SHRP2_symposium2013_Aros-Vera

1 On the Evaluation of Incentive Structures to Implement Off-Hour Deliveries Felipe Aros-Vera Researcher arosvm@rpi.edu 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 jhv@rpi.edu Motivation Traffic Congestion Supply Perspective Transportation Demand Management Motivation TDM has primarily focused on passenger cars Regrettably: the role that TDM could play on freight has been overlooked 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: 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 Overall process of the BMS Define range of incentives to receivers for OHD   Carrier/receiver synthetic generation Randomly select industry segment o Generate/locate carrier o Generate/locate receivers to serve Receiver behavioral simulation Model receiver’s decision to accept OHD Ordinal logit model (Holguin-Veras et al 2013) a) Base case (no OHD)   Carrier behavioral simulation Compute costs for base case and mixed operation Model carrier’s decision Repeat for another carrier-receivers set Change incentives, reset participation counts End Legend: Carrier depot Regular-hour receiver Off-hour receiver Output: Truck Trips Market Share Receivers Market Share b) Mixed operation Ordered logit model with random effects This model reproduces receivers’ response to incentives Incentives NAICS code Interaction terms: OTI and NAICS Interaction terms: TV and NAICS Model Independent variables Constant Number of deliveries Incentives One time incentive in $1000 (OTI) Carrier discount in percent (CDR*100) Business Support (BS) Public Recognition (PR) Trusted Vendor (TV) NAICS Clothing stores, binary variable Performing arts, binary variable Interaction terms: OTI and NAICS OTI for food and beverage stores OTI for apparel manufacture stores OTI for clothing stores OTI for nondurable wholesalers Interaction terms: CDR and NAICS CDR for personal laundry Interaction terms: Trusted vendor and NAICS TV for food and beverage stores TV for performing arts TV for clothing stores TV for miscellaneous stores retailers Parameters µ(1) µ(2) µ(3) Sigma n Log likelihood Model Parameter t-stat 0.61 (2.78) -0.07 (-9.17) Model Parameter t-stat 0.22 (1.00) -0.08 (-11.66) 0.18 3.00 0.55 0.34 (6.95) (6.81) (3.82) (2.24) 0.17 3.10 0.51 0.38 0.94 (6.76) (7.12) (3.52) (2.48) (4.29) -2.73 -1.96 (-4.57) (-5.69) -2.46 -4.80 (-4.32) (-12.38) 0.12 0.23 0.24 0.33 (2.56) (1.72) (3.18) ( 6.83) 0.20 0.11 0.25 0.37 (4.24) (1.88) (3.40) (7.62) -2.11 (-2.98) -2.08 (-3.25) 4.35 4.65 5.06 6.59 (7.29) (2.56) (8.28) (13.63) 2.02 13.49 2.24 3.17 (3.17) (11.16) (4.06) (5.86) 1.88 4.56 7.63 4.58 ( 21.54) (34.64) (40.45) (27.64) 1.91 4.56 7.55 4.74 (21.36) (34.14) (40.51) (25.83) 1522 -1390.89 1522 -1388.50 BMS Application to New York City 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 Population Establishments Bronx 1,332,650 15,528 Brooklyn 2,465,326 Manhattan Queens FTA (trips/day) FTP (trips/day) FTG (trips/day) 224,179 26,320 26,838 53,157 7.45% 44,043 521,992 75,865 73,431 149,295 20.92% 1,537,195 102,597 2,062,079 182,427 161,144 343,571 48.14% 2,229,379 41,551 518,953 71,447 68,883 140,330 19.66% 443,728 8,376 100,975 14,464 12,910 27,374 3.84% 8,008,278 212,095 3,428,177 370,522 343,206 713,728 100.00% County Staten Island Grand Total Employment % Case study: New York City 10 3 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 Use of the NYMTC Best Practice Model  Origins (NJ)  Destinations (businesses) in Manhattan  Industry sector (NAICS) determines:  Number of stops  Location of businesses 11 BMS Considerations: trip generation models 12 BMS Results 13 Truck Trips MS Receivers MS 16.0% 8.0% 14.0% 7.0% 12.0% 6.0% 10.0% 5.0% 8.0% 4.0% 6.0% 3.0% 4.0% 2.0% 2.0% 1.0% 0.0% 0.0% OTI ($ thousand) 10 OTI ($ thousand) 10 BMS Results 14 OTI = $4,000 avg = 3.4% max = 7.6% = 1.3% OTI = $2,000 avg = 2.7% max = 7.6% = 1.2% OTI = $6,000 avg = 4.3% max = 9.9% = 1.9% OTI = $8,000 avg = 5.5% max = 11.9% = 2.6% OTI = $10,000 avg = 7.0% max = 13.4% = 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 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 17 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 Deliveries Geographic oriented incentives Self Supported Freight Demand Management 18 Thanks!