Regional driving characteristics, regional driving cycles

Transportation Research Part D 8 (2003) 361–381 www.elsevier.com/locate/trd

Regional driving characteristics, regional driving cycles Jie Lin, D.A. Niemeier

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Department of Civil and Environmental Engineering, University of California, One Shields Ave., Davis, CA 95616, USA

Abstract The Environmental Protection AgencyÕs (EPA) MOBILE6 driving cycles were developed by combining chase car data collected in three cities: Baltimore, Spokane, and Los Angeles, and then organizing the data by facility and level of service (LOS) to eliminate regional variability in driving. EPAÕs approach to driving cycle construction presumes that regional driving variability is insignificant when controlling for facility type and LOS. In this study we re-visit the issue of regional driving variability and its potential impact on emissions using driving data recently collected in the Bay Area, Sacramento and Stanislaus, California. We begin by examining regional driving characteristics for four types of driving conditions: un-congested and congested freeway driving, and un-congested and congested arterial driving. The results suggest regional similarities in terms of the average speeds and accelerations, but marked differences in frequency, duration, and intensity of both steady state and acceleration modal events. A one-way ANOVA analysis indicates that regional driving variability exists even after controlling for LOS and facility type. We also show how these regional differences can result in driving cycles with significantly different compositions of modal events (i.e., cruise, idle, acceleration and deceleration) using a new method for constructing driving cycles. An examination of the cycles with respect to steady state driving and acceleration/deceleration modal events confirms that regional driving differences are sufficiently large enough to result in important driving cycle differences, which may also translate into important regional variability in vehicle emissions estimation. Ó 2003 Elsevier Ltd. All rights reserved.

1. Introduction The US Environmental Protection AgencyÕs (EPA) latest emission model (MOBILE6) includes eleven new facility-specific driving cycles (Table 1) that were constructed using chase car data collected in Baltimore, Spokane, and Los Angeles (US Environmental Protection Agency, 1997). The new MOBILE6 cycles are organized by facility type and level of service (LOS), a decision

*

Corresponding author. Tel.: +1-530-752-8918/0586; fax: +1-530-752-7872. E-mail address: [email protected] (D.A. Niemeier).

1361-9209/$ - see front matter Ó 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S1361-9209(03)00022-1

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J. Lin, D.A. Niemeier / Transportation Research Part D 8 (2003) 361–381

Table 1 Characteristics of the EPAÕs facility-specific driving cycles (source: US Environmental Protection Agency, 1997) Cycle

Average speed (mph)

Maximum speed (mph)

Maximum acceleration (mph/s)

Cycle length (s)

Cycle length (mile)

Freeway, high speed Freeway, LOS A–C Freeway, LOS D Freeway, LOS E Freeway, LOS F Freeway, LOS ‘‘G’’ Freeway ramps Arterials/collectors LOS A–B Arterials/collectors LOS C–D Arterials/collectors LOS E–F Local roadways

63.2 59.7 52.9 30.5 18.6 13.1 34.6 24.8 19.2 11.6 12.9

74.7 73.1 70.6 63.0 49.9 35.7 60.2 58.9 49.5 39.9 38.3

2.7 3.4 2.3 5.3 6.9 3.8 5.7 5.0 5.7 5.8 3.7

610 516 406 456 442 390 266 737 629 504 525

10.72 8.55 5.96 3.86 2.29 1.42 2.56 5.07 3.36 1.62 1.87

based on an analysis of chase car driving data showing that, after controlling for average speeds, significantly different amounts of constant speed cruise conditions were observed across different facility types (US Environmental Protection Agency, 1999). The resulting cycles, organized by facility and LOS, are considered independent of the city in which the data were collected (US Environmental Protection Agency, 1997). Regional driving variability has been shown to be a function of, among others, roadway type or the urban transportation environment (Ericsson, 2000), driver characteristics (e.g., aggressive or not), and traffic conditions (Vlieger et al., 2000), and that this variability can have a significant impact on vehicle emissions (e.g., LeBlanc et al., 1995; Sjodin and Lenner, 1994). Driving cycles used to characterize driving variability for the purpose of emissions estimation have likewise been shown to vary by region. Watson and Milkins (1983) found that driving cycles representative of cities in the US, Europe, and Japan did not adequately characterize the observed driving patterns in Australian cities with respect to speed and acceleration rates, and by extension fuel consumption and emissions. That regionally characterized driving cycles can produce significantly different base emissions has also been confirmed in a recent study using dynamometer testing of cycles generated specifically to be regionally representative (Niemeier, in press). The EPAÕs decision to organize cycles by facility type and LOS assumes that in doing so regional variability is reduced and driving characteristics specific to a city are, in a sense, averaged out. The purpose of this study is to evaluate this assumption using newly collected driving data in Northern California. We begin by exploring regional differences in driving in the data. We conduct an analysis of variance to test for regional contributions to driving variability. Based on this analysis, we use a new method for constructing driving cycles to demonstrate how regional driving patterns can subsequently influence driving cycle characteristics. 2. Chase car data For our study we used target vehicle data collected during a recent chase car data collection effort to study driving behavior in three areas of Northern California: the Metropolitan Bay Area,


