2017-2018 Institute of Transportation Engineers at Montana State University
Table of Contents Table of Contents Chapter 1. Introduction... 1 1.1. Scope of Work... 1 1.2. Site Description... 2 Chapter 2. Study Methodology... 3 2.1. Level of Efforts... 4 Chapter 3. Study Results... 5 3.1. Vehicle Traffic... 6 3.2. Pedestrian Traffic... 7 3.3. Bicycle Traffic... 9 3.4. Parking... 10 Chapter 4. Conclusion... 12 4.1. Special Thanks... 13 Appendix A. Trip Generation Forms... 14 Appendix B. Parking Forms... 27
Introduction Chapter 1. Introduction This report summarizes the work performed by ITE @ MSU for the ITE Western District Student Data Collection Program. The project was performed in southwest Montana s city of Bozeman. Although ITE @ MSU has a storied history, we have been fairly dormant over the last few years due to a high level of student turnover. However, the club currently has a strong base of students passionate in transportation engineering and we are excited for what our future holds. In the fall semester, we hosted ITE International President-elect, Michael Sanderson, for a large transportation presentation. We also held an informational session by representatives from the design and planning firm, Kittelson & Associates. The club was able to reach out to middle school students in town at an interactive station at MSU s annual Engineer-A-Thon. For the first time in our club s history, we sent student members to the ITE Student Leadership Summit. We are also thrilled to be further involved with the upcoming annual conferences of the ITE Western District and the ITE Intermountain Section. We are currently involved in a pilot project involving the addition of bike lanes at Montana State University on existing campus sidewalks. With all the new knowledge our members will gain from these events and projects, we plan on giving back to our university by supporting transportation initiatives such as bike safety, better parking, and additional public transportation. Multiple members of the club were involved with this data collection project, as provided in section 4.1 of this report. 1.1. Scope of Work This project included two main activities: Collection of continuous trip generation data for vehicles, bicycles, and pedestrians, grouped into 15-minute intervals. Collection of parking lot occupancy every hour throughout the duration of study. This work occurred over three different days of study, Wednesday February 21 st, Thursday February 22 nd, and Saturday February 24 th. Data collection occurred between 7 AM and 6 PM. 1
Vehicle Introduction 1.2. Site Description The study site was the apartment complex, following Land Use Code 225: Off-Campus Student Apartment. As shown in Figure 1, the complex is located directly west of the MSU football stadium on South 11th Avenue. It is a half mile south of the intersection of S. 11th Ave. and W. Grant St., the major intersection on the southwest corner of the MSU campus. This apartment complex was built in 2015 and serves primarily as offcampus student lodging. It currently houses approximately 500 residents and encompasses an area of six city blocks. also has a sandwich shop on the first floor facing S. 11th Ave. There is on-street public parking along S. 11 th Ave and private, permit-only parking for residents on-site. N Kagy Blvd Vehicle Apartments Vehicle 11 th Ave To MSU Ped/Bike Figure 1: Site Overview (image taken from Google Maps). Bozeman s only public transportation system, the Streamline bus service, does not serve the complex. The nearest bus stop is one mile away at MSU s student union building. Because most bus routes in south Bozeman are designed to service the university, the complex was deemed too close to justify being included in a route. This site was of interest because MSU, and Bozeman in general, has a rising population trend. Bozeman was recently ranked as the fastest growing micropolitan area in the United States (2018 U.S. Census Bureau). In addition, MSU set the 10 th straight spring enrollment record in 2018, at a growth rate of 37% (February 14, 2018 Bozeman Daily Chronicle). New housing developments will continue to be built around the city, creating new concentrations of trip generations. In addition to ITE, the data collected will be helpful to planners who seek to incorporate popular modes of transportation into new developments. Table 1: Statistics of Apartments Buildings 8 Stories 3 Apartments (approx.) 130 Residents (approx.) 500 Parking Spaces 515 Bike Racks* 10 * Bike racks each hold approximately 10 bicycles. 2
