Truck Tolling: Understanding Industry Tradeoffs When Using or Avoiding Toll Facilities (2012)

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

23 | P a g e Chapter 3 Findings and Applications Web-Based Survey Analysis The survey described in Chapter 2 featured three sections: a demographic and operating characteristics section, a stated pricing section, and an attitudinal section. A total of 965 web-based surveys were completed by a wide range of industry participants (with 661 surveys considered valid and included in this analysis). See Appendix B for the survey instrument and distribution. The initial demographics and operating characteristics section of the survey was designed both to capture baseline data for benchmarking and to serve as a tool for routing respondent to appropriate “revealed preference” items in the second section of the survey. The third, attitudinal section of the survey was designed to capture non-econometric influences on respondent behavior with regard to toll usage. Figure 1 and Figure 2 below show the respondents’ position within the trucking transaction, and the type of trucking operation (respectively). Figure 1: Position in the Trucking Transaction Drivers, 70% Dispatchers, 13% Owners , 10% Operations, 4% Trucking Executives, 2% Shippers, 1% Respondent Employment (N=965)

24 | P a g e Figure 2: Type of Trucking Operation While the revealed preference section of the survey used a standard stepwise model for establishing pricing levels for each respondent, the way scenarios were described was somewhat unique for this type of study. Typically, revealed preference transportation surveys will attempt to price an existing or proposed roadway: a proposed feeder highway accessing a port, for example. Since this project was directed at a national audience and national, regional, and local trucking firms, the project team designed “typical” tolling scenarios and focused pricing on the stated benefits of a roadway. Hence, respondents were given hypothetical scenarios that would typically save a given number of minutes and/or miles of travel. This allowed the respondent to focus on the proposed benefits and place the typical scenario into a “mental model” of a similar road they have used in the past. Online surveys asked respondents about their willingness to pay tolls on three toll road scenarios: Turnpike, Bypass, and Bridge. The turnpike scenario described a long-distance tolled highway, similar to existing facilities such as the Pennsylvania Turnpike, Massachusetts Turnpike, and the New Jersey Owner-Operator (37%) For-Hire Truckload Carrier (24%) Specialized (17%) Private Truckload (9%) LTL (4%) Dray (2%) Local Deliv. (2%) Other (5%) Respondent Operations (N=965)

25 | P a g e Turnpike. The “bypass” scenario described a tolled highway designed to allow quick traversal across an often congested urban area, similar to existing facilities such as the Texas highway 45 and I-66 in the Washington, DC region. The bridge scenario described a toll bridge designed to traverse a physical obstacle such as a body of water. Respondents were given an estimated time savings for each scenario and then asked if they would pay the toll if it was set at a given amount. The amount the toll was set at ranged from $0.50 to $32.00 depending on the scenario. For each scenario, a “hedonistic price” was developed based on respondents’ stated willingness to pay (The term “hedonistic price” describes two characteristics—intrinsic value and external factors—which together determine the price that consumers are willing to pay.) Beyond the hedonistic price analysis, eight factors were evaluated based on their impact on the willingness to pay for tolled road facilities: • Employment Position: Driver vs. non-driver (dispatcher, operations staff, ownership, etc.) • Industry Segment: Truckload vs. Non-truckload • Industry Tenure: More than 10 years vs. 10 years or less • Annual Miles Driven: Less than 100,000 vs. 100,000 or more • Typical Haul Mileage: Less than 500 miles vs. 500 miles or more • Typical Driving Environment: Urban vs. Rural • Opportunity to Access Tolls: 10% or less of current miles could be on tolled roads vs. more than 10% of current miles could be on toll roads • Owner-Operator Identification: Member of OOIDA vs. Non-OOIDA member Survey Results from Web Based Survey The data were analyzed to determine if the correlation between willingness to pay and each independent variable was most likely random chance or a statistically independent indicator. In the tables below, a ‘Sig’ of 0.10 or less indicates that there is a less than 10 percent chance that the correlation is due to random chance and therefore is considered statistically significant. These statistically significant factors are highlighted in bold text. All other factors are considered inconclusive whether or not the variable affects willingness to pay tolls. The beta coefficient or ‘B’ column indicates the magnitude of effect that variable has on the willingness to pay tolls. A larger beta indicates a larger effect on the willingness to pay tolls. A negative beta indicates a negative correlation. Due to the relatively small number of cases collected with complete pricing information, analysis was conducted using a multinomial logistic regression model rather than the preferred linear regression. In designing the model, all dependent pricing variables were coded into three categories: no willingness to pay ($0.00 price), marginal willingness to pay ($0.50 price), and real willing to pay (price greater than $0.50). Those with a marginal willingness to pay are represented in the ’token’ column, while those with a true willingness to pay are represented in the ‘real’ column. Turnpike Scenario The first scenario was nicknamed the “turnpike” scenario because it described a long-distance tolled highway, similar to existing facilities such as the Pennsylvania Turnpike, Massachusetts Turnpike, and the New Jersey Turnpike. Typically these pieces of infrastructure are used to travel long distances

26 | P a g e through areas with lower speed alternative roadways induced by traffic congestion (New Jersey) or a lack of high-speed alternative roadways (Pennsylvania). The scenario was described as follows to respondents: While delivering an interstate load, you need to travel across a long distance in an area where the only viable route options are tolled interstate freeways and non-tolled secondary roads. Examples of these areas include parts of New Jersey, Pennsylvania, New York, and Oklahoma. Using the tolled freeway to travel 100 miles consistently reduces travel time by 30 minutes compared to traveling on secondary roads. If the toll for the route was $10 per 100 miles, would you consistently use the tolled route? A simple graph (Figure 3) of the price distribution for the turnpike scenario shows that responses were skewed toward paying no toll or only nominal tolls for this scenario. Figure 3: Hedonistic Price for Turnpike Scenario 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% $0.00 $0.50 $5.00 $10.00 $15.00 $20.00 P er ce nt o f A ff ir m at iv e Re sp on se s by T ol l L ev el Toll Level Hedonistic Price for Turnpike Drivers Dispatchers

