MPC
Project Details

Title:	Constrained System-Optimal Route Planning in support of Fleet Route Planning, Ridesourcing, and Ridesharing
Principal Investigators:	Farnoush Banaei-Kashani
University:	University of Colorado Denver
Status:	Active
Year:	2018
Grant #:	69A3551747108 (FAST Act)
Project #:	MPC-585
RiP #:	01689678
RH Display ID:	15293
Keywords:	alternatives analysis, demand responsive transportation, fleet management, optimization, performance measurement, ridesharing, routes and routing, simulation

Abstract

Route (or path) planning is a core optimization problem to address for efficient and intelligent transportation in various transportation systems. While the algorithms designed for efficient and accurate route planning in transportation networks is extensive, to the best of our knowledge all existing solutions focus on planning optimal routes for individual travelers. With this approach, "optimality" is defined based on a criterion that captures best interest(s) of individual travelers rather than those of the transportation network as a whole. Although popular, this definition of optimality is not necessarily aligned with the strategic goals of the USDOT, which demand optimal utilization of the transportation network in terms of performance measures such as overall mobility and environmental sustainability.

With our previous MPC project, we addressed this misalignment by introducing system-optimal route planning, an alternative approach to route planning where optimality of the routes is defined based on their impact on overall utilization of the transportation network rather than benefits of individual users. In particular, these solutions leverage two big data methodologies, namely, guaranteed approximation and distributed and parallel computation, to scale up route planning for practical applications.

With this proposal, we plan to extend our system-optimal route planning solutions to consider scenarios where certain user constrains ought to be enforced for valid system-optimal route planning. In particular, we intend to develop constrained system-optimal route planning solutions for fleet route planning, ridesourcing and ridesharing, and perform an extensive simulation-based comparative analysis to evaluate performance of the proposed solutions versus existing state-of-the-art solutions.

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