Total cost of ownership: a diesel versus gasoline comparison (2012-2013)

There are dramatic changes taking place in the U.S. automotive industry as it moves to meet stringent government mandated Corporate Average Fuel Efficiency (CAFE) requirements. Clean diesel engine technology represents one of the technologies companies are using to improve fuel economy. This report not only compares the fuel efficiency of clean diesel vehicles to comparable gasoline versions of the same vehicle (sold at auction during the 2012-2013 timeframe), but it also compares the total cost of ownership (TCO) between the two types of technologies. The report is a followup to our previous work on the total cost of ownership comparison of vehicles sold at auction during the 2010 and 2011 timeframe. The TCO model is built by developing three- and five-year cost estimates of depreciation by modeling used-vehicle auction data, as well as developing estimates for fuel costs by modeling government data. This report differs from the previous report in that it controls for the trim levels of the different vehicles. The depreciation and fuel cost estimates are added to three- and five-year estimates for repairs, fees and taxes, insurance, and maintenance from an outside data source. The results show that clean diesel vehicles provide a return on investment in both the three- and five-year timeframes, though there are differences in the amounts of return among mass market vehicles, medium duty pickup trucks, and luxury vehicles, as well as passenger cars, sport utility vehicles (SUVs), and medium duty pickup trucks.Robert Bosch Corporationhttp://deepblue.lib.umich.edu/bitstream/2027.42/111893/1/103193.pd

Evaluating roadway surface rating technologies

Final Report 09/01/2013 to 9/30/14The key project objective was to assess and evaluate the feasibility and accuracy of custom software used in smartphones to measure road roughness from the accelerometer data collected from smartphones and compare results with PASER (Pavement Surface and Evaluation Rating System) and IRI (International Roughness Index) measurement values collected from the same roadway segments. This project is MDOT’s first large implementation of a customized Android smartphone to collect road roughness data using a methodology developed from previous research work performed by UMTRI. Accelerometer data collection was performed via Android-based smartphones using a customized software application called DataProbe. During the project’s initial phase smartphones were installed in each of nine Michigan Department of Transportation (MDOT) vehicles driven by MDOT employees. These same vehicles also were used during 2012 and 2013 tocollect data on road distress using PASER Ratings for comparison. The DataProbe software application was used to collect data and transmit it to a University of Michigan Transportation Research server, where it was sorted, stored, and analyzed. All MDOT regions are represented in this analysis that compares road roughness ratings for nearly 6000 one tenth of a mile road segments. For the second phase of the project, road distress (PASER Rating) data was collected in 2014 simultaneously with an MDOT vehicle equipped with an IRI device and two DataProbe smartphones and two UMTRI vehicles equipped with five DataProbe smartphones. The analysis of the 2012 and 2013 data found that there were a number of significant predictors of IRI road roughness including: the phone and the vehicle used to collect the data, the speed of the vehicle collecting the data, the type of road surface, date of data collection, and accelerometer variance. By including quadratic terms to adjust for non-linear relationships and interactions among the predictors studied in this project, the multiple regression model predicted nearly 45 percent and 43 percent of the variance in IRI scores, respectively. An analysis of commonly used IRI categories (3 level/5 level) using ordinal logistic regression found that DataProbe accurately predicted these categories 68/71 percent of the time (2012 data), 77/76 percent of the time (2013 data). Analysis of the data collected in 2014 showed multiple regression models with variance among accelerometer measurements and speed accounting for 37 percent of the variance, while the ordinal logistic regression accurately predicted the IRI (3 level/5 level) categories 86/83 percent of the time. These results are promising when considering the near term application of the DataProbe technology for smaller locales that drive over their local roads more often, generating web-based road roughness visuals of each of the roads in their jurisdiction. In the longer term, state-wide road roughness measurement may be performed through the crowd-sourcing model available through Connected Vehicle initiatives, where all vehicles will be equipped with devices that support safety applications as well as other applications such as those that measure road roughness.Michigan Department of Transportationhttp://deepblue.lib.umich.edu/bitstream/2027.42/111891/1/103192.pd