27 | P a g e Statistically analyzing the eight independent variables, only the “Opportunity to Access Tolls” had a significant effect within the model: Table 2: Turnpike Scenario Statistical Analysis Turnpike Scenario Willingness to Pay 'Token' 'Real' Factor B Sig B Sig Employment Position: Driver 0.721 0.404 0.255 0.702 Industry Segment: Truckload 0.388 0.526 -0.147 0.782 Industry Tenure: More than 10 years -0.369 0.472 -0.105 0.795 Annual Miles Driven: Less than 100,000 or more 0.359 0.458 -0.62 0.124 Typical Haul Mileage: 500 miles or more -0.259 0.684 -0.015 0.977 Typical Driving Environment: Urban 0.231 0.752 -0.018 0.974 Opportunity to Access Tolls: More than 10% of current miles could be on toll roads 0.968 0.023 -0.326 0.355 Owner-Operator Identification: Member of OOIDA -0.526 0.249 0.316 0.385 It appears that for turnpike styled tolled infrastructure, there may be a small effect of having experience in tolled environments (greater than 10% of miles potentially drivable on toll roads) and willingness to pay nominal fees ($0.50) rather than being a principled objector. This effect does not extend to willingness to move out of the token bracket to higher toll rates. This provides some evidence that regular experience with toll roads reduces the likelihood of principled objection to tolling.

28 | P a g e Bypass Scenario The next scenario was nicknamed the “bypass” scenario because it described a toll highway designed to allow quick travel through an often congested urban area. The scenario was described as follows to respondents: Midway through your hours-of-service driving shift you approach the outskirts of a large city. You must pass travel through the city to reach your ultimate delivery point later in the day. You have the option of using an existing interstate highway to travel through the city or a newly constructed tolled highway. Using the tolled highway to travel through the city consistently reduces travel time by 15 minutes during normal traffic conditions. If the toll for the route was $10, would you consistently use the tolled route? The hedonistic price to take a bypass (Figure 4) was even more heavily skewed than the “turnpike” scenario, with “$0.00” or “$0.50” making up more than 40 percent of the responses. Figure 4: Hedonistic Price for a Bypass Scenario 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% $0.00 $0.50 $5.00 $10.00 $15.00 $20.00 Pe rc en t o f A ff ir m at iv e Re sp on se s by T ol l L ev el Toll Level Hedonistic Price for Bypass Drivers Dispatchers

29 | P a g e Among the eight factors analyzed, only “Typical Haul Mileage” had a statistically significant effect within the model: Table 3: Bypass Scenario Statistical Analysis Bypass Scenario Willingness to Pay 'Token' 'Real' Factor B Sig B Sig Employment Position: Driver -0.824 0.233 0.902 0.184 Industry Segment: Truckload 0.638 0.184 -0.639 0.199 Industry Tenure: More than 10 years -0.485 0.22 -0.15 0.706 Annual Miles Driven: Less than 100,000 or more 0.018 0.961 -0.189 0.623 Typical Haul Mileage: 500 miles or more 0.369 0.478 -0.976 0.045 Typical Driving Environment: Urban 0.255 0.478 0.207 0.695 Opportunity to Access Tolls: More than 10% of current miles could be on toll roads 0.404 0.226 -0.051 0.886 Owner-Operator Identification: Member of OOIDA -0.155 0.667 -0.106 0.774 It appears that for the bypass scenario, there may be a small effect of having longer typical hauls (500 miles or longer) and unwillingness to pay toll fees (greater than $0.50). This effect does not extend to principled objection. Longer hauls may find these bypass routes less valuable, as small variations in travel time due to congestion can be smoothed out over longer trips. Bridge Scenario The next scenario was nicknamed the “bridge” scenario because it described toll bridge across a physical obstacle such as a body of water. The scenario was described as follows to respondents: Your delivery requires you to cross a large body of water via one of two bridges. The first bridge is most direct for your route, but requires a $16 toll. The second bridge requires you to extend your route by 10 miles and 20 minutes. Would you spend $16 to take the tolled bridge? Among drivers, the willingness to pay tolls for this scenario was similarly skewed toward low payment, though a fair percentage of truck drivers (29 percent) were willing to pay $8 or more for the toll alternative. Interestingly, almost 50 percent of dispatchers were willing to route

30 | P a g e trucks to the toll bridge in this scenario, which might reflect management placing a greater value on time than drivers (see Figure 5). Figure 5: Hedonistic Price for the Bridge Scenario 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% $0.00 $0.50 $8.00 $16.00 $24.00 $32.00 Pe rc en t o f A ff ir m at iv e Re sp on se s by T ol l L ev el Toll Level Hedonistic Price for Bridge Drivers Dispatchers

31 | P a g e Turning again to statistical analysis, among the eight factors, both “Typical Driving Environment” and “Owner Operator Status” had a statistically significant effect within the model. Table 4: Statistical Analysis of Bridge Scenario Bridge Scenario Willingness to Pay 'Token' 'Real' Factor B Sig B Sig Employment Position: Driver -0.648 0.99 1.672 0.171 Industry Segment: Truckload 14.801 0.991 0 NA Industry Tenure: More than 10 years 0.344 0.622 -0.627 0.293 Annual Miles Driven: Less than 100,000 or more 0.073 0.903 0.836 0.11 Typical Haul Mileage: 500 miles or more 0 NA 0 NA Typical Driving Environment: Urban -0.328 0.691 1.179 0.044 Opportunity to Access Tolls: More than 10% of current miles could be on toll roads -0.164 0.785 -0.795 0.108 Owner-Operator Identification: Member of OOIDA 1.249 0.059 -0.482 0.383 It appears that for a toll bridge scenario, principled objection is driven by owner-operator status (OOIDA membership) and willingness to pay higher toll fees (greater than $0.50) is influenced by frequency of driving in urban environments. For some owner-operators who disagree with tolling, bridge infrastructure may be the only tolled infrastructure they are forced to pay when accessing certain urban areas, making their objection more strongly stated here, whereas urban drivers may be exhibiting a bit of the “closeness makes the heart grow fonder” phenomenon seen in the turnpike scenario with long- haul drivers.

