Behavioral Micro-Simulation 1 Jose Holguin-Veras, Ph.D., P.E. William H. Hart Professor VREF’s...
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Transcript of Behavioral Micro-Simulation 1 Jose Holguin-Veras, Ph.D., P.E. William H. Hart Professor VREF’s...
Behavioral Micro-SimulationBehavioral Micro-Simulation
1
Jose Holguin-Veras, Ph.D., P.E.William H. Hart Professor
VREF’s Center of Excellence for Sustainable Urban Freight Systems
Center for Infrastructure, Transportation, and the Environment
Rensselaer Polytechnic Institute
Main goals
To produce a reasonable guess of freight traffic in metropolitan areas using:Freight trip generation estimates (using NCFRP 25
models)Known delivery patterns, such as tour length
distributions by industry sectors (obtained from data collected by RPI from carriers and receivers)
Observed traffic counts at key corridorsThe BMS was originally developed to assess the
impacts of alternative policies to foster off-hour deliveries (7PM to 6AM)
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Key components
Freight trip generation (FTG): estimated using the NCFRP 25 models and Zip Code Business Pattern data
Synthetic population of carriers (and receivers, if needed) is createdUsing the data collected by RPI, the sample data is
used to create the population of carriers needed to make all deliveries in the metro area
The origin of the deliveries are set to be the locations were warehouses and distribution centers are located
Delivery tours are created:Match the tour length (number of stops) by industry
sectorMatch the number of deliveries by ZIP code (or any
other level of geography used)
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Graphically: Synthetic population of carriersDifferent industry sectors have different tour
lengthsNYC and NJ (Holguin-Veras et al. 2012):
Average: 8.0 stops/tour; 12.6% do 1 stop/tour; 54.9% do < 6 stops/tour; 8.7% do > 20 stops
Synthetic population match observed traffic and FTG
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Tour simulations
Select a truck in an industry sector Number of stops is randomly
assigned Select receivers at random from
the group of receivers in that sector
Compute optimal tour and store it Repeat until delivery tours
satisfy the FTG for the entire area
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1) Origin of a truck that carries food products to five restaurants
2) Five receivers
BMS use in the off-hour delivery project8
Carrier/receiver synthetic generation Randomly select industry segment
o Generate/locate carriero Generate/locate receivers to serve
Receiver behavioral simulation Model receiver’s decision to accept OHD
Carrier behavioral simulation Compute costs for base case and mixed operation Model carrier’s decision
Repeat for another carrier-receivers set
End
Change incentives, reset participation counts
Define range of incentives to receivers for OHD
Ordinal logit model (Holguin-Veras et al 2013)
Regular-hour receiver
Off-hour receiver
a) Base case (no OHD) b) Mixed operation
