Using GPS to Monitor Driving and Parking Habits in Winnipeg for

PHEV Optimization

R.Smith1, D.Capelle1 and D.Blair1

1University of Winnipeg Department of Geography

Introduction

http://www.eeh.ee.ethz.ch/

What is a PHEV?

Introduction

• Power Requirements: Distance, Speed, Acceleration and Duration

• Time available for Battery Recharging

How do you design a PHEV?

Purpose

• Determine the energy demands placed on a PHEV by a typical driver

• Identify the most suitable public locations for recharging PHEVs

• Decrease vehicle emissions & petroleum dependence

Participants

• 100 Drivers from Winnipeg & nearby communities

• One year period

• Recruitment:– Local media– Word-of mouth – First come first served basis

Equipment

• 100 GPS receivers (Otto Driving Companion)– Store 300 hours of data @ one-second intervals– Plug-in to vehicle lighter socket– Transfer data to PC via USB cable

• Accuracy:– Position: 10 metres– Speed: 1 km/h

myottomate.com/checkoutotto.asp

Duty Cycle Analysis

arcx.com/sites/images/Photos/Underground parking lot at Square One.jpg

Vehicle Power Demand – the Duty Cycle

• A representative, 24-hour profile• Duty Cycles can indicate:

– Typical speed and acceleration demands– Hours of the day vehicle is in operation– Number of Trips / Day– Time available for Recharging

• Measured: Pre-determined route, single vehicle • Derived: Multiple vehicles, thousands of trips

over long periods of time

Duty Cycle Construction

• How many Trips / Cycle ?

• What is the trip origin and destination ?

• What hour of the day ?

• How long and far is the trip ?

• Speed and acceleration ?

• What is “Average” or “Typical” ?

Isolating Specific Trips

HOME

WORK

0

5

10

15

20

25

30

35

40

45

0:00

1:00

2:00

3:00

4:00

5:00

6:00

7:00

8:00

9:00

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

18:00

19:00

20:00

21:00

22:00

23:00

Hour of Day

Percentage

0

2

4

6

8

10

12

14

16

18

20

Sun Mon Tue Wed Thu Fri Sat

Weekday

Percentage

HOME to WORK

0

10

20

30

40

50

60

70

80

90

10:00:05PM

10:01:39PM

10:02:39PM

10:03:39PM

10:04:39PM

10:05:39PM

10:06:51PM

10:07:54PM

Speed (km/h)

CONGESTED FLOW

IDLE

IDLE

CREEP

UN-CONGESTED FLOW

Simplifying Trips

Creating “Blueprints”

0

10

20

30

40

50

60

MTT-1 MTT-2 MTT-3 MTT-4 MTT-5 MTT-6 MTT-7 MTT-8

Micro-Trip Type (MTT)

% of total micro-trips

Idling Micro-trips

Un-congested Traffic Flow

Creep

Congested Traffic Flow

%

Micro-Trip Types

Reconstructing Trips

0

10

20

30

40

50

60

70

80

8:00:00 8:01:30 8:03:00 8:04:30 8:06:00 8:07:30 8:09:00 8:10:30 8:12:00

Speed (km/h)

0

10

20

30

40

50

60

70

80

12:00:00 12:01:00 12:02:00 12:03:00 12:04:00 12:05:00 12:06:00

Speed (km/h)

0

10

20

30

40

50

60

70

80

13:00:00 13:01:00 13:02:00 13:03:00 13:04:00 13:05:00 13:06:00 13:07:00 0

10

20

30

40

50

60

70

80

16:00:00 16:01:30 16:03:00 16:04:30 16:06:00 16:07:30 16:09:00 16:10:30

Speed (km/h)

0

10

20

30

40

50

60

70

80

17:00:00 17:02:00 17:04:00 17:06:00 17:08:00 17:10:00 17:12:00

Speed (km/h)

