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Public transit systems with efficient designs and operating plans can reduce greenhouse gas (GHG) emissions relative to low-occupancy transportation modes, but many current transit systems have not been designed to reduce environmental impacts. This motivates the study of the benefits of design and operational approaches for reducing the environmental impacts of transit systems. For example, transit agencies may replace level-of-service (LOS) by vehicle miles traveled (VMT) as a criterion in evaluating design and operational changes. Previous studies have demonstrated in an idealized singletechnology transit system the potential of reducing GHG emissions by lowering the transit level-of-service (LOS) provided to the users. In this research, we extend the analysis to account for a more realistic case: a transit system with a hierarchical structure (trunk and feeder lines) providing service to a city where demand is elastic. By considering the interactions between the trunk and the feeder systems, the study provides a quantitative basis for designing and operating integrated urban transit systems that can reduce GHG emissions and costs to both transit users and agencies. The study shows that highly elastic transit demand may cancel emission reduction potentials resulting from lowering LOS, due to demand shifts to lower occupancy vehicles, causing unintended consequences. However, for mass transit modes, these potentials are still significant. Transit networks with buses, bus rapid transit or light rail as trunk modes should be designed and operated near the cost-optimal point when the demand is highly elastic, while this is not required for metro. We also find that the potential for unintended consequences increases with the size of the city. The results are robust to uncertainties in the costs and emissions parameters. The study also includes a discussion of a current transit system. Since many current transit systems have not yet been optimally designed, it should be possible to reduce their GHG emissions without sacrificing the LOS. A case study of the MUNI bus system in San Francisco is used to validate this conjecture. The analysis shows that reductions in GHG emissions can be achieved when societal costs are reduced simultaneously. The cost-optimal MUNI bus system has a societal cost of 0.15 billion $/year and emits 1680 metric tons of greenhouse gases. These figures only amount to about half of the cost and a third of the emissions in the current MUNI bus system. The optimal system has a lower spatial availability but a higher temporal availability of bus service than the current system, which highlights the potential benefits of providing more frequent express bus services.

The objective of infrastructure management is to provide optimal maintenance, rehabilitation and replacement (MR&R) policies for a system of facilities over a planning horizon. While most approaches in the literature have studied the decision-making process as a finite resource allocation problem, the impact of construction activities on the road network is often not accounted for. The state-of-the-art Markov decision process (MDP)-based optimization approaches in infrastructure management, while optimal for solving budget allocation problems, become internally inconsistent upon introducing network constraints. In comparison, approximate dynamic programming (ADP) enables solving complex problem formulations by using simulation techniques and lower dimension value function approximations. In this paper, an ADP framework is proposed which provides better results than randomized policy frameworks in the presence of network constraints.

Local bus operators can reduce costs and better serve their customers by matching service frequencies to those of the regional trunk lines that pass through their jurisdictions. For an entire region, trunk line and local bus operators should coordinate their schedules and the carrying capacities of their fleets. Metropolitan Planning Organizations (MPOs) are in a position to encourage this through their purse-string powers and ability to provide financial incentives. For future investments, facilities should be designed to allow quick and efficient transfers not just between the same types of vehicles (e.g. cross-platform transfers for rail systems), but also between different types of vehicles across different systems (e.g. small feeder buses and commuter rail lines). Before implementing these changes, several issues should be considered. One, additional costs will likely be incurred for technologies that improve scheduling and for upgrading stations. Also, improved bus-rail coordination can lengthen headways, thus reducing the frequency of services. Additionally, not all local feeder bus riders are destined for trunk line stops. Thus, the benefits of coordination should be weighed against the additional waiting times experienced by those riders.

Different highway agencies use different maintenance strategies. Our research indicates that each agency should compute its own cost estimates of maintenance on the basis of: • the measure of pavement performance that it uses to trigger maintenance (and accordingly, the appropriate value of the axle load power which is not necessarily equal to four), • and the actual maintenance activities and policies that it uses (which are not necessarily thick overlays applied at regular time intervals). If agencies were to move toward more realistic cost estimates, they would be able to better forecast maintenance costs and plan their maintenance programs accordingly. They also could establish fees that would be more equitable across truck sizes and potentially even raise additional needed dollars.

Bridge maintenance and replacement optimization methods use deterioration models to predict the future condition of bridge components. The purpose of this paper is to develop a framework for bridge maintenance optimization using a deterioration model that takes into account aspects of the history of the bridge condition and maintenance, while allowing the use of efficient optimization techniques. Markovian models are widely used to represent bridge component deterioration. In existing Markovian models, the state is the bridge component condition, and the history of the condition is not taken into account, which is seen as a limitation. This paper describes a method to formulate a realistic history-dependent model of bridge deck deterioration as a Markov chain, while retaining aspects of the history of deterioration and maintenance as part of the model. This model is then used to formulate and solve a reliability-based bridge maintenance optimization problem as a Markov decision process. A parametric study is conducted to compare the policies obtained in this research with policies derived using a simpler Markovian model.

Asset management systems help public works agencies decide when and how to maintain and rehabilitate infrastructure facilities in a cost effective manner. Many sources of error, some difficult to quantify, can limit the ability of asset management systems to accurately predict how built systems will deteriorate. This paper introduces the use of robust optimization to deal with epistemic uncertainty. The Hurwicz criterion is employed to ensure management policies are never ‘too conservative.’ An efficient solution algorithm is developed to solve robust counterparts of the asset management problem. A case study demonstrates how the consideration of uncertainty alters optimal management policies and shows how the proposed approach may reduce maintenance and rehabilitation (M&R) expenditures.

Asset management systems help public works agencies decide when and how to maintain and rehabilitate infrastructure facilities in a cost-effective manner. Many sources of error, some difficult to quantify, can limit the ability of asset management systems to accurately predict how built systems will deteriorate. This paper introduces the use of robust optimization to deal with epistemic uncertainty. The Hurwicz criterion is employed to ensure management policies are never ‘too conservative.’ An efficient solution algorithm is developed to solve robust counterparts of the asset management problem. A case study demonstrates how the consideration of uncertainty alters optimal management policies and shows how the proposed approach may reduce maintenance and rehabilitation (M&R) expenditures.