Conference proceeding
Dynamic Congestion Pricing for Ridesourcing Traffic: a Simulation Optimization Approach
2019 Winter Simulation Conference (WSC), pp.2868-2869
12/2019
DOI: 10.1109/WSC40007.2019.9004722
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.
Details
- Title: Subtitle
- Dynamic Congestion Pricing for Ridesourcing Traffic: a Simulation Optimization Approach
- Creators
- Qi Luo - University of Iowa, Business Analytics
- Resource Type
- Conference proceeding
- Publication Details
- 2019 Winter Simulation Conference (WSC), pp.2868-2869
- DOI
- 10.1109/WSC40007.2019.9004722
- eISSN
- 1558-4305
- Publisher
- IEEE
- Language
- English
- Date published
- 12/2019
- Academic Unit
- Business Analytics
- Record Identifier
- 9984696154002771
Metrics
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