The Electric Vehicle Charging
Station Location Problem:
A Parking-Based Assignment Method
T. Donna Chen
Dr. Kara M. Kockelman
Moby Khan
Department of Civil, Architectural, & Environmental Engineering
University of Texas at Austin
Overview
• Motivation
• Existing research
• Puget Sound region data Photo by Daniel Reese for KUT News
• Study methodology
▫ Forecasting zone-level parking demand
▫ Forecasting trip-level parking demand
▫ Locating optimal charging infrastructure
(Seattle application)
• Conclusions
Background
• Trend: Increased number of EVs in the market
▫ Higher fuel economy requirements
▫ Higher fuel costs
• Caveat: Range anxiety for EV owners/potential
buyers affects
▫ EV adoption rates
▫ Electrified mile shares
▫ Petroleum demand
▫ Power consumption across times of day
• Potential solution: Public charging station
provision
Background
• Charging station installation costs
▫ $3,000 to $15,000 per station using existing
infrastructure
▫ $40,000+ per station when requiring local
infrastructure upgrade
• Energy providers, cities, & MPOs need a
methodology to optimally locate public charging
stations that…
▫ Serve charging demand,
▫ Minimize access costs for EV drivers,
▫ Meet constrained budgets.
Existing Research
• Wang et al.’s (2010) numerical method for Chengdu,
China used distribution of gas-station demands as
proxy for charging demands.
• Sweda & Klabjan’s (2011) Chicago case study uses an
agent-based model to identify residential patterns of
EV ownership & driving activities to identify strategic
station locations.
• Frade et al.’s (2011) coverage model for Lisbon seeks
locations that maximize match for charging demands.
• Our work most closely tracks that of Hanabusa &
Horiguchi (2011): minimizing driver walk costs
while ensuring coverage (via buffer distances
between stations, in Seattle).
Our Approach
• Behavioral models calibrated to predict when & where
EVs are likely parked.
▫ Zone-level parking demand based on land use attributes
of destination zones.
▫ Trip-level parking demand based on individual trip
characteristics.
• Optimization routine (MIP) identifies charging station
locations in order to…
▫ Minimize station access penalties for EV drivers, while…
▫ Satisfying budget constraints, &
▫ Ensuring minimum station spacing requirements.
The Data
• Puget Sound Regional Council’s (PSRC) 2006
household travel survey data.
▫ 4 Washington Counties (King, Kitsap, Pierce &
Snohomish)
▫ 4,741 households
▫ 10,510 individuals
▫ 3,700 traffic analysis zones (TAZs)
▫ 1,177,140 parcels
▫ 87,600 total person-trips
▫ 48,789 trips by light-duty vehicles
Seattle’s 3,700 Traffic Analysis Zones
Summary Statistics of PSRC Data
Mean St Dev. Min Max
Person Records (N=10,510)
Age (years) 41.9 21.8 0 99
Male Indicator 0.47 0.50 0 1
Driver’s License Indicator 0.78 0.42 0 1
Student Indicator 0.21 0.4 0 1
Household Records (N=4,741)
Household Size 2.22 1.21 1 8
#Workers in Household 1.13 0.85 0 5
#Vehicles in Household 1.89 1.07 0 10
Annual Household Income $71,400 $42,300 $5,000 $175,000
Determining Parking Location & Duration
• Starting with 48,789 trips (55.7% of all person-
trips).
▫ Only trips ending away from one’s home parcel are
counted.
▫ Only trips with adequate parking duration (at a
single parcel) are counted (at least 15 minutes), to
help ensure adequate time for charging.
• 30,085 candidate parking periods emerged.
Forecasting Zone-Level Parking Demand
Summary Statistics of PSRC Zone Attributes
Variable (n = 3962) Mean Std. Dev. Min Max
Parking duration (mins/mile2) 2.41E+04 1.18E+05 0 2.43E+06
Population density (persons/mile2) 7.99E+03 1.88E+04 0 2.91E+05
Employment density (jobs/mile2) 1.26E+04 8.27E+04 0 2.07E+06
Student density (students/mile2) 1.64E+03 2.48E+04 0 1.02E+06
Housing density (units/mile2) 3.43E+03 8.20E+04 0 1.27E+05
Average price of daily paid parking ($) $0.145 $1.027 0 $21.3
Average price of hourly paid parking ($) $0.066 $0.465 0 $11.0
3-way intersections (1/2 mile radius) 45.8 22.52 0 119.3
4-way intersections (1/2 mile radius) 36.8 45.91 0 251.8
Express bus stops (1/4 mile radius) 2.25 6.369 0 55.6
Bus stops (1/4 mile radius) 6.21 9.016 0 69.6
OLS Results for Total Demand/SqMile
Y= Total Parking Demand in Zone (minutes/mile2)
Parameter Standardized
Variable t-stat
Estimate Coef.
Constant 3268 1.06
Density
Population density (residents/mile2) -0.294 -0.047 -3.50
Employment density (jobs/mile2) 0.583 0.408 27.0
Student density (students/mile2) 0.226 0.047 4.11
Parking Prices (within ¼ mile)
Average price of daily parking ($) 2.22 0.193 11.0
Transit Access & Network Connectivity
#3-way intersections (within ½ mile) -158.0 -0.030 -2.41
#4-way intersections (within ½ mile) 160.8 0.062 2.94
#Express bus stops (within ¼ mile) 1537 0.083 3.29
#Bus stops (within ¼ mile) 1624 0.124 4.17
Number of Observations 3,692 TAZs
Adjusted R-squared 0.521
Forecasting Trip-Level Parking Demand
Summary Statistics of PSRC Trip Attributes
Avg Parking Duration Std.