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Sacramento, and Stanislaus. The Bay Area is a well-developed metropolitan area with a relatively high population density and urban land use. Sacramento and Stanislaus are located in the Great Central Valley, a major agricultural area in California with a mix of rural, urban, and industrial land uses. Sacramento lies in the northern portion of the Great Central Valley in a region called Sacramento Valley and is home to the state legislature and many state departments and agencies. It has the largest population and highest population density in the valley. Stanislaus, located in Northern San Joaquin Valley, south of the Sacramento Valley, is predominantly farmland. The data consist of route-based second-by-second speeds collected using an enhanced version of the standard chase car study protocol (Morey et al., 2000; Gammariello and Long, 1993; Sierra Research, 1997). The data collection period covers driving during the AM, PM, and off peak periods from late February to the end of May 2000. Each areaÕs data contains speeds (in mph) and acceleration rates (in mph/s) for both the chase car (also called the patrol car) and the target car. Additional information provided in the data set includes the functional class as defined by the Highway Performance Monitoring System (HPMS), an approximation of LOS as defined in the Highway Capacity Manual (HCM), and road grade. There are a total of 263,227, 180,771, and 211,034 s of speed observations in the Bay Area, Sacramento, and Stanislaus, respectively. Of the 263,227 s of Bay Area driving, 23.6% (62,213 s) were recorded during the AM peak hours; 37.9% (99,766 s) were recorded during the off-peak hours, and 38.5% (101,248 s) were during the PM peak hours. In the Sacramento driving data, 25.2% (45,563 s), 30.0% (54,141 s) and 44.8% (81,067 s) were collected during AM peak, off-peak, and PM peak hours. Stanislaus driving data has a different frequency distribution from either the Bay Area or Sacramento. The majority of data (62.2%, or 131,322 s) was collected during off-peak hours and 15.0% (31,660 s) and 22.8% (48,052 s) were collected during AM peak and PM peak hours. More than half of the data recorded in the Bay Area (Fig. 1) exhibits traffic conditions from moderate to severe congestion (LOS D through F). In contrast, as might be expected given the prevalence of off-peak data collection, the majority (almost 95%) of the driving data recorded in Stanislaus are in relatively uncongested traffic conditions (LOS A through C).

Freeway

Arterial

100

60

LOS E-F LOS D LOS A-C

40 20

ta ni sl au s

am en to ac r

ea Ar ay

ta ni sl au s

to ra m en ac

Ar

ea

0 ay

%

80

Fig. 1. Frequencies of LOS.

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Table 2 Summary statistics of California chase car driving data Size (s)

Average speed (mph)

Standard deviation Standard deviation of of speed (mph) acceleration (mph/s)

Uncongested freeway

Bay Area Sacramento Stanislaus

47,694 28,148 35,007

67.0 67.4 68.3

6.2 5.2 5.6

2.22 0.69 0.58

Congested freeway


106,868 37,268 2068

33.0 46.7 54.5

21.0 19.2 9.7

1.61 1.06 0.89

Uncongested arterial


25,876 38,395 52,803

34.5 34.6 37.1

17.4 14.7 16.1

2.32 1.60 1.51

Congested arterial


65,918 51,703 38,153

15.3 14.9 15.7

15.0 16.0 15.7

1.90 1.67 1.65

Table 2 presents descriptive statistics for each of the three study areasÕ target vehicle driving data classified into four facility-congestion categories: uncongested (i.e., LOS A through C) freeways, congested (LOS D through F) freeways, uncongested arterials and congested arterials. Perhaps the most notable dissimilarity is the difference between the congested freeway sample sizes and average congested freeway speeds observed in each region. The Bay Area has the lowest observed average speed (33 mph) and the largest standard deviation (21 mph) of the three regions, while a fairly high average speed (54.5 mph) and small standard deviation (9.7 mph) was observed in Stanislaus. Sacramento data had an average speed of 46.7 mph and standard deviation of 19.2 mph. Comparing standard deviations of the acceleration rates across each of the four facility-congestion categories, the Bay Area driving data exhibit the highest standard deviation while again the Stanislaus driving data exhibit the lowest standard deviation. The data shown in Table 2 suggests that Sacramento driving is reasonably similar to Stanislaus driving with respect to both average speed and the standard deviation of acceleration.

