Steven Lavrenz

Resilience and Robustness in Long-term Planning of the National Energy and Transportation System

The most significant energy consuming infrastructures and the greatest contributors to greenhouse gases for any developed nation today are electric and freight/passenger transportation systems. Technological alternatives for producing, transporting and converting energy for electric and transportation systems are numerous. Addressing costs, sustainability and resilience of electric and transportation needs requires long-term assessment since these capital-intensive infrastructures take years to build with lifetimes approaching a century. Yet, the advent of electrically driven transportation, including cars, trucks and trains, creates potential interdependencies between the two infrastructures that may be both problematic and beneficial. We are developing modelling capability to perform long-term electric and transportation infrastructure design at a national level, accounting for their interdependencies. The approach combines network flow modelling with a multi-objective solution method. We describe and compare it to the state of the art in energy planning models. An example is presented to illustrate important features of this new approach.

Use of High Resolution Signal Controller Data to Identify Red Light Running

Intersection crashes are a safety concern for many transportation agencies, and crashes related to red light running (RLR) vehicles are of particular interest. Many camera-based RLR detection systems are controversial with the public, and there is relatively little published literature on the methodologies. This study proposes a methodology that combines high-resolution signal controller data with conventional stop bar loop detection to identify vehicles that enter the intersection after the start of red, when many of the most serious RLR crashes occur. The methodology was validated with on-site video collection at several locations, and the algorithm was refined to reduce the incidence of false RLR indications. One case study demonstrated that an increase on the side street of the green split from 20% to 24% of the cycle length was associated with a 34% reduction in daily RLR counts and a reduction in the likelihood of RLR by a factor of 1.7—a substantial safety improvement for minimal cost. Law enforcement and transportation agencies can use this technique to more efficiently manage and deploy safety resources, especially in cases for which detailed crash histories are unknown or infrequent.

Estimating the marginal cost of pavement damage by highway users on the basis of practical schedules for pavement maintenance, rehabilitation and reconstruction

Highway agencies worldwide strive to ensure that highway users pay fees that not only recover the costs of pavement damage but also are equitable. In addressing the limitations of past research and quantifying the resulting adverse consequences on their analysis outcomes, this paper presents a comprehensive framework to derive more representative estimates of pavement damage cost. The developed framework incorporates practical pavement repair schedules that include all the key repair categories as a basis for estimating the marginal pavement damage cost (MPDC). The framework was applied to pavements of different surface type, functional class and age. On average, the MPDC was found to range from

Characterizing Signalized Intersection Performance Using Maximum Vehicle Delay

0.0032 per ESAL-mile on Interstate highways to

Integrating Traffic Signal Performance Measures into Agency Business Processes

0.1124 per ESAL-mile on non-national highways. It was determined that in each highway functional class, the marginal cost of pavement damage is influenced significantly by the pavement material type, traffic levels and age. Within any specific functional class, it was determined that the marginal cost increases with increasing traffic level and pavement age. The study also determined that non-consideration of at least one key repair category such as reconstruction or routine maintenance leads to significant (27–45%) underestimation of the actual MPDC.

Environmental and Energy Impacts of Automated Electric Highway Systems

Average delay is perhaps the most commonly used measure for characterizing the performance of signalized intersections. Current methodologies for estimating the average delay rely on the use of models based on volumes and green times. In practice, it is challenging to develop such real-time measurements of delay, due to the difficulty of accurately measuring vehicle arrivals and departures. However, measuring wait time after the first vehicle arrival during the red interval can be an important performance measure for low and moderate volume conditions. The maximum wait time performance measure provides an upper bound, or maximum, on individual vehicle delay during a given cycle and facilitates comparison between different types of operation.

Costs and Revenues Associated With Overweight Trucks in Indiana

INTEGRATING TRAFFIC SIGNAL PERFORMANCE MEASURES INTO AGENCY BUSINESS PROCESSES This report discusses uses of and requirements for performance measures in traffic signal systems facilitated by high-resolution controller event data. Uses of external travel time measurements are also discussed. The discussion is led by a high-level synthesis of the systems engineering concepts for traffic signal control, considering technical and nontechnical aspects of the problem. This is followed by a presentation of the requirements for implementing data collection and processing of the data into signal performance measures. The remaining portion of the report uses an example-oriented approach to show a variety of uses of performance measures for communication and detector system health, quality of local control (including capacity allocation, safety, pedestrian performance, preemption, and advanced control analysis), and quality of progression (including evaluation and optimization).

Strategic Methods for Modernizing Traffic Signal Maintenance Management and Quantifying the Impact of Maintenance Activities

This article proposes a methodology that can be applied to evaluate the environmental and energy impacts of a de novo technology, the automated electric highway systems (AEHS). The authors used the Motor Vehicle Emission Simulator (MOVES) program to estimate the impacts on emissions and energy use associated with a hypothetical implementation of this technology on the Interstate 70 corridor in Missouri during the period 2011–2040. The estimation results suggest that application of AEHS on the study corridor would decrease fossil fuel energy use by more than 25%, while emissions would decrease by up to 27%, depending on the pollutant being considered. A sensitivity analysis was also performed, in order to assess the impact of different demand estimates for the system; a 10% reduction in AEHS vehicle miles traveled would have an effect of 5% or less on the criteria of interest. This article represents one of the first attempts to quantify the environmental benefits of this widely discussed technology, and can serve as a guiding methodology for evaluation of upcoming intelligent transportation system technologies.

Extending Link Pivot Offset Optimization to Arterials with Single Controller Diverging Diamond Interchange

This study estimated highway pavement and bridge damage costs, and analyzed the adequacy of permit revenues to cover these costs. The study began with an extensive review of the literature on the subject, thus facilitating identification of the gaps in the existing practice and research. The developed framework includes the establishment of asset families, realistic types and timings of reconstruction, rehabilitation, and maintenance, traffic volumes and growth projections. The cost of damage was estimated for each asset family and age group, and the sensitivity of asset damage cost with respect to key policy and analysis variables was explored. Finally, the study examined cost and operational issues associated with the enforcement of overweight truck policies. In addressing the gaps in existing research, this study showed that the damage cost of highway assets due to overweight trucks is influenced significantly by the asset type and age. For pavement assets, the pavement damage cost estimates were found to range from

Highway-user fees for overweight trucks: Consequences of deviations from the fourth-power law

0.006 per Equivalent Single Axle Load (ESAL)‐mile on Interstates to