____________________________________ 

2013-01-2400 

Published 09/24/2013

doi:10.4271/2013-01-2400

saecomveh.saejournals.org 

A Statistical Characterization of School Bus Drive Cycles

Collected via Onboard Logging Systems
	

Adam Duran and Kevin Walkowicz
	

National Renewable Energy Laboratory
	

ABSTRACT 

In an effort to characterize the dynamics typical of school bus operation, National Renewable Energy Laboratory 

(NREL) researchers set out to gather in-use duty cycle data from school bus fleets operating across the country. Employing 

a combination of Isaac Instruments GPS/CAN data loggers in conjunction with existing onboard telemetric systems 

resulted in the capture of operating information for more than 200 individual vehicles in three geographically unique 

domestic locations. In total, over 1,500 individual operational route shifts from Washington, New York, and Colorado 

were collected. 

Upon completing the collection of in-use field data using either NREL-installed data acquisition devices or existing 

onboard telemetry systems, large-scale duty-cycle statistical analyses were performed to examine underlying vehicle 

dynamics trends within the data and to explore vehicle operation variations between fleet locations. Based on the results of 

these analyses, high, low, and average vehicle dynamics requirements were determined, resulting in the selection of 

representative standard chassis dynamometer test cycles for each condition. 

In this paper, the methodology and accompanying results of the large-scale duty-cycle statistical analysis are presented, 

including graphical and tabular representations of a number of relationships between key duty-cycle metrics observed 

within the larger data set. In addition to presenting the results of this analysis, conclusions are drawn and presented 

regarding potential applications of advanced vehicle technology as it relates specifically to school buses. 

CITATION: Duran, A. and Walkowicz, K., "A Statistical Characterization of School Bus Drive Cycles Collected via
	

Onboard Logging Systems," SAE Int. J. Commer. Veh. 6(2):2013, doi:10.4271/2013-01-2400.
	

In an effort to better understand the operating INTRODUCTION 

characteristics of school buses across the country and Understanding vehicle usage plays a fundamental role in examine the dynamics challenges associated with the school assessing the performance potential of new vehicle bus vocation, National Renewable Energy Laboratory technologies, leading to more informed decision making, (NREL) researchers, with the cooperation of local school better test procedures [1, 2, 3, 4, 5, 6, 7], more commercially districts and Zonar Systems, collected drive cycle information successful designs, and lowered manufacturing and operating on class 6 and 7 school buses operating in three unique expenses. Of the methods available for modeling vehicle geographic locations. The class 6 and 7 school buses, with usage, operational drive/duty cycles have shown the ability to typical capacities of 71 to 84 passengers, were located in the accurately represent vehicle operating information in an states of Washington, New York, and Colorado. Although efficient and streamlined manner well suited to downstream smaller-sized vehicles exist that fall under the vocational 

analysis [8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. Typically category of school buses, no data was collected or examined consisting of speed and time information, these seemingly associated with this special subset, as vehicles falling within simple pieces of information, when properly understood, this group display operating characteristics dissimilar to their have been used to assess the value of new technology and to larger counterparts. aid in corresponding development and deployment. 

NREL/CP-5400-60068. Posted with permission.

Presented at the SAE 2013 Commercial Vehicle 

Engineering Congress.
Duran et al / SAE Int. J. Commer. Veh. / Volume 6, Issue 2(October 2013) 

Upon completion of the data collection phase of this 

study, detailed drive cycle analysis were performed to 

characterize typical school bus operating behavior. 

Examining a number of metrics associated with vehicle 

energy consumption and driving behavior, statistical 

relationships between key metrics of interest were explored, 

and representative standard chassis dynamometer test cycles 

were chosen corresponding to high, average, and low driving 

aggressiveness. 

