Urban Mobility Modeling

Multi-agent based simulation (MABS) is an important approach for studying complex systems. The Agent-based model often contains many parameters, these parameters are usually not independent, with differences in their range, and may be subjected to constraints. How to use MABS investigating complex systems effectively is still a challenge. The common tasks of MABS include: summarizing the macroscopic patterns of the system, identifying key factors, establishing a meta-model, and optimization. We proposed a framework of experimental design and data mining for MABS. In the framework, method of experimental design is used to generate experiment points in the parameter space, then generate simulation data, and finally using data mining techniques to analyze data. With this framework, we could explore and analyze complex system iteratively. Using central composite discrepancy (CCD) as measure of uniformity, we designed an algorithm of experimental design in which parameters could meet any constraints. We discussed the relationship between tasks of complex system simulation and data mining, such as using cluster analysis to classify the macro patterns of the system, and using CART, PCA, ICA and other dimensionality reduction methods to identify key factors, using linear regression, stepwise regression, SVM, neural network, etc. to build the meta-model of the system. This framework integrates MABS with experimental design and data mining to provide a reference for complex system exploration and analysis.

Download Full-text

Urban mobility modeling to reduce traffic congestion in Surabaya: a system dynamics framework

Journal of Modelling in Management ◽

10.1108/jm2-03-2019-0055 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Erma Suryani ◽

Rully Agus Hendrawan ◽

Philip Faster Eka Adipraja ◽

Arif Wibisono ◽

Lily Puspa Dewi

Keyword(s):

System Dynamics ◽

Public Transport ◽

Traffic Congestion ◽

Supply And Demand ◽

Urban Mobility ◽

Rapid Transit ◽

Content Type ◽

Mobility Modeling ◽

Delay Performance ◽

Mobility Performance

Purpose This paper aims to address the urban mobility and traffic congestion problem under environmental dynamics to improve mobility and reduce traffic congestion using system dynamics (SD) simulation and scenarios. Design/methodology/approach SD simulation was used to analyze urban mobility and traffic congestion. Data were collected from the Transportation Department of Surabaya City. Several scenarios to improve urban mobility and reduce traffic congestion were developed by modifying the structures and parameters of the model. Findings Several factors influence urban mobility, including modal split, trip frequency, delay performance and the ratio of public transport supply and demand. Urban mobility, daily traffic and road capacity are some factors that affect traffic congestion. Scenarios can be designed based on the assumptions of the proposed strategy. Research limitations/implications The study was conducted at Surabaya City, East Java, Indonesia, which is the fourth most-congested city in the world. Practical implications By implementing several strategies (mass rapid transit and bus rapid transit development and public transport delay reduction), mobility performance is projected to be improved by 70.34-92.96%. With this increased mobility, traffic congestion is projected to decline by 52.5-65.8%. Originality/value The novel contributions of this research are: formulating relationships between several variables; modeling dynamic behavior of urban mobility and traffic congestion; and building scenario models to improve mobility and reduce traffic congestion in Surabaya. With the increase in urban mobility and the decrease in average daily traffic, traffic congestion could be reduced by a minimum of 57.6% and a maximum of 69%.

Download Full-text

Analyzing Urban Mobility Carbon Footprint with Large-scale, Agent-based Simulation

Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems ◽

10.5220/0006662201430150 ◽

2018 ◽

Author(s):

Eduardo Felipe Zambom Santana ◽

Lucas Kanashiro ◽

Diego Bogado Tomasiello ◽

Fabio Kon ◽

Mariana Giannotti

Keyword(s):

Carbon Footprint ◽

Large Scale ◽

Urban Mobility ◽

Agent Based Simulation ◽

Agent Based

Download Full-text

A Unified Framework for Traditional and Agent-Based Social Network Modeling

Interdisciplinary Applications of Agent-Based Social Simulation and Modeling - Advances in Human and Social Aspects of Technology ◽

10.4018/978-1-4666-5954-4.ch011 ◽

2014 ◽

pp. 184-199 ◽

Cited By ~ 3

Author(s):

Enrico Franchi ◽

Michele Tomaiuolo

Keyword(s):

Social Network ◽

Conceptual Framework ◽

Network Models ◽

General Purpose ◽

Agent Based Modeling ◽

Unified Framework ◽

Modeling Tool ◽

Agent Based ◽

Meta Model ◽

Social Network Modeling

In the last sixty years of research, several models have been proposed to explain (i) the formation and (ii) the evolution of networks. However, because of the specialization required for the problems, most of the agent-based models are not general. On the other hand, many of the traditional network models focus on elementary interactions that are often part of several different processes. This phenomenon is especially evident in the field of models for social networks. Therefore, this chapter presents a unified conceptual framework to express both novel agent-based and traditional social network models. This conceptual framework is essentially a meta-model that acts as a template for other models. To support this meta-model, the chapter proposes a different kind of agent-based modeling tool that we specifically created for developing social network models. The tool the authors propose does not aim at being a general-purpose agent-based modeling tool, thus remaining a relatively simple software system, while it is extensible where it really matters. Eventually, the authors apply this toolkit to a novel problem coming from the domain of P2P social networking platforms.

Download Full-text