3. Regional driving variability It is fairly well established that the duration, rate and intensity of modal events impact vehicle emissions. Thus, we begin our examination of regional driving variability by defining a driving event, consistent with EPAÕs definition (US Environmental Protection Agency, 1999), as a sequence of second-by-second movements by the target vehicle that takes place under the same facility-congestion condition. We then further define a steady state event as a driving event that is at least 10 s long with average acceleration within 0.1 mph and speed variance less than 5 mph2 . Note that speed variance here is computed as the statistical variance. An average acceleration within 0.1 mph tolerance ensures that the overall trend of the event will be at a steady speed, while a maximum speed variance of 5 mph2 controls the speed variation within the event. A steady


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state event becomes an idle event when speed is zero. All other modal events are characterized as acceleration events. We can then compare the frequency and the average duration and intensity (speed) of steady state events and acceleration events between the EPAÕs 3-city driving data (a combined data set of Los Angeles, Baltimore, and Spokane chase car data), the combined California driving data, and data collected in each of the three areas (the Bay Area, Sacramento, and Stanislaus). According to Barth et al.Õs recent study, steady state speed has a parabolic relationship with emissions measured in grams-per-mile (Barth et al., 1999). Emissions are high at either low or high speeds and low at speeds in the range of 30–50 mph. The average duration gives a rough approximation of travel distance at the same steady state speed, and thus the total amount of steady state speed emissions. With respect to the acceleration events, we can compare the frequency, durations, and intensity of acceleration events between the EPA 3-city data, the combined California driving data, and the data collected in each of the three California areas (the Bay Area, Sacramento, and Stanislaus). The duration, intensity, and frequency of a vehicle operation mode have been shown to result in emissions variability between drivers (Holmen and Niemeier, 1998). Cernuschi et al. (1995) found that CO, VOC, and NOx had the highest emission rates at the high acceleration mode and the lowest emission rates at the low deceleration modes. Similar conclusions were also reached in a real-time vehicle hydrocarbon emissions study conducted by the air and energy engineering research laboratory associated with US EPA (Childress and Wilson, Jr., 1994). Note that we do not categorize or analyze the data in any of the five driving data sets by time of day (i.e., AM peak, PM peak, and off-peak period) when we carry out the comparisons. Current driving cycles do not distinguish between periods of the day in their representation.

3.1. Steady state events Table 3 presents the frequencies, average speeds, average durations, and associated standard deviations for the steady state events. The statistics suggest important differences between EPAÕs 3-City, CaliforniaÕs combined data and the driving reflected in each of the three individual California areas. For freeways, there is a notable difference in the frequency of events between the EPAÕs 3-city data and the combined California data. The EPAÕs 3-city steady state events comprise only 1.3% of all events under uncongested conditions and none are observed under congested conditions. This contrast to 7.9% and 3.2% under uncongested and congested conditions, respectively, in the California combined data. When comparing among the three individual California areas, the largest difference in the observed frequencies of steady state events occurs between the Bay Area and Stanislaus. The Bay Area freeway driving data had the lowest percent of steady state events observed under both uncongested (5.4%) and congested (2.2%) conditions among the three areas, while Stanislaus data has the highest percent of observed steady state events (11.1% and 5.4%, respectively). The steady state events contained in the EPAÕs 3-city data had an average steady state speed of 59.4 mph for uncongested freeways, about 7 mph lower than those observed, on average, in the California combined data. The average steady state speeds are quite close to each other between each two of the three individual areas, i.e., the Bay Area, Sacramento, and Stanislaus for both uncongested and congested freeway driving.