Carrier depotLegend:
Output: Joint Market Share (JMS) of OHD Receivers Market Share (RMS) at TAZ level
Ordered logit model with random effects
This model reproduces receivers’ response to incentives
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ModelIndependent variables Parameter t-stat Parameter t-stat
Constant 0.61 (2.78) 0.22 (1.00)Number of deliveries -0.07 (-9.17) -0.08 (-11.66)
IncentivesOne time incentive in $1000 (OTI) 0.18 (6.95) 0.17 (6.76)Carrier discount in percent (CDR*100) 3.00 (6.81) 3.10 (7.12)Business Support (BS) 0.55 (3.82) 0.51 (3.52)Public Recognition (PR) 0.34 (2.24) 0.38 (2.48)Trusted Vendor (TV) 0.94 (4.29)
NAICSClothing stores, binary variable -2.73 (-4.57) -2.46 (-4.32)Performing arts, binary variable -1.96 (-5.69) -4.80 (-12.38)
Interaction terms: OTI and NAICSOTI for food and beverage stores 0.12 (2.56) 0.20 (4.24)OTI for apparel manufacture stores 0.23 (1.72) 0.11 (1.88)OTI for clothing stores 0.24 (3.18) 0.25 (3.40)OTI for nondurable wholesalers 0.33 ( 6.83) 0.37 (7.62)
Interaction terms: CDR and NAICSCDR for personal laundry -2.11 (-2.98) -2.08 (-3.25)
Interaction terms: Trusted vendor and NAICSTV for food and beverage stores 4.35 (7.29) 2.02 (3.17)TV for performing arts 4.65 (2.56) 13.49 (11.16)TV for clothing stores 5.06 (8.28) 2.24 (4.06)TV for miscellaneous stores retailers 6.59 (13.63) 3.17 (5.86)
Parametersµ(1) 1.88 ( 21.54) 1.91 (21.36)µ(2) 4.56 (34.64) 4.56 (34.14)µ(3) 7.63 (40.45) 7.55 (40.51)Sigma 4.58 (27.64) 4.74 (25.83)
nLog likelihood -1390.89 -1388.50
1522
Model 1 Model 2
1522
Incentives
Interaction terms:OTI and NAICS
NAICS code
Interaction terms:TV and NAICS
BMS Results10
OTI = $0avg = 2.2%max = 6.2%min = 0.6%
OTI = $2,000avg = 2.7%max = 7.6%min = 1.2%
OTI = $4,000avg = 3.4%max = 7.6%min = 1.3%
OTI = $6,000avg = 4.3%max = 9.9%min = 1.9%
OTI = $8,000avg = 5.5%
max = 11.9%min = 2.6%
OTI = $10,00avg = 7.0%
max = 13.4%min = 3.5%
Geographically focused incentives: case of NYC50% of establishments are located
in Midtown Manhattan being responsible for 52% of the incoming freight trips to the city
Two geographic distribution have been considered: (1) Lower and Midtown (2) Central Park and Upper
Scenarios consider giving incentivesto either the entire Manhattanor only to Lower and Midtown Manhattan
Lower Manhattan (LM)
Midtown Manhattan (MM)
Central Park (CP)
Upper Manhattan (UM)
+
+
Results of geographically focused incentivesRatio Budget/JMS provides an idea about the
amount of resources required to achieve a 1% JMS
The results also show the superiority of geographically focused incentives which requires between 71% and 75% less expenditures than incentives spread out all over Manhattan
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OTI ($K)
JMS (%)
RMS (%)
Budget ($M)
JMS (%)
RMS (%)
Budget ($M)
1 6.5 1.7 1.4 6.7 1.8 0.4 2 7.0 1.9 3.4 7.0 1.9 0.8 3 7.4 2.1 5.5 7.0 1.9 1.2 4 7.8 2.3 8.5 7.5 2.2 2.1 5 8.0 2.4 11.2 8.2 2.4 2.8 6 8.6 2.7 15.2 8.4 2.6 3.7 7 8.9 2.7 19.2 8.6 2.7 4.5 8 9.7 3.2 26.1 9.1 2.9 5.9 9 9.6 3.3 29.7 9.7 3.3 7.3 10 10.3 3.6 36.2 9.9 3.4 8.8