Study Methodology Table 1 shows relevant statistics of the apartment complex. There are a variety of apartment sizes within the complex including studio, 1, 2, 3, 4, and 5-bedrooms. The exact number of each apartment size was not readily available, however there are approximately 3.8 bedrooms per apartment. The vast majority of bedrooms within the complex contain only one resident. Therefore, it was assumed that there are approximately 500 bedrooms within the complex. Due to the high demand for housing in Bozeman, the complex has been fully occupied since it opened. Chapter 2. Study Methodology For the three days of data collection, members of ITE @ MSU volunteered to collect data in shifts ranging between one hour and six hours. There were always two members on site, each performing one of two roles. The first role consisted of observing pedestrians, vehicles, and cyclists from the position indicated in Figure 2. This position allowed the observer to record the entire flow of traffic in and out of the complex, as both the north and northeast entrances were clearly in the line of sight of the observer while looking southwest. Pedestrians, vehicles, and cyclists were recorded by their respective mode of transportation and whether they were entering or exiting the complex. Observation Location Apartments The second role filled by club members consisted of 11 th Ave To MSU Figure 2: Observation Location (image taken from Google Maps). N Figure 3: View from the observation location (taken from http://www.stadiumviewliving.com/) recording the total volume of vehicles within the complex. One volume was recorded for each hour throughout the duration of the study. A drone was used for some of the counts as it allowed the user to quickly get an aerial view of the complex and record the total volume of vehicles. When the drone was not used, the counts were performed on foot, bike or by car. 3
Study Methodology In February, Montana was the only state whose average temperature was rated by NOAA as much below average. Montana experienced its sixth coldest February on record with a monthly temperature of 9.7 F below average, being the coldest for the state since 1989. Even though no precipitation occurred during the collection days, it is important to note that Montana s statewide precipitation (snowfall) was rated as much above average for the entire month. Due to the cold temperatures during each day of study, all data collection (with the exception of some hourly manual parking counts) was performed within a heated car parked at the study location indicated in Figure 2. Table 2: Weather during days of study (NOAA). Day Maximum Minimum Average Precipitation Temperature Temperature Wind Speed Sunrise Sunset Wednesday 15 F -15 F 0 In 2.4 MPH 7:17 AM 6:00 PM Thursday 18 F -5 F 0 In 3.5 MPH 7:15 AM 6:02 PM Saturday 32 F 13 F 0 In 8.9 MPH 7:12 AM 6:04 PM Although the weather during the 3 days of study may have impacted how residents decided to commute to school, the complex s proximity to the university makes driving impractical under any conditions. Unless a resident were to drive to the university earlier than 7:30, the closest available parking is only several hundred feet from the complex (and still about a 10-minute walk from most university buildings). Therefore, the vast majority of residents still walk to the university regardless of the weather conditions. Because of icy conditions, some residents may have opted to walk instead of bike. 2.1. Level of Efforts Each day of study lasted for 11 hours with at least 2 students always on site. Therefore, 66 total hours were spent on data collection. The project manager spent an additional 4 hours preparing for the study by building data collection sheets, outlining study instructions, and consulting with ITE @ MSU s professional mentor. The final 10 hours were spent analyzing the data and writing this report. Preparation of this report was performed by 5 ITE @ MSU members. 4
AM Peak (trips/h) PM Peak (trips/h) Daily Rate (trips/h) Total Trips AM Peak (trips/h) PM Peak (trips/h) Daily Rate (trips/h) Total Trips AM Peak (trips/h) PM Peak (trips/h) Daily Rate (trips/h) Total Trips Study Results Chapter 3. Study Results A full summary of counted trips by mode and day can be seen in Table 3. Note that the numbers reflect 11 hours of total data collection (7 AM 6 PM) for each day. The total number of trips generated over the three-day period is 3,568. The number of vehicles (2,110) and pedestrians (1,360) are far greater than those on bicycles (98). The different modes of transportation percentages are summarized in Table 4. The weekdays had much larger trip generation (1,297 on Wednesday and 1,581 on Thursday) than the weekend (690). Note that for the vehicle trips in Table 3, the numbers shown represent only the number of vehicles that were