Strategic Interventions in Transportation CPHS

car-driver-interaction

Background and Objectives

Supporting Agencies

ISC

Contributors

NSF NSF NSF NSF

Thrust 1: Strategic Information Design for Quantal Response Travelers

Selfish routing begets inefficiency in multi-agent transportation systems, leading to significant economic losses in our society. Although several powerful techniques (e. g., marginal cost pricing) have been proposed to mitigate price-of-anarchy (a measure of inefficiency), social welfare maximization still remains a huge challenge in selfish routing, especially when travelers deviate from maximizing their own expected utilities. This paper proposes a novel informational intervention to improve the efficiency of selfish routing, especially in the presence of quantal response travelers. Specifically, modeling the interaction between the system and travelers as a Stackelberg game, and develop a novel approximate algorithm, called LoRI (which stands for logit response based information) to steer the travelers’ logit responses towards social welfare using strategically designed information. Simulation results in diverse transportation settings demonstrate that LoRI significantly improves price of anarchy of selfish routing (both in terms of congestion and carbon emissions), even when the travelers use navigation services that recommend optimal shortest-paths according to their selfish interests.


References
  • S. Sanga, V. S. S. Nadendla, M. Telukunta, and S. K. Das, “Maximizing Social Welfare in Selfish Multi-Modal Routing using Strategic Information Design for Quantal Response Travelers,” in The 20th IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS 2023), 2023.

Thrust 2: Trust-Aware Stackelberg Routing

Stackelberg routing platforms (SRP) reduce congestion in one-shot traffic networks by proposing optimal route recommendations to selfish travelers. Traditionally, Stackelberg routing is cast as a partial control problem where a fraction of traveler flow complies with route recommendations, while the remaining respond as selfish travelers. In this effort, a novel Stackelberg routing framework is formulated where the agents exhibit probabilistic compliance by accepting SRP's route recommendations with a trust probability. A greedy Trust-Aware Stackelberg Routing algorithm (in short, TASR) is proposed for SRP to compute unique path recommendations to each traveler flow with a unique demand. Simulation experiments are designed with random travel demands with diverse trust values on real road networks such as Sioux Falls, Chicago Sketch, and Sydney networks for both single-commodity and multi-commodity flows. The performance of TASR is compared with state-of-the-art Stackelberg routing methods in terms of traffic congestion and trust dynamics over repeated interaction between the SRP and the travelers. Results show that TASR improves network congestion without causing a significant reduction in trust towards the SRP, when compared to most well-known Stackelberg routing strategies.


References
  • D. E. M. Brown, V. S. S. Nadendla, and S. K. Das, “TASR: A Novel Trust-Aware Stackelberg Routing Algorithm to Mitigate Traffic Congestion,” in 10th IEEE International Conference on Smart Computing (SMARTCOMP’24), (Best Paper Award) 2024