Assessing the changes in operating traffic conditions due to weather conditions

[email protected], [email protected]

Assessing the changes inoperating traffic conditionsdue to weather conditions

Olivier de Mouzon

Nour-Eddin El Faouzi

Transport and Traffic Engineering Laboratory (LICIT)

École Nationale des Travaux Publics de

l’État

[email protected] Effects of weather on traffic

Outline

• State of the art• Objectives of this work

• Some results of preliminary studies of weather effect on traffic at macro/micro/meso levels:– Study 1: 1 day of individual data– Study 2: 2 day comparison of individual data– Study 3: using traffic composition on 15 days of aggregated

data– Study 4: 1 year of semi-aggregated data

• Conclusion - Perspectives


Impact of adverse weather on traffic

Capacity Speed Speed Variations Volume Travel Time

Mobility

Congestion Severity Occurrence of Crashes

Safety Severity of Crashes Gap Platooning Acceleration Speed

Driver Behaviour

Modal Shift


Objectives• Weather impacts traffic + risk & severity of crashes.• Critical issue for all road operators.

• Very seldom studies in some parts of Europe.• Most existing studies consider traffic data at aggregated level.• Here, individual data is available.

• New sensor technologies and RWIS: real-time monitoring.• Create better decision support tools for road managers.

• More precisely, the goal of this study is to assess the weather impact on traffic risk and safety, through– headway,– speed and– platoon formation.


Study 1: 1 day of data, ASF, 3-lane motorway

0

1000

2000

3000

4000

5000

6000

31/1

0/20

02 0

5:00

31/1

0/20

02 0

7:00

31/1

0/20

02 0

9:00

31/1

0/20

02 1

1:00

31/1

0/20

02 1

3:00

31/1

0/20

02 1

5:00

31/1

0/20

02 1

7:00

31/1

0/20

02 1

9:00

31/1

0/20

02 2

1:00

31/1

0/20

02 2

3:00

01/1

1/20

02 0

1:00

Time

Veh

/ h

Very strong rain (05:00-05:30)

Strong rain (05:30-08:30)

Road wet, no rain (08:30-09:00)

Dry


Study 1 – macroscopic level

13239

23290

11711

12406

21184

9804

580

326

330

495

1461

1446

65

12

131

0

5000

10000

15000

20000

25000

Leftlane

Middlelane

Rightlane

(a) Vehicule counts

All w

eather conditions (1200 min)

Dry w

eather conditions (960 min)

Road w

et, no rain (30 min)

Strong rain (180 m

in)V

ery strong rain (30 min)

0,0%

9,8%

55,0%

0,0%

9,8%

52,5%

0,0%

6,0%

63,6%

0,2%

9,8%

68,9%

0,0%

21,5%

71,0%

0% 20% 40% 60% 80%

Leftlane

Middlelane

Rightlane

(b) Percentage of trucks

All w

eather conditions (veh: 13239 left, 23290 middle, 11711 right)

Dry w

eather conditions (veh: 12406 left, 21184 middle, 9804 right)

Road w

et, no rain (veh: 326 left, 580 middle, 330 right)

Strong rain (veh: 495 left, 1461 m

iddle, 1446 right)V

ery strong rain (veh: 12 left, 65 middle, 131 right)

108,4

89,8

108,1

90,7

114,2

87,8

110,0

85,4

100,0

84,2

126,8

127,0

126,6

124,2

116,7

80 90

100

110

120

130

Leftlane

Middlelane

Rightlane

(c) Average speed of vehicles (km/h)

All w

eather conditions (vehicles: 13239 left, 23290 middle, 11711 right)

Dry w

eather conditions (vehicles: 12406 left, 21184 middle, 9804 right)

Road w

et, no rain (vehicles: 326 left, 580 middle, 330 right)

Strong rain (vehicles: 495 left, 1461 m

iddle, 1446 right)V

ery strong rain (vehicles: 12 left, 65 middle, 131 right)


Study 1 – mesoscopic level (platoons)

0%25%50%75%

100%

Left lane Middle lane Right lane

% o

f ve

h.

in a

pla

too

n All weather conditions (platoons: 2206 left, 4014 middle, 2781 right)Dry weather conditions (platoons: 2036 left, 3568 middle, 2349 right)Road wet, no rain (platoons: 70 left, 110 middle, 81 right)Strong rain (platoons: 99 left, 324 middle, 331 right)Very strong rain (platoons: 1 left, 12 middle, 20 right)

2,75,3 5,05,5 5,2

2,75,0

3,92,63,0 3,4

2,52,0 2,2 2,5

0

2

4

6

Left lane Middle lane Right lane

Pla

too

n a

vg.

nb

. o

f ve

h. All weather conditions (platoons: 2206 left, 4014 middle, 2781 right)

