on the evaluation of incentive structures to implement off-hour deliveries 1 felipe aros-vera...
TRANSCRIPT
On the Evaluation of Incentive Structures to Implement
Off-Hour Deliveries
On the Evaluation of Incentive Structures to Implement
Off-Hour Deliveries
1
Felipe Aros-VeraResearcher
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
Motivation2
Traffic Congestion
Supply Perspective
Transportation Demand Management
Motivation
TDM has primarily focused on passengercars
Regrettably: the role that TDM could play on freight has been overlooked
3
Off-Hour Deliveries
An 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:
4
Delivery time!!!
Behavioral Micro-Simulation (BMS)
Behavioral Micro-Simulation (BMS)
Objective: simulate the carriers’ and receivers’ joint decision process to evaluate TDM policies
Features: 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
5
Overall process of the BMS6
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 effects
This model reproduces receivers’ response to incentives
7
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 Application to New York City
8
Case study: New York City
The 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
9
CountyPopulation
Establish-ments
Employ-ment
FTA (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 City
3 different incentives are evaluatedBusiness 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
10
Use of the NYMTC Best Practice Model11
Origins (NJ) Destinations
(businesses) in Manhattan
Industry sector (NAICS) determines: Number of
stops Location of
businesses
BMS Considerations: trip generation models
12
BMS Results13
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 Results14
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 OHD
OTI 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 incentives
One of the most remarkable results comes from geographic oriented incentives
The 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
Conclusions
The 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
17
Thanks!
18