Transportation Network

Transportation network research focuses on optimizing the efficiency, resilience, and fairness of transportation systems through data-driven modeling and algorithmic approaches. Current research emphasizes developing and applying advanced machine learning techniques, including graph neural networks, reinforcement learning, and optimal transport methods, to address challenges such as real-time traffic routing, predicting traffic flow and modal split, and mitigating the impact of attacks or uncertainties. These advancements have significant implications for improving urban planning, traffic management, and the overall user experience, leading to more efficient and sustainable transportation networks.

Papers

June 2, 2023

Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems
Hongde Wu, Sen Yan, Mingming Liu
Financial Application Graph Representation Recent Advance Intelligent Transportation System Transportation System Graph Based Learning Transportation Network

March 25, 2023

Edge Ranking of Graphs in Transportation Networks using a Graph Neural Network (GNN)
Debasish Jana, Sven Malama, Sriram Narasimhan, Ertugrul Taciroglu
Graph Neural Network Graph Drawing Large Scale Network Transportation Network Water Distribution Network Graph Edge Betweenness Centrality

November 26, 2022

A Critical Review of Traffic Signal Control and A Novel Unified View of Reinforcement Learning and Model Predictive Control Approaches for Adaptive Traffic Signal Control
Xiaoyu Wang, Scott Sanner, Baher Abdulhai
Reinforcement Learning Predictive Control Traffic Signal Control Unified View Control Design Transportation Network Control Algorithm Adaptive Traffic Signal Control

October 14, 2022

Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources
Peter Lippmann, Enrique Fita Sanmartín, Fred A. Hamprecht
Optimal Transport Theoretical Understanding Multiple Source Transportation Network Geometric Optimization

October 12, 2022

Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems
Tong Liu, Hadi Meidani
Transportation Infrastructure Transportation Network Network Reliability

March 31, 2022

Online Learning for Traffic Routing under Unknown Preferences
Devansh Jalota, Karthik Gopalakrishnan, Navid Azizan, Ramesh Johari, Marco Pavone
Online Learning Routing Problem Traffic Management Transportation Network Congestion Pricing

March 27, 2022

Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systems
Aron Brenner, Manxi Wu, Saurabh Amin
Interpretable Machine Learning Transportation System Public Transit High Dimensional Time Series Transportation Network Modal Regression Transit Network

February 21, 2022

Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network
Rezaur Rahman, Samiul Hasan
Novel Approach Graph Convolutional Neural Network Traffic Pattern Transportation Network Traffic Assignment Driven Network

Transportation Network

Papers

Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems

Edge Ranking of Graphs in Transportation Networks using a Graph Neural Network (GNN)

A Critical Review of Traffic Signal Control and A Novel Unified View of Reinforcement Learning and Model Predictive Control Approaches for Adaptive Traffic Signal Control

Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources

Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems

Online Learning for Traffic Routing under Unknown Preferences

Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systems

Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network