Dynamic Congestion Pricing for Ridesourcing Traffic: a Simulation Optimization Approach | IEEE Conference Publication | IEEE Xplore

Dynamic Congestion Pricing for Ridesourcing Traffic: a Simulation Optimization Approach

Publisher: IEEE

Abstract:

Despite the documented benefits of ridesourcing services, recent studies show that they can slow down traffic in the densest cities significantly. To implement congestion...View more

Abstract:

Despite the documented benefits of ridesourcing services, recent studies show that they can slow down traffic in the densest cities significantly. To implement congestion pricing policies upon those vehicles, regulators need to estimate the degree of congestion effect. This paper studies simulation-based approaches to address the two technical challenges arising from the representation of system dynamics and the optimization for congestion price mechanisms. To estimate the traffic state, we use a metamodel representation for traffic flow and a numerical method for data interpolation. To reduce the burden of replicating evaluation in stochastic optimization, we use a simulation optimization approach to compute the optimal congestion price. This data-driven approach can potentially be extended to solve large-scale congestion pricing problems with unobservable states.
Date of Conference: 08-11 December 2019
Date Added to IEEE Xplore: 20 February 2020
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ISSN Information:

Publisher: IEEE
Conference Location: National Harbor, MD, USA

References

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