American Transportation Research Institute Research Articles

THEORETICAL FRAMEWORKS OF ECOPFM PREDICTIVE MAINTENANCE (ECOPFM) PREDICTIVE MAINTENANCE SYSTEM

The Frameworks of EcoPFM Predictive Maintenance (PM) System presents a novel approach to maintenance optimization within eco-friendly power facilities, addressing the critical need for sustainable, efficient asset management. This paper introduces an integrated framework leveraging advanced predictive analytics, machine learning algorithms, and Internet of Things (IoT) technology to enable proactive maintenance interventions based on real-time data insights. Focusing on the context of the United States it highlights the significance of implementing such a system in the realm of eco-friendly energy infrastructure. The automotive and heavy-duty truck industries in the United States grapple with the challenge of optimizing maintenance strategies to ensure vehicle reliability, safety, and environmental sustainability. Traditional maintenance approaches, primarily reactive or scheduled maintenance, fall short in addressing the complexities of modern vehicle operations. The U.S. Department of Transportation reports that heavy-duty trucks transport approximately 70% of the nation's freight by weight, underscoring the sector's critical role in the economy. However, inefficiencies in maintenance strategies contribute to significant economic and operational setbacks. According to the American Transportation Research Institute, unscheduled truck maintenance and repairs are leading operational costs for fleets, with an average expense of 16.7 cents per mile in 2020, highlighting the financial strain of current maintenance practices. In the United States, the demand for eco-friendly power solutions is rapidly increasing, driven by a growing awareness of environmental sustainability and the imperative to reduce carbon emissions. As the nation transitions towards renewable energy sources and eco-friendly power facilities, the effective management of these assets becomes paramount to ensuring reliability, performance, and longevity. The EcoPFM PM System integrates diverse data sets sourced from eco-friendly power facilities across the USA, encompassing historical operational data, sensor readings, and environmental parameters. Through predictive analytics, the system identifies patterns and trends within these data sets to forecast equipment failures and performance degradation accurately. By prioritizing maintenance tasks based on risk assessment models and condition monitoring, the system enables organizations to optimize resource allocation, minimize downtime, and extend asset lifespan. Embracing the Frameworks of EcoPFM Predictive Maintenance System holds immense promise for organizations operating eco-friendly power facilities in the United States. By harnessing data-driven insights and proactive maintenance strategies, this system offers a pathway towards enhanced operational efficiency, cost reduction, and sustainability, ultimately contributing to the advancement of eco-friendly energy infrastructure in the nation.
 Keywords: Predictive Maintenance, System, ECOPFM, Technology.

Truck Parking Usage Patterns by Facility Amenity Availability

Truck parking is currently ranked by the American Transportation Research Institute (ATRI) as the fifth most critical issue for the trucking industry and, more importantly, as the second most important issue for truck drivers. Part of the problem can be attributed to inadequate supply of parking and federal hours of service (HOS) regulations. Recent truck driver stated-preference surveys reveal that amenities including restrooms, fuel, and showers are important considerations while seeking available parking. A link between parking usage patterns and facility amenity bundles can guide transportation agency investments in relation to the design and type of parking facilities with high potential to mitigate overcrowding issues, and can be used for predictive modeling in real-time parking availability algorithms and information systems. This paper used historical, anonymous truck global positioning system (GPS) data to determine the extent to which hourly parking usage patterns, that is, average parking duration, percentage of parked trucks, and parking usage ratio, vary by amenity availability. A K-means clustering model grouped parking facilities by time of day parking usage patterns, season, and geographic region. Each cluster, represented by parking usage patterns, was then tied to unique amenity bundles. Three usage pattern clusters were identified: overnight usage with long parking durations ( Cluster 1), off-peak usage with long parking durations, ( Cluster 2), and off-peak usage with short parking durations ( Cluster 3). In general, overnight and longer duration parking was associated with facilities that had fewer amenities, notably without showers, while peak and off-peak hours and shorter duration parking was associated with full-service facilities.

Data-Driven Synthetic Optimization Method for Driving Cycle Development

One of the key tasks for improving vehicle operating costs estimation is to develop representative driving cycles. A driving cycle is a vehicle speed-time profile. The cycles are a critical input for simulating VOCs in different road scenarios. The traditional methods could not generate driving cycles representing the speed pattern of the sample snippets. A new driving cycle development method, synthetic optimization, was developed and applied to generate synthetic driving cycles by applying the speed-acceleration frequency distribution matrix, speed-acceleration status transition matrix, and simulated annealing optimization algorithm. The data used for driving cycle development come from the new Strategic Highway Research Program (SHRP 2) naturalistic driving study (NDS) data and truck GPS trajectory data from American Transportation Research Institute (ATRI). Driving cycles of full-access-control facilities were developed as an example to show the performance of the proposed method. Compared to the conventional methods, the synthetic optimization approach provides, along with other advantages, driving cycles that better represent the speed patterns observed in the different scenarios.

