There are a number of publications in Tourism Planning. Clare Gunn’s book explores the fundamentals of tourism planning (i.e., sustainability, policy, growth, and ecotourism) on various scales from around the world [6]. Edward Inskeep’s book provides an overview of sustainable tourism planning (i.e., institutional, environmental, implementation, socioeconomic, strategic, and development). Seasonality is discussed in terms of capacity for attractions and methods to reduce seasonality (e.g., four-season resorts, promotions in off-seasons) [7]. Takayuki Hara’s book describes how quantitative analysis is applied to the tourism industry, including regression, forecasting, and social accounting [8]. Each of these references are practitioner based and general; however, they do not provide details for tourism traffic planning with seasonality [6-8].

The economic impacts of transportation planning are covered by the Victoria Transport Policy Institute [9]. This resource provides a comprehensive overview of transportation planning, including evaluation of transportation benefits, the economic value of walkability (for a locality), evaluation of non-motorized transportation benefits and costs, land use, and transportation diversity.

Environmentally, tourism gateway communities always have to depend on local residents as well as the economic and cultural factors [10]. Transportation impacts the environment and congestion via the carrying capacity of the roadways. These issues could negatively impact the recreational quality and tourism of an area. For example, emergency response services could be inhibited by transportation congestion during peak times or contribute to environmental impacts by increased emissions and fossil fuel consumption [11, 12]. Sustainable tourism may also depend on these environmental costs and the enhanced mobility [13]; however, emissions and fossil fuel consumption could be reduced if motorized traffic is replaced with an emphasis on public transport, walking, and cycling. Greenhouse gas emissions are dominated by transportation within the tourism industry; thus, infrastructure should integrate the types of transport, alternative designs, and environmental impacts with future importance on decreasing travel distances, increasing passenger load, and promoting technological efficiencies.

GIS and visualization are influential tools for predicting the aesthetic and environmental impacts of tourism development and alternative transportation plans. GIS can assist quantify land use changes on watersheds, support site selection and corridor suitability analysis, visually depict aesthetic impacts in line-of-sight and viewshed analyses, and environmental impact assessment [14]. The participatory GIS provides an approach for integrating community residential and planner participation and circumventing hurdles in communications [15] or alleviating environmental justice issues [16].

Citizen and public perception is important for planning projects. Obviously, residents should be questioned regarding their perceptions, experiences, and expectations. Then, one can evaluate how the residents assess tourism and they will interact with the tourists or businesses serving the tourists [17, 18].

Traffic prediction research spans many different timelines, from several hours to minutes. The research focuses on predicting traffic volume and predicting traffic behavior (e.g., an unexpected event such as a lane closure). Fries et al. evaluated software that was used to predict traffic conditions after a traffic incident. They found that large amounts of computational capacity were needed to make accurate predictions [19]. Stathopoulos et al. studied multivariate models to predict traffic volume in congested urban areas, which were more accurate than univariate time series models [20]. From a time span perspective, there is a need for different model specifications throughout the day. These methods can also be used for waterways, Lowry et al. determined recreational river traffic patterns by applying highway traffic simulation software and methods [21].

Han, Stone, and Huntsinger assigned traffic volumes to small networks using census data, maps, and traffic data in a spreadsheet-model to localities where more complex software had previously been used [22]. Zhong and Hanson used travel demand models to estimate traffic volume in rural areas. In these areas no traffic data was available. The travel demand models consistently overestimated traffic volume originally. However, the models increased in accuracy when reducing the size of traffic analysis zones, and including additional information (e.g., number of driveways per kilometer) [23]. Stutz and Runkler predicted long and short term traffic patterns using fuzzy neural networks [24].

K-factor = the proportion of AADT occurring during a given hour (e.g., the Design Hour Factor)

D-factor = the proportion of the total, two-way design hour traffic, traveling in the peak direction (e.g., the Directional Distribution)

DHV = AADT × K-factor

DDHV (Peak Direction) = AADT × K-factor × D-factor

DDHV (Non-peak Direction) = AADT × K-factor × (1 – D-factor)

Further discussion of the K-factor can be found in references [25-27].

For example, K₃₀ depicts the 30^th busiest hour (of the year, or study period) for various road types assuming two-way traffic and unconstrained by capacity. The 30^th hour is chosen because it has been shown to be effective for designing and planning; including, but not limited to, Florida and Nevada.

The K₃₀ values act as you would expect, notably:

• The K₃₀ factor will decrease as the AADT on a roadway increases.
• The K₃₀ factor will decrease as development density increases.
• The highest K₃₀ factor (i.e., hourly traffic as a percentage of AADT) occurs on recreational roadways which exhibit high seasonality.