32 | P a g e Trucking Industry Attitudes about Tolling Within the opinion section of the online survey, respondents were asked whether they agreed or disagreed with a number of statements about tolls. The simple frequency of responses was revealing as to the respondents’ “depth of passion” about different questions related to toll facilities. Figure 6 below shows a relatively normal distribution of options about a number of toll-related decision issues. Figure 6: Distribution of Responses for Certain Tolling Questions As opposed to the normal distribution curves shown in the previous figure, there were three questions related to the public sector motivation to develop toll roads, each of which elicited strong, negative attitudes about toll roads. 0 50 100 150 200 250 300 350 400 450 500 Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree Toll routes are a good strategy to save time. I only use toll roads if I am behind schedule. Driving is less stressful on tolled routes. Toll roads generally have less traffic than non-tolled alternatives. Toll roads can help drivers comply with hours of service (HOS) regulations. Having drivers use toll roads improves my company's on-time performance. Generally, trucks are less likely to be involved in an accident on a tolled road.

33 | P a g e Figure 7: Distribution of Responses Regarding Toll Facilities and Government Finance 0 100 200 300 400 500 600 700 800 900 1000 Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree Toll roads mainly exist for raising money for the government. There is a push to make all roadways tolled. Toll roads are a more fair way of funding road construction and maintenance than fuel taxes. REVERSE CODED

34 | P a g e For further analysis, the attitudinal responses to the survey were assigned a numeric value: • Strongly agree: 5 • Agree: 4 • Neither agree nor disagree: 3 • Disagree: 2 • Strongly Disagree: 1 The following table (Table 5) shows the average responses for each statement for various groups within the data set. A higher score indicates a higher general consensus with that statement whereas a lower score indicates a general disagreement with that statement.

