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Pavement deterioration models are an important part of any pavement management system. Many of these models suffer from endogeneity bias because of the inclusion of independent variables correlated with unobserved factors, which are captured by the model's error terms. Examples of such endogenous variables include pavement overlay thickness and maintenance and rehabilitation activities, both of which are not randomly chosen but are in fact decision variables selected by pavement engineers based on field conditions. Inclusion of these variables in a pavement deterioration model can result in biased and inconsistent model parameter estimates, leading to incorrect insights. Previous research has shown that continuous endogenous variables, such as pavement overlay thickness, can be corrected using auxiliary models to replace the endogenous variable with an instrumented variable that has lower correlation with the unobserved error term. Discrete endogenous variables, such as the type of maintenance and rehabilitation activities, have been accounted for by modeling the likelihood of each potential outcome and developing individual deterioration models for each of the potential responses. This paper proposes an alternative approach to accommodate discrete endogenous variables-the selectivity correction method-that allows a single model to incorporate the impacts of all discrete choices. This approach is applied to develop a pavement-roughness progression model that incorporates both continuous and discrete endogenous variables using field data from Washington State. The result is a roughness progression model with consistent parameter estimates, which have more realistic values than those obtained in previous studies that used the same data.

Greenhouse gas emissions reduction has garnered special importance in recent times in the transportation sector, including pavement design and management. In this study, we incorporate this environmental objective in pavement management. We present an optimization problem to minimize GHG emissions under multiple budget constraints by determining joint management strategies for a range of heterogeneous interventions, including maintenance, rehabilitation and reconstruction. We propose a computationally efficient bottom-up solution algorithm, which is built on Lagrangian Relaxation and Dynamic Programming. Finally, we apply our findings to a real-world highway network in California, where the results show a potential GHG emissions reduction of 20% through an increased combined budget of 35% on the Pareto frontier.

Public transit systems with high occupancy can reduce greenhouse gas (GHG) emissions relative to low-occupancy transportation modes, but 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. In previous work, we explored the unintended consequences of lowering transit LOS on emissions in a single-technology transit system. Herein, 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, we provide a quantitative basis for designing and operating integrated urban transit systems that can reduce GHG emissions and societal costs. We find that highly elastic transit demand may cancel emission reduction potentials resulting from lowering LOS, due to demand shifts to lower occupancy vehicles. 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 find that the potential for unintended consequences increases with the size of the city. Our results are robust to uncertainties in the costs and emissions parameters.

Pavement management systems, designed to minimize total lifecycle costs, will need to evolve to meet the needs of the future. Environmental concerns are likely to add an additional consideration for the state DOTs when allocating their financial resources. Transportation agencies will be concerned with determining maintenance, resurfacing and reconstruction policies for pavement segments in their systems while also addressing the environmental impact of these activities. In this paper, we propose an efficient solution to solve for pavement resurfacing and reconstruction policies that minimize societal (agency and user) costs under a Greenhouse Gas (GHG) emissions constraint. The main methodological contribution of this work relative to the state of the art is that we formulate the problem to include multi-dimensional pavement segment states and heterogeneous management activities. It allows for a more realistic representation of the majority of current pavements in the world. For example, the assumption that pavements are perpetual, i.e., do not need reconstruction during their lifetime, can be relaxed. A case study using California roads is performed; we find that, for that specific group of pavement segments, the optimal policies to minimize societal costs do not vary greatly from the policies that minimize GHG emissions. An agency can use these results to determine what GHG emission budgets are feasible for the highway system that it manages.

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.

