Pembina - Emerson Port of Entry Study
David Lettner: Manitoba Infrastructure and Transportation
Project Steering Committee (PSC)
2
Manitoba Infrastructure and Transportation (MIT)
 David Lettner, Project Manager and PSC Chair
 Walter Burdz (P. Eng.), Executive Director Highway Engineering
 Brett Wareham (P. Eng.), Director of Regional Operations (Region 1)
North Dakota Department of Transportation (NDDOT)
 Jack Olson, Assistant Division Director, Planning and Programming
 Les Noehre (P.E.), Grand Forks District Engineer
Transport Canada (TC)
 Susan Zacharias, Policy Coordinator (Prairie and Northern Region)
Canada Border Services Agency (CBSA)
 Blair Downey, Chief of Operations (Southern Manitoba District)
Customs and Border Protection (CBP)
 Jason Schmelz, Assistant Port Director (Pembina)
General Services Administration (GSA)
 Bryan Sayler, Property Manger (North Dakota Field Ofice)
3
TBWG: Bi-national institutional support






Policy context and alignment of planning strategies
Sharing of best practices / experiences
Technology applications, studies and data sources
Peer network (agency subject matter experts) and agency perspectives
Timely exposure to emerging funding opportunities
TBWG website (past plenary session archives)
NDDOT: Previous regional level initiatives



FHWA Coordinated Border Infrastructure Grant application (1999)
NDDOT lead agency (with Manitoba / Saskatchewan participation + funding)
Initial regional level work / study on border infrastructure coordination issues
4

Project Background and Context

Existing Situation / Historical Activity

Planning Principles / Best Practices


Key Study Methodologies
Proposed Port Concept / Measures of Effectiveness
5

2009
 Initial inter-agency meeting (precursor to project steering committee - PSC)
 GBCF Application to TC

2010
 GBCF Contribution Agreement (CA) signed between MIT and TC
 MOU and CA signed between MIT and NDDOT

2011
 PSC formed
 ESP registry / TOR finalized / RFP process / ESP retained (study start date Oct 5)

2012
 Phase 1-Concept Planning completed (broad agency consensus for concept)
 Phase 2-Functional Design mobilized (MIT: transportation infrastructure)
6
40+ stakeholders
30+ agency specialists
7
Primary Study Objectives:



At a conceptual level of detail: to prepare a long-range concept plan
for the P-E POE that identifies the general level of transportation
infrastructure and border services facility improvements required to
meet anticipated demand to the year 2035.
To gain consensus on the recommended long-range concept plan
for the P-E POE from all transportation agency funding partners
(TC, MIT, NDDOT) and bi-national border service agencies (CBSA,
CBP, GSA)
To develop and implement a long-term collaborative mechanism for
maintaining stewardship for the P-E POE concept plan and working
collaboratively toward implementation of the recommended concept
8
9
10
11
12
13
14
15
SB improvements:$1.7M
 $525K for VMS
 $1.2M for pavements
16
Optimize Public Investments:




CBP
1997
$14.5M
CBSA
1999
$10.5M
MIT
1996 / 2012
$7.0M +
Utilize appropriate methodologies to justify future transportation / port
improvements and expenditures (demand-capacity analysis / benefit-cost)
Hierarchical Land Assignment Strategies:
 Essential transportation and border service functions take priority over nonessential functions
 Locate non-essential port functions further from Canada-USA border wherever
possible (ex: duty free operations)
Operational and Phasing Considerations:
 Integrate and optimize advance notification, channelization and lane
assignment strategies to facilitate vehicle throughput
 Phasing considerations related to impact on businesses, project delivery
implications for public agencies (6-10 year project delivery cycle)
17
Transportation Demand Management (TDM)

TDM strategies are intended to reduce demand on facilities and
infrastructure during peak periods by modifying travel behaviour
 Providing access to delay and congestion data for port users
 Using ITS applications like BIFA to provide real time traveller information
 Promoting uptake of trusted traveller / trader programs (NEXUS / FAST)
Transportation System Management (TSM)

