Learning to Route via Theory-Guided Residual Network

05/18/2021
by   Chang Liu, et al.
0

The heavy traffic and related issues have always been concerns for modern cities. With the help of deep learning and reinforcement learning, people have proposed various policies to solve these traffic-related problems, such as smart traffic signal control systems and taxi dispatching systems. People usually validate these policies in a city simulator, since directly applying them in the real city introduces real cost. However, these policies validated in the city simulator may fail in the real city if the simulator is significantly different from the real world. To tackle this problem, we need to build a real-like traffic simulation system. Therefore, in this paper, we propose to learn the human routing model, which is one of the most essential part in the traffic simulator. This problem has two major challenges. First, human routing decisions are determined by multiple factors, besides the common time and distance factor. Second, current historical routes data usually covers just a small portion of vehicles, due to privacy and device availability issues. To address these problems, we propose a theory-guided residual network model, where the theoretical part can emphasize the general principles for human routing decisions (e.g., fastest route), and the residual part can capture drivable condition preferences (e.g., local road or highway). Since the theoretical part is composed of traditional shortest path algorithms that do not need data to train, our residual network can learn human routing models from limited data. We have conducted extensive experiments on multiple real-world datasets to show the superior performance of our model, especially with small data. Besides, we have also illustrated why our model is better at recovering real routes through case studies.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/13/2019

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

Traffic signal control is an emerging application scenario for reinforce...
research
05/28/2021

Towards a Very Large Scale Traffic Simulator for Multi-Agent Reinforcement Learning Testbeds

Smart traffic control and management become an emerging application for ...
research
02/20/2022

Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach

The ever-increasing heavy traffic congestion potentially impedes the acc...
research
02/26/2023

Q-Cogni: An Integrated Causal Reinforcement Learning Framework

We present Q-Cogni, an algorithmically integrated causal reinforcement l...
research
02/28/2023

City-scale Pollution Aware Traffic Routing by Sampling Max Flows using MCMC

A significant cause of air pollution in urban areas worldwide is the hig...
research
10/26/2020

Route Planning with Real-Time Traffic Predictions

Situation aware route planning gathers increasing interest as cities bec...
research
07/13/2023

Automatic Routing System for Intelligent Warehouses

Automation of logistic processes is essential to improve productivity an...

Please sign up or login with your details

Forgot password? Click here to reset