Evaluation and Testing of Driver-Assistive Truck Platooning: Phase 2 Final Results

Phase 2 final results are described for the FHWA Exploratory Advanced Research project titled Heavy Truck Cooperative Adaptive Cruise Control: Evaluation, Testing, and Stakeholder Engagement for Near Term Deployment, which evaluates the commercial feasibility of driver-assistive truck platooning (DATP). The project was led by Auburn University, in partnership with Peloton Technology, Peterbilt Trucks, Meritor WABCO, and the American Transportation Research Institute. DATP is a form of cooperative adaptive cruise control for heavy trucks (two-truck platoons). It takes advantage of the increasing maturity of vehicle-to-vehicle (V2V) communications (and the expected widespread deployment of V2V connectivity based on dedicated short-range communications over the next decade) to improve freight efficiency, fleet efficiency, safety, and highway mobility as well as reduce emissions. Notably, truck fleets can implement DATP regardless of the regulatory timeline for dedicated short-range communications. The Phase 2 analysis built on Phase 1 and included a testing program of a DATP prototype (with detailed SAE Type 2 fuel economy testing), wireless communications optimization, traffic modeling to understand the impact on roadways at various levels of market penetration, and additional analysis of methods to find DATP partners as well as aerodynamic simulations to understand drag on the vehicles. Detailed analysis of the fuel economy testing data is provided.

Truck–Rail Intermodal Connector Performance Evaluation: Illinois Case Study

This paper describes the development and use of an open source, geospatially enabled framework and applies that platform to the analysis of freight truck travel time data that were extracted from the National Performance Management Research Dataset (NPMRDS). The NPMRDS is provided by HERE-Navteq and the American Transportation Research Institute under a contract with the U.S. Department of Transportation. The analysis platform is based on the PostgreSQL database and uses the PostGIS extension to enable spatial analysis inside the database. The study team developed a methodology to combine information from the NPMRDS, the National Highway Planning Network, and the Illinois Department of Transportation’s Illinois Roadway Information System statewide roadway inventory. Geographic information system tools and the capabilities of the PostgreSQL database addressed incompatibilities and inconsistencies between supporting data sets and allowed the study team to find, access, use, and combine data from different sources. The study used these data sources to develop roadway performance measures for truck–rail intermodal connectors (IMCs) and compare these connectors with roads that had similar attributes. When compared with roadway segments with similar characteristics, speeds on IMCs often demonstrated greater uniformity and consistency.

Development of Algorithms to Convert Large Streams of Truck GPS Data into Truck Trips

This paper documents the development and validation of the algorithms and procedures used to convert large streams of raw GPS data available from the American Transportation Research Institute into a database of truck trips for use in analysis and modeling of statewide freight truck travel. Also presented are the results from the conversion of 4 months of raw GPS data from Florida, totaling more than 145 million GPS records, into a database of more than 1.2 million truck trips within, into, and out of Florida. The procedures and implementation details described in this paper will be useful to transportation planning agencies in converting raw GPS data streams into more usable truck trip databases for truck flow modeling and analysis at statewide and megaregional levels.

Estimation of Statewide Origin–Destination Truck Flows from Large Streams of GPS Data

This paper demonstrates the use of large streams of truck GPS data from the American Transportation Research Institute for the estimation of statewide freight truck flows in Florida. Raw GPS data streams, which comprised more than 145 million GPS records, were used to derive a database of more than 1.2 million truck trips that started or ended in Florida. The paper sheds light on the extent to which the trips derived from the GPS data captured the observed truck traffic flows in Florida. The paper includes insights into ( a) the truck type composition, ( b) the proportion of the observed truck traffic flows covered by the data, and ( c) the geographical differences in the coverage. The paper applies origin–destination (O-D) matrix estimation to combine the GPS data with the observed truck traffic volumes at different locations within and outside Florida to derive an O-D table of truck flows within, into, and out of the state. The procedures, implementation details, and findings discussed in the paper are expected to be useful to agencies that are considering the use of GPS data in freight travel demand modeling.

Assessing the Use of Navigation Systems in the Trucking Industry

There is currently a dearth of information and understanding on the role and the scope of the use of navigation systems in large trucks. Furthermore, the relationship between navigation system use in large trucks and safety benefits and consequences is unclear. Anecdotal evidence suggests that the use of navigation systems that are not specifically designed for the operation of large trucks has a detrimental impact on safety. Given the limited crash data that directly tied large truck crashes to navigation system usage, the American Transportation Research Institute (ATRI) explored alternative methodologies for investigating this important safety issue. The study developed two new approaches that (a) assessed navigation system usage in the industry and (b) attempted to identify the technical basis for navigation system failures and inadequacies from a truck operations perspective. ATRI collected and analyzed a large survey data sample on navigation system usage, including perceived utility and the associated benefits and risks for truck drivers who used navigation systems. Furthermore, the study identified the key priorities that navigation system providers should address to meet the needs of the trucking industry.

