using gps to monitor driving and parking habits in winnipeg for phev optimization
DESCRIPTION
Using GPS to Monitor Driving and Parking Habits in Winnipeg for PHEV Optimization. R.Smith 1 , D.Capelle 1 and D.Blair 1 1 University of Winnipeg Department of Geography. Introduction. What is a PHEV?. http://www.eeh.ee.ethz.ch/. Introduction. How do you design a PHEV?. - PowerPoint PPT PresentationTRANSCRIPT
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