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Open Access Publications from the University of California

Congestion Reduction via Personalized Incentives

  • Author(s): Ghafelebashi, Ali;
  • Razaviyayn, Meisam;
  • Dessouky, Maged
  • et al.

Published Web Location

https://doi.org/10.7922/G2VM49K4
The data associated with this publication are available at:
https://doi.org/10.5061/dryad.ncjsxkst8
Abstract

Rapid population growth and development in cities across the globe have fueled an inescapable urban burden: traffic congestion. Congestion causes huge economic losses and increased vehicle emissions, which contribute to poor air quality. Over the past several decades traffic engineers have tried various strategies to reduce congestion, ranging from increasing roadway capacity to transportation demand management programs.

An alternative travel demand management approach that has garnered less attention is the use of positive incentive programs— rewarding desirable behavior rather than penalizing undesirable behavior—to reduce congestion. Researchers at the University of Southern California developed a real-time, distributed algorithm for offering personalized incentives to individual drivers to make socially optimal routing decisions. The methodology relies on online and historical traffic data as well as individual preferences and routing options from drivers’ origins and destinations to estimate both the traffic condition and the drivers’ responses to the provided incentives. This policy brief summarizes the findings from that research and provides policy implications.

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