Integrated mobile observations 2.0

The key objective for this project was the implementation of a low cost data acquisition system that provides weather-related road information to weather analysts throughout the country in near-real time using a fleet of 60 vehicles along the I-94 corridor in southern Michigan. This work is part of an on-going research initiative to identify how state DOTs will use and benefit from the large quantities of data generated by future connected vehicle programs and to assist in refining connected vehicle system requirements. The project employed an Android-based customized smartphone system called DataProbe to gather information from the phone (date, time, latitude and longitude, altitude, number of satellites, speed, accelerometer data, and compass heading); the vehicle through its controller area network (CAN) (air and coolant temperature, odometer, barometer, tachometer, speedometer, throttle, brakes, anti-lock braking system (ABS), electronic stability control (ESC), engine traction control and braking traction control); and through external sensors, Surface Patrol, that measure road and air temperature, humidity, and dew point. This data is collected by the phone in one second intervals [except for accelerometer data which is gathered at 100 second intervals on three axis (x, y, and z)], written to a comma separated values (CSV) file for a period of five minutes, and sent via cell phone to a University of Michigan Transportation Research Institute (UMTRI) server where it is validated, stored, and sent to weather analysts in five locations throughout the U.S. The Android-based smartphone was also designed to take photos of the road manually or via a web portal designed to track vehicles in use and potentially send messages to drivers through the phone. These photos were also uploaded to the UMTRI server and sent to the weather analysts throughout the U.S. Over a period of 17 months, the project saw tremendous change in every part of the process including changing nearly half of the project vehicles, re-writing DataProbe source code, changing hardware, and changing the process for working with drivers and updating software. The project resulted in the delivery of 172 gigabytes of valid data representing 196,204 valid files transferred to weather analysts, with MDOT operators driving nearly 400,000 miles and taking 44,594 photos. The IMO 2.0 project has been extended to accommodate an IMO 2.0 demonstration at the 2014 ITS World Congress – Detroit in September to demonstrate how the IMO 2.0 data can be used for traveler information in the form of motorist advisory warnings posted to dynamic message signs and the MDOT MiDrive website. Projectcompletion for this phase is November, 2015.+Michigan Department of Transportationhttp://deepblue.lib.umich.edu/bitstream/2027.42/117506/1/103240.pdfDescription of 103240.pdf : Final repor

The connected driver: integrated mobile observations 2.0 (IMO 2.0)

This project is a continuation of the original Integrated Mobile Observations (IMO 2.0) project that ran from January, 2013 through March, 2014. The Connected Driver: Integrated Mobile Observations, 2014-2015 project ran from April, 2014 through October, 2015. The main goal for the project was to show the applicability and capability of a smartphone based data collection system to provide accurate and timely micro-level road condition data to weather analysts in order to generate road condition warnings for drivers via electronic road signs, website, and mobile phone application (apps). The project employed an Android-based customized smartphone software program called DataProbe to gather information from the phone (date, time, latitude and longitude, altitude, number of satellites, speed, accelerometer data, and compass heading); the vehicle through its controller area network (CAN) (air and coolant temperature, odometer, barometer, tachometer, speedometer, throttle, brakes, anti-lock braking system (ABS), electronic stability control (ESC), engine traction control and braking traction control); and through external sensors, Surface Patrol, that measure road surface and air temperature, humidity, and dew point. When looking at the two IMO 2.0 projects combined over 31 months of data collection, vehicle operators drove 901,126 miles (363 gigabytes) and took 99,569 photos (45 gigabytes). Finally, the demonstration project at the 2014 ITS World Congress displayed the ability of UMTRI and NCAR researchers to combine the data collected from DataProbe sensors with an NCAR-developed Motorist Advisory Warning (MAW) phone application that delivered timely warnings to drivers of rain, slippery roads, and rough roads on their phone as well as on electronic signs in an area of 400 feet on the test track. This demonstration clearly showed that it is possible to provide micro-level weather reports in a timely manner. Project completion for this phase was October 31, 2015.Michigan Department of Transportationhttp://deepblue.lib.umich.edu/bitstream/2027.42/117546/1/103242.pdfDescription of 103242.pdf : Final repor

High efficiency trucks: new revenues, new jobs, and improved fuel economy in the medium and heavy truck fleet

The move to high efficiency trucks can lead to new revenues and jobs for companies involved in the development and marketing of the technologies needed to make this transition. But in order for the medium and heavy truck industry to make this transition, there will be a number of barriers to overcome. This study, funded by CALSTART, examines these challenges, estimates the potential revenues and jobs that may be created, and discusses the policy options available to government. The basis for this analysis is a survey of the manufacturers and suppliers that make up the medium/heavy truck industry. We divided potential new technologies into three groups, aerodynamics, hybrid/electric, and other powertrain technologies supplied from a previous analysis by the Union of Concerned Scientists (UCS). There are significant differences in the cost and sophistication of the technologies within as well as among these groups. Our analysis is based on the responses of 31 companies (from an original 90) that are either marketing or developing 52 of the new technologies. Two of the three challenges to introducing these new technologies, as reported by the executives who participated in the survey, focus on building the business case for the trucking industry to introduce the new technologies and ensuring customer acceptance of the technologies. The other major challenge is the technology challenges that still exist for some of the new technologies. These are significant challenges because the medium/heavy trucking industry, which runs on narrow margins, makes technology decisions based not on emotion but on business economics.CALSTARThttp://deepblue.lib.umich.edu/bitstream/2027.42/91261/1/102867.pd

