Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/33926
Title: Multi-period transportation network investment decision making and policy implications using econometric framework
Authors: Haque, Khademul 
Mishra, Sabyasachee 
Golias, Mihalis 
Major Field of Science: Engineering and Technology
Field Category: ENGINEERING AND TECHNOLOGY
Keywords: Network design problem;Investment;Decision making;Econometric framework;Transportation planning
Issue Date: 1-Nov-2021
Source: Research in Transportation Economics, vol.89, 2021
Volume: 89
Journal: Research in Transportation Economics 
Abstract: Transportation infrastructure projects take numerous years of planning before they are scheduled for construction. Prioritization of such projects over a multi-period planning horizon (under a limited budget) is a difficult task, as it is usually formulated as a bilevel network design problem (NDP). Although multi-period network investment is studied in the literature, its application by public agencies is limited because of the complexities involved in network design problems and the computational time needed to analyze large scale networks (i.e., performing the traffic assignment at the lower level of the NDP). The contribution of this research is two-fold. First, it extends a previously published single year discrete network design formulation to a multi-period discrete network design problem (MPNDP) to capture both the spatial and temporal patterns of multi-period network investment decisions. Second, using the MPNDP investment results; and the network characteristics, this research develops and evaluates a new econometric model the Multi-Period Econometric Network Investment Model (MENIM). MENIM can be used by agencies in place of MPNDP to approximate network investments. The proposed model is calibrated and validated using medium to large scale networks and results show that it provides comparable results to MPNDP within acceptable computational times. Patterns of multi-period network investments from these numerical experiments are also extensively discussed along with policy recommendations for public agencies.
URI: https://hdl.handle.net/20.500.14279/33926
ISSN: 07398859
DOI: 10.1016/j.retrec.2021.101109
Type: Article
Affiliation : CDM Smith Inc. 
University of Memphis 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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