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The impact of congestion charging on vehicle speed and its implications for assessing vehicle emissions
ARTICLE IN PRESS
Atmospheric Environment 39 (2005) 6875–6884 www.elsevier.com/locate/atmosenv
The impact of congestion charging on vehicle speed and its implications for assessing vehicle emissions Sean D. Beeversa,, David C. Carslawb a
Environmental Research Group, King’s College London, Franklin Wilkins Building, 150 Stamford Street, London SE1 9NH, UK b Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK Received 15 April 2005; received in revised form 26 July 2005; accepted 4 August 2005
Abstract Previous analysis of London’s congestion charging scheme (CCS) has shown that changes in vehicle speed are an important factor in reducing vehicle emissions. Therefore, a detailed investigation of network average vehicle speed in both central and inner London has been undertaken using a combination of the non-parametric Wilcoxon sign ranks test and a method for calculating the cumulative difference between mean speeds pre- and post-CCS, or cumulative sum (CUSUM) analysis. Within the charging zone (CZ), the Wilcoxon test has shown that the difference in speed between pre- and postCCS periods has increased on average by 2.1 km h1 and that these changes are significant at the p ¼ 0:05 level. The CUSUM analysis has provided evidence of the timing of this change in mean speed in the CZ and this agrees well with the introduction of the CCS on the 17 February 2003. In combination, these results provide compelling evidence that the introduction of congestion charging has significantly increased vehicle speed in the CZ and by comparison with the results in inner London, that these changes are not part of a wider trend. To examine one impact of this change we used an instantaneous emissions model, the Vehicle Transient Emissions Simulation Software, to undertake a comparison between the change in vehicle emissions associated with changing driving characteristics, between pre- and post-charging periods, and those associated with a change in average speed. The analysis was limited to three vehicle types: a Euro II LGV, a Euro III diesel car and a Euro IV petrol car, but showed that driving characteristics in central London have a relatively small effect on emissions of NOX and CO2 compared with the average vehicle speed. However, for PM10 emissions from the Euro II LGV the opposite was found and for this vehicle the driving characteristics were more important than the average speed in estimating exhaust emissions. For this vehicle, emissions increased between pre- and post-CCS periods by 4%. For the Euro IV petrol car NOX emissions also increased by 6% between pre- and post-CCS periods. These findings will help to further understand the extent to which congestion charging reduces vehicle emissions in London. r 2005 Elsevier Ltd. All rights reserved. Keywords: Environment and transport planning; Road user pricing; Emissions modelling; CUSUM; Congestion charging
1. Background The London congestion charging scheme (CCS) was introduced in February 2003. Whilst the aim of Corresponding author.
E-mail address: [email protected] (S.D. Beevers). 1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.08.021
the CCS was to tackle vehicle congestion in central London, it also reduced vehicle emissions. The reduction in vehicle emissions, as a result of introducing the CCS but not including the effect of vehicle technology improvements, has been estimated to be 12.0712% (2s) for NOX and PM10 and 19.5% for CO2 (Beevers and Carslaw,
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2005). The change in emissions were brought about by a change in vehicle kilometre (1574% (2s)), but of equal importance was an increase in average speed of +4 km h1710% (2s) or +20%. The change in speed accounted for 65%, 71% and 48% of the change in emissions of NOX, PM10 and CO2, respectively. The importance of vehicle speed as a controlling influence in estimating vehicle emissions necessitates a more detailed assessment of the assumptions relating to changes in speed as well as understanding further the assessment techniques used in calculating vehicle emissions associated with the CCS. Three topics are addressed in this paper. First, we consider the significance of any change in vehicle speed between pre-CCS (2002) and postCCS (2003). Second, we determine whether these speed changes are associated with the introduction of congestion charging and not part of a wider trend in vehicle speed in London. Third, to gain a better understanding of current emissions inventory techniques, which relate vehicle emissions in gram per kilometre to the average vehicle speed, we determine whether these adequately assess how a change in vehicle speed effects exhaust emissions. Vehicle speed is an important determinant in estimating vehicle emissions and there are a number of methods used to measure it. These include roadside measurements using radar and traffic sensors, travel survey methods, instrumented vehicles and traffic models. A comprehensive review of European information (Andre and Hammarstrom, 2000) concludes that much of this information is limited and heterogeneous and that the quality of speed information may effect emissions by up to 30%. One approach that has been foremost in the development of real world drive cycles is the use of instrumented cars. These vehicles can record speed continuously and use several methods to reflect traffic speeds including: the chase car method (used in Paris), the floating car method (used in London) and privately owned instrumented vehicles used as part of their normal operation (Andre et al., 1999). 1.1. Background to driving dynamics and emissions estimates Vehicle drive cycle dynamics (the variation in average speed, acceleration and deceleration, power demand and time spent stationary) is
important in determining vehicle emissions and has been widely studied. Ntziachristos and Samaras (2000) described the development of speed dependent hot emission factors for passenger cars equipped with a three-way catalyst. Although 402 ‘in use’ vehicles were tested using 33 real world drive cycles (Andre et al., 1993), sample correlation coefficients between vehicle speed and emissions, for all pollutants but CO2, were very low (R2 o0:20). Even for the mean cycle emissions, values of R2 were o0.56. The likely cause of the poor agreement between vehicle emissions and drive cycles was either the highly dispersed values within a cycle or a weak association of emissions with speed. Real world drive cycles, i.e. those that attempt to replicate ‘on-road’ driving conditions rather than legislative cycles, are important however. For example, Joumard et al. (2000) tested a small sample of petrol and diesel cars over the following legislative cycles: the European NEDC (ECE15 followed by EUDC), the FTP75 cycle and the US Highway cycle; and real world cycles: INRETS cycles, Modem cycles and Pure Modem Hyzem cycles. Joumard et al. (2000) concluded that legislative drive cycles significantly underestimate hot emissions compared with real world driving cycles. These results were supported by de Haan and Keller (2000), who concluded that exhaust emissions predictions based on legislative cycles were significantly less than those based on real world alternatives. The reason for the disagreement is that modern catalyst cars have a very low baseline emission rate but that this is punctuated by very high peak values during high acceleration and high load operation (open loop catalyst operation) (Joumard et al., 1999). Detailed emissions characteristics have also been investigated by de Haan and Keller (2000) using 1 Hz emissions measurements from 20 Euro 1 petrol passenger cars to predict the differences between legislative and real world drive cycles. In this study, constant speed tests identified peaks of emissions, which although short lived, could represent half of the emissions of a drive cycle. It was concluded that the transient nature of these emissions occur during gear changing, in response to high power intervals, changes in speed dynamics and some without any obvious cause. The latter point was associated with the response times of the engine management and electronic systems to the lambda sensor and catalyst and that these differed markedly between vehicles.
