While the traditional approach to ease traffic congestion has focused on building infrastructure, the recent emergence of Connected and Automated Vehicles (CAVs) and urban mobility services (e.g., Autonomous Mobility-on-Demand (AMoD) systems) has opened a new set of alternatives for reducing travel times. This thesis seeks to exploit these advances to improve the operation and efficiency of Intelligent Transportation Systems using a network optimization perspective. It proposes novel methods to evaluate the prospective benefits of adopting socially optimal routing schemes, intermodal mobility, and contraflow lane reversals in transportation networks.
This dissertation makes methodological and empirical contributions to the transportation domain. From a methodological standpoint, it devises a fast solver for the Traffic Assignment Problem with Side Constraints which supports arbitrary linear constraints on the flows. Instead of using standard column-generation methods, it introduces affine approximations of the travel latency function to reformulate the problem as a quadratic (or linear) programming problem. This framework is applied to two problems related to urban planning and mobility policy: social routing with rebalancing in intermodal mobility systems and planning lane reversals in transportation networks. Moreover, it proposes a novel method to jointly estimate the Origin-Destination demand and travel latency functions of the Traffic Assignment Problem. Finally, it develops a model to jointly optimize the pricing, rebalancing and fleet sizing decisions of a Mobility-on-Demand service. Empirically, it validates all the methods by testing them with real transportation topologies and real traffic data from Eastern Massachusetts and New York City showing the achievable benefits obtained when compared to benchmarks.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/44773 |
| Date | 24 May 2022 |
| Creators | Wollenstein-Betech, Salomón |
| Contributors | Cassandras, Christos G., Paschalidis, Ioannis Ch. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-NonCommercial 4.0 International, http://creativecommons.org/licenses/by-nc/4.0/ |
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