0

10

20

30

40

50

60

70

80

8:00:00 8:01:30 8:03:00 8:04:30 8:06:00 8:07:30 8:09:00 8:10:30 8:12:00

0

10

20

30

40

50

60

70

80

8:00:00 8:01:30 8:03:00 8:04:30 8:06:00 8:07:30 8:09:00 8:10:30 8:12:00

0

10

20

30

40

50

60

70

80

17:00:00 17:02:00 17:04:00 17:06:00 17:08:00 17:10:00 17:12:00

0

10

20

30

40

50

60

70

80

16:00:00 16:01:30 16:03:00 16:04:30 16:06:00 16:07:30 16:09:00 16:10:30

0

10

20

30

40

50

60

70

80

13:00:00 13:01:00 13:02:00 13:03:00 13:04:00 13:05:00 13:06:00 13:07:00

0

10

20

30

40

50

60

70

80

12:00:00 12:01:00 12:02:00 12:03:00 12:04:00 12:05:00 12:06:000

10

20

30

40

50

60

70

80

13:00:00 13:01:00 13:02:00 13:03:00 13:04:00 13:05:00 13:06:00 13:07:00

X 100

Reconstructing Trips

0

5

10

15

20

25

30

4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10

Distance (km)

Frequency

0

2

4

6

8

10

12

14

16

18

17 18 19 20 21 22 23 24 25 26 27 28 29

Average Trip Speed (km/h)

Frequency

6.5 km 22 km/h

Distance Average Speed

Duty Cycle Construction

0

10

20

30

40

50

60

70

8:00:00 8:01:00 8:02:00 8:03:00 8:04:00 8:05:00 8:06:00 8:07:00 8:08:00 8:09:00 8:10:00 8:11:00 8:12:00 8:13:00 8:14:00 8:15:00 8:16:00 8:17:00

Time

Speed (km/h)

0

10

20

30

40

50

60

70

80

3:00:00 3:01:00 3:02:00 3:03:00 3:04:00 3:05:00 3:06:00 3:07:00 3:08:00 3:09:00 3:10:00

Time

Speed (km/h)

0

10

20

30

40

50

60

70

80

90

15:30:00 15:31:00 15:32:00 15:33:00 15:34:00 15:35:00 15:36:00 15:37:00 15:38:00 15:39:00 15:40:00 15:41:00

Time

Spee

d (k

m/h)

0

10

20

30

40

50

60

70

16:00:00 16:01:00 16:02:00 16:03:00 16:04:00 16:05:00 16:06:00 16:07:00 16:08:00 16:09:00 16:10:00

Time

Speed (km/h)

0

10

20

30

40

50

60

70

17:00:00 17:01:00 17:02:00 17:03:00 17:04:00 17:05:00 17:06:00 17:07:00 17:08:00 17:09:00 17:10:00 17:11:00

Time

Speed (km/h)

HOME to WORKWORK to SCHOOL

SCHOOL to HOMEHOME to SHOPPING

SHOPPING to HOME

TOTAL DISTANCE = 25.4 km

TOTAL DURATION = 1:02:54

Parking Analysis

arcx.com/sites/images/Photos/Underground parking lot at Square One.jpg

Suitability Criteria

• Maximum public availability– Widely-used parking lots

• Maximum re-charge potential– Long mean parking duration

• Low Impact on Electric Grid– “Off-peak electric demand” parking

Filtering & Manipulation

• Isolate only Trip-ends from data set– Parking locations

• Calculate Duration of all Parking Events

– Time difference between trip-end and next trip-start

• Parking On/Off-peak electric demand

Potentially Suitable Lots: Widely Used Areas

Potentially Suitable Lots:Individual Lot Analysis

78 / 85(0.92)

On-peak/ Off-peak

96 minsmean duration

68# participants

 STATISTICS

0.870.931.201.31.1On-peak/Off-peak

1181207210196Mean Duration (mins)

210206558# Participants

GP-5GP-4GP-3GP-2GP-1STATISTICS

Ranking Parking Lots

Suitability Criteria• Widely-used• Long mean-parking

duration• Low impact on

electric grid

  RANK

Lot A Lot B

Widely Used

1 2

Duration 2 1

Off peak 2 1

SUM 5 4

OVERALLless

desirablemore

desirable

Conclusion

The Good:

• GPS and GIS ideal for identifying suitable locations for PHEV recharge infrastructure

• Applicable to other cities

The Bad:

• Sample size too small

• GPS data errors

Acknowledgments• Soheil Shahidinejad, Department of Engineering, University of Manitoba• Dr. Jeff Babb, Department of Math and Stats, University of Winnipeg• Brad Russell, Department of Geography: Map Library, University of Winnipeg• Centre for Forest Interdisciplinary Research (C-FIR)• Pam Godin, Leif Norman and Laura Redpath• Terry Zdan and Dr. Arne Elias, The Centre for Sustainable Transportation (CST)

Funding and Support