APPROACH 

Data Collection and Drive Cycle Analysis 

Drive cycles used to model vehicle operation are 

composed of corresponding vehicle speed vs. time points. To 

capture the in-use drive cycle information analyzed as part of 

this study and to attempt to characterize the operation of 

school buses across the country, NREL researchers 

instrumented school buses located in three unique geographic 

regions (Colorado, Washington, New York) with onboard 

mounted Isaac Instruments DRU 900 and DRU 908 data 

loggers configured to collect 1-Hz global positioning system 

(GPS) data. The data sampling rate of 1 Hz was chosen based 

on the results of previous studies [1, 2, 3, 4, 6, 7]. In addition 

to the deployment of manually installed devices, data were 

also collected using preinstalled Zonar Systems telemetric 

GPS devices at the same 1-Hz sampling rate. Employing both 

systems, data from a total of 1,718 route shifts reflecting the 

operation of 212 vehicles were captured over a period of 

several weeks. An example of the Isaac Instruments data 

loggers employed as part of this study is shown in Figure 1. 

Figure 1. Isaac Industries data logging device 

Of the 1,718 route shift data collected as part of this 

study, 814 cycles were collected from source vehicles 

operating in Colorado, 874 from buses in Washington, and 

the 30 remaining from vehicles in New York. The 

distribution of source vehicles similarly paralleled the drive 

cycle distribution by geographic region. Of the 212 vehicles 

from which data were collected, 102 of these vehicles were 

located in Colorado, 107 in Washington, and 3 in New York. 

Both the Colorado and Washington locations included large 

numbers of vehicles with preinstalled telemetric GPS data 

logging devices, allowing for large-scale data acquisition, 

while the vehicles examined in New York required manual 

instrument installation, resulting in limited-scale deployment 

of data logging devices. 

Once the 1-Hz drive cycles had been captured, the raw 

field data stored on the data acquisition devices were 

downloaded onto a local computer for post processing and 

analysis. The raw drive cycles were filtered and analyzed 

using NREL's Drive Cycle Rapid Investigation, 

Visualization, and Evaluation tool (DRIVE) [18, 19] to 

generate individual drive cycle summary statistics. Using 

NREL's DRIVE tool, each drive cycle collected was 

characterized according to a total of 172 unique metrics, 

including key metrics such as operating speed distribution, 

stops per mile, acceleration rate, and vehicle kinetic intensity, 

a measure of the energy consumption nature of a cycle [6]. 

The analytical procedure performed as part of the DRIVE 

software was repeated using batch processing techniques, 

generating group-level statistics through the aggregation of 

individual drive cycles. Depending on how the data were 

aggregated into groups, researchers were able to examine 

statistical trends and relationships on multiple scales, ranging 

from the small-scale individual vehicle level, to geographic 

grouping, and finally to large-scale aggregation. As this study 

was concerned with characterizing overall school bus 

operation, the results presented herein reflect the analysis 

performed on the global conglomeration of experimental 

data. 

Representative Test Cycle Selection 

Once the data collection and preliminary drive cycle 

analysis had been completed, standard chassis dynamometer 

test cycles were chosen that reflected high, average, and low 

operating aggressiveness. For this particular study, the terms 

high, average, and low aggressiveness reflect the nature of 

energy consumption of the cycle, with the highly aggressive 

cycle representative of energy consumption primarily through 

stop-and-go driving behavior associated with urban driving 

conditions, and the low aggressive cycle representative of 

energy consumption through aerodynamic drag consistent 

with high-speed cruising associated with highway driving. As 

energy consumption was the key focus of cycle selection, the 

test cycles chosen as part of this study were selected using a 

multivariate least squares approach. The metrics kinetic 

intensity, stops per mile, and standard deviation of speed are 

examples of the primary selection parameters. To visualize 

the multivariate selection approach, a simplified single 

variable ranking example can be observed in Figure 2. 
Duran et al / SAE Int. J. Commer. Veh. / Volume 6, Issue 2(October 2013) 

Figure 2. Example single metric ranking distribution 

RESULTS 

Operating Duration, Distance, and Driving 

Speed Distribution 

Analyzing the aggregated results of the drive cycle 

analysis performed on the school bus data, one observes 

normality in the distribution of route duration data. 