The D₃₀ values are used to correct for traffic that is traveling primarily in one direction during the peak hour. Thus, more traffic lanes will be needed for that direction. The D-factor values should average to be 0.5 when averaging over both directions.

The values for K₃₀ and D₃₀ can be obtained using traffic counts, either continuous counts or short-term counts. They can be estimated based on known values for the recreational, rural, suburban, and urban areas. For example, the values for K₃₀ will be approximately 0.14 for recreational, 0.114 for rural, 0.103 for suburban, and 0.097 for urban. The values for D₃₀ would average 0.5 (i.e., equal traffic in both directions at peak hour) and adjust accordingly. Both of these parameter values would have to be adjusted for long-term planning, especially as new roadways are being constructed as road choices may change over the project’s horizon.

It should be noted that while Nevada in 2012 [26] used the K₃₀ and D₃₀ values depending on the roadway being studied, Florida has developed a new model that uses different K values (i.e., K₁₀₀), D values, and percent of vehicles that are trucks based on different locations within the state while tying-in budget considerations [27]. In other words, Florida is planning for the 100^th busiest hour rather than the 30^th busiest hour due to budget constraints and other restrictions.

The following standard K factors have been used by the Florida Department of Transportation since at least 2002 [25] and continued to be used as of 2014 [27]. Note that D factors would be determined using traffic counters measuring directionality of the traffic at the corresponding standard K factor representative time period. Thus, once DHV and DDHV are calculated, then a Level Of Service (LOS) analysis can be completed and plans can be made accordingly.

The primary recommendation with respect to the model would be to use the hourly traffic counts (either continuous or short-term) to develop K₃₀ and D₃₀ values for locations and then use DHVs and DDHVs rather than AADTs for planning purposes.

An intermediate approach was needed since hourly traffic counts were not readily available or reliable for all locations. Thus, an adaptation of the monthly traffic counts was used to approximate the K₃₀ values. The recommendation is to average the top two monthly counts. This was approximated using data available from Nevada and Florida where the K₃₀ ratio was 14% for high-tourism areas, 11.4% for rural areas, and so forth; and relating it to the Case Study locations. Due to the location of the Case Studies it was assumed the DHV and DDHV were equivalent (i.e., the directions of traffic did not matter). Since the average of the top two months will be used, then that will be defined as Design Volume (DV). The following recommendations for each Case Study location are based on the knowledge that transportation funding is limited. Thus, the overarching goal was to provide an expectation of what the two month average for DV would provide. AADT was eliminated as consideration for DV for various administrative, financial, and resource reasons.

Below are the figures for the Outer Banks area for the A2702 traffic counter, which will be included in Case Study 2. Fig. (1) is a repetition of Fig. (9) and Fig. (2) is a repetition of Fig. (10). As discussed further in Section 4 this area is considered high with respect to seasonal variation (month-to-month). This example provides details on how to interpret these figures.

Fig. (1). An example of seasonality.

Fig. (2). an example of seasonality.

Fig. (3). Data Summary for Case Study 1.

Fig. (4). AADT and DV Summary for Case Study 1.

The minimum, median, maximum, and standard deviation are based across the 12 months throughout the year; as percentages of AADT observed at the site for the entire year. Thus, for Dare County site A2702, the minimum of 5.11 (Fig. 1) was observed in January (Fig. 1); meaning 5.11% of the traffic passing A2702 for the entire year occurred in January. Likewise, the maximum was 12.38% occurring in July. The multiplier 2.42 Fig. (1) is determined by dividing the maximum (July at 12.38%) by the minimum (January at 5.11%) (i.e., 12.38% ÷ 5.11% = 2.42). The AADT was the actual observed value, and the DV is the average of the top two observed months; for A2702, July and June. Note, medians are reported rather than averages for the 12 months since averages would be 100% ÷ 12 = 8.33% for each location.

The seasonal variation is exhibited in the histogram in Fig. (2). In fact, June-August all exhibit twice as much traffic as January (the month with the least amount of traffic) and about 25% more than April and October, which are average months.

The seasonal variation is exhibited in the values within Figures 2.3 and 2.4. The Multiplier is the Maximum divided by the Minimum. A Multiplier of ≥ 2 (or nearly 2) is considered high variation, a 2 > Multiplier ≥ 1.4 is considered moderate, and below 1.4 is considered steady.

Formulas for Case Study:

DV = (ADT₁ + ADT₂) ÷ 2

Where ADT₁ is the highest average daily traffic for a single month in a calendar year, and ADT₂ is the second-highest average daily traffic for a single month in a calendar year.

The following locations were studied for Case Study 1. Information regarding the county, location of the traffic counter, route, and dates of data collection is provided in Fig. (3). Also provided is the research team’s evaluation of the seasonality depicted based on the data analyzed (in reference to the other Case Study traffic counter locations) in Fig. (4). Finally, the analysis for each counter location is provided in Figs. (5-7).

