Evaluating the safety of platooned heavy vehicles: A case study

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EVALUATING THE SAFETY OF PLATOONED HEAVY VEHICLES:

A CASE STUDY

Marco Guerrieri1, 2_{and Raffaele Mauro}1

1_{Department of Civil, Environmental and Mechanical Engineering, University of Trento, Italy} 2_{Euro-Mediterranean Institute of Science and Technology (IEMEST), Palermo, Italy}

E-Mail: [email protected]

ABSTRACT

In the right lane of motorways with trucks’ overtaking prohibition is very common the formation of platoons of heavy vehicles. Although this traffic controls strategies can increase the capacity of passing lane, in the right lane may occur increases of rear-end collisions. The purpose of this research was to evaluate the safety of the platooned heavy vehicles by means a closed-form stream model. The case study of the Italian motorway A22 was examined. The sampling of platoons was performed in four observation sections.In the research have been investigated platoons with 2÷20 heavy vehicles for each platoon. Many traffic parameters have been evaluated: frequencies of the number of platoons, minimum mean and maximum headways and speeds between heavy vehicles of each platoon, etc. We have found that the percentages of platoons whose vehicles travel with an average headway of less than 3 seconds are in the range 37.1%÷66.9%, depending on the motorway section. In addition, it was performed a comparative analysis between the minimum safety spaces “s” and the mean intra-vehicular distances “sa” of platooned heavy vehicles. The results show that the percentages of platoons whose vehicles travel, on average, in safety conditions (s = s-sa >0) are in the range 32%÷43%, depending on the motorway section.

Keywords: motorway, platoons, heavy vehicles, safety.

INTRODUCTION

The traffic processes in which heavy-duty vehicles travel with small headways give rise to the reduction of fuel consumption through the decreasing of aerodynamic drag (except for the leader vehicles).

Bonnet and Fritz [1] have shown that at a spacing of 10 m and at a velocity of 80 km/h, the fuel consumption reduction is about 21% of the fuel consumption of the trail truck in isolation.

This reduction, determines meaningful environmental and economic advantage. Furthermore, under specific traffic condition, traffic processes with closely-spaced vehicles can give benefits in terms of increase of road capacity. This occurs, for example, in the Automated Highway Systems (AHS), in which, instrumented vehicles with ITS technologies flow out in platoons on pre-selected lanes of highways. The utilization of this kind of vehicles provides quicker and more accurate responses than human drivers.

In addition, drivers’ capability to identify variations in vehicle gaps, trajectories, accelerations, etc. limits speed and precision of reaction. The lane’s capacity of a “traditional” highway with human drivers generally is below 2.200 per hour [2]. Instead, the lane’s capacity of the AHS is higher, as inferable by following equation [3]:

)] I ) 1 N ( i ) N L [(

N v 3600

C

    

 

 (1)

where: C is the lane capacity [veh/h], v is the speed [m/s], N is the number of vehicles in one platoon, L is the vehicles length [m], i is the spacing between vehicles in a platoon (intra-platoon spacing, generally 1÷2 m) [m], I is

the spacing between platoons (inter-platoon spacing, generally 30÷60 m) [m].

The values calculated with eq. (1) are in accordance to the capacity-speed curves obtained by Shladover [4] in which the maximum capacity for a lane it is reached in the case of 20-vehicle platoons and speed around 30 m/s [1]: C = 8.500 veh/h.

Platoons analyses turned out to be important in the study of car accidents and road safety [5].

The formation of platoons of heavy vehicles is very common in the right lane of motorways with truck lane changing restrictions or trucks’ overtaking prohibition. This traffic control strategies can increase the capacity of passing lanes but, at the same time, can increase the collisions in the right lanes.

The paper addresses the safety analysis of the platooned heavy vehicles using a closed-form stream model, based on empirical data of the Italian motorwayA22.

Many traffic surveys were done with the aim to estimate frequency of platoons, number of heavy vehicles for each platoons, minimum, mean, maximum values of intra-vehicular headways, speeds, theoretical safety spacing between vehicles, operating spacing.