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Table 3 Comparison of constant speed cruise events Congestion level Freeway

Arterial Average speed (mph)

Duration (s)

Standard deviation

Number of events (%)

Average

Standard deviation

68 0

61 0

6 (0.2) 1 (0.4)

39.4 0.0

30 23

29 0

Combined California data set Uncongested 90 (7.9) 66.5 Congested 56 (3.2) 61.4

54 37

58 24

26 (1.2) 14 (0.6)

41.2 13.4

30 24

19 14

Bay Area Uncongested Congested

29 (5.4) 25 (2.2)

65.3 61.1

38 28

22 20

4 (0.8) 5 (0.6)

45.9 12.6

13 22

3 17

Sacramento Uncongested Congested

25 (9.2) 28 (5.2)

67.2 61.9

52 48

45 25

10 (1.3) 0

44.5 0

31 0

13 0

Stanislaus Uncongested Congested

36 (11.1) 3 (5.4)

66.9 59.2

69 21

80 14

12 (1.3) 9 (1.3)

37.3 13.8

36 25

24 16

Number of events (%) EPA 3-city data set Uncongested 6 (1.3) Congested 0

Average speed (mph)

Duration (s) Average

59.4 0

Looking at the duration of steady state events, an uncongested freeway steady state event in the EPAÕs 3-city data had an average duration of 68 s (r ¼ 61 s), 14 s longer than that observed in the California combined driving data (54 s on average and standard deviation of 58 s). Comparing uncongested freeway steady state events between the three California areas, it can be seen that the Bay Area has the shortest average duration at 38 s, 14 and 31 s shorter than Sacramento and Stanislaus, respectively. Stanislaus has not only the longest average duration (69 s) but also the highest standard deviation (80 s), even higher than the average duration itself indicating a dramatically heterogeneous pattern in steady state driving for this area. The Sacramento congested freeway steady state events are 48 s in length on average, 20 and 27 s longer than the Bay Area and the Stanislaus events, respectively. The associated standard deviations are fairly close among the individual three California areas. For arterials, steady state events represent less than 2% of all modal events observed in all of the five data sets. There were 1.2% uncongested arterial steady state events contained in the combined California uncongested arterial data and 0.2% in the EPAÕs uncongested arterial data. Congested arterial steady state events were observed less often, representing less than 0.4% of all events in the EPAÕs chase car data and 0.6% of all events in the California combined chase car data. The fraction of observed uncongested arterial steady state events was fairly homogeneous across the three individual California areas. The Sacramento data did not contain any congested arterial steady state events. If we look at average speeds for steady state speed events, the EPAÕs 3-city and the California combined driving data are similar for uncongested arterials (39.4 mph and 41.2 mph, respec-


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tively). However, if we look at the average speeds for congested arterials, the difference between the two data sets becomes quite large (i.e., idle speed in the EPAÕs 3-city data versus 13.4 mph in the California combined data). Comparing average speeds among the three individual California areas, the Bay AreaÕs uncongested arterial steady state events exhibited an average speed of 45.9 mph, 1.4 mph faster than the average speed observed in Sacramento. The Stanislaus data showed a relatively low average speed of 37.3 mph, while the Bay Area and the Stanislaus congested arterial steady state events shared similar average speeds. We found very little difference in average durations of uncongested and congested arterial steady state events between the EPAÕs 3-city data and the California combined driving data. However, the standard deviation of duration observed in the EPAÕs 3-city uncongested arterial steady state events was higher than that observed in California combined data. Large differences in average duration are present in the uncongested arterial steady state events when comparing Stanislaus and the Bay Area. Stanislaus had the highest observed average duration of 36 s, 23 s longer than the Bay Area (13 s). The associated standard deviation for Stanislaus driving was 8 times as large as the Bay Area standard deviation of duration. 3.2. Acceleration events To examine how acceleration events differ among the five data sets, we plotted the 95% confidence intervals for the duration of acceleration events by facility-congestion category for the EPAÕs 3-city driving data, the California combined data, and the three individual California areasÕ driving data in Fig. 2(a)–(d). When comparing the acceleration events between the EPAÕs 3-city and the California combined data in Fig. 2(a)–(d), the EPAÕs 3-city data exhibits longer duration events on average with generally greater variation than the California combined data. Such differences imply regional driving variability exists between the regions defined by the EPAÕs data and the regions reflected in the California data. The large variation observed in the EPA data is also likely to be reflective of highly heterogeneous driving behavior in the three cities (i.e., Baltimore, Spokane, and Los Angeles) where the chase car study was conducted. When comparing uncongested freeway acceleration events between the Bay Area, Sacramento, and Stanislaus (Fig. 2(a)), the Sacramento and Stanislaus events share similar distributions and are longer, on average, than the durationÕs observed for the Bay Area uncongested freeway events. For congested freeway acceleration events from the three individual California data sets (Fig. 2(b)), the Stanislaus acceleration events tend to be much shorter, on average, than those observed in the other two data sets. For uncongested arterial acceleration events observed in the Bay Area, Sacramento, and Stanislaus data, Fig. 2(c) indicates a slightly higher average duration for the Stanislaus acceleration events. For congested arterial acceleration events from the three California areas, the average duration (roughly over 50 s) and variation of the Stanislaus acceleration events are less than those observed in the Bay Area and Sacramento data that have very similar average durations (about 80 s) and variations. It is important to note that the EPA durations for both uncongested and congested conditions were considerably greater than those observed in the California study. This analysis shows that the frequencies, average speeds, and average durations and associated standard deviations for both steady state and acceleration events are very different between the