Examining the distribution in Figure 3, it is shown that the 

average operational shift is 2.63 hours in duration, with a 

minimum duration of 0.14 hour and a maximum observed 

duration of 7.94 hours. Calculating the standard deviation in 

route duration, we find sigma to be 1.14 hours. Employing 

standard confidence interval assumptions based on normally 

distributed data and combining the observed average route 

duration with the calculated set standard deviation, the 99.7% 

confidence interval for route duration was found to be zero to 

6.04 hours. While the route duration statistics may appear 

small in comparison to other vocations, it is important to note 

that typical daily school bus operation consists of two route 

shifts, which when examined on a daily level would result in 

the doubling of the duration values mentioned previously. 

Taking into account dual shifts associated with daily 

behavior, the average school bus is typically in operation for 

5.26 hours out of a standard 8-hour work day. This difference 

in operating time may allow for potential inter-shift charging 

opportunities should battery electric or plug-in hybrid electric 

vehicle technology be implemented in future school bus 

design. 

Similar to the distribution of the route duration metric, the 

driving distances calculated as part of the drive cycle analysis 

performed on the aggregate data set revealed strong 

normality, as shown in Figure 4. Of the over 1,500 individual 

operating shifts analyzed as part of this study, the average 

driving distance observed was 31.73 miles, with a maximum 

observed driving distance of 127.36 miles and a minimum 

distance of 1.45 miles. Calculating the set standard deviation, 

sigma was found to be 15.17 miles. Again assuming 

normality based on the driving distance distribution, a 99.7% 

confidence interval on route distance was determined and 

found to be zero to 77.23 miles. Similarly to route duration, 

doubling the route distance to reflect daily operating 

distances, it is found that on average school buses travel 

73.46 miles, with a 99.7% cutoff on driving distance of 

154.46 miles. Given the relatively low mileage average route 

mileage requirements, there is significant potential for 

hybridization or electrification as energy storage 

requirements can be minimized to take advantage of the 

limited range requirements and potential for inter-shift 

charging. 

Figure 3. Route duration - 3-sigma distribution
	

Figure 4. Route distance - 3-sigma distribution
	
Duran et al / SAE Int. J. Commer. Veh. / Volume 6, Issue 2(October 2013) 

Having examined both route distance and duration in 

great detail, another set of global metrics of interest in 

characterizing overall vehicle information are the distribution 

of vehicle operating speed. Collecting and tabulating the 

operating speed information for each of the collected drive 

cycles, one observes zero speed operation as the primary 

speed occurring during vehicle operation. As shown in Table 

1, 53.25% of operating time is spent at zero speed, with 

approximately 98% of vehicle operation occurring at speeds 

below 50 mph. Given this speed distribution, we conclude 

that the vehicles examined rarely achieve highway speeds as 

part of their typical route operation. Additionally, since a 

significant portion of the drive cycle time is spent at idle, 

there is significant opportunity for fuel consumption savings 

via idle reduction technology or electrification/hybridization. 

Removing fuel consumed while the vehicle is stationary has 

the potential to result in a significant increase in vehicle fuel 

economy. 

Table 1. Observed Speed Distribution 

Route Kinematics Characterization 

In addition to characterizing the route on a distance and 

duration level, route kinematics were also characterized as 

part of the drive cycle analysis. Table 2 shows the 

distribution and confidence intervals associated with a 

number of key metrics associated with route kinematics. 

