link statistics

Direction de la Transition Transition Team

ALVARO GIL TRANSITION TEAM JULY 31 2013

Pedestrian flows through the link (pavilions K-D-H) projected for phase 1 General statistics


Circulation flow model

• There are several types of pedestrian flows in the hospital

• This presentation focuses only in the pedestrian flows that will be affected with the construction of pavilion K, more specifically at phase 1

• Different methodologies are applied to identify and model those flows

2

Introduction



3

Pedestrian flow types during phase 1 at Pavilion K



4


Pavilion K

Pavilion D

Pavilion H

Pavilion E Légaré Street

Current visitors parking



5


Patients coming to the emergency



6



Underground parking visitors



7




2nd floor walkway:

1) Patients going to the main building



8




2nd floor walkway:


2) Medical staff



9




2nd floor walkway:


2) Medical staff

S1 link:

1) Underground parking visitors to K & H



10




2nd floor walkway:


2) Medical staff

S1 link:


2) Logistics flows (K & H)



11




2nd floor walkway:


2) Medical staff

S1 link:



3) Visitors/Patients walking between H & K



12




2nd floor walkway:


2) Medical staff

S1 link:



3) Visitors/Patients walking between H & K

4) Clinical staff walking between H & K



• This traffic will be only for patients going to/from the emergency during phase 1.

• A complete analysis of the data base of the emergency records was made to identify all the possible trajectories outside the emergency and establish their projection during phase 1

• The impact study has been already submitted

• This presentation will focuses then in the traffic through the link (S1 level)

13

2nd floor walkway



14

Link layout

Pavilion H

Elevators from/to underground parking

Pavilion D

Pavilion K

Elevator pavilion K

Link



Logistic

• Laundry

• Kitchen

• Housekeeping

• Pharmacy

• Supply

• CSD

Pavilion H

• Visitors/patients

• Clinical staff

Parking • Visitors

15

Types of flow through the link



• 3 sources:

1. Physical simulation

2. Live observations

3. Computational simulation

16

Measurements



• 3 sources:




17

Measurements Used only for the logistic flows in order to identify possible issues once the link be ready



• 3 sources:




18

Measurements



• 3 sources:




19

Measurements



• 3 sources:




20

Measurements

• Most used method • Used for estimate the flows:

1. Parking visitors 2. Transients (current link

between H & D) 3. Speed measures



• 3 sources:




21

Measurements




• 5 days of measurements • Different days of the week and

hours to ensure the accuracy of the observations (day/night)



• 3 sources:




22

Measurements




• 5 days of measurements • Different days of the week and hours to ensure the

accuracy of the observations (day/night)

• A second series of observations was done to identify and separate the hospital staff



• 3 sources:




23

Measurements




• Shadowing of all the logistic services to identify the average speed and variation



• 3 sources:




24

Measurements




• Shadowing of all the logistic services to identify the average speed and variation

• Additionally, some speed measures of hospital staff and visitors were made at the hospital main building

• This information is used to feed the computer simulation models



• 3 sources:




25

Measurements

http://100177a/Tools/Video2.html http://youtu.be/RdH30Pl2uI8 (from outside the hospital)

Video

http://100177a/Tools/Video2.html



http://youtu.be/RdH30Pl2uI8

http://youtu.be/RdH30Pl2uI8



26

Numerical results



• Measures in meter / second (1m/s = 3.6 km/h)

• A variation coefficient (CV) was also calculated (average / standard deviation). Higher CV’s (>50%) indicate non stable process or non predictable walking times in this case.

• A resume graph is presented in the next slide

27

Speed measures



28

Walking speed and variation

(m/s)

Tran

sfu

sio

n

serv

ices

0.72 0.87

Kit

chen

Lau

nd

ry

0.83

0.77 1.01 1.35

Ph

arm

acy

0.89 0.92

Ho

use

keep

ing

0.94

1.31

Han

dic

app

ed

use

rs

Stan

dar

d

wal

ker

Eld

erly

use

rs

Staf

f

Logi

stic

s

(in

tern

al

del

iver

y)

104%

(%)

Var

iati

on

20%

74%

43% 45%

20% 23%

20% 16% 20%

Spe

ed


29

Traffic flow measures



• The table below resumes all the projected daily visits to the pavilions K and H during phase 1

• Includes always both directions

30

Logistic services

Hour CSR Housekeeping Kitchen Laundry Logistics Pharmacy Total

6 0 2 0 6 0 0 8

7 2 0 6 12 0 0 20

8 2 0 0 12 2 0 16

9 2 6 2 12 2 0 24

10 8 4 6 2 2 0 22

11 4 6 0 2 2 2 16

12 2 2 4 2 2 2 14

13 6 6 2 2 6 0 22

14 4 4 4 2 4 0 18

15 0 0 0 2 0 0 2

16 0 2 0 2 0 0 4

17 0 2 4 2 0 0 8

18 0 0 6 2 0 0 8

19 0 6 2 2 0 0 10

20 0 4 0 2 0 0 6

21 0 6 0 4 0 0 10

22 0 4 0 4 0 0 8

23 0 0 0 2 0 0 2

218



• Measures made at the Légaré parking for visitors

• The measures include the cars entering and exiting the parking (two different pedestrian flows)