35 | P a g e To ll ro ut es a re a g oo d st ra te gy to s av e tim e To ll ro ad s ar e to o ex pe ns iv e To ll ro ad s ex is t m ai nl y fo r r ai si ng m on ey fo r t he g ov er nm en t To ll ro ad s ar e to o ex pe ns iv e fo r w ha t t he y pr ov id e I a vo id to ll ro ad s w he ne ve r I c an I o nl y us e to ll ro ad s if I c an g et re im bu rs ed I o nl y us e to ll ro ad s if I a m b eh in d sc he du le Tr af fic is w or se o n se co nd ar y ro ad s on ce to ll ro ad s ar e op en ed Th e av ai la bi lit y of p re fe rre d fa ci lit ie s af fe ct s m y de ci si on to ta ke a to lle d ro ut e D riv in g is le ss s tre ss fu l o n to lle d ro ut es To ll ro ad s ge ne ra lly h av e le ss tr af fic th an n on -to lle d al te rn at iv es I a m w ill in g to d riv e fa r o ut si de o f m y w ay to a vo id a to ll To ll ro ad s ca n he lp d riv er s co m pl y w ith h ou rs o f s er vi ce re gu la tio ns H av in g dr iv er s us e to ll ro ad s im pr ov es m y co m pa ny 's o n- tim e pe rfo rm an ce R ei m bu rs in g to lls is a n ad m in is tra tiv e bu rd en Th er e is a p us h to m ak e al l r oa dw ay s to lle d To ll ro ad s ar e a m or e fa ir w ay o f f un di ng ro ad c on st ru ct io n an d m ai nt en an ce th an fu el ta xe s G en er al ly , t ru ck s ar e le ss li ke ly to b e in vo lv ed in a n ac ci de nt o n a to lle d ro ad Tr uc ks fa ce h ei gh te d en fo rc em en t o f r eg ul at io ns o n to ll ro ad s Th e ab ilit y to p ay to lls e le ct ro ni ca lly u si ng a tr an sp on de r m ak es m e m or e lik el y to u se a to ll r oa d If I t ak e a to ll ro ad I ca n' t a cc es s m y pr ef er re d se rv ic e pr ov id er s E le ct ro ni c to lli ng s im pl ifi es re co rd k ee pi ng Which best describes your job in the trucking industry? Driver - All 2.9 4.7 4.5 4.6 4.4 3.4 2.9 3.7 3.1 2.8 2.9 3.4 2.6 2.6 na na na 2.4 3.2 3.1 3.6 3.3 Driver - Specialized 2.9 4.8 4.5 4.6 4.4 3.4 2.9 3.8 3.1 2.8 3.0 3.4 2.6 2.6 na na na 2.4 3.2 3.1 3.7 3.3 Driver - Less-Than-Truckload 3.1 4.6 4.6 4.8 4.1 4.0 2.9 3.8 3.7 3.0 2.9 3.0 2.8 3.0 na na na 2.4 3.3 3.5 3.1 3.4 Driver - Private Fleet Truckload 3.0 4.7 4.5 4.6 4.2 3.8 2.4 3.9 2.9 2.6 3.0 3.2 2.9 3.0 na na na 2.6 3.1 3.4 3.6 3.5 Driver - For-Hire Carrier/Contract 2.8 4.7 4.6 4.7 4.3 3.7 2.9 3.6 3.3 3.0 3.1 3.3 2.7 2.7 na na na 2.6 3.4 3.4 3.6 3.5 Driver - For-Hire Owner Operator 2.9 4.8 4.4 4.6 4.5 3.1 3.0 3.7 2.8 2.6 2.7 3.5 2.5 2.5 na na na 2.3 3.3 2.6 3.5 3.0 Driver - Other (incl. Drayage & Local) 3.5 4.7 4.6 4.6 4.6 3.1 2.8 3.9 2.9 3.2 3.3 3.4 2.9 2.9 na na na 2.8 2.9 3.4 3.7 3.6 Non-Driver - All 2.7 4.5 4.1 4.3 na na na na na na 3.1 na 2.7 2.6 3.8 3.5 2.0 2.5 3.1 3.0 3.7 3.3 Dispatcher / Fleet Manager 2.8 4.5 4.0 4.2 na na na na na na 3.2 na 2.9 2.7 3.9 3.4 2.1 2.7 3.2 3.1 3.5 3.4 Shipper / Receiver / 3PL 2.0 4.0 3.9 3.3 na na na na na na 2.1 na 2.0 2.1 3.3 3.1 2.1 2.4 3.1 2.4 3.9 2.7 Trucking Executive 2.5 4.6 3.9 4.4 na na na na na na 3.3 na 2.4 2.8 3.8 3.8 2.2 2.7 3.2 3.4 3.8 3.5 Fleet Owner 2.7 4.7 4.3 4.5 na na na na na na 3.0 na 2.6 2.4 3.6 3.8 1.9 2.3 3.0 2.8 3.8 3.1 Other Trucking Professional 3.0 4.5 4.1 4.3 na na na na na na 3.2 na 3.0 2.9 4.0 2.9 2.2 2.5 3.5 3.4 3.8 3.7 Real Willingness to Pay 3.3 4.6 4.2 4.3 4.0 3.5 3.0 3.6 3.2 3.1 3.1 2.8 3.1 3.0 3.6 3.3 2.5 2.7 3.2 3.6 3.5 3.7 Nominal Willingness to Pay 2.6 4.8 4.4 4.7 4.7 3.3 3.0 3.9 3.2 2.6 3.0 3.8 2.4 2.4 3.9 3.9 1.8 2.3 3.2 2.5 3.8 3.0 No Willingness to Pay 2.0 4.8 4.6 4.7 4.7 3.3 2.4 3.9 2.6 2.3 2.7 4.1 2.0 2.0 4.1 3.8 1.3 1.9 3.3 2.1 3.7 2.6 250 miles or less 3.1 4.8 4.6 4.5 4.3 4.0 3.1 3.8 3.3 2.8 2.7 3.3 2.8 3.0 na na na 2.7 3.1 3.6 3.5 3.7 251 - 500 miles 2.8 4.7 4.5 4.5 4.3 3.4 2.9 3.7 3.1 2.8 2.9 3.3 2.6 2.6 na na na 2.4 3.2 3.1 3.5 3.2 501 - 1,000 miles 2.9 4.7 4.5 4.6 4.3 3.4 2.9 3.8 3.2 3.0 3.0 3.2 2.8 2.7 na na na 2.5 3.4 3.1 3.6 3.4 1,001 - 1,500 miles 2.9 4.8 4.5 4.7 4.5 3.4 2.8 3.6 2.6 2.5 2.7 3.6 2.3 2.6 na na na 2.5 3.4 3.0 3.4 3.1 1,501 - 2,000 miles 2.8 4.9 4.5 4.9 4.5 3.6 3.3 3.9 3.1 2.4 2.7 3.4 2.4 2.7 na na na 2.3 3.6 2.9 3.6 3.0 more than 2,000 miles 3.1 4.8 4.7 4.7 4.7 3.0 2.7 4.1 3.1 2.6 3.0 3.7 2.4 2.2 na na na 2.3 2.8 2.4 4.0 2.8 5 years or less 2.9 4.7 4.5 4.5 4.2 3.2 2.4 3.6 3.1 2.6 2.9 3.3 2.8 2.7 na na na 2.2 3.2 3.0 3.7 3.2 6 - 10 years 3.1 4.8 4.5 4.6 4.4 3.8 3.0 3.6 3.4 2.9 2.9 2.9 2.6 2.6 na na na 2.6 3.2 3.3 3.6 3.4 11 - 20 years 3.0 4.7 4.5 4.6 4.3 3.3 2.9 3.8 2.8 2.8 3.0 3.5 2.5 2.6 na na na 2.4 3.3 3.0 3.4 3.3 21 - 30 years 2.8 4.8 4.5 4.6 4.3 3.4 3.0 3.9 2.9 2.8 3.0 3.4 2.6 2.6 na na na 2.4 3.3 3.1 3.5 3.2 more than 30 years 2.9 4.8 4.6 4.8 4.6 3.4 3.0 3.7 3.2 2.8 2.7 3.5 2.7 2.7 na na na 2.4 3.2 2.9 3.7 3.2 5% or less of the time 2.7 4.7 4.4 4.5 4.6 3.4 2.8 3.7 3.0 2.6 2.9 3.5 2.3 2.5 na na na 2.4 3.3 2.9 3.5 3.1 6% - 10% of the time 3.2 4.7 4.5 4.6 4.1 3.4 2.9 3.9 3.0 3.0 2.9 3.1 2.8 2.8 na na na 2.5 3.1 3.1 3.6 3.5 11% - 25% of the time 3.0 4.8 4.6 4.8 4.3 3.3 2.9 3.8 3.1 2.8 2.9 3.3 2.7 2.6 na na na 2.4 3.2 3.1 3.7 3.4 26% - 50% of the time 2.7 4.8 4.6 4.7 4.4 3.2 3.0 3.5 3.1 2.7 2.6 3.5 2.5 2.6 na na na 2.3 3.3 3.0 3.5 3.1 more than 50% of the time 3.2 4.9 4.6 4.8 4.5 3.6 3.0 3.7 3.1 3.1 3.1 3.5 2.8 2.9 na na na 2.7 3.3 3.1 3.5 3.3 Almost entirely rural 3.0 5.0 4.9 4.9 4.9 3.8 3.4 3.9 2.7 2.8 2.9 3.6 2.9 2.6 na na na 2.9 3.8 2.9 3.6 2.9 Mostly rural with some urban 2.9 4.8 4.5 4.6 4.5 3.3 3.0 3.8 3.0 2.8 2.9 3.4 2.6 2.5 na na na 2.6 3.0 2.9 3.5 3.2 Equal amounts rural and urban 2.9 4.8 4.6 4.7 4.3 3.4 2.9 3.7 3.1 2.8 2.8 3.4 2.5 2.6 na na na 2.3 3.3 3.1 3.6 3.3 Mostly urban with some rural 3.0 4.7 4.3 4.5 4.2 3.3 2.6 3.4 3.2 3.0 3.2 3.3 2.6 2.8 na na na 2.5 3.6 3.2 3.7 3.3 Almost entirely urban 3.2 4.8 4.4 4.6 4.3 4.1 2.5 3.8 3.7 3.1 3.3 3.1 2.8 3.3 na na na 2.9 3.3 3.3 3.3 3.5 Shading Key 5 - Strongly Agree 4 - Agree 3 - Neither Agree or Disagree 2- Disagree 1 - Strongly Disagree What percentage of the time could be spent on toll roads? (Driver Only) What sort of environment do you typically drive in? (Driver Only) What is the length of your typical haul? (Drivers Only) How many years have you worked as a commercial driver? (Drivers Only) What is your willingness to pay tolls? Table 5: Truck Industry Opinions Concerning Toll Roads in the U.S.

