Online Learning for Traffic Routing under Unknown Preferences

03/31/2022
by   Devansh Jalota, et al.
0

In transportation networks, users typically choose routes in a decentralized and self-interested manner to minimize their individual travel costs, which, in practice, often results in inefficient overall outcomes for society. As a result, there has been a growing interest in designing road tolling schemes to cope with these efficiency losses and steer users toward a system-efficient traffic pattern. However, the efficacy of road tolling schemes often relies on having access to complete information on users' trip attributes, such as their origin-destination (O-D) travel information and their values of time, which may not be available in practice. Motivated by this practical consideration, we propose an online learning approach to set tolls in a traffic network to drive heterogeneous users with different values of time toward a system-efficient traffic pattern. In particular, we develop a simple yet effective algorithm that adjusts tolls at each time period solely based on the observed aggregate flows on the roads of the network without relying on any additional trip attributes of users, thereby preserving user privacy. In the setting where the O-D pairs and values of time of users are drawn i.i.d. at each period, we show that our approach obtains an expected regret and road capacity violation of O(√(T)), where T is the number of periods over which tolls are updated. Our regret guarantee is relative to an offline oracle that has complete information on users' trip attributes. We further establish a Ω(√(T)) lower bound on the regret of any algorithm, which establishes that our algorithm is optimal up to constants. Finally, we demonstrate the superior performance of our approach relative to several benchmarks on a real-world transportation network, thereby highlighting its practical applicability.

READ FULL TEXT
research
04/27/2022

Online Learning in Fisher Markets with Unknown Agent Preferences

In a Fisher market, agents (users) spend a budget of (artificial) curren...
research
03/31/2021

Balancing Fairness and Efficiency in Traffic Routing via Interpolated Traffic Assignment

System optimum (SO) routing, wherein the total travel time of all users ...
research
10/18/2018

On Socially Optimal Traffic Flow in the Presence of Random Users

Traffic assignment is an integral part of urban city planning. Roads and...
research
06/26/2021

Contextual Inverse Optimization: Offline and Online Learning

We study the problems of offline and online contextual optimization with...
research
03/11/2021

Utility of Traffic Information in Dynamic Routing: Is Sharing Information Always Useful?

Real-time traffic information can be utilized to enhance the efficiency ...
research
03/21/2023

Online Learning for Equilibrium Pricing in Markets under Incomplete Information

The study of market equilibria is central to economic theory, particular...
research
11/27/2022

A Data-driven Pricing Scheme for Optimal Routing through Artificial Currencies

Mobility systems often suffer from a high price of anarchy due to the un...

Please sign up or login with your details

Forgot password? Click here to reset