Transportation agencies are being urged to reduce their greenhouse gas (GHG) emissions. One possible solution within their scope is to alter their pavement management system to include environmental impacts. Managing pavement assets is important because poor road conditions lead to increased fuel consumption of vehicles. Rehabilitation activities improve pavement condition, but require materials and construction equipment, which produce GHG emissions as well. The agency's role is to decide when to rehabilitate the road segments in the network. In previous work, we sought to minimize total societal costs (user and agency costs combined) subject to an emissions constraint for a road network, and demonstrated that there exists a range of potentially optimal solutions (a Pareto frontier) with tradeoffs between costs and GHG emissions. However, we did not account for the case where the available financial budget to the agency is binding. This letter considers an agency whose main goal is to reduce its carbon footprint while operating under a constrained financial budget. A Lagrangian dual solution methodology is applied, which selects the optimal timing and optimal action from a set of alternatives for each segment. This formulation quantifies GHG emission savings per additional dollar of agency budget spent, which can be used in a cap-and-trade system or to make budget decisions. We discuss the importance of communication between agencies and their legislature that sets the financial budgets to implement sustainable policies. We show that for a case study of Californian roads, it is optimal to apply frequent, thin overlays as opposed to the less frequent, thick overlays recommended in the literature if the objective is to minimize GHG emissions. A promising new technology, warm-mix asphalt, will have a negligible effect on reducing GHG emissions for road resurfacing under constrained budgets.

This report evaluates the performance of Continuous Risk Profile (CRP) compared with the Sliding Window Method (SWM) and Peak Searching (PS) methods. These three network screening methods all require the same inputs: traffic collision data and Safety Performance Functions (SPFs), however, depending on how these input parameters are analyzed at the network screening level, the result of the analysis can vary significantly. Findings indicated that the CRP method produced far fewer false positives than SWM and PS. The false negative rates for CRP, SWM and PS were comparable. These findings indicate that by using the CRP method, California Department of Transportation (Caltrans) can significantly reduce the resources spent on investigating falsely identified locations and better utilize the resources in improving high collision concentration locations. It will also help Caltrans in reducing the backlog in Caltrans Table C.

This report summarizes the layout and content of a website created for the CEC to make the information in the Energy Aware Planning Guide readily available to California cities and counties, to assist them in their efforts to reduce energy use. The website consists of an electronic version of the entire Planning Guide that allows easy maneuverability among the many sections of the document, and quick downloading of references cited in the document. The website also contains individual briefing pages for 46 land use- and transportation-related measures, including 21 briefs that provide information on additional transportation-related measures that are not specifically identified in the Planning Guide. The website also summarizes estimates of the effectiveness of individual measures described in the briefings, as well as the cost-effectiveness of each measure, derived from a comprehensive national study of the potential to reduce energy use from the transportation sector

The paper explores how the coordination of vehicle schedules in a public transit system affects generalized costs. We consider an idealized system that delivers its users to a common destination by requiring each to transfer from a feeder- to a trunk-line vehicle. Continuum models are used first to analyze cases in which the trunk-line vehicle schedule is given exogenously. We find that when feeder vehicles are dispatched in coordination with this exogenous trunk-line schedule, the reduction in user cost often outweighs the added cost to the feeder operation. In cases when the frequencies of trunk and feeder services can be established jointly, the models show that coordination can be Pareto improving, meaning that operator and user costs both diminish. Conditions that give rise to these cost savings are specified. Practical implications are discussed.

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.

The US parking infrastructure is vast and little is known about its scale and environmental impacts. The few parking space inventories that exist are typically regionalized and no known environmental assessment has been performed to determine the energy and emissions from providing this infrastructure. A better understanding of the scale of US parking is necessary to properly value the total costs of automobile travel. Energy and emissions from constructing and maintaining the parking infrastructure should be considered when assessing the total human health and environmental impacts of vehicle travel. We develop five parking space inventory scenarios and from these estimate the range of infrastructure provided in the US to be between 105 million and 2 billion spaces. Using these estimates, a life-cycle environmental inventory is performed to capture the energy consumption and emissions of greenhouse gases, CO, SO2, NOX, VOC (volatile organic compounds), and PM10 (PM: particulate matter) from raw material extraction, transport, asphalt and concrete production, and placement (including direct, indirect, and supply chain processes) of space construction and maintenance. The environmental assessment is then evaluated within the life-cycle performance of sedans, SUVs (sports utility vehicles), and pickups. Depending on the scenario and vehicle type, the inclusion of parking within the overall life-cycle inventory increases energy consumption from 3.1 to 4.8 MJ by 0.1–0.3 MJ and greenhouse gas emissions from 230 to 380  g CO2e by 6–23 g CO2e per passenger kilometer traveled. Life-cycle automobile SO2 and PM10 emissions show some of the largest increases, by as much as 24% and 89% from the baseline inventory. The environmental consequences of providing the parking spaces are discussed as well as the uncertainty in allocating paved area between parking and roadways.

