Control (management)

Scheme (mathematics)

Engineering

Transport engineering

Pure mathematics

Biology

Multi-agent system

Field (mathematics)

Computer science

Visual arts

Distributed computing

Controller (irrigation)

Architecture

Operations research

Mathematics

Object (grammar)

Agronomy

Mathematical analysis

Artificial intelligence

Recommender system

Intersection (aeronautics)

Traffic congestion

Machine learning

IEEE Transactions on Systems Man and Cybernetics Systems

Strategic traffic management is crucial for combating traffic congestion at the macroscopic level. However, such a field is still relatively unexplored, particularly for microscopic control objects, such as intersections and coordinated intersection groups. This article proposes a human-in-the-loop recommendation system for strategic urban traffic management, which follows an agent-based structure. A regional agent dispatcher is defined to assign agents for operation whenever “operation on-demand” is required. Such a requirement is identified by a daily-dependent operational mode on strategic traffic operations at a control object level. The strategic management scheme for each control object is guided by a strategic agent (customized), which is essentially a deep recommender model with a specific architecture. By featuring the multiagent design, a customized operational scheme can be generated at the intersection level, which instructs the corresponding controller to take specific operations. The utility of the recommendation system is demonstrated via a case study using real-world traffic data. In both offline and online evaluations, the system performs consistently at traffic operational recommendations in different scenarios and has the potential to provide more reasonable traffic operational strategies than a human-operated system.

An Agent-Based Traffic Recommendation System: Revisiting and Revising Urban Traffic Management Strategies