Use of Logbook Data to Assess 34-Hour Restart Charges

The Federal Motor Carrier Safety Administration's (FMCSA) hours-of-service (HOS) rules govern the number of hours that a commercial driver may be on duty and operate a commercial motor vehicle and how much rest is required between periods of work. Recent changes to the HOS, which included a change to the 34-h restart rule, were supported by an FMCSA regulatory impact analysis (RIA) that assessed the costs and benefits of the changes. The foundation of this analysis was logbook data from a small sample of carriers undergoing FMCSA safety audits and compliance reviews and thus more likely to have compliance issues. To assess the accuracy of the FMCSA cost–benefit analysis, which found a net benefit of $133 million annually for the new restart provisions, the American Transportation Research Institute (ATRI) research team replicated the most critical analyses of the RIA. As part of this process, a larger logbook data set that represented normal trucking operations was utilized. ATRI found that FMCSA's intense-schedule drivers, a group that was critical to FMCSA's net benefit findings, did not exist within normal carrier operations. Consequently, the agency's cost–benefit results were found to be overestimated. Analysis of the normal logbook data showed that a net loss, not a net benefit, would result from the new restart provisions. In addition, costs not captured by FMCSA's analysis were incorporated in the analysis to show an annual loss of $189 million considering that the average driver might lose a modest 15 min of productivity per week because of the new provisions.

Generating Reliable Freight Performance Measures with Truck GPS Data

Building on previous efforts to study freight mobility and reliability, a truck GPS data analysis methodology was developed to study the freight performance of heavy commercial vehicles along key freight corridors in the Twin Cities metropolitan area in Minnesota. Twelve months of truck GPS data collected in 2012 were obtained from the American Transportation Research Institute. Several performance measures, such as truck mobility, delay, and reliability measures, were derived and statistically analyzed by route, roadway segment, and time of day. The derived performance measures were validated with available benchmark data to ensure data quality. Data quality validation is important particularly in urban areas where satellite reception may be limited and traffic congestion is more common. For data quality verification, average truck speed and hourly volume percentage computed from the truck GPS data were validated with data from weigh-in-motion sensors, loop detectors, and automatic traffic recorders at selected locations. The analysis methodology using truck GPS data offers potential opportunities for freight planners and managers to generate reliable measures in a timely manner. Findings from this research indicate that those measures derived from the truck GPS data can be used in supporting the U.S. Department of Transportation performance measure initiative and truck and freight modeling in metropolitan areas. The performance measures can provide truck-specific information to support regional surface freight planners in identifying freight bottlenecks and infrastructure improvement needs and in developing operational strategies to promote efficient freight movement for the industry.

Accommodation of Large Trucks in Roundabouts

Roundabouts increasingly are used as alternatives for signalized and stop-controlled intersections in the United States, where nearly 200 new roundabouts are constructed every year. Proponents often cite numerous safety improvements, including reduced overall crash rates at converted intersections, as justification to increase roundabout development. However, the growing use of roundabouts may inhibit the safe and efficient movement of large trucks because of roundabout design elements that conflict with truck operations. In collaboration with Kansas State University, researchers from the American Transportation Research Institute conducted an online survey of motor carriers to explore features that may hinder roundabout use by large trucks. The survey collected industry observations regarding the challenges experienced by commercial truck drivers in negotiating roundabouts. Those observations are synthesized, and the anecdotal experiences that may allow researchers and roadway designers to identify and categorize unknown roundabout design issues and possible solutions are documented. It is hoped that the research results can be used as the first conversation in a productive dialogue between transportation planners and the trucking industry on the issue of roundabout design for large truck use.

Synthesis of Commercial Motor Vehicle Safety Technology Surveys

This paper documents and synthesizes the major qualitative survey efforts within the United States relating to stakeholder design, use, and perspectives of onboard truck safety technologies. The Federal Motor Carrier Safety Administration (FMCSA) and American Transportation Research Institute (ATRI) have conducted a survey synthesis and meta-analysis to consolidate, analyze, and identify gaps in existing survey research on safety technologies. Over the past several years, there has been an increasing interest in onboard truck safety technologies. At different times, entities such as FMCSA, FHWA, American Trucking Associations (ATA), and ATRI have undertaken different survey projects to document and understand onboard safety technology applications. While a wide variety of data has been collected by these projects, there has been little effort to aggregate data across projects. The current synthesis includes analysis of more than 11 survey, interview, and focus group instruments, representing 558 respondents and 56 unique questions. Preliminary results indicate that while existing research addresses many issues related to carrier purchase and use of technologies, there is a considerable gap in research addressing driver attitudes, experiences, and preferences. Satellite or cellular communication between terminal and vehicle and Global Positioning System are the technologies most commonly used by carriers. Most carriers that have a safety technology installed on their fleets have already realized the desired safety benefits, often intangible from a quantitative perspective. Cost and lack of clear data about benefits are the two biggest factors preventing wider installation of safety technologies.

