1. Analysis of Fiat Ecodrive data
WLTP-DHC-xxx
Analysis of Fiat Ecodrive data
By H. Steven
This is a shortened version of the detailed presentation from
The numbers of figures and tables were kept the same as in the detailed version for easier comparison 1

2. Introduction
Within the discussion about weighting factors for the 4 phases of the WLTC the author was asked to analyse in-use driving behavior data provided by FIAT.
The following text is copied from a FIAT presentation:
Fiat Group Automobiles in November 2008 introduced a eco-technology called eco:Drive™ in vehicles to encourage environmentally aware driver behaviour.
Fiat eco:Drive™ allows customers to collect driving data from their vehicles, These data, through a personal computer application, are analyzed by specific algorithms in order to obtain personalized feedback on how to change driving style to achieve maximum fuel efficiency from the vehicle. 2

3. Database and preprocessing
The data was delivered as text files ( in total). The first line contained information about the vehicle and the driver. Information about the country could be obtained from a cross reference list.
Table 1 gives an overview of vehicles, countries and number of drivers.
The data was imported in a series of ACCESS databases. Each text file was interpreted as journey or cycle and got a cycle number as identifier. Each user got a driver ID.
The cycle durations varied from 21 s to 4,8 h (average 12,2 min). 3

4. Database, number of drivers per veh.
Table 1 4

5. Preprocessing
On this data the same preprocessing was applied as for the WLTP in-use database.
The vehicle speed trace was smoothed by a Hanning filter,
The data was separated into short trips and stop periods.
The acceleration a was calculated based on the smoothed speed signal.
Short trips were indicated as not ok, if
a > 4 m/s² or a < -4,5 m/s²,
v_start and or v_end > 12,6 km/h (incomplete ST),
if the absolute difference between original speed and smoothed speed is > 7 km/h (jumps in the speed trace) 5

6. Preprocessing
Within a cycle the short trips and the stops were numbered in ascending order. The stops were linked to the short trips by these numbers.
Stops were indicated as not ok, if
The allocated short trips were not ok,
The duration exceeded 600 s (1365 of 1,95 mio stops).
Erroneous cycles were excluded from the further analysis. 6

7. Further check of the results
The remaining dataset consists of 3332 different vehicle/driver combinations. Since vehicle speeds and stop percentages are highly influenced by the area of the abode of the driver, a comparison of the results should be based on driver specific analyses.
This requires a statistically significant amount of data in terms of monitoring days and mileage.
362 vehicle/driver combinations (11%) had just one monitoring day, 1775 vehicle/driver combinations (53%) had less than 14 monitoring days.
Since these combinations represent only 13,5% of the total mileage, they were disregarded for the further analysis. 7

8. Vehicle/driver specific results
In a first analysis step for the remaining vehicle/driver combinations mileage, drive time, stop time, average speeds and stop percentages were calculated per monitoring day.
The same analysis was done for the EU WLTP in-use database.
The results for all vehicle/driver combinations and all monitoring days are shown in figure 3. The correspon-ding values for the WLTC 5.3 are shown for comparison.
Both datasets cover the same area and have similar trend lines. As one would expect, the WLTC 5.3 average is close to the average of the EU WLTP in-use database. 8

9. p_stop vs v_ave
Figure 3 9

10. Vehicle/driver specific results
The Fiat ecodrive dataset has a lower average speed (-15% compared to the WLTC 5.3) and a higher stop percentage (+15% compared to the WLTC 5.3).
Both datasets show large variances not only in average speeds and stop percentages but also in the average daily distances (see figure 4).
And there is a certain correlation between the average speed and the average distance per day.
The 142 vehicle/driver combinations of the WLTP DB show a significant higher average daily distance than the Fiat ecodrive DB.
This might at least partly be explained by differences in the vehicle sample (see table 2). 10

11. v_ave vs d_ave per day
Figure 4 11

12. Vehicle samples
Table 2 12

13. Vehicle/driver specific results
Figure 5 shows the average values for each vehicle/driver combination of the two datasets.
But there are large day to day variations for a given vehicle/driver combination.
Figure 6 shows examples for day to day variations.
Similar ranges were found for the WLTP DB. 13