Figures 5, 6, 7, 8 confirm the normality of the distributions of 

a subset of these metrics. Examining the metrics contained in 

Table 2, one can draw a number of conclusions. First, the 

mean maximum driving speed observed in the collection of 

drive cycles matches the speed distribution determined 

previously. With a mean maximum speed of 54.48 mph, it 

can be confirmed that the majority of vehicle operation 

occurs at speeds below 55 mph. In addition, examining the 

mean average driving speed and mean standard deviation of 

speed, we find that school buses operate along low speed 

routes with high amounts of speed variation. However, much 

of this variation may be attributed to the stop frequency of the 

vehicle. Examining both the stops-per-mile and stops-perminute metrics, it is found that, as one would expect, school 

buses make frequent stops on both distance traveled and time 

scales. Combining high frequency of stops with a similarly 

high standard deviation of speed, we observe a mean kinetic 

intensity of 1.63 which is indicative of more energy 

consumption over the cycle being attributable to vehicle 

acceleration than to aerodynamic losses. As the school bus 

drive cycle contains a high number of stops per mile and 

operates at a low average driving speed, there is energy 

saving potential in the recapture of braking energy and 

reduction in vehicle launch energy requirements. 

Table 2. Key Route Drive Cycle Kinematic Metrics
	

Summary
	
Duran et al / SAE Int. J. Commer. Veh. / Volume 6, Issue 2(October 2013) 

Figure 5. Stops per mile - 3-sigma distribution
	

Figure 6. Stops per minute - 3-sigma distribution
	

Figure 7. Average acceleration - 3-sigma distribution
	

Figure 8. Average deceleration - 3-sigma distribution 

Bivariate Route Analysis 

Building upon the univariate analysis performed to 

examine global kinematics and route information; additional 

bivariate analyses were performed on the drive cycle data set 

to investigate the influence of geography on key metrics. In 

addition to exploring the influence of geography on vehicle 

operation, the bivariate analyses were performed to study any 

potential relationships between variables. Examining the 

variables in Table 2, only a handful displayed any level of 

correlation, including average driving speed, kinetic intensity, 

and stops per mile. 

Examining the correlation between average driving speed 

and kinetic intensity, one observes a strong power correlation 

between the two variables. Evaluating the Pearson correlation 

coefficient for the power fit of the data (R2 = 0.7533), the 

presence of a statistically significant relationship can be 

confirmed to the 10−7 order. Even more interesting than the 

presence of a strong power relationship, is the clustering of 

data by geographic region. We see in Figure 9 that the data 

collected from Colorado had a higher average driving speed 

and lower corresponding kinetic intensity than either the data 

sets collected from Washington or New York. This could be 

associated with potentially less-congested driving conditions 

or the presence of higher speed limits in the area. 

When average driving speed is examined with respect to 

the number of stops per mile, as in Figure 10, another power 

correlation is observed. Once again evaluating the Pearson 

correlation coefficient for the power fit of the data (R2 = 

0.6338), we observe the relationship between stops per mile 

and average driving speed to be statistically significant to the 

10−7 order. Once again, similar to the previous relationship, 

we see that geography plays a significant role in the location 

of data along the power curve. New York in particular 

exhibits a higher number of stops per mile than the other two 

geographic regions examined, while displaying a lower 

average driving speed. Based on this relationship we can 

conclude that average driving speed is influenced by the 

number of stops per mile, with routes possessing large 
Duran et al / SAE Int. J. Commer. Veh. / Volume 6, Issue 2(October 2013) 

numbers of stops per miles also possessing a low overall 

average driving speed. This conclusion also agrees with the 

relationship presented in Figure 9, as the greater number of 

stops per mile would result in greater energy consumption 

occurring due to acceleration from a stopped state. 

Finally, examining the relationship between the number 

of stops per mile and kinetic intensity of the cycle revealed 

another interesting relationship within the data set. 

Examining the aggregated data set shown in Figure 11, it 

appears as though there is a linear relationship between the 

number of stops per mile on a specific route and its 

corresponding kinetic intensity. However, what is of greater 

interest in this case is the influence of geography on the linear 

relationship. It appears as though both Colorado and New 

York follow a similar linear trend, while the Washington data 

display a slightly different linear behavior. 