The percentages of platoons that travel in safety conditions have been calculated by means the comparison of the aforementioned spacing.

THE A22 MOTORWAY

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The A22 is a typical divided four-lane motorway, with two hard shoulder; its overall length is about 313 km. Figure-1shows the overtaking prohibitions along the A22.

Figure-1. A22 motorway layoutand overtaking prohibitions.

The Annual average daily traffic (AADT) is between 41.907 and 62.464 vehicles per day, as shown in Table-1.

Table-1. AADTs values.

N. Initial _(Km) Final _(Km) Name _(veh/day)AADT

1 0+000 15+870 Brennero - _Vipiteno 41.907

2 15+870 38+030 _BressanoneVipiteno - 44.159

3 38+030 47+600 Bressanone- _Bressanone 48.388

4 47+600 53+070 Bressanone - _Chiusa 46.896

5 53+070 77+470 _{Bolzano nord}Chiusa – 50.362

6 77+470 85+330 Bolz. Nord- _{Bolz. sud} 48.778

7 85+330 101+800 Bolzano sud - _{Egna Ora} 57.553

8 101+800 121+450 _S.MicheleEgna – 58.810

9 121+450 131+440 S. Michele - _{Trento nord} 57.865

10 131+440 136+460 _{Trento centro}Trento nord - 52.464

11 136+460 142+000 Trento centro - _{Trento sud} 52.874

12 142+000 157+850 _{Rovereto nord}Trento sud - 61.066

13 157+850 166+740 Rov.to nord - _{Rov.to sud} 60.366

14 166+740 179+125 Rov.to sud – _{Ala Avio} 62.075

15 179+125 206+670 Ala Avio –

Affi 62.646 16 206+670 225+370 _{Verona nord}Affi – 48.216

17 225+370 228+000 Verona nord - _{int. A4} 60.431

18 228+000 243+670 _Nogaroleint. A4 - 62.675

19 243+670 256+180 _{Mantova nord}Nogarole - 61.054

20 256+180 265+000 Mant. nord – _{Mant. sud} 61.197

21 265+000 276+710 Mant. sud -

Pegognaga 61.669 22 276+710 285+630 _{Reggiolo Rolo}Pegognaga - 54.637

23 285+630 302+175 Reggiolo Rolo - _Carpi 55.440

24 302+175 312+150 _{Campogalliano}Carpi - 59.668

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CAPACITY ESTIMATION AND DISTRIBUTIONS OF HEAVY VEHICLES BETWEEN LANES

The capacity analysis was done for two observation periods: 5÷11 May 2014 and 8÷14 December 2014.

The observation sections are given in Table-2.

The macroscopic flow parameters (i.e. flow “q”, speed “v” and density “k”) have been deducted for intervals of 5 minutes and 15 minutes. Traffic flows were estimated in terms of passenger car unit (pcu).

In all, has been carried out N5’ = 24.192 couples (v; k), (q; k), (v; q).

Table-2. Observation sections.

Observation

section Location Horizontal alignment Vertical alignment; slople (%) Kofler 063+500 Tangent Curve: R = 10.000 m; _{slope =0,41 %}

S. Michele 123+960 Tangent _{slope =0,03 %}Tangent,

Portale Affi 205+500 _{R = 1000 m}Curve: Curve: R = 10.000 m; _{slope = 0,23 %}

Mantova Sud 271+900 Tangent Curve: R = 15.000 m; _{slope = 0,00 %}

For each section, the flow diagrams are obtained by means of May model [7, 8, 9, 10] in which the relationship between speed (v) and density (k) is given by the following equation:

2 jam kk ) ( 2 1

f e

v

v    (2)

A typical fundamental diagram for the right lanesis given in Figure-2 [11].

Figure-2. Speed - flow scatter plot for the right lane, section San Michele, northbound roadway.