Lower and Midtown Manhattan
Central Park and Upper Manhattan OTI
($K)Manhattan
Lower and Midtown Manhattan
1 0.31 0.22 2 0.67 0.48 3 1.05 0.75 4 1.45 1.08 5 1.89 1.40 6 2.41 1.76 7 2.85 2.16 8 3.46 2.68 9 4.07 3.10
10 4.68 3.52
Ratio Budget/JMS
Self supported freight demand managementA self-supported freight demand management
system (SS-FDM), is one that generates the funds required for a continuing improvement towards sustainability
The incentives to be handed out to the receivers are generated by a toll surcharge to the vehicles that travel in the regular hours
The analyses consider tolls to only trucks (per axle) or both; trucks and cars. Finally, different levels of toll collection efficiency were also considered
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Results: tolls to trucks (per axle)
Toll collection 100%
Toll collection 75%
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$1 $2 $5 $8 $10
$0 7.1% 8,467 $0.00 $0.00 $58.08 $116.16 $290.40 $464.63 $580.79
$1,000 7.6% 9,031 $5.65 $1.88 $57.78 $115.56 $288.91 $462.26 $577.82
$2,000 8.2% 9,783 $26.32 $8.77 $57.39 $114.77 $286.93 $459.10 $573.87
$3,000 8.8% 10,463 $59.89 $19.96 $57.03 $114.06 $285.15 $456.23 $570.29
$4,000 9.5% 11,265 $111.95 $37.32 $56.61 $113.21 $283.04 $452.86 $566.07
$5,000 10.3% 12,209 $187.14 $62.38 $56.11 $112.22 $280.55 $448.89 $561.11
$6,000 11.0% 13,058 $275.51 $91.84 $55.66 $111.33 $278.32 $445.31 $556.64
$7,000 11.9% 14,175 $399.58 $133.19 $55.08 $110.15 $275.38 $440.62 $550.77
$8,000 12.8% 15,200 $538.65 $179.55 $54.54 $109.08 $272.69 $436.30 $545.38
$9,000 13.7% 16,279 $703.14 $234.38 $53.97 $107.94 $269.85 $431.76 $539.70
$10,000 14.9% 17,754 $928.70 $309.57 $53.19 $106.39 $265.97 $425.56 $531.95
Freight vehicle surcharge per axle:One-time-incentive
% OHDOHD tours (year)
Total incentive
budget
Yearly incentive
budget
Yearly toll revenues (car surcharge = $0)
$1 $2 $5 $8 $10
$0 7.1% 8,467 $0.00 $0.00 $77.44 $154.88 $387.19 $619.51 $774.39
$1,000 7.6% 9,031 $5.65 $1.88 $77.04 $154.09 $385.21 $616.34 $770.43
$2,000 8.2% 9,783 $26.32 $8.77 $76.52 $153.03 $382.58 $612.13 $765.16
$3,000 8.8% 10,463 $59.89 $19.96 $76.04 $152.08 $380.19 $608.31 $760.39
$4,000 9.5% 11,265 $111.95 $37.32 $75.48 $150.95 $377.38 $603.81 $754.76
$5,000 10.3% 12,209 $187.14 $62.38 $74.81 $149.63 $374.07 $598.51 $748.14
$6,000 11.0% 13,058 $275.51 $91.84 $74.22 $148.44 $371.09 $593.75 $742.19
$7,000 11.9% 14,175 $399.58 $133.19 $73.44 $146.87 $367.18 $587.49 $734.36
$8,000 12.8% 15,200 $538.65 $179.55 $72.72 $145.43 $363.59 $581.74 $727.17
$9,000 13.7% 16,279 $703.14 $234.38 $71.96 $143.92 $359.80 $575.68 $719.60
$10,000 14.9% 17,754 $928.70 $309.57 $70.93 $141.85 $354.63 $567.41 $709.26
Yearly toll revenues (car surcharge = $0)Freight vehicle surcharge per axle:
One-time-incentive
% OHDOHD tours (year)
Total incentive
budget
Yearly incentive
budget
Note: The shaded cells represent non-feasible combinations of financial incentives to receivers and tolls.