36 | P a g e General Findings An assessment of the data found that actors in the trucking transaction generally strongly agreed with the following statements: • Toll roads are too expensive • Toll roads exist mainly for raising money for the government • Toll roads are too expensive for what they provide • I avoid toll roads whenever I can Members of the actors in the trucking transaction generally agreed with the following statements: • Traffic is worse on secondary roads once toll roads are opened • If I take a toll road I can’t access my preferred service providers Members of the actors in the trucking transaction generally disagreed with the following statements: • Toll roads can help drivers comply with hours of service regulations • Having drivers use toll roads improves my company’s on-time performance • Generally, trucks are less likely to be involved in an accident on a toll road • Toll roads are a more fair way of funding road construction and maintenance than fuel taxes Members of the actors in the trucking transaction generally neither agreed nor disagreed with the statement ‘Toll routes are a good strategy to save time.’ There were no significant differences in attitudes between drivers and non-drivers of the questions asked. Shorter haul, urban drivers felt that there were benefits from using a transponder for tolling whereas longer haul, rural drivers generally saw less benefit from using a transponder. Evidence of this attitude was shown through drivers responses to the following two statements, which appeared to be affected by the typical haul length: ‘The ability to pay tolls electronically using a transponder makes me more likely to use a toll road’ and ‘Electronic tolling simplifies record keeping.’ • Drivers whose typical haul was short tended to agree with these statements whereas drivers whose typical haul was long tended to disagree with these statements. • Drivers who drove more in urban environments than rural tended to agree with these statements whereas drivers who drove more in rural environments than urban tended to disagree with these statements. It is possible that a longer length of haul impacts the number of different toll roads a driver may encounter, and therefore the number of different toll systems which require a different transponder and account. A shorter average length of haul—for example urban driving environments—might only encounter one toll facility and therefore only need to maintain one toll transponder and account.

37 | P a g e Rural drivers generally agree with the statement ‘I only use toll roads if I am behind schedule,’ whereas urban drivers tended to disagree with that statement. This may indicate that urban drivers have other factors that affect the decision of whether or not to take a toll road—such as delivery points and routing--whereas rural drivers do not see any benefits from toll roads other than time savings. Urban drivers generally agree with the statement ‘The availability of preferred facilities affects my decision to take a tolled route’ whereas rural drivers tended to disagree with that statement. This may reflect the same general conclusion as shown in the paragraph above: urban drivers may have more factors to consider than time savings; whereas time savings may be the main determining factor for rural drivers. Specific Findings While the average response offers an insight in to the general opinions of the industry, some statements warranted further analysis to understand the data beyond what a simple average revealed. Question 12: I am willing to drive far outside of my way to avoid tolls When analyzing responses to the above statement, it was found that responses varied by driver type and years of experience. Over half of the owner/operator drivers either ‘agreed’ or ‘strongly agreed’ with the above statement whereas carrier/contract drivers showed a relatively normal distribution centered on ‘neither agree nor disagree’ with the same statement.

38 | P a g e Figure 8: Affect of Driver Experience on Toll Avoidance Analyzing the responses of the owner/operator drivers further revealed that opinions concerning the above statement differed even more when years of experience was taken in to consideration. Owner/operators with 10 years or less of experience typically ‘disagreed’ with the statement whereas owner/operators with more than 10 years of experience generally ‘agreed’ or ‘strongly agreed’ with the statement. It appears that long-time owner/operators are much more likely to add miles to their route in order to avoid a toll than their less experienced counterparts who seem to generally accept tolls. Question 15: Reimbursing tolls is an administrative burden The above statement was only asked of non-drivers within the trucking business. Responses from for- hire owner/operator companies differed greatly from for-hire carrier/contract operations. While only approximately 12% of each group disagreed or strongly disagreed with the statement, 42% of carrier/contract operators strongly agreed with the statement whereas only 23% of owner/operators strongly agreed. This may indicate that carrier/contract operators feel a heavier administrative burden due to operating more trucks on more varied routes and therefore encountering different toll systems than owner/operators feel affect their business. 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree I am willing to drive far outside of my way to avoid tolls (For-Hire Owner/Operators Only) 10 years or less More than 10 years

39 | P a g e Figure 9: Opinion on Administrative Burden of Tolls Personal Surveys at Trucking Industry Trade Shows Data for this analysis were obtained through independent surveys conducted at trucking industry trade shows in Charlotte, North Carolina and Dallas, Texas (see Appendices “C” and “D” for the survey instruments). Through these surveys, truckers were polled on various characteristics of their company’s operations, and most importantly, whether or not they choose to use an un-tolled route in lieu of a toll road. To interpret these results, statistical analysis was used to answer four main questions: 1) Does the type of firm (independent owner-operator, company driver, other) influence toll avoidance? 2) Does the company’s type of trucking operation (Less than Truckload (LTL), full truckload, et al) influence toll avoidance? 3) Does toll reimbursement or ability to pass tolls on to customers influence toll avoidance? 4) Does the party responsible for trip routing (owner, dispatcher, or driver) influence toll avoidance? The Charlotte and Dallas surveys each included a slightly different set of questions. After the survey was tested at the Dallas truck show, updates were made to improve the effectiveness of the survey 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree Reimbursing tolls is an administrative burden For-Hire Carrier/ Contract For-Hire Owner/ Operator