The Tables 3-4provide the traffic flow parameters (free-flow speed “vf”, capacity “C”, jam density “kjam”) obtained for the section San Michele. For comparison, Tables 5, 6 show the same types of information for the section Adige, but deducted for traffic of the year 2003.

Table-3. Traffic flow parameters (section San Michele).

Lane-carriageway

San Michele (km 123+960) - northbound roadway vf

(km/h) k

jam

(pcu/lane/km) (pcu/h)C v

jam

(km/h) right lane 108 23 1484 66 passing lane 134 23 1901 81 carriageway 121 46 3361 73

Table-4. Traffic flow parameters (section San Michele).

Lane - carriageway

San Michele (km 123+960) - southbound roadway vf

(km/h) k

jam

Table-5. Traffic flow parameters (section Adige).

Adige (km 187+300) - northbound roadway vf

(km/h) k

jam

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Table-6. Traffic flow parameters (Section Adige).

Adige (km 187+300) - southbound roadway vf

(km/h) k

jam

The Figures 3-6 show the frequency distribution histograms of the percentage of heavy vehicles in the traffic stream on the right lane and on the overpassing lane, respectively for weekdays and weekends (section San Michele). It is immediate to verify that the overtaking prohibitions are generally respected by heavy vehicles over 7,5 t, thoughthis leads to the formation of many platoons.

Figure-3. Frequency distribution of the percentage of heavy vehicles. Section San Michele, northbound

roadway, right lane, weekdays.

roadway, passing lane, weekdays.

roadway, right lane, weekends.

roadway, passing lane, weekends.

ANALYSIS OF PLATOONS

A heavy-vehicles platoon is a group of heavy vehicles that travel in close proximity to one another (cfr. Figure-7) [12, 13].

The leader vehicle is followed by a number of other vehicles that closely match their speed.

Figure-7. Example of two-vehicle platoon where 1 is the lead vehicle and 2 is the follower vehicle [13].

The main advantages of heavy vehicles platoons are:

 potential increases of the lane capacity;

 reduction of the wind resistance (except for the leader vehicle) and fuel savings;

 lower freight costs;

 less severity of accidents.

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 different types of heavy vehicles will have differing braking and acceleration characteristics, whit increase of the related rear-end collisions risk;

 unobstructed view may be limited by heavy vehicles platoons;

 some drivers may attempt to drive in a platoon without the right skills;

 while the accidents severity might decrease, the accidents rate might increase.

In all, the number of annual rear-end collisions between platooned heavy vehicles on the A22 motorway was of 38 in the years 2014 and 2015 (cfr. Figure-8).

Figure-8. Rear-end collisions between platooned heavy vehicles.

For the A22 the sampling of platoons has been carried out at observation sections shown in Table-2 for the day 7 May 2014 (Wednesday). The flow parameters of platoons, with 2÷20 heavy vehicles for each platoon, have been investigated by collecting data regarding speeds, headways and mass of the vehicles. Among others, have been calculated the following parameters:

 frequencies of the number of platoons size of “i” heavy vehicles (i = 1,2,…20);

 minimum headway (min), mean headway (mean), and maximum headway (max) between vehicles of each platoon;

 minimum speed (Vmin), mean speed (Vmean), and maximum speed (Vmax) of vehicles of each platoon;  the number of platoons whose vehicles travel with

headways comprised within predetermined values (from 0 <≤ 1 seconds, to 1< ≤ 20 seconds).

Table-7 shows the results for the section San Michele north bound roadway. The relative frequencies of the number of platoons with “i” vehicles are delivered in

Figure-9. The size of platoons rarely exceeds ten vehicles (i >10).

Figure-9. Frequency of platoons as function of the number of vehicles for each platoon. Section San Michele,

northbound roadway.

Figure-10 shows the trends of minimum headway (min) and mean headway (mean), whilst the Figure-11 shows the trends of maximum headway (max).

The mean headway is characterized by low fluctuations; instead, the minimum headway increases monotonously as function of the platoons size.