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2. Method 2.1. Source of data The speed data used in this paper were derived from the moving car observer (MCO) or floating car method, recorded at 1 Hz. The floating car aims to travel at the average speed of vehicles on the road network. This is achieved by the car overtaking the same number of vehicles as it is overtaken by. The data were recorded across the network of roads shown in Fig. 1 and is representative of 70–80% of the vehicle kilometre travelled in this area of London, calculated from vehicle flows in the London Atmospheric Emissions Inventory 2002 (GLA, 2004). The data were summarised in three different ways. First, for the two statistical analyses of speed change, the data were summarised as the average network speed, calculated by the total distance travelled across the entire road network, divided by the time take to travel that distance. Second, average speeds were calculated in the same way but for each road separately. Third, 1 Hz speed data were analysed, in combination with the Vehicle Transient Emissions Simulation Software (VETESS) emissions model to calculate emissions on each road link separately. VETESS is a computer simulation tool developed by the European Union funded DECADE project (http://www.cle.de/ umwelt/decade/index.html). All available data from 2002 to 2004 were used in this analysis. These data were recorded by Transport for London as part of their assessment of the
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CCS. During that period the speed data were resampled at 2 monthly intervals. The samples were taken during weekdays only, over six periods of the day: 06:00–07:15, 07:45–09.15, 10:00–12:00, 14:00–16:00, 16:45–18.15, 18:45–20:00. Care was taken to avoid seasonal effects such as holiday periods as well as specific problems such as road works. As a minimum requirement, during any one sampling campaign, each link was surveyed in both directions during all periods of the day. 2.2. Wilcoxon sign ranks test To test the change in speed between pre- and post-CCS, two analyses were undertaken. The first was a Wilcoxon sign ranks test for paired samples. The non-parametric Wilcoxon method was adopted in preference to the Student t-test because according to the Shapiro–Wilk test the distribution of average speed was not normal. Only four of the sample periods were used in this analysis 07:45–09:15 to 16:45–18:15 and only data from the periods March to June in 2002, 2003 and 2004. This was to maintain consistency between the charging zone (CZ) sampling regime and the more limited data set available in inner London. The samples were paired using unique road link identifiers (a node and b node). This procedure was performed for all of the road links in the test; in this case, 462 roads in the CZ and 993 links in inner London. The Wilcoxon test takes each sample pair (e.g. speed on road link #1 in 2002 and on road link #1 in 2003/4) and considers the difference between them,
Fig. 1. The Greater London road network and the roads in inner London used to analyse of the effects of the CCS on vehicle speed.
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which can be positive or negative. These differences are ranked and result in a number of negative ranks (2003/4 speedo2002 speed) and the number of positive ranks (2003/4 speed42002 speed). In the next step, the method calculates the mean rank (MR), which gives the average difference between the before and after speed and the sum of ranks (SOR), which is the product of the negative or positive rank and the MR. Finally, a test of significance between the two sets of results is made. To do this the probability of the smaller SOR value occurring by chance is tested, given that the null hypothesis (2002 speed ¼ 2003 speed), is true. The test for significance is two tailed and uses a z-score. Road links with the same before and after speed (ties) are ignored, although in all cases the number was not 44. The analysis was undertaken using the statistical software SPSS v11.5. 2.3. Cumulative sum (CUSUM) analysis Proving the significance of a change in speed in the CZ does not point conclusively at the CCS being the cause. Therefore, a CUSUM analysis was made to investigate the timing of the change and to determine whether the change in speed was coincident with the introduction of the scheme. This test used a bi-monthly time series of average network speed in the CZ, for 2002, 2003 and for the first 8 months of 2004. The network average speed measurements were extended to cover all of the six time periods during the day, i.e. from 06:00 to 20:00. CUSUM analysis is a method used widely to detect persistent changes in an observed process and is the CUSUM of the deviation of observations from a mean value (Manly and Mackenzie, 2000; Scandol, 2003). The CUSUM value is given by Sn ¼ SðX i 2X 0 Þ, where X 0 is the average CCS network speed in 2002, summarised in Table 1, and X i the bi-monthly average network speed throughout 2002/3 and for the first 8 months of 2004. If a positive change in mean speed occurs, the CUSUM line will deviate above x-axis (the zero CUSUM line) or below it if the mean reduces. If the change in mean persists then the line will continue in its upwards (or downwards) trend at constant gradient. If the change in mean increases, the gradient of the line will also increase, but if the change is temporary the line will return towards the x-axis. Several statistical criteria are required for the CUSUM analyses to be valid. These include the
Table 1 Target variable: 2002 average CCS speed Sample period
Average speed (X0) (km h1)
06:00–07:15 07:45–09:15 10:00–12:00 14:00–16:00 16:45–18:15 18:45–20:00
21.0 14.4 14.1 14.1 14.5 15.4
independence of the measurements and the avoidance of seasonality effects. These potential problems have been ‘designed out’ of the measurement campaign. For example, problems with autocorrelation are avoided because the data were sampled at 2 monthly intervals. Seasonality is also minimised because the aim of the MCO survey is to provide speeds during ‘normal’ periods of the year. Normal periods are those that avoid network disruption due to road works and holidays. The choice to represent X 0 as the average speed in 2002 is a limitation to the analysis because some of the works required for the preparation for the CCS scheme may also have affected the speed in 2002. Ideally, speeds during 2001 would have been used in the analysis but were not available. 2.4. Modal emissions model The effect of speed as an indicator of the change in emissions was tested by running the VETESS modal emissions model (Pelkmans et al., 2004). The VETESS emissions model uses new methods based on experimental characterisation of engines to calculate emissions over any drive cycle and aims to provide a more realistic simulation by incorporating transient engine behaviour. VETESS uses specific vehicle details (a drive train model and engine file), gear change schemes, deceleration fuel cut-off, auxiliary power use (air conditioning), vehicle load, rolling resistance, aerodynamic resistance, climbing resistance and acceleration to calculate engine speed and torque, which is then combined with engine maps to provide an emission for each point in the drive cycle. VETESS was developed using measurements from chassis dynamometers using both steady-state and dynamic tests. The vehicle measurements were made using Vito’s VOEMlow system, measuring at 1 Hz. The VOEMlow system was itself validated against a Constant
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Volume Sampling chassis dynamometer. In total, 130 measurements were used in the comparison revealing differences in emissions measurements of typically 10% (Lenears et al., 2003). A comprehensive validation of the model was also undertaken using EU and MOL real world drive cycles, which are representative of city, rural and motorway driving. Additionally, the vehicles were tested using in-vehicle measurements in real traffic in MOL and in Barcelona. The typical accuracy for both diesel vehicles for NOX and PM10 were in the range of 10–20%. Predictions of CO2 for all three vehicles had the highest accuracy of o5%. However, the validation tests also showed that the most difficult to predict was NOX for the VW Polo and here catalyst behaviour played an important role (Pelkmans et al., 2004). The model required the use of 1 Hz speed data, on each road separately. Because the calculated speed is affected by both the precision and the accuracy with which it is taken (distance is recorded to 1/ 1000th mile at 1 s intervals) it was necessary to smooth the raw speed data to avoid any unrealistic speed changes. Without this post-processing an over emphasis of the transient nature of speed changes can create unrealistic emissions ‘spikes’ in the vehicle emission data. A bi-weight kernal smoothing technique (de Haan and Keller, 2001) was adopted to reduce the noise of the speed signal and to produce a smoothly varying speed trace for each road every second. Additionally, very short cycles, along short road sections (o20 s), were ignored and care was taken to avoid breaks in the time series of the data. Furthermore, only links with all six periods of the day were used for comparative purposes. From these data, 1518 runs (May–June 2002) and 1892 runs (May–June 2003) were run using the VETESS model.
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VETESS is limited, however, because it represents only three vehicle types: a Euro II LGV, a Euro III diesel car and a Euro IV petrol car, described in Table 2. As a consequence, VETESS cannot provide fleet emission estimates. It can, however, indicate the range in emissions associated with those vehicle types for a number of pollutants, over different drive cycles. In this case, NOX, PM10 and CO2 have been considered. The assumptions used in the model include flat terrain, ‘normal’ driving and gear change assumptions (aggressive driving techniques can be chosen) and no air conditioning or additional payload carried by the vehicle. Automatic deceleration fuel cut-off was also specified.
3. Results and discussion Results of the Wilcoxon paired sample tests are summarised in Tables 3 and 4 and provide a number of statistics to assist in describing the difference between the speed in 2002 and 2003/4 in inner and central London. Data in Table 3 indicate three key points. First, the average vehicle speed in the CZ, before the introduction of the CCS, is lower than in inner London by 7 km h1. Second, the changes in speed between the years 2002 and 2003/4 are greater in magnitude in the CZ compared with inner London and that changes in the CZ are always positive, i.e. speed in 20034speed in 2002. On average, speeds within the CZ have increased by 1.6 and 2.1 km h1, between pre-CCS (2002) and postCCS (2003 and 2004) periods, respectively. This compares with 0.1 and 0.7 km h1 in the inner area. In the inner area, changes in speed are less significant, whilst in the CZ congestion is reducing. The difference between inner and CZ speeds in 2004 has therefore reduced to 4 km h1 from 7 km h1.
