Willingness to Pay for Reliability in Road Freight Transportation: Evidence from a Stated Preference Survey in Florida Xia Jin, Kollol Shams Florida International University, Miami, FL Rickey Fitzgerald Florida Department of Transportation

Outline Objectives Survey Design Data Methodology Model Results Discussions

Objectives Conduct a stated preference survey to collect choice behavioral information from the users (shippers, carriers, and forwarders); and Estimate value of reliability (VOR) and value of time (VOT) with special attention on user heterogeneity.

Survey Design Four modes: road, rail, water, air Four experiment types to capture additional trade- offs: between modes (road vs. rail) and departure times (peak vs. off-peak)

Survey Design (cont’d) Three main sections: Base shipment information SP scenarios General questions (optional)

Survey Data Survey implementation Roadway dataset Meetings with various agencies and associations to gather feedbacks on the survey design January – May 2016 Through multiple channels Roadway dataset Raw data -150 participants Cleaned data – 97 participants, 387 observations

Survey Data 11 commodity types User types : Carrier, Shippers with and without transportation , 3PL Shipping distance : Less than 50 miles, 50 to 300 miles, & greater than 300 miles Shipment duration : 0-8 hrs, 8- 24 hrs, and 1-3+ days Truck size and trucking types Survey question was designed to collect responses from all types of the user & shipment types.

Shippers with Transportation Shippers w/o Transportation   Carrier Shippers with Transportation Shippers w/o Transportation 3PL Sample Size 108 7 28 Commodity Type Agriculture & Food Products 41.70% 78% 46% 43% Heavy Manufacturing 21.3% 0% 12% Petroleum Products 5.6% 4% Paper , Chemicals 7.4% 8% Construction Materials 6.5% 11% 15% Others 17.5% 11.0% 15.0% 14.0% Shipping Distance 0-50 miles 5% 29% 50-300 miles 19% 56% >300 miles 77% 33% 88% 71% Shipping Duration 0-8 hrs 22% 16% 14% 8-24 hrs 76% 34% 68% 1-3+ days 20% 44% 86% Sample 11 commodity types Nearly 50% Agriculture & Food Products Manufacturing equipment & Auto-parts Mostly longer distance (>300 miles) Shipment duration Carriers & Shippers without transportation : 8-24 hours 3PL : 1- 3+ days The statistics suggested that the sample is mostly made up of shipments of long distance and heavy volume. As such, a large share of the shipments were carried by heavy trucks and full truck load (FTL). Agricultural Minerals Lumber Paper, Chemicals Petroleum Products Warehousing Non-municipal Waste Construction Materials (Concrete, Glass, Clay, Stone) Others, Please Specify Food Products Nondurable Manufacturing

Shippers with Transportation Shippers w/o Transportation   Carrier Shippers with Transportation Shippers w/o Transportation 3PL Sample Size 108 7 28 Shipping Weight (ton) Min 0.5 3 0.25 5 Max 40 20 Mean 24.17 11.5 30.70 21.25 Shipping Cost <$600 33% 43% 100% 25% $600-$1800 42% 29% 0% $1800+ 50% Trucking Type Light 2% 11% Medium 16% 22% 20% Heavy 82% 67% 80% Truck Type Less Than a Truck Load 1% 40% Full Truck Load 71% Refrigerated Drayage Others Monetary Penalty for late delivery Yes 12% 57% No 89% 88% Sample Mostly Heavy truck & Full Truck Load Most of them had no provision of monetary penalty for late delivery In addition, most of the respondents stated that there was no provision of monetary penalty for late delivery, except for 3PLs. It should be noted that while the descriptive analysis provides the necessary background to understand the sample and therefore their choice behavior, this sample is not meant to be representative of the freight transportation industry in Florida or any region for that matter.