Figure-12 shows the speed range of vehicles as function of the platoons size. The amplitude of the range of speeds decreases systematically and significantly with the increases of the number of vehicles within platoons.

In Table-8 are given the number of platoons whose vehicles travel with headways comprised within predetermined classes (I ÷ j).

We have found that a great part of the vehicles travel with very low headways and, for this reason, in potential unsafe conditions. In fact, the percentages“p” of platoons whose vehicles travel with an average headway of less than 3 seconds are:

 p = 37.1%, section Kofler, northbound roadway;

 p = 66.9%, section Kofler, southbound roadway;

 p = 40.9%, section San Michele, northbound roadway;

 p = 39.4%, section San Michele, southbound roadway;

 p = 36.6%, section Portale Affi, northbound roadway;

 p = 44.0%, section Portale Affi, southbound roadway;

 p = 39.1%, section Mantova Sud, northbound roadway;

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Table-7. Platooned heavy vehicles flow parameters -Section San Michele, northbound roadway.

Number of

platoons Vehicles for each platoon min (s) max(s) mean(s) Vmin(km/h) Vmax

(km/h) V

mean

(km/h)

224 2 0,50 33,30 4,71 73,50 121,50 89,40

122 3 1,35 22,65 4,88 79,67 111,67 88,00

116 4 0,87 12,03 3,94 69,25 103,00 85,47

78 5 1,13 11,40 3,49 77,40 106,00 85,35

52 6 1,58 12,80 4,52 71,83 101,33 85,52

58 7 1,75 9,72 3,92 74,29 98,29 84,39

34 8 1,27 7,94 3,28 77,38 91,88 83,87

28 9 1,65 13,66 3,75 76,67 90,89 83,60

21 10 2,53 7,38 3,74 81,00 92,10 84,50

13 11 2,24 5,67 3,46 80,27 89,27 84,17

14 12 2,19 4,38 3,32 77,17 85,58 82,59

14 13 2,18 5,62 3,36 76,08 87,54 83,13

7 14 1,75 4,05 2,84 81,00 84,64 82,46

7 15 2,24 5,34 3,70 78,87 87,87 82,65

4 16 2,08 5,08 3,98 71,31 88,75 78,98

5 17 2,50 5,69 3,73 75,88 87,88 82,34

1 18 3,20 3,20 3,20 83,44 83,44 83,44

3 19 2,97 3,04 3,01 72,89 83,47 76,74

2 20 2,90 3,21 3,05 74,70 83,25 78,98

Figure-10. Relationship headway –platoons size (red line: min; blue line: mean).Section San Michele, northbound

roadway.

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Figure-12. Mean speed of vehicles in each platoon (each dot represents a platoon). Section San Michele,

northbound roadway.

Table-8. Number of platoons whose vehicles travel with headways comprised within classes of headway. Section

San Michele, northbound roadway. i

(s)

j (s)

*=(i + j )/2

(s) N

Vm (km/h)

0 1 0,5 15 87,28

1 2 1,5 90 85,07

2 3 2,5 225 84,6

3 4 3,5 179 85,51

4 5 4,5 109 86,25

5 6 5,5 71 88,06

6 7 6,5 33 90,45

7 8 7,5 27 92,45

8 9 8,5 18 89,86

9 10 9,5 13 94,04

10 11 10,5 6 90,31

11 12 11,5 6 87,36

12 13 12,5 4 94,25

13 14 13,5 5 89,47

14 15 14,5 1 81,5

15 16 15,5 3 88,22

16 17 16,5 2 90,92

17 18 17,5 0 -

18 19 18,5 0 -

19 20 19,5 0 -

Figure-13. Mean speed of vehicles travel with headway falling within the class with mean value*.Section San

Michele, northbound roadway.

Figure-14. Relative cumulative frequency of platoons whose vehicles travel with headway falling within the

class with mean value*. Section San Michele, northbound roadway.

EVALUATION OF MINIMUM SAFETY SPACE

The minimum safety spaces “s” between platooned heavy vehicles can be calculated as follows.