Table 2 The vehicle types considered in the VETESS emissions model
Engine size Fuel system Euro class Max. power Max. torque Engine aspiration Exhaust gas recirculation Emissions control device
Citroen Jumper 2.5D
Skoda Octavia 1.9Tdi
VW Polo 1.4, 16V
2446 cm3 diesel engine Indirect injection EURO II certified 63 kW at 4350 rpm 153 Nm at 2250 rpm
1896 cm3 diesel engine Direct injection EURO III certified 66 kW at 4000 rpm 210 Nm at 1900 rpm Turbo+intercooler Yes Oxidation catalyst
1390 cm3 petrol engine Multipoint fuel injection EURO IV certified 74 kW at 6000 rpm 126 Nm at 4400 rpm
Yes Oxidation catalyst
Yes Lambda control three-way catalyst+small precatalyst (ox.)
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Table 3 Network average speed for inner London and charging zone (CZ), for pre-CCS (2002) and post-CCS (2003/4) between the hours of 07:45–18:15 Zone
2002 2003 2004 2002 2003 2004
CZ CZ CZ inner inner inner
N
07:45–09:15 Mean (s)
10:00–12:00 Mean (s)
14:00–16:00 Mean (s)
16:45–18:15 Mean (s)
462 462 462 993 993 993
16.7 19.3 19.4 24.1 23.5 21.8
17.2 17.9 18.7 25.0 24.9 24.7
17.0 18.3 18.9 23.6 25.1 23.9
16.9 18.8 19.1 22.9 22.6 22.3
(7.9) (7.6) (7.8) (10.9) (11.7) (11.1)
(8.7) (7.8) (8.5) (11.2) (11.8) (11.3)
(8.4) (8.0) (8.1) (11.3) (11.8) (11.2)
(8.7) (7.9) (8.1) (11.6) (11.8) (11.3)
Table 4 Rank details of network average speed for inner London and charging zone (CZ), pre- and post-congestion charging scheme (CCS), 07:4509:15 to 10:00–12:00
2003 2003 2004 2004 2003 2003 2004 2004
CZo2002 CZ CZ42002 CZ CZo2002 CZ CZ42002 CZ innero2002 inner inner42002 inner innero2002 inner inner42002 inner
N 07:45–09:15
Mean rank 07:45–09:15
Sum of ranks 07:45–09:15
N 10:00–12:00
Mean rank 10:00–12:00
Sum of ranks 10:00–12:00
166 296 168 29 522 468 590 402
213 242 207 245 509 481 526 453
35,338 71,615 34,710 71,781 265,635 224,909 310,451 182,077
214 248 206 255 492 498 506 487
220 242 211 247 501 490 507 487
47,026 59,927 43,534 62,957 246,701 243,843 256,271 237,249
The results of statistics calculated using the Wilcoxon test are summarised in Table 4. For the sake of brevity, the equivalent calculations for the period 14:00–18:15 have not been included. It is desirable that the difference between positive and negative SOR value is as large as possible. If there is a higher positive SOR value this indicates that 2003 speeds in the CZ are higher than in 2002. To test the significance of these results the smaller SOR values, the negative SOR in the case of the CZ speeds between 2002 and 2003, were tested by estimating the probability of the result occurring by chance. An example of this is the comparison between the 07:45–09:15 2003 CZ speed and 2002 CZ speed, which results in the majority of speed change being positive (2003 SOR of 71,615 vs. 2002 SOR of 35,338). These are summarised in Table 5, and for the period 07:45–09:15 the significance test indicates a probability of zero, to three decimal places. Therefore, the null hypothesis can be rejected and the conclusion drawn that the positive change in speed is significant at greater than the p ¼ 0:05 level. Overall the tests for the CZ speeds (2003/4 vs. 2002)
are all positive, and are all significant at the p ¼ 0:05 level. The results for inner London contrast with those for the CZ. The differences in speed are smaller, and in four out of eight cases are not significant at the p ¼ 0:05 level. Furthermore, during time periods 07:45–09:15 (in 2003/4) and 16:45–18:15 (2004 only), significant reductions in speed occur rather than increases seen in the CZ. In summary, the results of the Wilcoxon paired sample tests show that across all time periods the change in speeds associated with the CZ area are greater in magnitude, are all positive and significant at the p ¼ 0:05 level. In contrast, the inner London changes in speeds are both positive and negative, are smaller in magnitude and as a consequence half of them are not significant at the p ¼ 0:05 level. 3.1. CUSUM analysis The Wilcoxon sign ranks tests support the argument that the introduction of congestion charging has significantly affected vehicle speed in the CZ and that this is not part of a wider trend in
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Table 5 The Wilcoxon test for significance of network average speed for inner and CCS areas of London, pre- and post-CCS Time period
Speed 2003 CCS–speed 2002 CCS
Speed 2004 CCS–speed 2002 CCS
Speed 2003 inner–speed 2002 inner
Speed 2004 inner–speed 2002 inner
07:45–09:15
Z Significance
6.3a 0.000
6.5a 0.000
2.3b 0.024
7.1b 0.000
10:00–12:00
Z Significance
2.2a 0.025
3.4a 0.001
0.2b 0.874
1.1b 0.293
14:00–16:00
Z Significance
3.9a 0.000
4.6a 0.000
3.8a 0.000
0.6a 0.558
16:45–18:15
Z Significance
5.1a 0.000
5.1a 0.000
0.8b 0.439
2.5b 0.012
a
Based on negative ranks. Based on positive ranks.
b
15 Cumulative Sum
speeds of the kind seen in inner London. However, it could equally be argued that it was as the result of some other factor that exists in the CZ and not within inner London. It is important, therefore, to determine the timing of change in speed. A CUSUM analysis has been used for this purpose. Only one plot is shown, however, because all plots for the period 06:00–20:00 show very similar trends. It can be seen from the results summarised in Fig. 2 that during 2002 there are some small deviations in network average speed, all of which are temporary and result in the trend line returning to zero. During the sample period January and February 2003, however, a positive trend in the average speed occurs for all time periods, and the gradient of the line remains reasonably constant throughout 2003 and 2004. This indicates both a positive change in mean network speed in the CZ and that the change is permanent throughout 2003 and into 2004. The point at which the deviation in speed occurs is important in this case and for all time periods agrees very well with the start date of the CCS itself (17 February 2003). The fact that the positive deviation also occurs outside the CCS hours of operation also indicates that the effect of the CCS is not limited to its operational hours alone. In summary, the magnitude and significance of changes in mean speed between pre- and post-CCS periods, calculated using the Wilcoxon sign ranks test, and the timing of that change, identified using CUSUM analysis provide conclusive evidence that the cause of the change in speed in central London was as a direct consequence of the CCS.
10 5 0 -5 02
02
b
n Ja
20
Fe
M
ay
n
Ju
2
20
p Se
O
n Ja
b
20
Fe
M
ay
3
03
03
00
2 ct
n
Ju
p Se
04
00
20
O
2 ct
n Ja
Fe
b
20
ay
Ju
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20
04
M
Fig. 2. CUSUM plot average charging zone network speeds between 10:00 and 12:00 during 2002–2004.
3.2. Vehicle emissions under transient driving conditions The final analysis questions whether a change in average speed of the magnitude brought about by the CCS can be justifiably made, using vehicle emissions plotted against average vehicle speed. This is a common method used in calculating regional and national vehicle emissions in the UK. The results presented in Figs. 3 and 4 are for 2002 and 2003 and represent 1518 and 1892 model runs, respectively. Note that to reduce the number of plots, the NOX and CO2 speed related emissions curves for 2003 are not shown, as they are very similar to those in Fig. 3 for 2002. These results were processed to provide an average speed and emission estimate (g km1), for the three vehicle types described in Table 2 and for NOX, PM10 and CO2. The resulting plots are similar in shape to the
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PM10 Emissions (g km-1)
0.4
0.3
0.2
0.1
0.0 0
10
20
30
40
50
60
70
Speed km hr-1
Fig. 4. Emissions estimates from two vehicles: Euro II LGV (open circles) and Euro III diesel car (open triangles) run over 1892 drive cycles derived from second-by-second speed estimates for central London roads in 2003.