40 | P a g e instrument itself. Therefore it was not statistically valid to comingle the results. An independent analysis was conducted for each set of responses and results of each analysis are reported separately. Methodology The results presented in this section are based on binomial logit regression analysis conducted using EVIEWS software—EVIEWS is a PC-based statistical modeling tool specifically designed to conduct regression analysis of various types, including binomial logit regression modeling. All of the results in this analysis are interpretations of the regression outputs at a 95-percent confidence level, meaning there is a 95-percent certainty of the results being correct. Logistic regression is a statistical method commonly used to predict the likelihood of an event occurring based on a number of given variables. In this case, the event occurring is the avoidance of a toll road, and the variables include ownership, operation, and reimbursement policies. “Binomial” refers to the fact that there are only two possible outcomes that the regression can predict; in this case, whether a driver will avoid a toll road or not avoid a toll. The results of a logit model allow an analyst to determine which variables, if any, increased the probability of a decision being made and by how much. In short, logit regression modeling identifies which factors best explain the likelihood of toll avoidance, and the magnitude of each factor’s influence. Each question examined was conducted as a separate model, holding all other factors constant unless otherwise stated (to “hold all else constant” is to ignore all other known and unknown variables which may influence the relationship between the variable in question (e.g. type of firm) and decision to take a toll road). This means that one cannot compare the coefficient magnitudes of one model against the magnitudes of another; they must be taken in isolation with their individual model. In the results below, which show probability of an event presented as magnitude of influence, it is important to note how to interpret the figures; interpreting a finding as being “8.3 times more likely” can be explained in two steps: 1. Of the options respondents were given (e.g., independent owner-operator, company driver, other), this statistic compares those who answered “independent owner-operators” to those who answered the other two options combined. 2. If an independent owner-operator is 8.3 times more likely than all other respondents to avoid a toll road, he will do so 8.3 times for every one time all other respondents (company drivers and “other”) do collectively. Although the magnitudes of the probabilities can be calculated from the logit regression coefficients, they must be observed with caution. These impacts can change in any given model depending on how many, and which types of other variables are included. A better way of looking at these magnitudes is to assess the relative differences among the variables, which allows one to see which variable had the strongest influence over the probability of toll avoidance strictly in comparison only to the other variables examined.

41 | P a g e Results from each survey are presented in the following sections first with a summary, followed by detailed answers to the questions outlined earlier in this section. Charlotte Analysts working on this research project attended the Charlotte Diesel Super Show, October 8 and 9, 2010, at the Z-Max Dragway in Concord, North Carolina. The analysts staffed a booth situated with other vendors, and intercepted attendees for their voluntary responses to the toll road survey. Does the type of firm influence the decision to take a toll road? At the Charlotte truck show, respondents could classify themselves as working for three different types of trucking firm: • Independent Owner Operator • Company Driver • Other Respondents identifying as independent owner-operator or company driver showed an increased probability of toll avoidance. Independent owner-operators were 8.3 times more likely than all other respondents to have chosen a free and/or alternate route instead of taking a toll road. Company drivers also showed an increased probability of toll avoidance, although less so at 5.1 times more likely than all other respondents. Respondents identifying as “other” were neither more nor less likely to have avoided a toll than all other respondents at a statistically significant level. Figure 10: Probability of Avoiding Toll Road, by Type of Firm These responses suggest that overall, respondents who have independence over routing are much more likely than any other type of operation to choose a free route over a toll road. Similarly, respondents 0 2 4 6 8 10 Owner-operator Company Driver 8.3 5.1 Magnitude of Probability

42 | P a g e identified as company drivers are still more likely to avoid a toll than all other respondents, suggesting that, in general, most drivers in either situation are likely to have avoided a toll road. Does the company’s type of trucking operation (LTL, full truckload, specialized, drayage, other) influence the decision to take a toll road? This question required a slightly different analytical approach because respondents were able to select more than one option: • LTL • Full Truckload • Specialized • Drayage • Other (please specify) For purposes of this analysis, responses in which the respondent selected “drayage” were excluded due to an insufficient sample size. Of the remaining trucking types, the only statistically significant factor was among respondents who selected “full truckload.” These drivers were 16.3 times more likely than all other respondents to have chosen a free and/or alternate route instead of taking a roll road. All other responses, including LTL, specialized, and “other” did not influence the probability of avoiding a toll at a statistically significant level. Does toll reimbursement or ability to pass tolls on to customers influence the decision to take a toll road? Two questions assessed whether toll reimbursement affected the decision to take a toll road: • Do you get reimbursed for tolls? (yes, no) • Does the cost of the tolls you pay get passed on to the customer? (yes, no, don’t know) Based on surveys from all Charlotte respondents, neither toll reimbursement nor passing tolls on to customers influenced whether a respondent chooses to use a free and/or alternate route instead of taking a roll road. On face value, this appears to be counterintuitive, as one may expect that drivers who are not reimbursed would be more likely to avoid tolls. Similarly, one may expect drivers who are reimbursed to prefer toll roads under the assumption that it is the fastest route. There may be other factors involved to explain these results. For example, toll reimbursement in and of itself wouldn’t factor into a routing decision, if in fact a driver did not have the cash in-pocket to pay the toll. Does the party responsible for trip routing (owner-operator, dispatcher, driver, other) influence the decision to take a toll road? The Charlotte survey queried “who has control over truck routing?” • Owner/operator

