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Impacts of Autonomous Vehicles on Parking and Congestion
Sina Bahrami, Ph.D. CandidateMatthew J. Roorda, Professor
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AVs legislation and policy
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Speed up the integration of AVs
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Street Access
STR
EET
DR
IVEW
AY
Building
Conventional Car-parks
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AV Car-parks
Street Access
STR
EET
DR
IVEW
AY
Building
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1- Design Demand 2- Plot Dimensions
Optimal Parking Facility Geometry
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Any vehicle can be discharged at any given point in time
Relocation Policy
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Vehicle Relocation in Larger Islands
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Expected Relocations Per Vehicle Retrieval
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Solution Methodology
A mixed integer program with a non-linear objective function.
The purpose of the [MP] is to iteratively generate different layouts until the best layout is found.
The [SP] finds the optimal allocation of the demand between the islands.
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Impact of Demand on Optimal Layout
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Plot Shape Analysis
Capacity560
Capacity540
Capacity500
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Parking capacity increase
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Where to park?
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Full information scenario
All arrival and departure times are known in
advance.
The problem is modelled as an integer
program.
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Full information scenario[π΄π΄1, π΄π΄2,π΄π΄3,π΄π΄4,π΄π΄5,π·π·4,π΄π΄6,π΄π΄7, π΄π΄8,π΄π΄9,π΄π΄10,π΄π΄11,π·π·9,π΄π΄12,π·π·7, π·π·2,π·π·3,π·π·5,π·π·8,π·π·1,π·π·12,π·π·6,π·π·10,π·π·11]
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1
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44
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66
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88
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1010
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91212
7
2
3
5
8 1
12
6
10
1111
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Partial information scenario
Sequential stochastic optimization model
Infinite state space
Test and compare different policies using a
simulation model
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Allocation policies
Arrival time
oOnly considers the arrival time
Clustering based on dwell time
oCluster AVs as short term vs long term
Blockage probability
o Blockage probability based on average dwell
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Key operational findings
Blocking probability is the best scenario when all the islands are sizeable or arrival rate is high.
Arrival policies compete with blockage probability because they consider future arrivals.
Considering Retrieving vehicles from the rear side does not reduce the number of relocations.
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Regular Vehicle Parking Autonomous Vehicle Parking
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Parking options
Home
o πΆπΆβ = 2π₯π₯βπππ‘π‘
Car-park
o πΆπΆππ = 2π₯π₯πππππ‘π‘ + ππππ(π‘π‘ππ β 2π₯π₯ππ)
Cruise
o πΆπΆππ = π‘π‘πππππ‘π‘
π₯π₯ Travel timeππ Travel costππ Parking rateπ‘π‘ Activity time
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Hypothetical city
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Base case scenario with ππππ = 3[ $βππ
] and π‘π‘ππ = 12[ $βππ
]
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Parking cost sensitivity analysis
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Travel cost sensitivity analysis
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Parking location analysis
Daily spatial distribution of cruising
Daily spatial distribution of Parking
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Key findingsNo
policy
Same parking
price
Zero-occupant
toll
5 pm traffic flow snapshot
18 min
12 min
47 min
-
30 min
10 min
50 min
+1 %
15 min
11.5 min
43 min
- 3.5 %
Maximum cruising
timeAverage
travel time to
car-parks
Maximum travel
time to car-parks
Change in VKT
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Vehicle to Vehicle
Vehicle to Infrastructure
Capacity enhancement
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Relation between link capacity and AV proportion
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The Equilibrium Condition
The equilibrium condition can be formulated
as NCP.
The UE does not have a unique solution
because the travel time function changes
regarding HV and AV flows is not symmetric.
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A simple example
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Best User Equilibrium flow
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Traffic management policies
HV exclusive, AV exclusive, or shared links. There are 3 π΄π΄ different scenarios for a
network πΊπΊ(ππ,π΄π΄). System optimal traffic assignment is used as
the lower bound. For a real size network, policies can decrease
the gap between user equilibrium and system optimal to less than 1%.
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Review
Car-park dimensionsDesign demand
Optimal car-park layout Design
Arrival and departure information of each individual
Optimal operation of the car-park
Location and price of each car-park
Optimal parking policiesOptimal traffic management policies
Parking Design
Parking Operation
Parking and Network PolicyParking Choice
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Thank You!
Sina BahramiEmail: sina.bahrami@mail.utoronto.caLinkedIn: www.linkedin.com/in/bahrami-sina
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