This paper focuses on the estimation of highway maintenance marginal costs. Highway maintenance marginal cost has been estimated in the literature using the perpetual overlay indirect approach. This approach assumes that pavement overlay costs dominate maintenance costs and ignores other maintenance activities. This paper focuses on two questions. First, is it acceptable to ignore the less costly activities? Second, if multiple maintenance activities are to be considered, is it acceptable to ignore their interdependence? The results show that less costly maintenance activities cannot be ignored. Furthermore, if multiple activities are to be considered, their interdependence should be taken into account.

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.

In recent years, the reduction of freight vehicle trips during peak hours has been a common policy goal. To this end, policies have been implemented to shift logistics operations to nighttime hours. The purpose of such policies has generally been to mitigate congestion and environmental impacts. However, the atmospheric boundary layer is generally more stable during the night than the day. Consequently, shifting logistics operations to the night may increase 24‐hour average concentrations of diesel exhaust pollutants in many locations. This paper presents realistic scenarios for two California cities, which provide exhaust concentration and human intake estimates after temporal redistributions of daily logistics operations. Estimates are made for multiple redistribution patterns, including from 07:00‐19:00 to 19:00‐0:700, similar to daytime congestion charging polices and from 03:00‐18:00 to 18:00‐03:00, corresponding to the PierPASS program at the ports of Los Angeles and Long Beach. Results for these two redistribution scenarios indicate that 24‐hour average exhaust concentrations would increase at most locations in California, and daily human intake is likely to worsen or be unimproved at best. These results are shown to be worse for inland than coastal settings, due to differences in meteorology. Traffic congestion effects are accounted for, using a new graphical method, which depicts how off‐peak policies can be environmentally improving or damaging, depending on traffic speeds and meteorology. An investigation into the decreasing marginal environmental benefits of off‐policies is then provided, through the application of traffic flow theory. Finally, related environmental and human exposure concerns are considered to provide a comprehensive policy discussion of the environmental effects of shifting logistics operations from day to night.

In recent years, the reduction of freight truck trips has been a common policy goal. To this end, policies aimed at influencing load consolidation, load factors and increasing maximum truck weight limits have been suggested and implemented, resulting in higher gross vehicle weights. The purpose of such policies has generally been to mitigate congestion and environmental impacts. However, trucks cause most of the damage incurred by pavements. The supply chain associated with pavement maintenance and construction releases significant air emissions, raising the question of whether increased vehicle weights may cause unintended environmental consequences. This paper presents scenarios with estimated emissions resulting from load consolidation and changes in load factors. These scenarios reveal several points having to do with the tradeoff between tailpipe versus pavement supply‐chain emissions. In some cases, unintended emissions from the pavement supply‐chain are found to be significant. Emissions associated with pavement construction are also found to increase as a result of pavement design specifications that account for heavier trucks.

This paper documents a new method for monitoring traffic collision data from continuous roadway facilities to detect high collision concentration locations. Many existing methods for detecting collision concentration locations require segmentation of roadways and assume traffic collision data are spatially uncorrelated, resulting in both false positives (i.e., identifying sites for safety improvements that should not have been selected) and false negatives (i.e., not identifying sites that should have been selected). The proposed method does not require segmentation of roadways; spatial correlation in the collision data does not affect the results of analysis. This new method has a lower false positive rate than the conventional sliding moving window approach. This paper shows how the proposed method can proactively identify high collision concentration locations and capture the benefit of safety improvements observed in the project location and in neighboring sites.

This report documents a review of the literature for commercial motor vehicle inspection and compliance stations and its relationship with the growth of truck travel over the next 25 years and the lack of concurrent capacity increases in staffing at such stations. Problems result in that more commercial vehicles will need to stop for inspection with longer queues at weigh stations associated with increased congestion, increased wait times, more idling trucks, and increased safety hazards. Alternatively, without stopping at inspection and compliance stations other problems will result such as roadway pavement and structure damage, and safety-related and security-related issues. Proposed advanced technological solutions to these problems are examined focusing on the four areas of pavement damage, safety, air quality, and security.

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.