The minimum, median, maximum, and standard deviation are based across the 12 months throughout the year; as percentages of AADT observed at the site for the entire year. Thus, for Avery County site A0501, the minimum of 7.01 (Fig. 4) was observed in January (Fig. 5); meaning 7.01% of the traffic passing A0501 for the entire year occurred in January. Likewise, the maximum was 9.26% occurring in August. The multiplier 1.32 (Fig. 4) is determined by dividing the maximum (August at 9.26%) by the minimum (January at 7.01%) (i.e., 9.26% ÷ 7.01% = 1.32). The AADT was the actual observed value, and the DV is the average of the top two observed months; in the case of A0501, August and October.

Note, medians are reported rather than averages for the 12 months since averages would be 100% ÷ 12 = 8.33% for each location.

The Case Study 1 location provides a number of interesting issues with respect to transportation planning, urban planning, and urban form issues. The data shows that there is moderate seasonality in one of the three locations studied for the data collected, compared to other areas studied (i.e., it does not depict as an extreme seasonality component as compared to other Case Study locations). This is likely due to the fact that the university and surrounding community provide a good deal of routine traffic in the area. The typical peaks are in July and October (with August being third highest). The design volume (DV) for the various locations are relatively close to AADT (i.e., when compared to other case study locations) due to the minimal and/or moderate seasonality exhibited by these locations.

The following locations were studied for Case Study 2. Information regarding the county, location of the traffic counter, route, and dates of data collection are provided in Fig. (8). Also provided is the research team’s evaluation of the seasonality depicted based on the data analyzed (in reference to the other Case Study traffic counter locations) in Fig. (9). Finally, the analysis for each counter location is provided in Figs. (10-11).

The minimum, median, maximum, and standard deviation are based across the 12 months throughout the year; as percentages of AADT observed at the site for the entire year. Thus, for Dare County site A2702, the minimum of 5.11 (Fig. 9) was observed in January (Fig. 10); meaning 5.11% of the traffic passing A2702 for the entire year occurred in January. Likewise, the maximum was 12.38% occurring in July. The multiplier 2.42 Fig. (9) is determined by dividing the maximum (July at 12.38%) by the minimum (January at 5.11%) (i.e., 12.38% ÷ 5.11% = 2.42). The AADT was the actual observed value, and the DV is the average of the top two observed months; for A2702, July and June.

Fig. (5). AADT Summary for Avery (Location A0501).

Fig. (6). AADT Summary for Avery (Location A9401).

Fig. (7). AADT Summary for Avery (Location A9403).

Fig. (8). Data Summary for Case Study 2.

Fig. (9). AADT and DV Summary for Case Study 2.

Fig. (10). AADT Summary for Dare (Location A2702).

Fig. (11). AADT Summary for Dare (Location A2703).

The Case Study 2 location provides a number of interesting issues with respect to transportation planning, urban planning, and urban form issues with respect to seasonality in traffic. The local residents exhibited extreme behavior with respect to traffic avoidance and other plans. The opportunity for alternative transportation (i.e., walking, bicycles) could be further explored, but would require additional infrastructure and/or infrastructure improvements. The abundance of parking in some areas actually encourages more tourism traffic. The data shows that there is high seasonality in both of the two locations studied for the data collected, compared to other areas studied (i.e., it does depict an extreme seasonality component as compared to other Case Study locations). This is likely due to the fact that the permanent population is relatively small when compared to the tourism population. The obvious peaks are in the summer (June-August), and the lows in the winter (December-February). The extreme seasonality represents a significant issue with respect to traffic count variation, essentially making the AADT meaningless for this region. Due to the economy of Dare County (and the Outer Banks, in general) relying heavily on tourism, it makes the transportation network essential to the prosperity of this area and its residents [28, 29].

The design volume for the various locations is significantly larger than AADT (i.e., when compared to other case study locations) due to the high seasonality and peak demand exhibited by these locations.

The following locations were studied for Case Study 3. Information regarding the county, location of the traffic counters, routes, and dates of data collection is provided in Fig. (12). Also provided is the research team’s evaluation of the seasonality depicted based on the data analyzed (in reference to the other Case Study traffic counter locations) in Fig. (13). Finally, the analysis for each counter location is provided in Figs. (14-15).

Fig. (12). Data Summary for Case Study 3

Fig. (13). AADT and DV Summary for Case Study 3.

Fig. (14). AADT Summary for Wilmington (Location A6403).

Fig. (15). AADT Summary for Wilmington (Location A6405).