Figure-15 shows the locations of the leading and following vehicles, travelling in platoons, at moment in which the leading vehicle begins to decelerate; at the end of the stopping maneuver of the following vehicle is required a safety margin (s0) [14].

Using the following notations can be found the relationship between speed v, deceleration and spacing s [24]:

 v initial speed of the two vehicles;

 d1 deceleration rate of the leading vehicle;  d2 deceleration rate of the following vehicle;  perception-reaction time of the following vehicle;

 s0safety margin at the end of deceleration phase (s0 = 0,5 m) ;

 s2(t)is the space of the following vehicle during the perception and reaction time s₂(t)v);

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 s2(t) is the distance covered during the deceleration of the following vehicle;

 L is the length of vehicles (L = 5,25 m).

L s ) t ( s ) t ( s ) t ( s

s ₂  ₂  ₁  ₀ (3)

In which: 1 2 1 d 2 v ) t ( s 

 (4)

2 2 2 d 2 v ) t ( s 

 (5)

Introducing equation (4) and equation (5) in the equation (3), we obtain:

L s d 2 v d 2 v v ) v ( s 0 2 2 1 2          (6)

Figure-15. Safety spacing between platooned vehicles.

The following values of deceleration are of interest for the operation’s safety levels:

 dn = normal or comfortable deceleration;  de = emergency deceleration;

 ∞ = “instantaneous” deceleration.

The first one (normal) is related to passenger comfort. The instantaneous deceleration is a theoretical value that occurs when an accident or a stalled vehicle comes within the perception filed of the subject vehicle.

Can be considered the safety regimes shown in Table-9.

In the light of the above considerations, denoting with sa the mean intra-vehicular distances (sa = 3,6·Vm/*) and with “s” the safety distances obtained using the eq. (9), the safety conditions of platoons are ensure if s > sa (or s = s-sa>0).

Therefore have been evaluated number and percentage of platoons whose heavy vehicles travel in safety conditions. As example, in Table-10 are given the

Table-9.Safety regimes definitions.

Regime Deceleration of leading vehicle

Deceleration of following

vehicle

a ∞ dn

b de dn

c ∞ de

d dl=df

e (no braking)

With the values of Table-9, and taking into account the Italian model for evaluation the perception-reaction time of the following vehicle (2,80,036v) [15], have been obtained the relationships

a) s(v)= v (2,8 – 0,036 v) + n 2

d 2

v _{+ s}

0 + L (7)

b) s(v)= v (2,8 – 0,036 v) + n 2 d 2 v _- e 2 d 2

v _{+ s}

0 + L (8)

c) s(v)= v (2,8 – 0,036 v) + e 2

d 2

v _{+ s}

0 + L (9)

d) s(v)= v (2,8 – 0,036 v) + s0 + L (10)

s(v)= s = s0 + L (11)

Figure-16. Safety space versus speed (eqs. (7)÷(11), dn = 2 m/s2_{; d}

e = 7,3 m/s2; s0 =0,9 m, L = 5,25 m).

As summarized in Table-10, up to headway (on average) * = 3,5 seconds, (classes min = 3 sec., max = 4 sec.) heavy vehicles travel in potentially unsafe conditions.

With the aid of the cumulative frequency distributions (shown in Table-10),it derives that the percentage of platoons whose vehicles travel, on average, in safety conditions (s = s-sa>0) is equal to 37% of the total for the section San Michele, northbound roadway.

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  = 43%, section Kofler, northbound roadway;

  = 19%, section Kofler, southbound roadway;

  = 35%, section San Michele, southbound roadway;

  = 43%, section Portale Affi, northbound roadway;

  = 36%, section Portale Affi, southbound roadway;

  = 42%, section Mantova Sud, northbound roadway;

  = 32%, section Mantova Sud, southbound roadway.

The maximum operating conditions, relatively to the smallest value in the average headway for which all vehicles travel safely (*=4,5 sec.), are the same for all the sections analyzed.