This paper presents a discussion directed at determining the most progressive options for shifting the freight logistics industry towards having more sustainable-oriented goals. The authors first discuss transportation sustainability and the concept of green logistics. This is followed by a discussion on externalities resulting from vehicle emissions and indirect environmental externalities. The problem of environmental externalities is then examined, with focus on emissions and other related data. The authors then examine options for reducing environmental externalities. They discuss impact considerations, options focused on emissions reduction, changing freight sector operations, and considerations regarding demand and economic development.

The report attempts to deliver a proof of concept that optimal planning and management strategies can be formulated through applying robust optimization methodology such that limited resources could be allocated more rationally, and reliability of a highway network improved more efficiently.The report focuses on two applications: the network design problem under demand uncertainty, and fleet allocation for freeway services patrols. Their corresponding decision-makings are formulated as several optimization models. By solving these models robust optimal strategies can be obtained. Numerical examples and simulation tests are presented to demonstrate the validity and usefulness of the proposed models.The report has proved that through applying robust optimization methodology, robust optimal improvement strategies can be obtained to improve substantially the capacity of a highway system against high-consequence scenarios incurred by fluctuations in travel demand or irregular incidents.

The network-level infrastructure management problem involves selecting and scheduling Maintenance, Repair, and Rehabilitation (MR&R) activities on networks of infrastructure facilities so as to maintain the level of service provided by the network in a cost-effective manner. This problem is frequently formulated as a Markov Decision Problem (MDP) solved via Linear Programming (LP). The conditions of facilities are represented by elements of discrete condition rating sets, and transition probabilities are employed to describe deterioration processes. Epistemic and parametric uncertainties not considered within the standard MDP/LP framework are associated with the transition probabilities used in infrastructure management optimization routines. This paper contrasts the expected costs incurred when model uncertainty is ignored with those incurred when this uncertainty is explicitly considered using Robust Optimization. A case study involving a network-level pavement management MDP/LP problem demonstrates how explicitly considering uncertainty may limit worst case MR&R expenditures. The methods and results can also be used to identify the costs of uncertainty in transition probability matrices used in infrastructure management systems.

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.

Predicting pavement performance under the combined action of traffic and the environment provides valuable information to a highway agency. The estimation of the time at which the pavement conditions will fall below an acceptable level (failure) is essential to program maintenance and rehabilitation works and for budgetary purposes. However, the failure time of a pavement is a highly variable event; terminal conditions will be reached at different times at various locations along a homogeneous pavement section. A common problem in modeling event duration is caused by unobserved failure events in a typical data set. Data collection surveys are usually of limited length. Thus, some pavement sections will have already failed by the day the survey starts, others will reach terminal conditions during the survey period, while others will only fail after the survey is concluded. If only the failure events observed during the survey are included in the statistical analysis (disregarding the information on the after and before events), the model developed will suffer from truncation bias. If the censoring of the failure events is not accounted for properly, the model may suffer from censoring bias.In this paper, an analysis of the data collected during the AASHO Road Test is presented. The analysis is based on the use of probabilistic duration modeling techniques. Duration models enable the stochastic nature of pavement failure time to be evaluated as well as censored data to be incorporated in the statistical estimation of the model parameters. The results, based on sound statistical principles, show that the failure times predicted with the model match the observed pavement failure data better than the original AASHO equation.

A recursive non-linear model was developed for the prediction of pavement performance as a function of traffic characteristics, pavement structural properties and environmental conditions. The model highlights some of the advantages of relaxing the linear restriction that is usually placed on the specification form of pavement performance models. First, a functional form that better represents the physical deterioration process can be used. Second, the estimated parameters are unbiased, owing to a proper specification and the use of sound statistical techniques. Finally, the standard error of the prediction is reduced by half that of the equivalent existing linear model. This improved accuracy has important economic implications in the context of pavement management.The model developed as part of this research enables the determination of an unbiased exponent of the so-called power law and of the equivalent loads for different axle configurations. The estimated exponent confirms the value of 4.2 traditionally used. However, it should be noted that this exponent is only to be used for determining damage in terms of serviceability. On the other hand, equivalent loads estimated for different axle configurations tend to differ from traditionally used values, especially in the case of single axles with single wheels.