Evaluating Transportation User Costs Based on Simulated Regional Network Models. Issue 1 (2017)

• Record Type: Journal Article
• Authors: Twumasi-Boakye, Richard; Sobanjo, John O.
• Is Part Of: Transportation research record. Volume 2612:Issue 1(2017)
• Page Start: 121
• Page End: 131
• Publication Date: 2017
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.3141/2612-14
• Languages: English
• ISSNs: 0361-1981

Abstract

Bridges are important infrastructure and essential components of a transportation network. Temporary, long-term, and partial closures of bridges can result in enormous cost implications. However, bridge closures are inevitable not only because of the likelihood of hazard-induced damages but also because routine maintenance, repair, and rehabilitation activities may also warrant closures. It is current practice that vehicles are rerouted to the shortest alternative route during bridge closures (the detour approach). In this study, a scenario-based network approach for evaluating the impact of bridge closures on transportation user cost was undertaken and is proposed in this paper. Both the detour-based and network-based approaches were applied to the Tampa Bay, Florida, regional network model, and five bridge closure scenarios were considered. User costs were computed in terms of delay and vehicle operating costs. For closures to the I-275, Gandy, Highway 580, and West Courtney Campbell Causeway bridges, there were increases of about 42%, 18%, 61%, and 45%, respectively, in total user costs for the network-based approach compared with the current detour-only approach. These findings indicate that a significant network impact was captured by the network-based approach. The proposed method captures the effects of bridge closures on all road segments within the regional network jurisdiction; provides a more rigid framework for analysis by ensuring that user costs are computed efficiently but without overestimation; takes into account that road users may have advance knowledge of roadway conditions before trips and that this knowledge may significantly influence route choices; and provides sufficient information for agencies to implement preemptive measures to cater for network-level disruptions attributable to bridge closures.