Results: tolls to trucks (per axle) and cars
Toll collection 100%
Toll collection 75%
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$1 $2 $5 $8 $10
$0 7.1% 8,467 $0.00 $0.00 $382.94 $460.38 $692.69 $925.01 $1,079.89
$1,000 7.6% 9,031 $5.65 $1.88 $382.54 $459.59 $690.71 $921.84 $1,075.93
$2,000 8.2% 9,783 $26.32 $8.77 $382.02 $458.53 $688.08 $917.63 $1,070.66
$3,000 8.8% 10,463 $59.89 $19.96 $381.54 $457.58 $685.69 $913.81 $1,065.89
$4,000 9.5% 11,265 $111.95 $37.32 $380.98 $456.45 $682.88 $909.31 $1,060.26
$5,000 10.3% 12,209 $187.14 $62.38 $380.31 $455.13 $679.57 $904.01 $1,053.64
$6,000 11.0% 13,058 $275.51 $91.84 $379.72 $453.94 $676.59 $899.25 $1,047.69
$7,000 11.9% 14,175 $399.58 $133.19 $378.94 $452.37 $672.68 $892.99 $1,039.86
$8,000 12.8% 15,200 $538.65 $179.55 $378.22 $450.93 $669.09 $887.24 $1,032.67
$9,000 13.7% 16,279 $703.14 $234.38 $377.46 $449.42 $665.30 $881.18 $1,025.10
$10,000 14.9% 17,754 $928.70 $309.57 $376.43 $447.35 $660.13 $872.91 $1,014.76
Yearly toll revenues (car surcharge = $1)Freight vehicle surcharge per axle:
One-time-incentive
% OHDOHD tours (year)
Total incentive
budget
Yearly incentive
budget
$1 $2 $5 $8 $10
$0 7.1% 8,467 $0.00 $0.00 $363.58 $421.66 $595.90 $770.13 $886.29
$1,000 7.6% 9,031 $5.65 $1.88 $363.28 $421.06 $594.41 $767.76 $883.32
$2,000 8.2% 9,783 $26.32 $8.77 $362.89 $420.27 $592.43 $764.60 $879.37
$3,000 8.8% 10,463 $59.89 $19.96 $362.53 $419.56 $590.65 $761.73 $875.79
$4,000 9.5% 11,265 $111.95 $37.32 $362.11 $418.71 $588.54 $758.36 $871.57
$5,000 10.3% 12,209 $187.14 $62.38 $361.61 $417.72 $586.05 $754.39 $866.61
$6,000 11.0% 13,058 $275.51 $91.84 $361.16 $416.83 $583.82 $750.81 $862.14
$7,000 11.9% 14,175 $399.58 $133.19 $360.58 $415.65 $580.88 $746.12 $856.27
$8,000 12.8% 15,200 $538.65 $179.55 $360.04 $414.58 $578.19 $741.80 $850.88
$9,000 13.7% 16,279 $703.14 $234.38 $359.47 $413.44 $575.35 $737.26 $845.20
$10,000 14.9% 17,754 $928.70 $309.57 $358.69 $411.89 $571.47 $731.06 $837.45
Freight vehicle surcharge per axle:One-time-incentive
% OHDOHD tours (year)
Total incentive
budget
Yearly incentive
budget
Yearly toll revenues (car surcharge = $1)
Note: in this case all combinations of financial incentives to receivers and tolls are feasible
Potential Uses
The BMS will replicate freight traffic in any metro area
The BMS could be used to:Produce realistic estimates of freight VMTAnalyze the impacts of alternative logistical
configurations (using a Urban Consolidation Center, transfers of cargo to environmentally friendly modes like freight bicycles)
Analyze the impacts of retiming of deliveries, or receiver-led consolidation programs by receivers
Analyze the impacts of policies that change operational patterns, technologies, or infrastructure used by carriers
Changes in work hours, limited emission zones, etc.
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Expected outputs of the BMS
Acceptance rate of technology/ operations/ infrastructure in response to policy measures
Freight (large and small trucks) VMT by industry segment for the initiatives considered, including time of day for some
Freight traffic by origin-destination before/after, a key input for traffic simulation models
Cost impacts on carriers and receivers
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Limitations
Estimation of air pollutionThe BMS is not a traffic simulator, it does not account
for traffic behavior in networksPotential solution:
Use the BMS output as an input to traffic simulators Purchase GPS data for key metro areas and post-process
it with MOVES to produce estimates, add the estimates to BMS
The BMS is very good for urban freight modeling, though it does not consider intercity freight (and things like truck stop electrification, etc.)Potential solution: create modules that perform
these computations, add to BMS
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Conclusions
The BMS is an important tool to evaluate TDM policies
The application to the Manhattan case study provides insight into the potential benefits, and limitations: Off-Hour DeliveriesGeographic oriented incentivesSelf Supported Freight Demand Management
Other extensions of the BMS include the analysis of incentives according to industry segments
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