14. p_stop vs v_ave
Figure 5 14

15. p_stop vs v_ave
Figure 6 15

16. Comparison of the results for countries and vehicle classes
The average values for v_ave and p_stop are summarized for the Fiat ecodrive data in tables 3a and 3b per vehicle model and country.
A comparison of v_ave between the ecodrive data and the EU WLTP data is shown in table 4 per country and in table 5 per vehicle class.
The data does not show any systematic influence of vehicle model or country.
Much more detailed results about country related differences can be found in the detailed presentation. 16

17. v_ave per country and vehicle
Table 3a 17

18. p_stop per country and vehicle
Table 3b 18

19. v_ave per country
Table 4 19

20. v_ave per vehicle class
Table 5 20

21. Additional analysis results
Since it was criticized that the analysis so far is only based on average speeds and stop percentages, analysis results about
Short trip distance distributions (not discussed here, see detailed presentation),
Vehicle speed distributions and
Acceleration distributions (not discussed here, see detailed presentation)
are added to this updated version. 21

22. Vehicle speed distributions
Figure 26 shows the overall vehicle speed distributions of the EU WLTP DB and the Fiat ecodrive DB for vehicle speeds above 2,5 km/h.
As could be expected from the average speed analysis, the Fiat ecodrive distribution has higher shares for lower speeds.
But once again, there are significant differences between the datasets from the different member states in both databases (see figures 27 and 28) and these differences are higher than the differences between the 7 vehicle models in the Fiat ecodrive DB (see figure 28 and 29). 22

23. Vehicle speed distributions
Figure 26 23

24. Vehicle speed distributions
Figure 27 24

25. Vehicle speed distributions
Figure 28 25

26. Vehicle speed distributions
Figure 29 26

27. Vehicle speed distributions
Two things need to be mentioned in particular:
The vehicle speed distribution for NL in the Fiat ecodrive DB is closest to the EU WLTP overall distribution and shows significant higher shares for high speeds than the distribution for DE (see figure 28).
It is most probable, that this difference is not in line with the “true” distributions for both countries.
Secondly, the speed distribution for the Fiat Bravo with Diesel engine fits pretty good to the WLTP overall distribution. This vehicle model represents a bit more than km mileage in the Fiat ecodrive DB. 27

28. Summary and conclusions
Within the discussion about weighting factors for the 4 phases of the WLTC the author was asked to analyse in-use driving behaviour data provided by Fiat.
The data was delivered as text files ( in total). The first line contained information about the vehicle and the driver. Information about the country could be obtained from a cross reference list.
The data covers 7 Fiat car models, 1 mid size, 2 compact, 2 subcompact and 2 mini cars. The pairs represent the same model, once with a Petrol and once with a Diesel engine.
On this data the same pre-processing was applied as for the WLTP in-use database. 28

29. Summary and conclusions
Erroneous cycles were excluded from the further analysis. This reduced the total mileage from 2,44 million km to 2,33 million km (-4,5%).
The remaining dataset consists of 3332 different vehicle/driver combinations.
Since the analysis required a statistically significant amount of data in terms of monitoring days and mileage, vehicle/driver combinations with less than 14 monitoring days were excluded.
This reduced the mileage to 1,9 million km and the number of vehicle/driver combinations to Table 6 shows the mileage distribution on countries and vehicle models. 29

30. Mileage per country and vehicle
Table 6 30

31. Summary and conclusions
The EU WLTP in-use data was recalculated in the same way for comparison reasons.
The results in terms of average speed and stop percentage for a vehicle/driver combination (and a monitoring period of at least 14 days) can vary between 15 km/h and 40% and 77 km/h and 3%.
No systematic influence of vehicle class or country was found.
It can rather be concluded that the area (agglomeration or rural) and the individual usage of the car (driver demand) are the most influencing factors.
Both factors cannot be scaled for the ecodrive dataset as well as for the EU WLTP dataset. 31