Synthesis of Commercial Motor Vehicle Safety Technology Surveys: What Has Been Learned?

This paper documents and synthesizes the major qualitative survey efforts within the United States relating to stakeholder design, use, and perspectives of onboard truck safety technologies. The Federal Motor Carrier Safety Administration (FMCSA) and American Transportation Research Institute (ATRI) have conducted a survey synthesis and metaanalysis to consolidate, analyze, and identify gaps in existing survey research on safety technologies. Over the past several years, there has been an increasing interest in onboard truck safety technologies. At different times, entities such as FMCSA, FHWA, American Trucking Associations (ATA), and ATRI have undertaken different survey projects to document and understand onboard safety technology applications. While a wide variety of data has been collected by these projects, there has been little effort to aggregate data across projects. The current synthesis includes analysis of more than 11 survey, interview, and focus group instruments, representing 558 respondents and 56 unique questions. Preliminary results indicate that while existing research addresses many issues related to carrier purchase and use of technologies, there is a considerable gap in research addressing driver attitudes, experiences, and preferences. Satellite or cellular communication between terminal and vehicle and Global Positioning System are the technologies most commonly used by carriers. Most carriers that have a safety technology installed on their fleets have already realized the desired safety benefits, often intangible from a quantitative perspective. Cost and lack of clear data about benefits are the two biggest factors preventing wider installation of safety technologies.

Energy and Emissions Impacts of Operating Higher-Productivity Vehicles

The American Transportation Research Institute and Cummins, Inc. teamed up to investigate the energy and emissions impacts from operating commercial vehicles at weights equal to or greater than existing federal limits. Six vehicle configurations and four gross vehicle weights (GVWs) were modeled over a representative route to estimate fuel usage and corresponding tailpipe emissions. The results provide a comparative estimate of the potential energy and emission impacts from operating different vehicle configurations at various weights. When six configurations were modeled over a representative route with the Cummins, Inc., vehicle mission simulation model and a simplified algorithm to estimate emissions, fuel consumption and emissions generally decreased for each ton-mile of freight transported when compared with two standard configuration vehicles at 80,000 lb GVW. With the exception of one configuration, decreases in fuel consumption and emissions per ton-mile were 4% to 19% at 100,000 lb GVW, 15% to 22% at 120,000 lb GVW, and 27% at 140,000 lb GVW. The lone exception was for the heaviest vehicle, which experienced an increase in fuel consumption and emissions per ton-mile at a GVW of 100,000 lb when compared with the two standard configurations. At this weight, the added payload weight was insufficient to offset the additional fuel consumption demands of the heavier vehicle. Other than this exception, operating higher-productivity vehicles to accommodate higher GVWs can be expected to decrease fuel consumption and emissions on a ton-mile basis when compared with standard configuration vehicles at 80,000 lb GVW.

Energy and Emissions Impacts of Operating Higher-Productivity Vehicles

The American Transportation Research Institute and Cummins, Inc. teamed up to investigate the energy and emissions impacts from operating commercial vehicles at weights equal to or greater than existing federal limits. Six vehicle configurations and four gross vehicle weights (GVWs) were modeled over a representative route to estimate fuel usage and corresponding tailpipe emissions. The results provide a comparative estimate of the potential energy and emission impacts from operating different vehicle configurations at various weights. When six configurations were modeled over a representative route with the Cummins, Inc., vehicle mission simulation model and a simplified algorithm to estimate emissions, fuel consumption and emissions generally decreased for each ton-mile of freight transported when compared with two standard configuration vehicles at 80,000 lb GVW. With the exception of one configuration, decreases in fuel consumption and emissions per ton-mile were 4% to 19% at 100,000 lb GVW, 15% to 22% at 120,000 lb GVW, and 27% at 140,000 lb GVW. The lone exception was for the heaviest vehicle, which experienced an increase in fuel consumption and emissions per ton-mile at a GVW of 100,000 lb when compared with the two standard configurations. At this weight, the added payload weight was insufficient to offset the additional fuel consumption demands of the heavier vehicle. Other than this exception, operating higher-productivity vehicles to accommodate higher GVWs can be expected to decrease fuel consumption and emissions on a ton-mile basis when compared with standard configuration vehicles at 80,000 lb GVW.