43 | P a g e • The driver • The dispatcher • Depends on the situation (please explain) Respondents who indicated that the driver had control over trip routing were 11.8 times more likely than all other respondents to avoid a toll. Respondents who identified the owner-operator as responsible were 4.4 times more likely than other all respondents to avoid a toll. These strong magnitudes, particularly among driver control, suggest that the party responsible for choosing the trip route is a powerful determinant of toll toad avoidance. Figure 11: Toll Road Avoidance, by Responsibility for Routing (Charlotte) It should be noted that due to the wording of the question, it is possible for “driver” and “owner- operator” to have the same meaning. In all cases, the owner-operator of a vehicle is also the driver and as a result of this ambiguity, the respondent’s choice to select “driver” rather than “owner-operator” may have been arbitrary. Responses indicating dispatcher responsibility or that responsibility “depends on the situation” did not influence the probability of avoiding a toll at a statistically significant level. Summary of Results from Charlotte • Those acting as independent owner-operators were the type of respondent most likely to avoid a toll. This is followed by company drivers, which also increased likelihood, but not as strongly. 0 2 4 6 8 10 12 Driver Owner-Operator 11.8 4.4 Magnitude of Probability

44 | P a g e • Those performing full truckload operations were the type of respondent most likely to avoid a toll. LTL, specialized, drayage, and all other types did not have any statistically significant influence over toll avoidance. • Neither toll reimbursement nor passing toll costs onto customers predicted a respondent’s likelihood of avoiding a toll at a statistically significant level. • Respondents were most likely to avoid a toll when the control over trip routing was left to the driver, followed by those whose routing decisions were made by the owner-operator. Although oftentimes the owner-operator and the driver are the same entity, respondents who selected “driver” may also be company drivers, and as such the two must be considered independently for purposes of this analysis. Dallas Analysts working on this research project attended the Great American Trucking Show, August 26 – 28, 2010, at the Dallas Convention Center in Dallas Texas. The analysts staffed a booth situated with other vendors, and intercepted attendees for their voluntary responses to the toll road survey. • Several types of trucking operations increased the likelihood of toll avoidance relative to all other respondents. In order of magnitude, they include: 1) other operation type; 2) for hire truckload, carrier/contract; 3) for hire truckload, self-employed owner-operator; 4) private fleet truckload; and 5) specialized. • Toll reimbursement did not influence the likelihood of toll avoidance across all respondents. • Respondents were most likely to avoid a toll when control over trip routing was left to the driver. When responsibility for trip routing was left to the company, respondents were neither more nor less likely to avoid a toll. Does the type of firm influence the decision to take a toll road? Does the company’s type of trucking operation (LTL, full truckload, et al) influence the decision to take a toll road? Unlike the Charlotte survey, respondents in Dallas were not asked directly what type of firm they worked for. Instead, respondents could identify themselves as owner-operators when asked to describe their company’s operation. The following company operation types were statistically significant in their influence of toll avoidance:

45 | P a g e Figure 12: Toll Road Avoidance, by Type of Firm (Dallas) The strongest influence over likelihood of toll avoidance is for those selecting “Other operation type,” followed by “For hire TL, carrier contract.” However, the “Other” category has broad meaning and does not have enough consistent responses to interpret further. Also, some responses in the “Other” category may fall into the other identified categories. Overall, for-hire truckload drivers, whether carrier/contract or self-employed owner-operator, appeared to be the most likely to avoid a toll. This is consistent with the Charlotte survey, where those identified as owner-operator were the most likely to avoid a toll. Because the category options between the two surveys were different, it is not feasible to directly compare the two. Does toll reimbursement influence the decision to take a toll road? The Dallas survey differed from Charlotte in that respondents were not asked about the ability to pass the cost of tolls directly on to customers. Additionally, respondents in Dallas were asked to provide more detail into toll reimbursement, with four different scenarios and a four-point scale from “always” to “never.” Respondents were asked if they are reimbursed when a) they take a toll to save time; b) they take a toll road because there is no alternative route; c) they take a toll road because a dispatcher directs them to; or d) they take a toll for any reason. Based on surveys from all respondents, there was no evidence to suggest that toll reimbursement influenced whether a respondent had avoided a toll road. Like in the Charlotte survey, this again 0 2 4 6 8 10 12 Specialized Private Fleet TL Self employed owner-operator Carrier/contract 4.1 4.8 7.2 10.8 Magnitude of Probability

46 | P a g e appears counterintuitive, as one would expect there to be a statistically significant relationship due to the reasons mentioned previously. Because both surveys suggested no relationship, it becomes more plausible that that toll reimbursement does not play a significant role in influencing whether or not a driver will use or avoid a toll road, and that the other variables examined are more effective at explaining the likelihood of toll avoidance. Does the party responsible for trip routing (driver, company, other) influence the decision to take a toll road? Respondents who selected “driver” or “depends on the situation,” were statistically more likely to avoid a toll if all else is held constant. Drivers who made their own routing decision were 5.9 times more likely than all other respondents to avoid a toll road. Drivers who selected “depends on the situation” were 15.4 times more likely than all other respondents to avoid a toll road. Figure 13: Toll Avoidance, by Party Responsible for Routing (Dallas) That driver-routing increased the likelihood of toll avoidance is consistent with the Charlotte survey, suggesting there is something about the driver’s behavior and attitudes towards tolling that influences them to avoid tolls. Conversely, companies who make routing decisions do not appear to guide their drivers to avoid tolls one way or another. The “routing depends on the situation” option likely derives its strong magnitude due to the trip allowing for an alternative toll-free route to be considered should one be available. Many drivers elaborated on this reply by explaining that these circumstances often involved both the driver and 0 5 10 15 20 Depends on Situation Driver 15.4 5.9 Magnitude of Probability

47 | P a g e company deciding together which route to take. Others indicated that the commodity type or sensitivity of time would alter the routing decision.