The minimum, median, maximum, and standard deviation are based across the 12 months throughout the year; as percentages of AADT observed at the site for the entire year. Thus, for New Hanover County site A6403, the minimum of 5.78 Fig. (13) was observed in December Fig. (14); meaning 5.78% of the traffic passing A6403 for the entire year occurred in December. Likewise, the maximum was 11.44% occurring in July. The multiplier 1.98 Fig. (13) is determined by dividing the maximum (July at 11.44%) by the minimum (December at 5.78%) (i.e., 11.44% ÷ 5.78% = 1.98). The AADT was the actual observed value, and the DV is the average of the top two observed months; in the case of A6403, July and June.

The Case Study 3 location provides a number of interesting issues due to its population size and diversity. The figures and data indicate that the Wilmington area (within the city) and the connecting highways exhibit a relatively steady traffic flow. There are obvious bottlenecks and pinch points. One of the counter locations exhibited extreme seasonality, particularly during the summer months since this was a route that captures the summer tourism traffic; thus, the design volume for this location was significantly higher than AADT. The other counter was relatively steady, not exhibiting seasonality; thus, the design volume and AADT were practically equivalent.

The following locations were studied for Case Study 4. Information regarding the county, location of the traffic counter, route, and dates of data collection is provided in Fig. (16). Also provided is the research team’s evaluation of the seasonality depicted based on the data analyzed (in reference to the other Case Study traffic counter locations) in Fig. (17). Finally, the analysis for each counter location is provided in Fig. (18).

The minimum, median, maximum, and standard deviation are based across the 12 months throughout the year; as percentages of AADT observed at the site for the entire year. Thus, for Swain County site A8602, the minimum of 5.19 Fig. (17) was observed in January Fig. (18); meaning 5.19% of the traffic passing A8602 for the entire year occurred in January. Likewise, the maximum was 12.52% occurring in July. The multiplier 2.41 Fig. (17) is determined by dividing the maximum (July at 12.52%) by the minimum (January at 5.19%) (i.e., 12.52% ÷ 5.19% = 2.41). The AADT was the actual observed value, and the DV is the average of the top two observed months; in the case of A8602, July and August.

The Case Study 4 location provides a number of interesting issues due to its proximity to the Great Smoky Mountains National Park and its seasonality in the summer months. The figures and data indicate that the Bryson City area exhibits extreme seasonality, nearly equivalent to the levels exhibited in the Dare County (Outer Banks) study (Case Study 2). Interestingly enough, the peak traffic occurs during the summer (same as Dare County), even though Bryson City is in the mountains. This is due to visitors to the Great Smoky Mountains National Park and surrounding areas. The design volume for the counter location is significantly larger than AADT (i.e., when compared to other case study locations) due to the high seasonality and peak demand exhibited by this location.

The following locations were studied for Case Study 5. Information regarding the county, location of the traffic counters, routes, and dates of data collection is provided in Fig. (19). Also provided is the research team’s evaluation of the seasonality depicted based on the data analyzed (in reference to the other Case Study traffic counter locations) in Fig. (20). Finally, the analysis for each counter location is provided in Figs. (21-22).

Fig. (16). Data Summary for Case Study 4.

Fig. (17). AADT and DV Summary for Case Study 4.

Fig. (18). AADT Summary for Bryson City (Location A8602).

Fig. (19). Data Summary for Case Study 5.

Fig. (20). AADT and DV Summary for Case Study 5.

Fig. (21). AADT Summary for Asheville (Location A1001).

Fig. (22). AADT Summary for Asheville (Location A1003).

The minimum, median, maximum, and standard deviation are based across the 12 months throughout the year; as percentages of AADT observed at the site for the entire year. Thus, for Buncombe County site A1001, the minimum of 7.48 Fig. (20) was observed in January Fig. (21); meaning 7.48% of the traffic passing A1001 for the entire year occurred in January. Likewise, the maximum was 8.71% occurring in October. The multiplier 1.17 Fig. (20) is determined by dividing the maximum (October at 8.71%) by the minimum (January at 7.48%) [i.e., 8.71% ÷ 7.48% = 1.17]. The AADT was the actual observed value, and the DV is the average of the top two observed months; in the case of A1001, June and October.

The Case Study 5 location provides a number of interesting issues due to its population size and diversity. The figures and data indicate that the Asheville area (within and nearby the city) and the connecting highways exhibit a relatively steady traffic flow with a moderate seasonality for one of the two counters studied. Essentially, the road is not used as much in January, perhaps due to extreme weather events and holidays. The road is used much more often during the summer months (when compared to January). There are obvious bottlenecks and pinch points.

The design volume for one location was approximately equivalent to the AADT and for the other location moderately higher, which is due to the lack of seasonality exhibited by these locations.