1. 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

2. Outline Objectives Survey Design Data Methodology Model Results
Discussions

3. 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.

4. 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)

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

6. 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

7. 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, 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.

8. 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%
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 : 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

9. 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.

10. 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
(-3.76)
-0.039(-2.80)
Travel Cost
(-2.84)
(-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
(-2.60)
Initial Log likelihood
Final Log likelihood
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 Halton draws were applied for the model estimation.

11. 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)
(-0.33)
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)
(-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
Final Log likelihood
-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.

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

13. 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

14. 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

15. Thanks for your attention!
Questions?
Xia Jin
Phone: 305/
Survey Link