Fig. 3. Emissions estimates from three vehicles: Euro II LGV (open circles), Euro III diesel car (open triangles) and Euro IV petrol car (open squares) run over 1518 drive cycles derived from second-by-second speed estimates for central London roads in 2002.
speed vs. emissions curves used in the analysis of the CCS, which are created using best-fit relationships plotted through emissions data from a number of vehicles within each vehicle class. Figs. 3 and 4, however, represent only one vehicle and thus this
comparison can only be qualitative. However, as each vehicle is run over 41500 separate drive cycles they do provide useful information on how changes in different drive cycles effect vehicle emissions. Furthermore, the Euro class category of each vehicle used in the analysis represents a significant proportion of the vehicles within each vehicle category in 2003. These proportions are 44%, 51% and 10% for the Citroen van, Skoda Octavia and the VW Polo, respectively. For NOX it is notable that for all vehicles very little scatter in emissions exists over the entire speed range. This is surprising given the scatter associated with vehicle measurements. This is partly associated with the fact that model results are likely to be less scattered, because for the same vehicle, run over the same drive cycle, the modelled NOX result will be the same, whereas the measured results will not. However, it can be concluded from these results that a best-fit line describing the relationship between emissions of NOX and average vehicle speed would be a reasonable approximation and that for these vehicle types different drive cycles have a small effect on emissions. This in turn supports the analysis method adopted for the CCS (Beevers and Carslaw, 2005). The CO2 plots presented are similar to that of NOX, displaying a relatively small scatter over the different drive cycles with similar average speeds. This too supports the emissions inventory methods used to analyse the effects of the CCS on CO2 emissions. The PM10 results are very different, however. First, these are only calculated
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for the Euro II LGV and Euro III diesel car because PM10 emissions are not included in the VETESS model for the Euro IV petrol car. The most striking result is the large scatter of emissions for a single average speed. For example, at a typical average speed of between 19 and 21 km h1 results in emissions of PM10 in 2002 vary by a factor 4 for the LGV and a factor of 6 for the diesel car. It is accepted that some of these emissions variations may cancel out once the effects of other vehicles are introduced; however, it shows a much greater dependence of emissions to the drive cycle on which the vehicle is operating. This does not support the method by which the effects of the CCS have been assessed for PM10. Furthermore, the effect of a switch from 2002 to 2003 speed has increased the scatter of PM10 emissions. Comparing the emissions totals from the VETESS runs, for 2002 and 2003, and for each vehicle and pollutant combination, tested these conclusions further. The total emissions for each vehicle were calculated using the sum of the average gram per second emissions for each road link in 2002 and in 2003 multiplied by the time taken to cross the link in seconds, for all road links. These results are summarised in Table 6. A sample of 572 road links, 13 of the total number of VETESS runs were used, because they are the same as those used in the London Atmospheric Emissions Inventory. On average the speed increase associated with these emissions calculations was between 23.9 and 26.0 km h1, an increase of 2.1 km h1, similar to average increase for all links in the CCS, but at a higher average speed. Again, it is only possible to make a qualitative comparison between these results and those of the CCS. For the Euro II LGV an overall increase in 2.1 km h1 gives a 6.2% change in NOX emissions and a 2.8% change in CO2 emissions between 2002 and 2003. This indicates a greater dependence of NOX to changes in average speed than CO2. This is similar to the conclusions drawn by Beevers and Table 6 Percentage change in emissions of NOX, PM10 and CO2, between 2003 and 2002, for a Euro II LGV, a Euro III diesel car and a Euro IV petrol car
Euro II LGV Euro III diesel car Euro IV petrol car
NOX
PM10
CO2
6.2 6.2 5.9
3.7 4.7
2.8 4.9 5.3
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Carslaw (2005), which shows that speed effects make up 65% of NOX reductions but only 48% of CO2 reductions. The Euro III diesel car also shows the same percentage change in NOX emissions compared with the Euro II LGV, but has a slightly larger response to changes in CO2. Once again, however, emissions of PM10 provide contrasting evidence to that of NOX and CO2. In the case of the Euro II LGV, in particular, there is a rise of +3.7% which contrasts with the results of the Euro III diesel car which shows an overall reduction of 4.7%. This suggests that the effect on driving characteristics is greater than the effect of average speed change between the 2 years. Finally, the Euro IV petrol car also shows an increase in NOX emissions of +5.9%, although for this vehicle too, the increase in speed gives a reduction in CO2, of 5.3%. The conclusion for this vehicle too is that it is also more susceptible to changes in driving characteristics than to the change in average speed. However, the emissions from such a modern vehicle are tightly controlled by the exhaust catalyst and are very small. Therefore, this effect is likely to be small compared with other vehicles within the fleet. It is difficult to provide a conclusive explanation for these results but it is clear that the vehicles respond very differently to the same driving characteristics. The evidence presented suggests the Euro class of the vehicle, the fuel system and whether the engine is turbo charged may be significant. Also, the power of the vehicle may be important, as this will effect the gear change options and engine load. Conversely, the use of fuel cut-off can effectively reduced the vehicle emissions to zero during periods of deceleration, which means that for the same average speed a vehicle may have high emissions, under acceleration and high load, or zero emissions, under fuel cut-off. 4. Conclusion The detailed analysis, given in this paper, has provided compelling evidence that the CCS alone is the cause of the change in vehicle speed, rather than a more general trend in central London. However, using an instantaneous emission model for over 1500 separate drive cycles has shown that for some vehicle and pollutant combinations an increase in speed equates to an increase in emissions, not a decrease. Whilst this analysis is only qualitative it highlights the complexity of assessing the emissions of vehicles at a street scale and also assessing the
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effect of traffic management schemes that affect vehicle speed and driving dynamics. Furthermore, the complexity of vehicles is increasing, as is the technology with which their emissions are controlled and it seems likely that correctly assessing the emissions response of newer vehicles to changes in driving characteristics is also likely to become more complex. An improved understanding of these issues will become necessary for both air pollution assessments of traffic management schemes as well as for the development of emissions inventories. Acknowledgements The authors wish to thank Transport for London for their continued support and for those involved in the DECADE project for making available the VETESS emissions model.
References Andre, M., Hammarstrom, U., 2000. Driving speeds in Europe for pollutant emissions estimation. Transportation Research Part D: Transport and Environment (5), 321–335. Andre, M., Hassel, D., Hickman, A.J., Joumard, R., 1993. Cycles de conduite realistes pour la mesure des emissions de polluants et consommation des vehicules legers sur banc a rouleaux. The Science of the Total Environment (134), 171–187. Andre, M., Hammarstrom, U., Reynaud, I., 1999. Driving statistics for the assessment of pollutant emissions from road transport. Meet Project, Cost 319 Action, INRETS: Report LTE 9906, pp. 28–31.