Dry weather conditions (platoons: 2036 left, 3568 middle, 2349 right)Road wet, no rain (platoons: 70 left, 110 middle, 81 right)Strong rain (platoons: 99 left, 324 middle, 331 right)Very strong rain (platoons: 1 left, 12 middle, 20 right)


Study 1 – microscopic level (speeds)

0%5%

10%15%20%25%

60-6

4

65-6

9

70-7

4

75-7

9

80-8

4

85-8

9

90-9

4

95-9

9

100-

104

105-

109

110-

114

115-

119

120-

124

125-

129

130-

134

135-

139

140-

144

145-

149

150-

Speed (km/h)

Per

cen

tag

e o

f ca

rsAll weather conditions (19 022 cars)Dry weather conditions (17 103 cars)Light rain (550 cars)Strong rain (1 318 cars)Very strong rain (51 cars)

Middle lane

0%5%

10%15%20%25%

60-6

4

65-6

9

70-7

4

75-7

9

80-8

4

85-8

9

90-9

4

95-9

9

100

-104

105

-109

110

-114

115

-119

120

-124

125

-129

130

-134

135

-139

140

-144

145

-149

150

-

Speed (km/h)

Per

cen

tag

e o

f ca

rs All weather conditions (5 266 cars)Dry weather conditions (4 659 cars)Road wet, no rain (120 cars)Strong rain (449 cars)Very strong rain (38 cars)

Right lane


Study 1 – microscopic level (time headways)

0%

10%

20%

30%

40%

50%

0-2

2-4

4-6

6-8

8-10

10-1

2

12-1

4

14-1

6

16-1

8

18-2

0

20-2

2

22-2

4

24-2

6

26-2

8

28-3

0

Time Headway (s)

Per

cen

tag

e o

f ca

rs All weather conditions (19 022 cars)Dry weather conditions (17 103 cars)Road wet, no rain (550 cars)Strong rain (1 318 cars)Very strong rain (51 cars)

Middle lane


Study 2: 2 days, ASF, 3-lane motorway• A9• January 2006

– Satuday 7th

• Medium rain 1pm-3pm

– Satuday 14th

• Dry weather conditions

• Similar weather forecasts


Left (fast) lane Middle lane Right (slow) lane

Date and Time Weather

conditions Nb of

vehicles % of

trucks Nb of

vehicles % of

trucks Nb of

vehicles % of

trucks Total number

of vehicles Traffic state

Saturday 01/07/2006

1:53 - 3:12 PM Rainy 512 0 % 1851 1 % 1525 37 % 3888

Uncongested For all lanes

Saturday 01/14/2006

1:53 - 3:12 PM Dry 482 0 % 1821 3 % 1571 44 % 3874

Uncongested For all lanes

Study 2

0%10%20%30%40%50%

0-2

2-4

4-6

6-8

8-10

10-1

2

12-1

4

14-1

6

16-1

8

18-2

0

20-2

2

22-2

4

24-2

6

26-2

8

28-3

0

Time Headway (s)

Per

cen

tag

e o

f ca

rs Dry weather conditions (482 cars)

Medium rain (512 cars)


Study 3: 15 days, aggregated dataFree flow speeds from aggregated data (3 lanes, 6 minutes)

90

95

100

105

110

115

120

125

130

0 10 20 30 40 50 60 70 80 90 100

Traffic composition (% of trucks)

Sp

eed

(km

/h)

Dry

Dry: upper bound of 95% confidence level

Dry: lower bound of 95% confidence level

Dry (extrapolation)

Medium rain

Medium rain: upper bound of 95% confidence level

Medium rain: lower bound of 95% confidence level

Medium rain (extrapolation)

20-40 %

40-60 %

60-80 %

80-100 %


Study 4: 1 year of semi-aggregated data

Fre

qu

ency

Speed (km/h)

Dry (66 421 vehicles)

Rain (52 810 vehicles)

Snow (8 612 vehicles)

Fre

qu

ency

Speed (km/h)

Dry (77 568 vehicles)

Rain (57 758 vehicles)

Snow (9 008 vehicles)

DayNight


Conclusion

• Results have to be confirmed on larger databases (with congestion)

• Some effects have been shown on the available data– Microscopic level: Time headways , speeds ,

day/night & rain/snow combined effects– Macroscopic level: Free flow speed depending on

traffic composition– Mesoscopic level (platooning): unclear


Perspectives

• Development of such studies at European level

• Integration to traffic regulation

• PhD of Romain Billot

• COST TU-07-02– Title: Real-time monitoring, surveillance and control of road

networks under adverse weather conditions– Coordinator: Nour-Eddin El Faouzi


Questions ?

Olivier de Mouzon [email protected] Nour-Eddin El Faouzi [email protected] Romain Billot [email protected]

Documents

Assessing the changes in operating traffic conditions due to weather conditions