32. Summary and conclusions
It can further be assumed that the ecodrive dataset, which is dominated by the two mini cars and a subcompact car, is not in line with statistics about the mileage distribution on vehicle classes.
This mileage distribution may vary between different countries. And it can also be assumed that the mini cars quite often are used as second car within a household.
This assumption is supported by the fact that the average daily distances in the ecodrive dataset is significantly lower than for the EU WLTP dataset.
Since there is a certain correlation between the average speed and the daily driven distance, the difference in average speed can partly be explained by this difference. 32

33. Summary and conclusions
Without any weighting the average speed of the ecodrive dataset is 39,8 km/h and the stop percentage is 15,1%. This means that the average speed is 14,4% lower than the average speed of WLTC 5.3 and the stop percentage is 1,7% higher than the stop percentage of the WLTC 5.3 (in absolute values).
If one would apply a mileage weighting for the different countries within the ecodrive DB based on the TREMOVE data used within the WLTP development process, the average speed for the ecodrive data would be 40,4 km/h and the stop percentage 14,8%.
This is 13% lower than the average speed of WLTC 5.3 and the stop percentage is 1,4% higher than the stop percentage of the WLTC 5.3 (in absolute values). 33

34. Summary and conclusions
If one would merge the ecodrive dataset and the EU WLTP dataset and restrict the averaging process to those countries that are represented in the WLTP database and use the same weighting factors which were used within the WLTP development process, the average speed would be 42,3 km/h and the stop percentage 13,9%.
This is 9% lower than the average speed of WLTC 5.3 and the stop percentage is 0,5% higher than the stop percentage of the WLTC 5.3 (in absolute values).
These results do not support the application of the weighting factors proposed by France, which would result in an average speed of 36,5 km/h and a stop percentage of 16,9%. 34

35. Summary and conclusions
But the analysis showed also quite clearly, that it is difficult to determine the “true” value of average speed for Europe without additional statistical data.
Further investigations are necessary in order to verify the results. Statistical data about the fleet composition in terms of vehicle subcategories and annual mileage would be helpful.
But with respect to the stop percentage it can clearly be stated based on this analysis, that the stop percentage of the WLTC suits well to its average speed. 35

36. Summary and conclusions
With respect to the short trip distance distributions it can be concluded that there is no difference in the traveled distances for 75% of the total driving time. This corresponds to short trips up to 1200 m.
For higher short trip distances the Fiat data shows higher shares for lower distances compared to the EU WLTP DB.
It cannot be assessed whether this difference is caused by the limited vehicle sample of the Fiat DB compared to the broader sample of the EU WLTP DB.
The vehicle speed distributions confirm what was already concluded from the average speed analysis. 36

37. Summary and conclusions
At the first glance one could conclude from the acceleration distribution analysis that there is no difference at all between the Fiat ecodrive DB and the EU WLTP DB.
But this is due to the fact that the acceleration values decrease with increasing vehicle speed and that the vehicle speeds of the EU WLTP DB are somewhat higher than the speeds of the Fiat ecodrive overall DB.
When one compares the corresponding vehicle speed distributions for specific vehicle speed classes, one can see that the accelerations in the Fiat ecodrive DB are a bit lower than in the EU WLTP DB. 37

38. Final remarks
The WLTC is intended to be used for the determination of pollutant exhaust emissions and CO2 emissions in a couple of years from now.
That means it should be orientated more on future trends rather than on the past or the current situation.
Jourmard estimated in [1] almost 10 years ago already the following trend in the mileage distribution:
Slight decrease of the urban part from 35% in 1970 to 33% in 2020, significant decrease of the rural part (from 62% to 39%) and a significant increase of the motorway part (from 3% to 28%).
The percentages estimated for 2020 reflect already quite well the current situation for Germany. 38

39. Final remarks
This trend would undoubtedly lead to an increase of the average speeds.
Another argument can be deducted from the efforts to reduce pollutant emissions at hotspots in agglomera-tions.
The by far most effective measure – apart from further reductions by improvements of the aftertreatment systems at the vehicle – is the reduction of time shares of stop & go conditions in the traffic condition mix.
Several research projects are aiming at the development of measures to achieve this goal.
Also this would result in an increase of the average speeds. 39

40. Literature
[1] INRETS report LTE 0420, Transport routier - Parc, usage et émissions des véhicules en France de 1970 à 2025, Jourmard, September 2004, 40