Beevers, S.D., Carslaw, D.C., 2005. The impact of congestion charging on vehicle emissions in London. Atmospheric Environment 39, 1–5. de Haan, P., Keller, M., 2000. Emission factors for passenger cars: application of instantaneous emission modelling. Atmospheric Environment (34), 4629–4638. de Haan, P., Keller, M., 2001. Real world drive cycles for emissions measurements: Artemis and Swiss cycles. Swiss Agency for Environment, Forests and Landscape, INFRAS, Report No. B7014b2-14, Switzerland, pp. 15–16. GLA, 2004. The London Atmospheric Emissions Inventory. The Greater London Authority, London /http://www. london.gov.uk/mayor/environment/air_quality/research/ emissions-inventory.jspS. Joumard, R., Philippe, F., Vidon, R., 1999. Reliability of the current models of instantaneous pollutant emissions. The Science of the Total Environment (235), 133–142. Joumard, R., Andre, M., Vidon, R., Tassel, P., Pruvost, C., 2000. Influence of driving cycles on unit emissions from passenger cars. Atmospheric Environment 45 (34), 4621–4628. Lenears, G., Pelkmans, L., Debal, P., 2003. The realisation of an onboard emission measuring system serving as a R&D tool for ultra low emitting vehicles. International Journal of Vehicle Design 31 (3), 253–268. Manly, B.F.J., Mackenzie, D., 2000. A cumulative sum type of method for environmental monitoring. Environmetrics (11), 151–166. Ntziachristos, L., Samaras, Z., 2000. Speed-dependent representative emission factors for catalyst passenger cars and influencing parameters. Atmospheric Environment (34), 4611–4619. Pelkmans, L., Debal, P., Hood, T., Hauser, G., Maria-Rosa, D., 2004. Development of a simulation tool to calculate fuel consumption and emissions of vehicles operating in dynamic conditions. In: Proceedings of the Society of Automotive Engineers International conference, Toulouse, France, June 2004, 2004-01-1873, ISBN 0-7680-1480-8. Scandol, J.P., 2003. Use of cumulative sum (CUSUM) control charts of landed catch in the management of fisheries. Fisheries Research 45 (64), 19–36.
Atmospheric Environment 39 (2005) 6875–6884 www.elsevier.com/locate/atmosenv
The impact of congestion charging on vehicle speed and its implications for assessing vehicle emissions Sean D. Beeversa,, David C. Carslawb a
Environmental Research Group, King’s College London, Franklin Wilkins Building, 150 Stamford Street, London SE1 9NH, UK b Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK Received 15 April 2005; received in revised form 26 July 2005; accepted 4 August 2005
Abstract Previous analysis of London’s congestion charging scheme (CCS) has shown that changes in vehicle speed are an important factor in reducing vehicle emissions. Therefore, a detailed investigation of network average vehicle speed in both central and inner London has been undertaken using a combination of the non-parametric Wilcoxon sign ranks test and a method for calculating the cumulative difference between mean speeds pre- and post-CCS, or cumulative sum (CUSUM) analysis. Within the charging zone (CZ), the Wilcoxon test has shown that the difference in speed between pre- and postCCS periods has increased on average by 2.1 km h1 and that these changes are significant at the p ¼ 0:05 level. The CUSUM analysis has provided evidence of the timing of this change in mean speed in the CZ and this agrees well with the introduction of the CCS on the 17 February 2003. In combination, these results provide compelling evidence that the introduction of congestion charging has significantly increased vehicle speed in the CZ and by comparison with the results in inner London, that these changes are not part of a wider trend. To examine one impact of this change we used an instantaneous emissions model, the Vehicle Transient Emissions Simulation Software, to undertake a comparison between the change in vehicle emissions associated with changing driving characteristics, between pre- and post-charging periods, and those associated with a change in average speed. The analysis was limited to three vehicle types: a Euro II LGV, a Euro III diesel car and a Euro IV petrol car, but showed that driving characteristics in central London have a relatively small effect on emissions of NOX and CO2 compared with the average vehicle speed. However, for PM10 emissions from the Euro II LGV the opposite was found and for this vehicle the driving characteristics were more important than the average speed in estimating exhaust emissions. For this vehicle, emissions increased between pre- and post-CCS periods by 4%. For the Euro IV petrol car NOX emissions also increased by 6% between pre- and post-CCS periods. These findings will help to further understand the extent to which congestion charging reduces vehicle emissions in London. r 2005 Elsevier Ltd. All rights reserved. Keywords: Environment and transport planning; Road user pricing; Emissions modelling; CUSUM; Congestion charging
1. Background The London congestion charging scheme (CCS) was introduced in February 2003. Whilst the aim of Corresponding author.
E-mail address: [email protected] (S.D. Beevers). 1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.08.021
the CCS was to tackle vehicle congestion in central London, it also reduced vehicle emissions. The reduction in vehicle emissions, as a result of introducing the CCS but not including the effect of vehicle technology improvements, has been estimated to be 12.0712% (2s) for NOX and PM10 and 19.5% for CO2 (Beevers and Carslaw,
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2005). The change in emissions were brought about by a change in vehicle kilometre (1574% (2s)), but of equal importance was an increase in average speed of +4 km h1710% (2s) or +20%. The change in speed accounted for 65%, 71% and 48% of the change in emissions of NOX, PM10 and CO2, respectively. The importance of vehicle speed as a controlling influence in estimating vehicle emissions necessitates a more detailed assessment of the assumptions relating to changes in speed as well as understanding further the assessment techniques used in calculating vehicle emissions associated with the CCS. Three topics are addressed in this paper. First, we consider the significance of any change in vehicle speed between pre-CCS (2002) and postCCS (2003). Second, we determine whether these speed changes are associated with the introduction of congestion charging and not part of a wider trend in vehicle speed in London. Third, to gain a better understanding of current emissions inventory techniques, which relate vehicle emissions in gram per kilometre to the average vehicle speed, we determine whether these adequately assess how a change in vehicle speed effects exhaust emissions. Vehicle speed is an important determinant in estimating vehicle emissions and there are a number of methods used to measure it. These include roadside measurements using radar and traffic sensors, travel survey methods, instrumented vehicles and traffic models. A comprehensive review of European information (Andre and Hammarstrom, 2000) concludes that much of this information is limited and heterogeneous and that the quality of speed information may effect emissions by up to 30%. One approach that has been foremost in the development of real world drive cycles is the use of instrumented cars. These vehicles can record speed continuously and use several methods to reflect traffic speeds including: the chase car method (used in Paris), the floating car method (used in London) and privately owned instrumented vehicles used as part of their normal operation (Andre et al., 1999). 1.1. Background to driving dynamics and emissions estimates Vehicle drive cycle dynamics (the variation in average speed, acceleration and deceleration, power demand and time spent stationary) is
important in determining vehicle emissions and has been widely studied. Ntziachristos and Samaras (2000) described the development of speed dependent hot emission factors for passenger cars equipped with a three-way catalyst. Although 402 ‘in use’ vehicles were tested using 33 real world drive cycles (Andre et al., 1993), sample correlation coefficients between vehicle speed and emissions, for all pollutants but CO2, were very low (R2 o0:20). Even for the mean cycle emissions, values of R2 were o0.56. The likely cause of the poor agreement between vehicle emissions and drive cycles was either the highly dispersed values within a cycle or a weak association of emissions with speed. Real world drive cycles, i.e. those that attempt to replicate ‘on-road’ driving conditions rather than legislative cycles, are important however. For example, Joumard et al. (2000) tested a small sample of petrol and diesel cars over the following legislative cycles: the European NEDC (ECE15 followed by EUDC), the FTP75 cycle and the US Highway cycle; and real world cycles: INRETS cycles, Modem cycles and Pure Modem Hyzem cycles. Joumard et al. (2000) concluded that legislative drive cycles significantly underestimate hot emissions compared with real world driving cycles. These results were supported by de Haan and Keller (2000), who concluded that exhaust emissions predictions based on legislative cycles were significantly less than those based on real world alternatives. The reason for the disagreement is that modern catalyst cars have a very low baseline emission rate but that this is punctuated by very high peak values during high acceleration and high load operation (open loop catalyst operation) (Joumard et al., 1999). Detailed emissions characteristics have also been investigated by de Haan and Keller (2000) using 1 Hz emissions measurements from 20 Euro 1 petrol passenger cars to predict the differences between legislative and real world drive cycles. In this study, constant speed tests identified peaks of emissions, which although short lived, could represent half of the emissions of a drive cycle. It was concluded that the transient nature of these emissions occur during gear changing, in response to high power intervals, changes in speed dynamics and some without any obvious cause. The latter point was associated with the response times of the engine management and electronic systems to the lambda sensor and catalyst and that these differed markedly between vehicles.
