An agent-based model of the emergence of cooperation and a fair and stable system optimum using ATIS on a simple road network
Authored by Nadav Levy, Ido Klein, Eran Ben-Elia
Date Published: 2018
DOI: 10.1016/j.trc.2017.11.007
Sponsors:
Israeli National Science Foundation
Platforms:
JavaScript
Model Documentation:
Other Narrative
Flow charts
Pseudocode
Mathematical description
Model Code URLs:
https://github.com/idshklein/system-optimal-ATIS
Abstract
Traffic congestion threats the growth and vitality of cities. Policy
measures like punishments or rewards often fail to create a long term
remedy. The rise of Information and Communication Technologies (ICT)
enable provision of travel information through advanced traveler
information systems (ATIS). Current ATIS based on shortest path routing
might expedite traffic to converge towards the suboptimal User
Equilibrium (UE) state. We consider that ATIS can persuade drivers to
cooperate, pushing the road network in the long run towards the System
Optimum (SO) instead. We develop an agent based model that simulates
day-to-day evolution of road traffic on a simple binary road network,
where the behavior of agents is reinforced by their previous
experiences. Scenarios are generated based on various network designs,
information recommendation allocations and incentive mechanisms and
tested regarding efficiency, stability and equity criteria. Results show
that agents learn to cooperate without incentives, but this is highly
sensitive to the type of recommendation allocation and network-specific
design. Punishment or rewards are useful incentives, especially when
cooperation between agents requires them to change behavior against
their natural tendencies. The resulting system optimal states are to
most parts efficient, stable and not least equitable. The implications
for future ATIS design and operations are further discussed.
Tags
Agent-based model
game theory
Cooperation
Congestion
Equilibrium
Transportation networks
social dilemmas
Real-time information
Route-choice behavior
Pre-trip information
Route-choice
System optimum
User constraints
Travel
demand
Car use