Stuck in traffic: analyzing real time traffic capabilities of personal navigation devices and traffic phone applications

Field Operational Test: May, 2013 to July, 2013The global positioning system (GPS) market is a fast changing, highly competitive market. Products change frequently as they try to provide the best customer experience for a service that is based on the need for real-time data. Two major functions of the GPS unit are to correctly report traffic jams on a driver’s route and provide an accurate and timely estimated time of arrival (ETA) for the driver whether he/she is in a traffic jam or just following driving directions from a GPS unit. This study measures the accuracy of traffic jam reporting by having Personal Navigational Devices (PNDs) from TomTom and Garmin and phone apps from TomTom, INRIX, and Google in the same vehicle programmed to arrive at the same destination. We found significant differences among the units in terms of their ability to recognize an upcoming traffic jam. We also found differences in how well the devices responded to jams when driving on surface streets versus highways, and whether the jams were shorter or longer in length. We see potential for auto manufacturers to employ real-time traffic in their new vehicles, providing potential growth for real-time traffic providers through access to new vehicles as well as the aftermarket.TomTom Group, Southfield, MIhttp://deepblue.lib.umich.edu/bitstream/2027.42/102509/1/102984.pd

Alternative powertrain strategies and fleet turnover in the 21st century

The changes taking place in the global automotive industry related to alternative powertrains and fuels are affecting each country or region differently. Each country or region has its own policies in place to monitor and manage vehicle fuel consumption and emissions. Countries or regions also have different numbers of new vehicles sold annually and the total numbers of vehicles in their fleets. This analysis looks at the current and future direction of alternative powertrains/fuels across four developed economies (United States, Western Europe, Japan, and South Korea) and four developing economies (Brazil, Russia, India, and China) in order to measure the impact of increasing the number of alternative powertrains/fuels in their fleets. In particular, the analysis looks at the how much of each country’s fleet will turn over to vehicles based only on alternative powertrains/fuels by 2050 by introducing three different alternative powertrain/fuel models (less aggressive, moderately aggressive, and very aggressive). A less aggressive approach will yield fleet turnover rates of 60 percent or more for most countries, a moderately aggressive approach will yield fleet turnover rates of over 80 percent for most countries, and a very aggressive approach will yield fleet turnover rates of nearly 90 percent or more for most countries.The University of Michigan Sustainable Worldwide Transportation, Ann Arbor, Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/78001/1/102673.pd

Tracking the use of onboard safety technologies across the truck fleet

Special ReportThe Transportation Safety Analysis and the Automotive Analysis Divisons at the University of Michigan Transportation Research Institute (UMTRI) initiated the Onboard Safety Technologies project in 2007, supported by FMCSA, to collect detailed information about the penetration of onboard safety technologies in the trucking fleet and future use of these technologies. The five technologies examined included: lane departure warning (LDWS), electronic stability control (ESC), forward and side collision warning (FCWS/SCWS), and vehicle tracking systems (TRACKING). Previous work in estimating the penetration of onboard safety technologies never approached the question of technology penetration by sampling the popluation of trucking companies. This project uses that approach through the use of a random sample survey of the entire fleet of trucking companies to measure current penetration, future use, and the advantages available to companies employing these technologies. The source for the sample was the 2007 Motor Carrier Management Information System (MCMIS) file. Interviews were also conducted with companies with high penetration of the technologies as well as system suppliers of the technologies, in order to gather more detailed information about usage and future technology direction. The results of the survey show the expected low levels of usage of LDWS, FCWS, and SCWS, slightly higher levels of usage of ESC, and much higher usage of TRACKING. Analysis shows higher usage related to larger company size. Company usage of these technologies is expected to double over the next five years. The main factors noted by participants for using the technologies that vary little among the technologies include: proven safety benefits of the technologies, positive feedback by drivers, driver improvement, improved safety culture, reduced cost of accidents, and insurance benefits. The interviews yielded important views about the cost advantages of usage, the difficulty of justifying the purchase of the technologies, alternatives to safety technologies, and the future of technology integration.Federal Motor Carrier Safety Administration, Washington, D.Chttp://deepblue.lib.umich.edu/bitstream/2027.42/91262/1/102868.pd