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2. Method 2.1. Source of data The speed data used in this paper were derived from the moving car observer (MCO) or floating car method, recorded at 1 Hz. The floating car aims to travel at the average speed of vehicles on the road network. This is achieved by the car overtaking the same number of vehicles as it is overtaken by. The data were recorded across the network of roads shown in Fig. 1 and is representative of 70–80% of the vehicle kilometre travelled in this area of London, calculated from vehicle flows in the London Atmospheric Emissions Inventory 2002 (GLA, 2004). The data were summarised in three different ways. First, for the two statistical analyses of speed change, the data were summarised as the average network speed, calculated by the total distance travelled across the entire road network, divided by the time take to travel that distance. Second, average speeds were calculated in the same way but for each road separately. Third, 1 Hz speed data were analysed, in combination with the Vehicle Transient Emissions Simulation Software (VETESS) emissions model to calculate emissions on each road link separately. VETESS is a computer simulation tool developed by the European Union funded DECADE project (http://www.cle.de/ umwelt/decade/index.html). All available data from 2002 to 2004 were used in this analysis. These data were recorded by Transport for London as part of their assessment of the
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CCS. During that period the speed data were resampled at 2 monthly intervals. The samples were taken during weekdays only, over six periods of the day: 06:00–07:15, 07:45–09.15, 10:00–12:00, 14:00–16:00, 16:45–18.15, 18:45–20:00. Care was taken to avoid seasonal effects such as holiday periods as well as specific problems such as road works. As a minimum requirement, during any one sampling campaign, each link was surveyed in both directions during all periods of the day. 2.2. Wilcoxon sign ranks test To test the change in speed between pre- and post-CCS, two analyses were undertaken. The first was a Wilcoxon sign ranks test for paired samples. The non-parametric Wilcoxon method was adopted in preference to the Student t-test because according to the Shapiro–Wilk test the distribution of average speed was not normal. Only four of the sample periods were used in this analysis 07:45–09:15 to 16:45–18:15 and only data from the periods March to June in 2002, 2003 and 2004. This was to maintain consistency between the charging zone (CZ) sampling regime and the more limited data set available in inner London. The samples were paired using unique road link identifiers (a node and b node). This procedure was performed for all of the road links in the test; in this case, 462 roads in the CZ and 993 links in inner London. The Wilcoxon test takes each sample pair (e.g. speed on road link #1 in 2002 and on road link #1 in 2003/4) and considers the difference between them,
Fig. 1. The Greater London road network and the roads in inner London used to analyse of the effects of the CCS on vehicle speed.
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which can be positive or negative. These differences are ranked and result in a number of negative ranks (2003/4 speedo2002 speed) and the number of positive ranks (2003/4 speed42002 speed). In the next step, the method calculates the mean rank (MR), which gives the average difference between the before and after speed and the sum of ranks (SOR), which is the product of the negative or positive rank and the MR. Finally, a test of significance between the two sets of results is made. To do this the probability of the smaller SOR value occurring by chance is tested, given that the null hypothesis (2002 speed ¼ 2003 speed), is true. The test for significance is two tailed and uses a z-score. Road links with the same before and after speed (ties) are ignored, although in all cases the number was not 44. The analysis was undertaken using the statistical software SPSS v11.5. 2.3. Cumulative sum (CUSUM) analysis Proving the significance of a change in speed in the CZ does not point conclusively at the CCS being the cause. Therefore, a CUSUM analysis was made to investigate the timing of the change and to determine whether the change in speed was coincident with the introduction of the scheme. This test used a bi-monthly time series of average network speed in the CZ, for 2002, 2003 and for the first 8 months of 2004. The network average speed measurements were extended to cover all of the six time periods during the day, i.e. from 06:00 to 20:00. CUSUM analysis is a method used widely to detect persistent changes in an observed process and is the CUSUM of the deviation of observations from a mean value (Manly and Mackenzie, 2000; Scandol, 2003). The CUSUM value is given by Sn ¼ SðX i 2X 0 Þ, where X 0 is the average CCS network speed in 2002, summarised in Table 1, and X i the bi-monthly average network speed throughout 2002/3 and for the first 8 months of 2004. If a positive change in mean speed occurs, the CUSUM line will deviate above x-axis (the zero CUSUM line) or below it if the mean reduces. If the change in mean persists then the line will continue in its upwards (or downwards) trend at constant gradient. If the change in mean increases, the gradient of the line will also increase, but if the change is temporary the line will return towards the x-axis. Several statistical criteria are required for the CUSUM analyses to be valid. These include the
Table 1 Target variable: 2002 average CCS speed Sample period
Average speed (X0) (km h1)
06:00–07:15 07:45–09:15 10:00–12:00 14:00–16:00 16:45–18:15 18:45–20:00
21.0 14.4 14.1 14.1 14.5 15.4
independence of the measurements and the avoidance of seasonality effects. These potential problems have been ‘designed out’ of the measurement campaign. For example, problems with autocorrelation are avoided because the data were sampled at 2 monthly intervals. Seasonality is also minimised because the aim of the MCO survey is to provide speeds during ‘normal’ periods of the year. Normal periods are those that avoid network disruption due to road works and holidays. The choice to represent X 0 as the average speed in 2002 is a limitation to the analysis because some of the works required for the preparation for the CCS scheme may also have affected the speed in 2002. Ideally, speeds during 2001 would have been used in the analysis but were not available. 2.4. Modal emissions model The effect of speed as an indicator of the change in emissions was tested by running the VETESS modal emissions model (Pelkmans et al., 2004). The VETESS emissions model uses new methods based on experimental characterisation of engines to calculate emissions over any drive cycle and aims to provide a more realistic simulation by incorporating transient engine behaviour. VETESS uses specific vehicle details (a drive train model and engine file), gear change schemes, deceleration fuel cut-off, auxiliary power use (air conditioning), vehicle load, rolling resistance, aerodynamic resistance, climbing resistance and acceleration to calculate engine speed and torque, which is then combined with engine maps to provide an emission for each point in the drive cycle. VETESS was developed using measurements from chassis dynamometers using both steady-state and dynamic tests. The vehicle measurements were made using Vito’s VOEMlow system, measuring at 1 Hz. The VOEMlow system was itself validated against a Constant
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Volume Sampling chassis dynamometer. In total, 130 measurements were used in the comparison revealing differences in emissions measurements of typically 10% (Lenears et al., 2003). A comprehensive validation of the model was also undertaken using EU and MOL real world drive cycles, which are representative of city, rural and motorway driving. Additionally, the vehicles were tested using in-vehicle measurements in real traffic in MOL and in Barcelona. The typical accuracy for both diesel vehicles for NOX and PM10 were in the range of 10–20%. Predictions of CO2 for all three vehicles had the highest accuracy of o5%. However, the validation tests also showed that the most difficult to predict was NOX for the VW Polo and here catalyst behaviour played an important role (Pelkmans et al., 2004). The model required the use of 1 Hz speed data, on each road separately. Because the calculated speed is affected by both the precision and the accuracy with which it is taken (distance is recorded to 1/ 1000th mile at 1 s intervals) it was necessary to smooth the raw speed data to avoid any unrealistic speed changes. Without this post-processing an over emphasis of the transient nature of speed changes can create unrealistic emissions ‘spikes’ in the vehicle emission data. A bi-weight kernal smoothing technique (de Haan and Keller, 2001) was adopted to reduce the noise of the speed signal and to produce a smoothly varying speed trace for each road every second. Additionally, very short cycles, along short road sections (o20 s), were ignored and care was taken to avoid breaks in the time series of the data. Furthermore, only links with all six periods of the day were used for comparative purposes. From these data, 1518 runs (May–June 2002) and 1892 runs (May–June 2003) were run using the VETESS model.