48 | P a g e Shipper/Beneficial Cargo Owner Attitudes In spite of extensive outreach to shippers, the web-based survey did not get enough responses to draw statistically valid data. So instead, the research team relied on previous portions of the research, where shippers were interviewed about their role in toll transactions and willingness to pay, and follow up interviews at the Council of Supply Chain Management Professionals and National Industrial Transportation League. The research found that shippers prefer to deal on a basis of service and price, with a minimum of surcharges or extra cost items to account for. Shippers expect that their third party logistics service providers, or their trucking companies, will include toll charges in their bid. Decisions on routing, or whether or not to use toll routes, are most often left to the trucking company, whose bid must reflect their assumptions about the need and benefit of using toll facilities to meet the shipper’s cost/service demands. Key Findings The research developed a large dataset which could be analyzed and parsed to develop the most important findings. Overall, the research team was operating under the hypothesis that one or more of the following factors would affect the willingness to pay tolls: Position in the Trucking Transaction • Driver (representing the employee who has the primary interface and decision with tolling) • Dispatcher/fleet manager (representing management) • Shipper/receiver/third-party logistics agent (3PL): representing the cargo owner, and/or the entity that arranges freight transportation, including cost and service parameters, and possibly accessorial charges such as for fuel and tolls. Type of Trucking Services • Local delivery • Drayage • Specialized • Local LTL • Private Fleet TL • For hire TL, Carrier/contract • For hire TL, self employed Owner Operator Other Factors • Toll reimbursement policies • Party responsible for trip routing (owner, dispatcher, driver) • Industry Tenure • Typical Haul Mileage • Typical Driving Environment • Opportunity/Familiarity with Toll Roads

49 | P a g e Attitudes about Toll Roads While sophisticated analytical techniques provide statistical certainty for survey analysis, the starting point should begin with attitudes about toll roads, because these attitudes might color all other survey responses. The negative opinion of toll roads and tolling as finance policy were so strong, the research team ascribed some respondents as “principled objectors” to toll roads, meaning that their passionate opinions could affect their attitudes about using toll roads. This passion is reflected in the overwhelming agreement to the following statements: • “Toll roads are too expensive” • “Toll roads exist mainly to make money for the government” • “Toll roads are too expensive for what they provide” • “I avoid toll roads whenever I can” Conversely, there was overwhelming disagreement with these positive statements about toll roads: • “Toll roads are a more fair way of funding maintenance and construction” • “Toll roads help drivers comply with hours of service rules” • “Toll roads improve on time performance” Due to the principled objection to tolls of many survey respondents, the research team believes that some survey data is skewed, with lower willingness to pay tolls than would otherwise be observed. Other Findings There was a divergence in the responses of Owner/Operator drivers, regarding their willingness to avoid a toll road, based on their tenure in the industry. Those drivers with 10 years or less in the industry were far less likely to avoid toll roads, than were those drivers with 10 or more years in the industry. Regarding tolls as an administrative burden, there was a divergence between for-hire carrier/contract respondents, and for-hire owner operators; in this instance, 42 percent of carrier/contract operators strongly agreed that reimbursing tolls is an administrative burden, whereas only 23 percent of owner/operators responded similarly. One explanation might be that owner operators have far less paperwork or number of employees to reimburse, so the question was moot to them. Statistically Significant Web-Based Survey Analysis The survey administered through a web portal provided respondents with three hypothetical toll road scenarios, each with associated time savings and toll charges. Respondents had the ability to select different toll rates for each scenario, which would in turn reflect their “willingness to pay” tolls in exchange for certain level of mileage or time savings. While the survey respondents were presented with realistic toll options (e.g., up to $32 in one of the scenarios), their stated willingness to pay skewed toward $0.00 or $0.50, which was nowhere near to the value of time presented in the scenario. The research team believes this skew toward very low tolls reflects a principled objection mentioned earlier. There were a few scenarios which revealed, with statistical significance, that certain drivers would be willing to pay some toll in exchange for time savings.

50 | P a g e Turnpike/Long Distance Toll Road Scenario The turnpike scenario involved the option of using a toll road over a distance of 100 miles, or secondary roads that would increase travel time by 30 minutes. In this scenario, the research team found that drivers who were familiar with toll road alternatives in their daily work were willing to pay a “token” toll amount. No other factor showed statistical significance in the willingness to pay tolls. Bypass Scenario The bypass scenario envisioned a toll road alternative that would reduce travel time by 15 minutes over a long haul drive. This scenario drew statistically significant conclusions from long haul drivers (500 miles or more typical), who showed strong unwillingness to pay more than a token toll amount. The reason for this unwillingness to pay might be that long haul truckers factor in delays over their trip length, such that the bypass scenario presented did not offer a strong value for the toll. Bridge Scenario The bridge scenario produced a number of interesting results; it presented an urban environment, with a toll bridge alternative for $16, versus a free bridge that added 20 minutes to the trip. In a simple hedonistic stated preference for this scenario, there was a significant difference between drivers and dispatchers in their willingness to take the toll bridge. Dispatchers were much more willing to take the toll bridge route than drivers, perhaps reflecting that dispatchers perceived a greater value for time benefit in this instance. This drivers who described their typical driving environment as “urban” expressed a strong willingness to pay a “real” toll for this scenario, rather than just some token amount. This could be explained by the urban drivers being more familiar with toll bridges and recognizing their time savings value. In Person Survey Analysis Type of Trucking Driver: Analysis from the Charlotte Truck Show indicated that Owner-Operators were 8.3 times more likely to avoid a toll road than all other types of firms; and that Company Drivers were 5.1 times more likely. Analysis from Dallas showed carrier/contract 10.8 times more likes to avoid tolls; owner operator 7.2 times more likely; private fleet TL 4.8 times more likely; and specialized 4.1 times more likely The only statistically significant response here was drivers who selected full truckload, which were 16.3 times more likely than other respondents to avoid tolls. Ability to pass tolls to customers/get reimbursed: No significant responses (Charlotte or Dallas) Responsibility for Trip Routing: Driver was 11.8 times more likely to avoid a toll road, than owner operator or dispatcher, though driver and owner/operator could be one in the same here (Charlotte). Driver 5.9 times more likely in Dallas, and ‘depends on situation’ is 15.4 times more likely.

51 | P a g e Shipper/Beneficial Cargo Owner Attitudes The research did not reveal any bias on the part of shippers, or third party logistic service providers, to use or avoid toll roads. Rather, shippers are requested a bid based on their service requirements, and expect trucking companies or third party logistic service providers to price tolls or any other ancillary charges into their bid.