• The period between 6PM and 5AM wasn’t measured, however some data was added based in interviews to the staff

• A ratio of 1.4 persons per car is used (1 out of 3 cars is occupied only by the driver)

• Finally, a 1.3 factor in the parking occupancy was added to consider the increase of capacity (from 200 to 300 spots in phase 1)

31

Parking

HourTotal cars

IN

Total cars

OUT

Visitors

IN

Visitors

OUT

Total

Visitors

Total

Visitors

(projected)

06 14 1 19 1 20 26

07 58 7 81 10 91 118

08 73 21 105 30 135 176

09 68 36 94 51 145 189

10 50 57 71 81 152 198

11 34 55 49 78 127 165

12 38 51 54 74 128 166

13 48 56 69 81 150 195

14 35 53 50 78 128 166

15 13 58 20 84 104 135

16 3 37 6 53 59 77

17 0 2 0 3 3 4

434 434 618 624 1242 1615



• A final measure was made to estimate the LOS (length of stay) in the parking

• A strong inverse correlation was found between the arrival time and the total LOS per car (earlier the vehicle arrives, higher the LOS will be)

• The average LOS per car is 1.6 hours (96 minutes) with 45% of variability

• When a car enters to the system very early (before 8am), the probability of expend 160 minutes or more is 60%, but this expected LOS is reduced in 13 minutes for each subsequent hour until 4PM where the average LOS is 30 minutes

32

Parking



• In the present, we have 2000 trajectories per day in both ways

• 55% among them are visitors (users) and 45% belongs to the hospital staff

33

Visitors and staff between pavilion H and D



H Logistic Parking Visitors Staff Total Traffic Traffic/min

0 0 5 0 0 5 0.1

1 0 1 0 2 3 0.1

2 0 1 0 4 5 0.1

3 1 1 0 7 9 0.2

4 0 7 0 9 16 0.3

5 0 20 0 10 30 0.5

6 8 26 25 6 65 1.1

7 20 118 31 27 196 3.3

8 16 176 100 85 377 6.3

9 24 189 129 50 392 6.5

10 22 198 146 69 435 7.3

11 16 165 124 81 386 6.4

12 14 166 100 114 394 6.6

13 22 195 121 121 459 7.7

14 18 166 91 79 354 5.9

15 2 135 65 57 259 4.3

16 4 77 50 43 174 2.9

17 8 16 51 44 119 2.0

18 8 10 16 12 46 0.8

19 10 9 10 9 38 0.6

20 6 8 5 13 32 0.5

21 10 7 1 12 30 0.5

22 8 5 0 10 23 0.4

23 2 4 0 7 13 0.2

219 1705 1065 871 3860

34

Total projected flow through the link



H Logistic Parking Visitors Staff Total Traffic Traffic/min

0 0 5 0 0 5 0.1

1 0 1 0 2 3 0.1

2 0 1 0 4 5 0.1

3 1 1 0 7 9 0.2

4 0 7 0 9 16 0.3

5 0 20 0 10 30 0.5

6 8 26 25 6 65 1.1

7 20 118 31 27 196 3.3

8 16 176 100 85 377 6.3

9 24 189 129 50 392 6.5

10 22 198 146 69 435 7.3

11 16 165 124 81 386 6.4

12 14 166 100 114 394 6.6

13 22 195 121 121 459 7.7

14 18 166 91 79 354 5.9

15 2 135 65 57 259 4.3

16 4 77 50 43 174 2.9

17 8 16 51 44 119 2.0

18 8 10 16 12 46 0.8

19 10 9 10 9 38 0.6

20 6 8 5 13 32 0.5

21 10 7 1 12 30 0.5

22 8 5 0 10 23 0.4

23 2 4 0 7 13 0.2

219 1705 1065 871 3860

35

Total projected flow through the link

Normal zone

Hot zone

Rush



• The hot zone starts at 9AM and finish at 3PM

• In between, there are two hours (ranges) where the heaviest traffic is expected (10AM-11AM and, 1PM-2PM)

• At these hours, the expected concurrent traffic is higher than 7 pedestrians per minute (see graph)

36

Flow analysis



• This crowd effect can be worst if we consider the logistics carts transiting at the same time through the link (22 transports projected at 1PM where the traffic is expected to be at 7.3 passengers per minute), what can generate a snow ball effect for the multiple bottlenecks in the same space

• A final physical simulation is proposed to measure this effect

37

Flow analysis

link statistics

Technology

types of flow

pedestrian flow types

types of pedestrian

elevator pavilion

construction of pavilion

computational simulation

physical simulation

medical staff s1 link