TSM strategies are intended to optimize the use of existing or
proposed facilities and infrastructure through better management
and operational practices
 Data collection
 Advance notification, channelization, lane assignment strategies
 Optimizing flexibility & cross-over functions of PIL infrastructure
18
Vehicle Demand Forecasts
 Hourly arrivals by vehicle category (autos, trucks)
 Hourly data: Synchro-Sim Traffic and undertake LOS analysis
Synchro-Sim Traffic Simulation Model
 30th highest hour: establish design parameters (ex: # PIL booths)
 Establish trigger points for phasing in improvements
Level of Service (LOS) Framework
 Sensitivity analysis to verify and refine phasing of improvements
 Capability to assess various processing / infrastructure scenarios
 Verification of Synchro-Sim queue lengths
19
Methodology Integration | Best Practices
Hourly traffic volumes to
2035
Vehicle
Forecasts
LOS
Analysis
LOS and Synchro-Sim
corroboration / cross-validation
Sensitivity analysis to assess various
processing / infrastructure scenarios
Synchro
Sim
30th highest hour / 99th percentile to
establish facility design requirements
20

Objective: To develop custom algorithms which take annual
forecasts (to 2035) by vehicle type and distribute this data on an
hourly and daily basis to uncover peaking characteristics and
patterns for the two primary vehicle classes (trucks / autos)

Needs: Historical hourly data by vehicle type (trucks / auto) needed
to develop algorithms which replicated historical patterns. CBSA and
CBP provided excellent historical data (hourly volumes by vehicle
type) over a 7-10 year period for the P-E POE

Benefits: Ability to assess daily and hourly peaking impacts on port
facilities and determine requirements for key infrastructure
components such as PIL booths with greater degree of statistical
confidence (+ / - .5% standard deviation)
21
Vehicle Forecasts: Compilation of three vehicle categories
 Buses (ex: 2011: 2,250 vehicles / 0.2% of total traffic)
 Trend line analysis (exponential trend line decrease from 1993-2010)
 Autos (ex: 2011: 642,348 vehicles / 62.3% of total traffic)
 Trend line analysis (linear trend line increase of 3.5% from 1993-2010)
 Custom algorithms developed to distribute annual forecast data for:
 Every hour of every day (to 2035) based on custom expansion factors
 Trucks (ex: 2011: 385,725 vehicles / 37.5% of total traffic)
 NFFI data for top 30 commodity groups converted into truck movements
 Custom algorithms developed to distribute annual forecast data for:
 Every hour of every day (to 2035) based on custom expansion factors
 FHWA vehicle classes / Gross vehicle weight / volume
 route splits (I-29 and I-94) / traffic splits (SB / NB)
 Percentage of empty backhauls
22
•NB and SB bus traffic exhibited an
exponentially decreasing trend-line
between 1993 and 2010
SB Buses
• From 2012 to 2035 a “bottom”
equilibrium value was established that
reflected 40% of mid-1990 values
3,000
2,500
2,000
•As buses were such a small amount
of the traffic stream (< 0.2%), and in
declining numbers, bus traffic was
removed from the forecast projections
1,500
1,000
500
0
1990
BUSES
2000
Proxy
2010
2020
2030
2040
NB Buses
3,000
2,500
2,000
1,500
1,000
500
0
1990
BUSES
2000
Proxy
2010
2020
2030
2040
23
Annual Autos Bi-Directional
2,000,000
1,800,000
1,600,000
1,400,000
1,200,000
1,000,000
800,000
600,000
400,000
200,000
0
High +1%
Medium
Low -1%
• From 1993 to 2010, auto traffic increased by 3.5% for both NB and SB directions
• A 3.5% annual growth rate (Med) was applied to autos for the 2012 to 2035 forecast period
24
Annual Trucks Bi-Directional
High +1%
Medium
Low -1%
• NFFI commodity data used to establish truck forecasts from 2012-2025
• NFFI data was extrapolated for the 2025-2035 forecast period
25
Capturing Forecast Peaks
The Rationale Behind Developing Algorithms
14000.0
14000
Algorithms were developed to translate
annual forecasts into daily and hourly
forecasts to more accurately capture
peaking patterns and characteristics
necessary to assess facility and
infrastructure requirements
12000.0
10000.0
Traffic
Volume
9 million
12000 data points
were required to
obtain hourly arrival
10000
rates
to the year 2035
8000.0
8000
6000.0
6000
4000.0
4000
Annual Growth
Forecast Trendline
2000.0
2000
0.0
0
0
20
40
60
80
100
120
Forecast Period
26
27
28
Calc for Max hourly theoretical PIL capacity:
(3 different auto dwell time scenarios)
120 sec PIL dwell time = 30 veh /hr / PIL
(30 veh x 6 PILs = 180 veh / hr)
90 sec PIL dwell time = 40 veh / hr / PIL
(40 veh x 6 PILS = 240 veh / hr)
60 sec PIL dwell time = 60 veh / hr / PIL
(60 veh x 6 PILS = 360 veh / hr)
29
Calc for Max hourly theoretical PIL capacity:
(3 different auto dwell time scenarios)
120 sec PIL dwell time = 30 veh /hr / PIL
(30 veh x 4 PILS = 120 veh / hr)
90 sec PIL dwell time = 40 veh / hr / PIL
(40 veh x 4 PILS = 160 veh / hr)
60 sec PIL dwell time = 60 veh / hr / PIL
60 veh x 4 PILS = 240 veh / hr)
30
31





