A model of information propagation in transportation networks

TL;DR

This paper presents a macroscopic model for simulating transportation networks using information propagation among nodes, incorporating flow dynamics and demand routing to analyze complex control strategies.

Contribution

It introduces a novel information propagation model (IPM) for transportation networks that integrates flow dynamics and demand routing under complex environments.

Findings

The model effectively simulates dynamic traffic control strategies.

Case studies demonstrate the model's applicability to real-world scenarios.

The IPM provides reliable insights into transportation network behavior.

Abstract

This paper introduces a new macroscopic perspective for simulating transportation networks. The idea is to look at the network as connected nodes. Each node sends an information package to its neighbors. Basically, the information package contains a state change that a specific node experienced, and it might affect the traffic network state in the future. Different types of information can be counted in transportation network. Each information type has different characteristics. It propagates through the network and interacts with other IPs and nodes. As a result, the model enables implementing and analyzing dynamic and inconvenient control strategies. This paper focus on flow dynamics and demand routing information under complex environment. The flow dynamics flows the LWR theory along the links. The demand routing follows a node equilibrium model. The node model takes into account the users preferences and choices under dynamic control agents such as dynamic tolling and sends the needed information for other facilities to operate. A DNL model is developed based on the IPM. Several case studies demonstrate the use IPM and its potential to provide a reliable and results for real world complicated transportation applications.