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VETESS is limited, however, because it represents only three vehicle types: a Euro II LGV, a Euro III diesel car and a Euro IV petrol car, described in Table 2. As a consequence, VETESS cannot provide fleet emission estimates. It can, however, indicate the range in emissions associated with those vehicle types for a number of pollutants, over different drive cycles. In this case, NOX, PM10 and CO2 have been considered. The assumptions used in the model include flat terrain, ‘normal’ driving and gear change assumptions (aggressive driving techniques can be chosen) and no air conditioning or additional payload carried by the vehicle. Automatic deceleration fuel cut-off was also specified.
3. Results and discussion Results of the Wilcoxon paired sample tests are summarised in Tables 3 and 4 and provide a number of statistics to assist in describing the difference between the speed in 2002 and 2003/4 in inner and central London. Data in Table 3 indicate three key points. First, the average vehicle speed in the CZ, before the introduction of the CCS, is lower than in inner London by 7 km h1. Second, the changes in speed between the years 2002 and 2003/4 are greater in magnitude in the CZ compared with inner London and that changes in the CZ are always positive, i.e. speed in 20034speed in 2002. On average, speeds within the CZ have increased by 1.6 and 2.1 km h1, between pre-CCS (2002) and postCCS (2003 and 2004) periods, respectively. This compares with 0.1 and 0.7 km h1 in the inner area. In the inner area, changes in speed are less significant, whilst in the CZ congestion is reducing. The difference between inner and CZ speeds in 2004 has therefore reduced to 4 km h1 from 7 km h1.
Table 2 The vehicle types considered in the VETESS emissions model
Engine size Fuel system Euro class Max. power Max. torque Engine aspiration Exhaust gas recirculation Emissions control device
Citroen Jumper 2.5D
Skoda Octavia 1.9Tdi
VW Polo 1.4, 16V
2446 cm3 diesel engine Indirect injection EURO II certified 63 kW at 4350 rpm 153 Nm at 2250 rpm
1896 cm3 diesel engine Direct injection EURO III certified 66 kW at 4000 rpm 210 Nm at 1900 rpm Turbo+intercooler Yes Oxidation catalyst
1390 cm3 petrol engine Multipoint fuel injection EURO IV certified 74 kW at 6000 rpm 126 Nm at 4400 rpm
Yes Oxidation catalyst
Yes Lambda control three-way catalyst+small precatalyst (ox.)
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Table 3 Network average speed for inner London and charging zone (CZ), for pre-CCS (2002) and post-CCS (2003/4) between the hours of 07:45–18:15 Zone
2002 2003 2004 2002 2003 2004
CZ CZ CZ inner inner inner
N
07:45–09:15 Mean (s)
10:00–12:00 Mean (s)
14:00–16:00 Mean (s)
16:45–18:15 Mean (s)
462 462 462 993 993 993
16.7 19.3 19.4 24.1 23.5 21.8
17.2 17.9 18.7 25.0 24.9 24.7
17.0 18.3 18.9 23.6 25.1 23.9
16.9 18.8 19.1 22.9 22.6 22.3
(7.9) (7.6) (7.8) (10.9) (11.7) (11.1)
(8.7) (7.8) (8.5) (11.2) (11.8) (11.3)
(8.4) (8.0) (8.1) (11.3) (11.8) (11.2)
(8.7) (7.9) (8.1) (11.6) (11.8) (11.3)
Table 4 Rank details of network average speed for inner London and charging zone (CZ), pre- and post-congestion charging scheme (CCS), 07:4509:15 to 10:00–12:00
2003 2003 2004 2004 2003 2003 2004 2004
CZo2002 CZ CZ42002 CZ CZo2002 CZ CZ42002 CZ innero2002 inner inner42002 inner innero2002 inner inner42002 inner
N 07:45–09:15
Mean rank 07:45–09:15
Sum of ranks 07:45–09:15
N 10:00–12:00
Mean rank 10:00–12:00
Sum of ranks 10:00–12:00
166 296 168 29 522 468 590 402
213 242 207 245 509 481 526 453
35,338 71,615 34,710 71,781 265,635 224,909 310,451 182,077
214 248 206 255 492 498 506 487
220 242 211 247 501 490 507 487
47,026 59,927 43,534 62,957 246,701 243,843 256,271 237,249
The results of statistics calculated using the Wilcoxon test are summarised in Table 4. For the sake of brevity, the equivalent calculations for the period 14:00–18:15 have not been included. It is desirable that the difference between positive and negative SOR value is as large as possible. If there is a higher positive SOR value this indicates that 2003 speeds in the CZ are higher than in 2002. To test the significance of these results the smaller SOR values, the negative SOR in the case of the CZ speeds between 2002 and 2003, were tested by estimating the probability of the result occurring by chance. An example of this is the comparison between the 07:45–09:15 2003 CZ speed and 2002 CZ speed, which results in the majority of speed change being positive (2003 SOR of 71,615 vs. 2002 SOR of 35,338). These are summarised in Table 5, and for the period 07:45–09:15 the significance test indicates a probability of zero, to three decimal places. Therefore, the null hypothesis can be rejected and the conclusion drawn that the positive change in speed is significant at greater than the p ¼ 0:05 level. Overall the tests for the CZ speeds (2003/4 vs. 2002)
are all positive, and are all significant at the p ¼ 0:05 level. The results for inner London contrast with those for the CZ. The differences in speed are smaller, and in four out of eight cases are not significant at the p ¼ 0:05 level. Furthermore, during time periods 07:45–09:15 (in 2003/4) and 16:45–18:15 (2004 only), significant reductions in speed occur rather than increases seen in the CZ. In summary, the results of the Wilcoxon paired sample tests show that across all time periods the change in speeds associated with the CZ area are greater in magnitude, are all positive and significant at the p ¼ 0:05 level. In contrast, the inner London changes in speeds are both positive and negative, are smaller in magnitude and as a consequence half of them are not significant at the p ¼ 0:05 level. 3.1. CUSUM analysis The Wilcoxon sign ranks tests support the argument that the introduction of congestion charging has significantly affected vehicle speed in the CZ and that this is not part of a wider trend in
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Table 5 The Wilcoxon test for significance of network average speed for inner and CCS areas of London, pre- and post-CCS Time period
Speed 2003 CCS–speed 2002 CCS
Speed 2004 CCS–speed 2002 CCS
Speed 2003 inner–speed 2002 inner
Speed 2004 inner–speed 2002 inner
07:45–09:15
Z Significance
6.3a 0.000
6.5a 0.000
2.3b 0.024
7.1b 0.000
10:00–12:00
Z Significance
2.2a 0.025
3.4a 0.001
0.2b 0.874
1.1b 0.293
14:00–16:00
Z Significance
3.9a 0.000
4.6a 0.000
3.8a 0.000
0.6a 0.558
16:45–18:15
Z Significance
5.1a 0.000
5.1a 0.000
0.8b 0.439
2.5b 0.012
a
Based on negative ranks. Based on positive ranks.
b
15 Cumulative Sum
speeds of the kind seen in inner London. However, it could equally be argued that it was as the result of some other factor that exists in the CZ and not within inner London. It is important, therefore, to determine the timing of change in speed. A CUSUM analysis has been used for this purpose. Only one plot is shown, however, because all plots for the period 06:00–20:00 show very similar trends. It can be seen from the results summarised in Fig. 2 that during 2002 there are some small deviations in network average speed, all of which are temporary and result in the trend line returning to zero. During the sample period January and February 2003, however, a positive trend in the average speed occurs for all time periods, and the gradient of the line remains reasonably constant throughout 2003 and 2004. This indicates both a positive change in mean network speed in the CZ and that the change is permanent throughout 2003 and into 2004. The point at which the deviation in speed occurs is important in this case and for all time periods agrees very well with the start date of the CCS itself (17 February 2003). The fact that the positive deviation also occurs outside the CCS hours of operation also indicates that the effect of the CCS is not limited to its operational hours alone. In summary, the magnitude and significance of changes in mean speed between pre- and post-CCS periods, calculated using the Wilcoxon sign ranks test, and the timing of that change, identified using CUSUM analysis provide conclusive evidence that the cause of the change in speed in central London was as a direct consequence of the CCS.
