On the Evaluation of Incentive Structures to Implement

Off-Hour Deliveries

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Felipe Aros-VeraResearcher

arosvm@rpi.edu

Jose Holguin-Veras, Ph.D., P.E.William H. Hart Professor

VREF’s Center of Excellence for Sustainable Urban Freight SystemsCenter for Infrastructure, Transportation, and the Environment

Rensselaer Polytechnic Institutejhv@rpi.edu

Motivation 2

Traffic Congestion

Supply Perspective

Transportation Demand Management

MotivationTDM has primarily focused on passenger

cars

Regrettably: the role that TDM could play on freight has been overlooked

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Off-Hour DeliveriesAn important freight TDM measure involves the

use of public sector incentives to induce a change in delivery times from the regular to the off-hours (7PM to 6AM).

Complexity:

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Delivery time!!!

Behavioral Micro-Simulation (BMS)

Behavioral Micro-Simulation (BMS)Objective: simulate the carriers’ and receivers’

joint decision process to evaluate TDM policiesFeatures: deep behavioral foundation

embedded in the decision making process followed by carriers and receivers

Fundamental insight: in order for OHD to be implemented, both carriers and receivers have to be better off

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Overall process of the BMS 6

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: Truck Trips Market Share Receivers Market Share

Ordered logit model with random effectsThis 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

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Model 1 Model 2

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Incentives

Interaction terms:OTI and NAICS

NAICS code

Interaction terms:TV and NAICS

BMS Application to New York City

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Case study: New York CityThe island of Manhattan is the economic center

of a large metropolitan area of a total population of 20 million with NYC, and its eight million residents, as its center

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CountyPopulation Establish-

mentsEmploy-

mentFTA

(trips/day)FTP

(trips/day)FTG

(trips/day)%

Bronx 1,332,650 15,528 224,179 26,320 26,838 53,157 7.45%

Brooklyn 2,465,326 44,043 521,992 75,865 73,431 149,295 20.92%

Manhattan 1,537,195 102,597 2,062,079 182,427 161,144 343,571 48.14%

Queens 2,229,379 41,551 518,953 71,447 68,883 140,330 19.66%

Staten Island 443,728 8,376 100,975 14,464 12,910 27,374 3.84%

Grand Total 8,008,278 212,095 3,428,177 370,522 343,206 713,728 100.00%

Case study: New York City3 different incentives are evaluated

Business support (BS)Public recognition (PR)One time incentive (OTI)

Data: New York Metropolitan Transportation Council

(NYMTC) Best Practice Model (BPM): demand model for the NY metropolitan area

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Use of the NYMTC Best Practice Model 11

Origins (NJ) Destinations

(businesses) in Manhattan

Industry sector (NAICS) determines: Number of

stops Location of

businesses

BMS Considerations: trip generation models

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BMS Results 13

0 1 2 3 4 5 6 7 8 9 100.0%2.0%4.0%6.0%8.0%

10.0%12.0%14.0%16.0%

Truck Trips MS

OTI ($ thousand)

0 1 2 3 4 5 6 7 8 9 100.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

Receivers MS

OTI ($ thousand)

BMS Results 14

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 = $10,000avg = 7.0%

max = 13.4%min = 3.5%

Results: incentives and impact on OHDOTI of $1,000 + BS + PR would move more

than 2,300 deliveries to the night hours; this corresponds to a reduction of 2% of deliveries. Budget of $2.4 millions

If the incentive reaches $10,000, more than 8,000 deliveries could be moved to the night. Budget of $70 million

Each delivery is estimated to take between 45 and 90 minutes in the regular hours (pilot tests show delivery times of less than 30 min during OHD)

Results: geographic oriented incentivesOne of the most remarkable results comes from

geographic oriented incentivesThe most congested parts of the city; lower and

midtown Manhattan, has the largest economic and social benefits

OTI of $10,000, requiring $36 million, could move around 4,100 deliveries, similar numbers than giving incentives to the entire city with the exception that these deliveries are made in the most congested part of the city

ConclusionsThe BMS is an important tool for evaluating

TDM policies; in this case the set of incentives to foster Off Hour Deliveries

The application to the Manhattan case study provides significant insight into the potential benefits, and limitations: Off-Hour DeliveriesGeographic oriented incentivesSelf Supported Freight Demand Management

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Thanks!

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