Figure 9. Route drive-cycle level comparison of average
	

driving speed to kinetic intensity
	

Figure 10. Route drive cycle level comparison of stops
	

per mile to average driving speed
	

Figure 11. Route drive cycle level comparison of stops 

per mile to kinetic intensity 

Representative Test Cycle Selection 

Completing the drive cycle analysis, representative test 

cycles were selected using a multivariate least squares 

method. Table 3 shows the three standard chassis test cycles 

chosen as well as the values of a number of the key metrics 

used as part of the selection process. The cycles are listed in 

order from most aggressive (OCTA) to least aggressive 

(HHDDT) as defined previously. 

Table 3. Representative Cycle Selection Results 

Examining the metrics for the cycles chosen, we see that 

the low-aggression cycle displays high-speed highway 

driving behavior, while the high-aggression cycle reflects 

lower-speed city driving. These differences are also 

highlighted in the difference in stops per miles between the 

cycles as well as their kinetic intensity. 

CONCLUSION 

Based on the results of the duty cycle analysis performed 

on the school bus fleet data, is has been shown that a number 

of interesting relationships appear as part of the underlying 

vehicle operation. Strong exponential relationships exist 

between a number of significant operating metrics, including 

average driving speed, stops per mile, and kinetic intensity. In 

addition, basic results correlating the influence of geography 

on route behavior have been observed. It may be of interest to 

explore further efforts to greater examination of the effect 
Duran et al / SAE Int. J. Commer. Veh. / Volume 6, Issue 2(October 2013) 

geography plays on vehicle operation, specifically in the area 

of operational duty cycle. 

In addition to observing interesting relationships between 

duty cycle metrics and geographic influences, it is 

worthwhile to note the significant idle time occurs as part of 

typical school bus operation. Due to the large amount of time 

spent at zero speed as part of daily route operation, it may be 

of interest to investigate additional idle reduction 

technologies or charging strategies in the case of hybrid 

electric vehicle applications. 

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Alliance for Sustainable Energy, LLC. All Rights Reserved. 

CONTACT INFORMATION 

Adam Duran is a research engineer working with the 

Transportation and Hydrogen Systems Center at the National 

Renewable Energy Laboratory. Adam's work focuses 

primarily in the areas of drive cycle analysis and 

characterization, custom drive cycle development, and 

medium/heavy-duty fleet evaluations. He may be reached at 

Adam.Duran@nrel.gov. 

Kevin Walkowicz is the Team Leader for Fleet Testing 

and Evaluation at the National Renewable Energy Laboratory 

in Golden, CO and one of the principal investigators for the 

U.S. Department of Energy's Advanced Vehicle Testing 

Activity. His primary focus is on capturing, analyzing and 

reporting in-use data related to electric drive medium- and 

heavy-duty vehicles. He can be reached at 

Kevin.Walkowicz@nrel.gov. 

ACKNOWLEDGMENTS 

The authors wish to thank Lee Slezak, U.S. Department of 

Energy Vehicle Technologies Office, as well as the U.S. 

Department of Energy for its support in conducting this 

project. In addition, the authors would like to acknowledge 

Zonar Systems for their assistance in the procurement of 

telemetric data examined as part of this study. 

DEFINITIONS/ABBREVIATIONS 

CARB HHDDT - California Air Resources Board Heavy 

Heavy-Duty Diesel Truck Cycle 

DRIVE - Drive Cycle Rapid Investigation, Visualization, and 

Evaluation tool 

EV - electric vehicle 

GPS - global positioning system 

HEV - hybrid electric vehicle 

mph - mile per hour 

NREL - National Renewable Energy Laboratory 

OCTA - Orange County Transit Authority Bus Cycle. Also 

known as Orange County Bus Cycle. 

RUCSBC - Rowan University Composite School Bus Cycle 

s - second