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From page 30... ... 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) Read the entire page →
From page 31... ... 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. Read the entire page →
From page 32... ... 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. Read the entire page →
From page 33... ... 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) Read the entire page →
From page 34... ... 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) Read the entire page →
From page 35... ... 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. Read the entire page →
From page 36... ... 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) Read the entire page →
From page 37... ... 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) Read the entire page →
From page 38... ... 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) Read the entire page →
From page 39... ... 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. Read the entire page →
From page 40... ... 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. Read the entire page →
From page 41... ... 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. Read the entire page →
From page 42... ... 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 ntim 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. Read the entire page →
From page 43... ... 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. Read the entire page →
From page 44... ... 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. Read the entire page →
From page 45... ... 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. Read the entire page →
From page 46... ... 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) Read the entire page →
From page 47... ... 40 \| P a g e instrument itself. Therefore it was not statistically valid to comingle the results. Read the entire page →
From page 48... ... 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. Read the entire page →
From page 49... ... 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) Read the entire page →
From page 50... ... 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. Read the entire page →
From page 51... ... 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. Read the entire page →
From page 52... ... 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. Read the entire page →
From page 53... ... 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. Read the entire page →
From page 54... ... 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. Read the entire page →
From page 55... ... 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. Read the entire page →
From page 56... ... 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. Read the entire page →
From page 57... ... 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. Read the entire page →
From page 58... ... 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. Read the entire page →

From page 30...

... 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)