Trip Attribute (min. per trip) Trip Attribute Mean Dev. Min Max
Activity: Work 379.7 Parking duration
142.0 199.5 15.0 2120
Activity: School (K-12) 338.8 (min/trip)
Activity: College 222.5 Trip distance 0.23
6.71 7.14 67.6
Activity: Eating out 46.1 (miles) 0
Activity: Personal business 46.8 Passengers
0.421 0.811 0 6
Activity: Everyday shopping 27.7 (excluding driver)
Activity: Major shopping 47.6
Activity: Religious/community 116.8
Activity: Social 127.6
Activity: Recreation-participate 103.5
Activity: Recreation-watch 107.4
58.8
Activity: Accompany someone else
Activity: Pick up/drop off 15.5
Activity: Turn around 53.0
Vehicle: Car 147.2
Vehicle: SUV 133.2
Vehicle: Van 103.3
Vehicle: Truck 173.7
OLS Results for Parking Durations/Trip
Y = Parking Duration of Trip (min/trip)
Variable Parameter Estimate Standard. Coef. t-stat
Constant 372.2 125.5
Activity: Work (base case) - - -
Activity: School (K-12) -21.28 -0.009 -2.37
Activity: College -157.6 -0.069 -18.4
Activity: Eating out -306.6 -0.396 -95.0
Activity: Personal business -313.2 -0.603 -135.6
Activity: Everyday shopping -324.5 -0.609 -133.9
Activity: Major shopping -308.0 -0.196 -51.5
Activity: Religious/community -246.3 -0.170 -44.6
Activity: Social -241.9 -0.238 -60.7
Activity: Recreation-participate -259.7 -0.302 -75.6
Activity: Recreation-watch -254.2 -0.161 -41.8
Activity: Accompany someone else -298.9 -0.141 -37.2
Activity: Pick up/drop off -344.1 -0.587 -127.3
Activity: Turn around -303.8 -0.102 -27.5
Number of Observations 30,085 Parking durations
Adjusted R-squared 0.590
Anticipating Best Sites for Charging Stations
Mixed Integer Programming (MIP) in GAMS:
Minimizes total access cost as function of walk distances
(cij) weighted by parking demand duration (yij).
Parking demand (di) in zone i
is met by station in zone j.
Ensures all demand is met.
Total # of stations won’t exceed budgeted #of stations (L).
Ensures minimum spacing (spread) between stations.
Ensures met parking demands are non-negative.
Indicator (xj) is 1 when station is assigned to zone j; 0 otherwise.
Indicator (δij) is 1 if minimum
spacing (r) is met; 0 otherwise.
Application: Optimal Station Locations in
Seattle
• 218 TAZs (i & j =218)
• Number of charging
stations limited to L=20
• Minimum station spacing
r=1 mile
Application (2)
Selected TAZs Own-Zone Demand Rank
Demand (mins) (out of 218)
13 2232 20
19 69 167
30 6449 2
31 2311 19
37 552 93
39 771 74
53 1481 40
56 749 76
65 118 152
70 8959 1
90 5063 7
93 678 77
101 2445 18
152 376 109
153 0 207
162 341 113
174 2466 17
180 421 106
199 603 85
209 593 87
Comparing MIP Results to Simple
Assignment
Top 20 Zones by
Optimal Solution
Demand
127,510 132,762
Total Cost (z)
mile-minutes mile-minutes
Average parking access
0.69 miles 0.72 miles
distance
Maximum parking
1.53 miles 3.80 miles
access distance
% of drivers accessing
94.5% 79.6%
parking within 1 mile
Conclusions
Parking demand at the zone level…
▫ Rises significantly with job & student densities.
▫ Is higher in more connected networks & transit-
served zones.
Parking duration at the trip level…
▫ Is influenced most by trip purpose, with work &
school trips having longest durations.
▫ Also rises with trip distance & vehicle type (cars
park longer than trucks, SUVs, & vans).
Conclusions (2)
This study provides a basic framework for MPOs,
cities, & energy providers to…
▫ Anticipate parking demand, &
▫ Efficiently locate EV charging infrastructure in new
settings subject to a variety of constraints.
Potential extensions include…
▫ Determining optimal capacity at each charging station
▫ Behavioral models to anticipate type of vehicles
parked in zones, &
▫ Sensitivity tests to determine fluctuations in charging
station zone selection due to changes in inputs.
References
Frade, I., Ribeiro, A., Goncalves, G. & Antunes, A.P. (2011) Optimal
Location of Charging Stations for Electric Vehicles in a Neighborhood
in Lisbon, Portugal. In Transportation Research Record: Journal of
the Transportation Research Board, No. 2252: 91-98.
Hanabusa, H. & Horiguchi, R. (2011) Lecture Notes in Computer
Science in Knowledge-based & Intelligent Information & Engineering
Systems, Volume 6883/2011, pp. 596-605.
Sweda, T. & Klabjan, D. (2011) An Agent-Based Decision Support System
for Electric Vehicle Charging Infrastructure Deployment. 7th IEEE
Vehicle Power & Propulsion Conference, Chicago, Illinois.
Wang, H., Huang, Q., Zhang, C. & Xia, A. (2010) A Novel Approach for the
Layout of Electric Vehicle Charging Station. Apperceiving Computing
& Intelligence Analysis Conference.
Thank you for your time!
Questions?