Modeling Results – Model Estimation (multiplicative WTP space) Coefficients MNL Model (additive) MNL Model (multiplicative WTP space) ML Model Constant Specific - Alt 2 -0.20(-1.43) 52.0 (0.86) -0.026(-0.57) Constant Specific - Alt 3 0.187(1.40) -54.3 (-0.88) 0.023(0.48) Travel Time -0.061(-4.33) - -0.026(-3.19) Travel time Reliability -0.0773(-3.76) -0.039(-2.80) Travel Cost -0.0013(-2.84) -0.0007(-4.55) Coeff_VOT 46.5 (4.64) Coeff_VOR 73.0 (4.07) scale 3.96 (5.58) STD. of Travel Time 0.0481(4.67) STD. of Travel Time Reliability -0.0467(-2.60) Initial Log likelihood -425.16 Final Log likelihood -397.60 -386.49 -331.10 Adjusted R-Square 0.05 0.08 0.25 Number of Observations 387 Number of Individuals 97 Value of Time ($/hr) 46.9 46.5 37.0 Value of Reliability ($/hr) 59.46 73.0 55.0 Model Structures MNL (Additive) : 𝑈= 𝛽 𝑐 𝐶+ 𝛽 𝑇 𝑇+ 𝛽 𝑅 𝜎 + 𝜀 MNL (Multiplicative) : 𝑈=𝜆∗ 𝑙𝑜𝑔 𝐶+𝑉𝑂𝑇∗𝑇+𝑉𝑂𝑅∗𝜎 +𝜖 Mixed Logit : 𝑈 𝑖𝑛 = 𝛽′ 𝑛 𝑋 𝑖𝑛 + 𝜀 While the MNL specification for both additive and log WTP multiplicative space models showed similar goodness-of-fit measures, the ML model showed better performance with higher R-square value. All the time and cost coefficients showed expected signs. To account for multiple observations from the same respondent, both MNL and ML models were estimated with individual-specific (panel specification) method in Biogeme . Travel time and travel time reliability were treated as random parameters with a normal distribution. 1000 Halton draws were applied for the model estimation.

Modeling Results – User Specific Shippers & 3PL are merged together to get statistically significant results Shippers without transportation were more likely to be sensitive toward travel time savings and less sensitive to travel time reliability, and vice versa. Coefficients Carriers Shippers and 3PL – with transportation Shippers and 3PL – w/o transportation Constant Specific - Alt 2 -0.42(-1.19) -0.18(-0.82) -0.42 (-1.12) Constant Specific - Alt 3 -0.61(-1.40) 0.02(0.09) -0.61 (-1.40) Main Variables Travel Time Mean -0.06(-2.46) -1.45(-3.28) -0.077 (-0.33) Travel Reliability Mean -0.25(-3.03) -0.20(-1.90) -1.60 (-4.15) Travel Cost -0.005(-5.4) -0.005(-2.47) -0.005 (-2.59) STD. of Travel Time -0.05(-1.96) -0.79(-2.86) -0.67 (-3.38) STD. of Travel Reliability -0.28(-3.18) 0.32(1.23) 0.46 (2.0) Interaction Variables ownership *Reliability 1.33(2.57) -1.34 (-3.28) ownership * Travel Time -1.25(-3.25) 1.25 (3.11) No of Observations 194 193 Initial Log likelihood -213.13 -212.03 Final Log likelihood -177.90 -89.36 -89.42 Adjusted R-square 0.13 0.54 Value of Time ($/hr.) 12.8 24.0 283.0 Value of Reliability ($/hr.) 51.0 290.0 70.0 Reliability Ratio (RR) 3.98 12.08 0.25 Shippers showed better model performance. This could be due to less variability among the shippers sample, particularly shippers without transportation. As seen in Table 2, most shipments for shippers without transportation were of long distances (>300 miles), used Full truck load (FTL), and costs less than 600 In terms of WTP, shippers showed higher VOT and VOR values than carriers. This finding is reasonable in the sense that carriers and transportation providers are typically responsible for the delay of shipment and bear financial liability (27), therefore exhibit higher VOR value than other groups. On the other hand, shippers without own are more interested in reducing the overall shipping time.

Model Results – Commodity Types Most of the groups suggest a statistically significant model results, except heavy manufacturing & auto parts group

Model Results – VOT and VOR Components VOT ($/hr) VOR ($/hr) User Specific All $37 $55 Transportation service Related $12 $29 Cargo/Goods Related $22 – $277 $75 - $177 Industry Specific Agriculture and Food $22 $74 Heavy Manufacturing $30 $25 Paper, Chemicals & Non-durable manufacturing $40 $17 Petroleum & Minerals $21 $24 Goods Specific Perishable $28 $79 Non-Perishable $23 $56

Discussions Average values for VOT and VOR are $37 and $55 per hour, respectively, and the RR ratio is about 1.5. These values are within the ranges indicated in the literature. Among the user groups, the VOT and VOR values ranged from $12.8 to $283, and $51 to $290, respectively. Carriers showed the lowest WTP; while shippers without transportation were more sensitive toward time savings and showed the highest VOT, while shippers with transportation showed the highest VOR. As expected, perishable products showed higher VOT and VOR values than non-perishable products. Similarly, agriculture and food products reflected the highest VOR value RR values among the commodity groups. Provided empirical evidence of freight road users’ WTP for the improvement in transportation related attributes in Florida User types, Commodity groups, Shipping Characteristics Identified potential sources for the large variation of road user’s willingness to pay Proposed framework for the valuation of travel time reliability Robust Model Estimation Recommended VOT and VOR values for the benefit cost analysis

Thanks for your attention! Questions? Xia Jin Email: xjin1@fiu.edu Phone: 305/348-2825 Survey Link http://lctr.org/freight