Frequency: Number of occurrences
Magnitude: Delay to individual vehicles
Duration: length of delay period
Day and Date: Holidays (day of the week or fixed date)
Vehicle Type: Truck / Auto peaking characteristics
32
LOS Framework Criteria
Magnitude of delay:
Delay to individual vehicles
Duration of delay:
Delay period for queued vehicles
Volume / Capacity Ratio:
Ratio of hourly arrivals to
max. theoretical processing capacity
33
Develop custom algorithms that calculate:
 Average wait times per vehicle for each forecast hour
based on the “state” of demand (unsaturated, build-up,
saturated, dissipation)
 Wait times converted to LOS categories (A to F) based on
custom service time parameters stipulated in LOS
framework
 Total number of hours in each LOS category
(A, B, C, D, E, F) aggregated by forecast year
34
TYPICAL PEAKING SCENARIO
State 1: Unsaturated – No delay
State 2: Build-up – Arrivals exceed processing capacity
State 3: Saturated – Arrivals and / or queue exceed processing capacity
State 4: Dissipation – Arrivals and queue less than processing capacity
35
Flexibility of custom algorithms that calculate LOS:
 The algorithms (once developed) have unlimited simulation
capability to test infrastructure and service level parameters
based on the LOS framework
 PIL dwell time: Impact of processing protocols / technologies
 Number of PILS: Impact of built infrastructure / staffing levels
 This is the distinct advantage of the LOS framework over
models like Synchro-Sim which would require simulation
runs for every hour in a year (8,760 hourly runs) to obtain
the same result for any given forecast year
36
37
38
39
40
41
42
43
44
Southbound and Northbound
 Utilize PTH 75 and I-29 for passenger vehicle traffic approach to PIL plaza
 Construct 2 new dedicated commercial lanes (FAST / non-FAST)
Southbound
 Convert all CBP commercial PILS to high / low booths
 New secondary commercial inspection facility
Northbound
 New CBSA commercial plaza (4 PILS, VACIS, secondary)
 New commercial service road connection to PTH 75
Access Management
 New Emerson Access Road at PTH 75 / PR 243 junction
45
Preliminary Order of Magnitude Cost Estimates
 $47.4M
 24-1 benefit-cost ratio for full concept build-out
Canada (NB and SB)
 $3M: MIT (transportation infrastructure)
 $30.5M: CBSA (border service facilities)
United States (NB and SB)
 $1.7M: NDDOT ( transportation infrastructure)
 $12.2M: CBP (border service facilities)
46
47
48
49
David E. Lettner, BA, MPA, MCIP
Project Manager: Pembina-Emerson Port of Entry Transportation Study
Senior Transportation Planning Consultant
Manitoba Infrastructure and Transportation
Transportation Systems Planning Branch
CONTACT:
215 Garry Street, Winnipeg, Manitoba, Canada, R3C 3P3
T: 204.945.5270
E: david.lettner@gov.mb.ca
50