The value of the flow associated withmaximum operating conditions is Q = 3600/*=800 veh/h.

Table-10. Safety evaluation of platooned heavy vehicles -Section San Michele, northbound roadway.

min (s) 

max (s) 

*

(s)

Number of platoons

Cumulative frequency

Relative Cumulative

frequency

(%)

Vm

(km/h) s a

(m) (m) s s = s(m) a-s Safety ensured

0 1 0,50 15 15 1,86 87,28 12,12 93,91 -81,79 NO

1 2 1,50 90 105 13,01 85,07 35,45 91,24 -55,80 NO

2 3 2,50 225 330 40,89 84,60 58,75 90,68 -31,93 NO

3 4 3,50 179 509 63,07 85,51 83,14 91,78 -8,64 NO

4 5 4,50 109 618 76,58 86,25 107,81 92,66 15,15 YES

5 6 5,50 71 689 85,38 88,06 134,54 94,86 39,68 YES

6 7 6,50 33 722 89,47 90,45 163,32 97,79 65,53 YES

7 8 7,50 27 749 92,81 92,45 192,61 100,26 92,36 YES

8 9 8,50 18 767 95,04 89,86 212,18 97,06 115,12 YES

9 10 9,50 13 780 96,65 94,04 248,17 102,23 145,94 YES

10 11 10,50 6 786 97,40 90,31 263,39 97,60 165,79 YES

11 12 11,50 6 792 98,14 87,36 279,06 94,01 185,06 YES

12 13 12,50 4 796 98,64 94,25 327,26 102,49 224,77 YES

13 14 13,50 5 801 99,26 89,47 335,50 96,58 238,92 YES

14 15 14,50 1 802 99,38 81,50 328,26 86,98 241,28 YES

15 16 15,50 3 805 99,75 88,22 379,85 95,06 284,79 YES

16 17 16,50 2 807 100,00 90,92 416,70 98,36 318,34 YES

17 18 17,50 0 807 100,00 - - - - -

18 19 18,50 0 807 100,00 - - - - -

19 20 19,50 0 807 100,00 - - - - -

CONCLUSIONS

The traffic flow processes and the vehicles distribution (“free-moving” or “platooned” vehicles) on highway and motorway have always had fundamental importance in highway engineering, with especially reference to topics like traffic operations, car accidents, road safety and air pollution emissions.

Heavy vehicles platoons formation in a traffic stream is very common in the right lane of motorways and highways with truck lane changing restrictions or trucks’ overtaking prohibition. This traffic control strategies can increase the capacity of the passing lane but, at the same time, can increase the collisions in the right lane.

In light of this, the study suggests a method for

closed-form stream model. The case study of the Italian motorway A22, with an overtaking prohibitionsfor heavy vehicles,was examined. To this aim, first were determined the macroscopic flow parameters (free-flow speed “vf”, capacity “C”, jam density “kjam) in four different road sections.

The overtaking prohibitions are generally respected by heavy vehicles over 7,5 t; this leads to the formation of many platoons.

In the research have been investigatedonly platoons with 2÷20 heavy vehicles for each platoon.

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The results of the analysis show that a great part of vehicles travel with very low headways.

The percentages of platoons whose vehicles travel with an average headway of less than 3 seconds are in the range 37.1%÷66.9%, depending on the motorway section.

The comparative analysis between the minimum safety spaces “s” and the mean intra-vehicular distances “sa” allowed to determine the percentages of platoons whose vehicles travel, on average, in safety conditions (s=s-sa >0). These percentages are between 32% and 43%depending on the motorway section.

In conclusion, despite the brake reaction time of professional drivers may be better than the general driving population, considering the mean speeds, generally the intra-vehicular distances within platoons are too small to avoid collisions.

ACKNOWLEDGEMENTS

The Authors wish to thanks Dr. Eng. Walter Pardatscher, CEO of the “Autostrada del Brennero SpA”, for the constant willingness during the development of this research.

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