10 5 0 -5 02
02
b
n Ja
20
Fe
M
ay
n
Ju
2
20
p Se
O
n Ja
b
20
Fe
M
ay
3
03
03
00
2 ct
n
Ju
p Se
04
00
20
O
2 ct
n Ja
Fe
b
20
ay
Ju
n
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M
Fig. 2. CUSUM plot average charging zone network speeds between 10:00 and 12:00 during 2002–2004.
3.2. Vehicle emissions under transient driving conditions The final analysis questions whether a change in average speed of the magnitude brought about by the CCS can be justifiably made, using vehicle emissions plotted against average vehicle speed. This is a common method used in calculating regional and national vehicle emissions in the UK. The results presented in Figs. 3 and 4 are for 2002 and 2003 and represent 1518 and 1892 model runs, respectively. Note that to reduce the number of plots, the NOX and CO2 speed related emissions curves for 2003 are not shown, as they are very similar to those in Fig. 3 for 2002. These results were processed to provide an average speed and emission estimate (g km1), for the three vehicle types described in Table 2 and for NOX, PM10 and CO2. The resulting plots are similar in shape to the
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PM10 Emissions (g km-1)
0.4
0.3
0.2
0.1
0.0 0
10
20
30
40
50
60
70
Speed km hr-1
Fig. 4. Emissions estimates from two vehicles: Euro II LGV (open circles) and Euro III diesel car (open triangles) run over 1892 drive cycles derived from second-by-second speed estimates for central London roads in 2003.
Fig. 3. Emissions estimates from three vehicles: Euro II LGV (open circles), Euro III diesel car (open triangles) and Euro IV petrol car (open squares) run over 1518 drive cycles derived from second-by-second speed estimates for central London roads in 2002.
speed vs. emissions curves used in the analysis of the CCS, which are created using best-fit relationships plotted through emissions data from a number of vehicles within each vehicle class. Figs. 3 and 4, however, represent only one vehicle and thus this
comparison can only be qualitative. However, as each vehicle is run over 41500 separate drive cycles they do provide useful information on how changes in different drive cycles effect vehicle emissions. Furthermore, the Euro class category of each vehicle used in the analysis represents a significant proportion of the vehicles within each vehicle category in 2003. These proportions are 44%, 51% and 10% for the Citroen van, Skoda Octavia and the VW Polo, respectively. For NOX it is notable that for all vehicles very little scatter in emissions exists over the entire speed range. This is surprising given the scatter associated with vehicle measurements. This is partly associated with the fact that model results are likely to be less scattered, because for the same vehicle, run over the same drive cycle, the modelled NOX result will be the same, whereas the measured results will not. However, it can be concluded from these results that a best-fit line describing the relationship between emissions of NOX and average vehicle speed would be a reasonable approximation and that for these vehicle types different drive cycles have a small effect on emissions. This in turn supports the analysis method adopted for the CCS (Beevers and Carslaw, 2005). The CO2 plots presented are similar to that of NOX, displaying a relatively small scatter over the different drive cycles with similar average speeds. This too supports the emissions inventory methods used to analyse the effects of the CCS on CO2 emissions. The PM10 results are very different, however. First, these are only calculated
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for the Euro II LGV and Euro III diesel car because PM10 emissions are not included in the VETESS model for the Euro IV petrol car. The most striking result is the large scatter of emissions for a single average speed. For example, at a typical average speed of between 19 and 21 km h1 results in emissions of PM10 in 2002 vary by a factor 4 for the LGV and a factor of 6 for the diesel car. It is accepted that some of these emissions variations may cancel out once the effects of other vehicles are introduced; however, it shows a much greater dependence of emissions to the drive cycle on which the vehicle is operating. This does not support the method by which the effects of the CCS have been assessed for PM10. Furthermore, the effect of a switch from 2002 to 2003 speed has increased the scatter of PM10 emissions. Comparing the emissions totals from the VETESS runs, for 2002 and 2003, and for each vehicle and pollutant combination, tested these conclusions further. The total emissions for each vehicle were calculated using the sum of the average gram per second emissions for each road link in 2002 and in 2003 multiplied by the time taken to cross the link in seconds, for all road links. These results are summarised in Table 6. A sample of 572 road links, 13 of the total number of VETESS runs were used, because they are the same as those used in the London Atmospheric Emissions Inventory. On average the speed increase associated with these emissions calculations was between 23.9 and 26.0 km h1, an increase of 2.1 km h1, similar to average increase for all links in the CCS, but at a higher average speed. Again, it is only possible to make a qualitative comparison between these results and those of the CCS. For the Euro II LGV an overall increase in 2.1 km h1 gives a 6.2% change in NOX emissions and a 2.8% change in CO2 emissions between 2002 and 2003. This indicates a greater dependence of NOX to changes in average speed than CO2. This is similar to the conclusions drawn by Beevers and Table 6 Percentage change in emissions of NOX, PM10 and CO2, between 2003 and 2002, for a Euro II LGV, a Euro III diesel car and a Euro IV petrol car
Euro II LGV Euro III diesel car Euro IV petrol car
NOX
PM10
CO2
6.2 6.2 5.9
3.7 4.7
2.8 4.9 5.3
6883
Carslaw (2005), which shows that speed effects make up 65% of NOX reductions but only 48% of CO2 reductions. The Euro III diesel car also shows the same percentage change in NOX emissions compared with the Euro II LGV, but has a slightly larger response to changes in CO2. Once again, however, emissions of PM10 provide contrasting evidence to that of NOX and CO2. In the case of the Euro II LGV, in particular, there is a rise of +3.7% which contrasts with the results of the Euro III diesel car which shows an overall reduction of 4.7%. This suggests that the effect on driving characteristics is greater than the effect of average speed change between the 2 years. Finally, the Euro IV petrol car also shows an increase in NOX emissions of +5.9%, although for this vehicle too, the increase in speed gives a reduction in CO2, of 5.3%. The conclusion for this vehicle too is that it is also more susceptible to changes in driving characteristics than to the change in average speed. However, the emissions from such a modern vehicle are tightly controlled by the exhaust catalyst and are very small. Therefore, this effect is likely to be small compared with other vehicles within the fleet. It is difficult to provide a conclusive explanation for these results but it is clear that the vehicles respond very differently to the same driving characteristics. The evidence presented suggests the Euro class of the vehicle, the fuel system and whether the engine is turbo charged may be significant. Also, the power of the vehicle may be important, as this will effect the gear change options and engine load. Conversely, the use of fuel cut-off can effectively reduced the vehicle emissions to zero during periods of deceleration, which means that for the same average speed a vehicle may have high emissions, under acceleration and high load, or zero emissions, under fuel cut-off. 4. Conclusion The detailed analysis, given in this paper, has provided compelling evidence that the CCS alone is the cause of the change in vehicle speed, rather than a more general trend in central London. However, using an instantaneous emission model for over 1500 separate drive cycles has shown that for some vehicle and pollutant combinations an increase in speed equates to an increase in emissions, not a decrease. Whilst this analysis is only qualitative it highlights the complexity of assessing the emissions of vehicles at a street scale and also assessing the
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effect of traffic management schemes that affect vehicle speed and driving dynamics. Furthermore, the complexity of vehicles is increasing, as is the technology with which their emissions are controlled and it seems likely that correctly assessing the emissions response of newer vehicles to changes in driving characteristics is also likely to become more complex. An improved understanding of these issues will become necessary for both air pollution assessments of traffic management schemes as well as for the development of emissions inventories. Acknowledgements The authors wish to thank Transport for London for their continued support and for those involved in the DECADE project for making available the VETESS emissions model.
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