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Fig. 2. Ninety-five percent confidence interval of acceleration event durations: (a) uncongested freeway data, (b) congested freeway data, (c) uncongested arterial data and (d) congested arterial data.

regions defined by the EPAÕs 3-city data and the combined California data after controlling for facility type and congestion condition. Even when we compare among the three individual California areas across facility-congestion conditions, the duration and variation of the steady state events can vary significantly by region. For example, the driving observed in Stanislaus was considerably different than the driving observed in the Bay Area.

4. Statistical analysis of regional variability The EPA assumed that organizing data by LOS/facility type would control for any city-to-city variability in driving. To test the EPAÕs assumption, we have designed several one-way analyses of variance (ANOVAs) conducted separately on the modal events of the four subsets of the California sample data (uncongested freeway, congested freeway, uncongested arterial, and congested arterial). Using this experimental design enables us to identify the regional effect on driving behavior after controlling for facility type and congestion condition, which is our main interest. A modal event is as defined previously described. In the ANOVA model, the independent variable is region (i.e., the Bay Area, Sacramento, and Stanislaus) and the dependent variables used were the average modal event speed and road power. Average speed is the most common descriptor used in


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mobile emissions modeling. Average road power is positively correlated with emissions and often used as a proxy. Road power is a function of travel speed and acceleration rate: Road power ¼ ð86:3Vt þ 0:0459Vt 3 þ 317ðVt Vt1 ÞVt Þ=1000 if Vt > Vt1 ; else Road power ¼ ð86:3Vt þ 0:0459Vt 3 Þ=1000 where Vt is the instantaneous speed at time t. We define the regional effects as the mean differences in the dependent variable (i.e., average speed or road power) between any two of the three levels in aj , i; j ¼ 1; 2; 3, and i 6¼ j) given one of the four facility-congestion cateregional type (i.e., ai gories (i.e., uncongested freeway, congested freeway, uncongested arterial, and congested arterial). If the probability of the F -statistic is less than the significance level (set as 0.05), the test result indicates a statistically significant difference in means for a given facility-congestion condition. It is important to note that if differences in the means of either of the two dependent variables are found significant, then regional variability exists even after controlling for facility type and congestion level. For uncongested freeways, the mean differences in average speeds are statistically significant (p < 0:0001 in Table 4) across regions. For congested freeways, both average speeds and average road powers are statistically different across regions. For arterial driving under either uncongested or congested conditions, the ANOVA statistics indicate a regional variability in road power. To summarize, as we might expect from the previous analyses, at least one out of the two driving descriptors (i.e., average speed and/or average road power) is statistically different by region, which indicates that a regional variability exists even after controlling for the facility type and congestion level. We have shown that the EPAÕs 3-city data does not adequately reflect driving variability observed in California and that driving variability exists by region even after controlling for facilitytype and congestion level. Thus, driving cycles based on combined driving data collected from various regions may not reflect important regional driving characteristics. To examine how cycles might change under the assumption of regional variability, we constructed a set of new driving cycles and demonstrate how they vary with respect to driving variability.

Table 4 One-way ANOVA for regional differences ‘‘Region’’ effect on average speed Uncongested freeways Congested freeways Uncongested arterials Congested arterials ‘‘Region’’ effect on average road power Uncongested freeways Congested freeways Uncongested arterials Congested arterials

F -statistics

Pr > F

11.79 43.53 2.79 0.50