Road Pricing with Autonomous Links

This research seeks to examine road pricing on a network of autonomous highway links. “Autonomous” refers to the links’ being competitive and independent and having the objective of maximizing their own profits without regard for either social welfare or the profits of other links. The principal goal of this research is to understand the implications of the adoption of road pricing and privatization on social welfare and the distribution of gains and losses. The specific pricing strategies of autonomous links are evaluated first under the condition of competition for simple networks. An agent-based modeling system is then developed; it integrates an equilibrated travel demand, route choice, and travel time model with a repeated game of autonomous links setting prices to maximize profit. The levels of profit, welfare consequences, and potential cooperative arrangements undertaken by autonomous links are evaluated. By studying how such an economic system may behave under various circumstances, the effectiveness of road pricing and road privatization as public policy can be assessed.

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Investigating the Effects of the Spread of Contagious Disease and Immunization of Population on Social Welfare Using Agent Based Modeling

Social Welfare ◽

10.29252/refahj.18.70.181 ◽

2019 ◽

Vol 18 (70) ◽

pp. 181-208

Author(s):

Ghasem Shirkhodaie ◽

Arash Rahman ◽

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Keyword(s):

Social Welfare ◽

Agent Based Modeling ◽

Agent Based ◽

Contagious Disease

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Policy Modeling and Applications: State-of-the-Art and Perspectives

Complexity ◽

10.1155/2019/5041681 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11

Author(s):

Bernardo A. Furtado ◽

Miguel A. Fuentes ◽

Claudio J. Tessone

Keyword(s):

Public Policy ◽

Evolutionary Game ◽

Network Models ◽

Complexity Science ◽

Agent Based Modeling ◽

Urban Systems ◽

Policy Makers ◽

Agent Based ◽

Corporate Strategies ◽

Actual Field

The range of application of methodologies of complexity science, interdisciplinary by nature, has spread even more broadly across disciplines after the dawn of this century. Specifically, applications to public policy and corporate strategies have proliferated in tandem. This paper reviews the most used complex systems methodologies with an emphasis on public policy. We briefly present examples, pros, and cons of agent-based modeling, network models, dynamical systems, data mining, and evolutionary game theory. Further, we illustrate some specific experiences of large applied projects in macroeconomics, urban systems, and infrastructure planning. We argue that agent-based modeling has established itself as a strong tool within scientific realm. However, adoption by policy-makers is still scarce. Considering the huge amount of exemplary, successful applications of complexity science across the most varied disciplines, we believe policy is ready to become an actual field of detailed and useful applications.

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POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations

Transportation Research Part C Emerging Technologies ◽

10.1016/j.trc.2015.07.017 ◽

2016 ◽

Vol 64 ◽

pp. 101-116 ◽

Cited By ~ 60

Author(s):

Joshua Auld ◽

Michael Hope ◽

Hubert Ley ◽

Vadim Sokolov ◽

Bo Xu ◽

...

Keyword(s):

Travel Demand ◽

Agent Based Modeling ◽

Modeling Framework ◽

Agent Based ◽

Framework Development

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Modeling Framework and Implementation of Activity- and Agent-Based Simulation: An Application to the Greater Boston Area

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118798970 ◽

2018 ◽

Vol 2672 (49) ◽

pp. 146-157 ◽

Cited By ~ 10

Author(s):

Isabel Viegas de Lima ◽

Mazen Danaf ◽

Arun Akkinepally ◽

Carlos Lima De Azevedo ◽

Moshe Ben-Akiva

Keyword(s):

Travel Demand ◽

Time Of Day ◽

Agent Based Modeling ◽

Travel Patterns ◽

Activity Schedules ◽

Modeling Framework ◽

Agent Based ◽

Similar Activity ◽

Home Based ◽

Wide Range

This paper presents a utility-maximizing approach to agent-based modeling with an application to the Greater Boston Area (GBA). It leverages day activity schedules (DAS) to create a framework for representing travel demand in an individual’s day. DAS are composed of a sequence of stops that make up home-based tours with activity purposes, intermediate stops, and subtours. The framework introduced in this paper includes three levels: (1) the Day Pattern Level, which determines if an individual will travel and, if so, what types of primary activities and intermediate stops they will do; (2) the Tour Level, which models the mode, destination, and time-of-day of the different primary activities; and (3) the Intermediate Stop Level, which generates intermediate stops. The models are estimated for the GBA using the 2010 Massachusetts Travel Survey (MTS). They are then implemented in SimMobility, the agent-based, activity-based, multimodal simulator. It run in a microsimulation using a Synthetic Population. Produced results are consistent with the MTS. Compared with similar activity-based approaches, the proposed framework allows for more flexibility in modeling a wide range of activity and travel patterns.

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