counted and does not consider the occupancy of each vehicle. Vehicle occupancy was not counted in this study, however the average occupancy rate was likely close to 1. Table 3: Summary of peak hour and daily averages of trips/h. Wednesday, February 21 Thursday, February 22 Saturday, February 24 Mode Vehicle 50 94 58 637 92 102 79 870 61 101 55 603 Walking 75 61 56 612 75 82 61 666 13 12 7 82 Bicycle 7 7 4 48 9 7 4 45 1 3 0.5 5 All Modes 132 162 118 1297 176 191 144 1581 75 116 62.5 690 Table 4: Percentage of transportation modes for each day. Mode Split Day Vehicle Walking Bicycle Wednesday 49 % 47% 4% Thursday 55% 42% 3% Saturday 87% 12% 1% Among the ITE Trip Generation Data Forms, part 2 had cells to provide AM and PM Peak Hour of adjacent street traffic. The peak hours were determined by data collection done by Sanderson Stewart (civil engineering design firm) on Wednesday, January 11 th, 2018. The counts were collected at the intersection of S. 11 th Ave. and W. Kagy Blvd., just north of Apartments. 5
Trips Study Results 3.1. Vehicle Traffic The number of vehicles per hour is summarized in figures 4, 5, and 6. The weekday AM peak hour is between 10:45 and 11:45 with an average rate of 71 trips/h. The PM peak is 16:15 17:15 respectively, at a rate of 98 trips/h. The weekend peak hours are similar with an AM peak at 10:30 11:30, and a PM peak at 16:45 17:45. Weekend peak rates were lower than weekday in the morning at 61 trips/h, but higher in the evening at 101 trips/h. More vehicles are departing than arriving the apartment complex in the morning, while the number of vehicles arriving and departing are more equal in the evening. Wednesday Vehicle Trips 100 90 80 70 60 50 40 30 20 10 0 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Time Enter Exit Figure 4: Wednesday vehicle trips. 6
Trips Trips Study Results Thursday Vehicle Trips 140 120 100 80 60 40 20 0 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Time Enter Exit Figure 5: Thursday vehicle trips. Saturday Vehicle Trips 100 90 80 70 60 50 40 30 20 10 0 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Time Enter Exit Figure 6: Saturday vehicle trips. 3.2. Pedestrian Traffic The amount of pedestrian traffic is equal to that of vehicles on the weekdays but is significantly lower on the weekend. The amount of foot traffic arrivals and departures throughout the day is summarized in figures 7, 8, and 9. During the mornings, there is a greater number of people departing the apartments, while in the evening there is a larger number of people arriving at the complex. AM peak hour for 7
Trips Trips Study Results pedestrians is 9:30 10:30 at peak rate of 75 trips/h. The PM peak hour is at 15:15 16:15 at a rate of 71.5 trips/h. The peak hour rates for the weekend are much lower. The AM peak hour is 10:30 11:30 with a rate of 13 trips/h and the PM is 16:15 17:15 at a rate of 12 trips/h. Wednesday Pedestrian Trips 80 70 60 50 40 30 20 10 0 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Time Enter Exit Figure 7: Wednesday pedestrian trips. Thursday Pedestrian Trips 80 70 60 50 40 30 20 10 0 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Time Enter Exit Figure 8: Thursday pedestrian trips. 8
Trips Trips Study Results Saturday Pedestrian Trips 16 14 12 10 8 6 4 2 0 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Time Enter Exit Figure 9: Saturday pedestrian trips. 3.3. Bicycle Traffic The amount of bicycle traffic had much lower trip generation compared to the other two modes of transportation. The AM peak hour during the week is at 9:00 10:00 with a rate of 8 trips/h. The PM peak hour is at 14:15 15:15 at a rate of 7 trips/h. There was minimal bicycle traffic during the weekend with a total of 5 trips throughout the day. 12 10 8 Wednesday Bicycle Trips 6 4 2 0 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Time Enter Exit Figure 10: Wednesday bicycle trips. 9
Trips Trips Study Results Thursday Bicycle Trips 10 9 8 7 6 5 4 3 2 1 0 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Time Enter Exit Figure 11: Thursday bicycle trips. Saturday Bicycle Trips 2.5 2 1.5 1 0.5 0 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Time Enter Exit Figure 12: Saturday bicycle trips. 3.4. Parking There is a general downward trend for parking occupancy throughout the day (Figure 13). As seen in Table 5, the parking lot never reached over 67% full (indicating that almost a third of residents may not own a vehicle). Over the course of the three days studied, there are on average less cars parked over the 10
Vehicles Study Results weekend than during the week. Note that with 515 spaces provided for 130 dwelling units, there is a parking supply ratio of 4 spaces per dwelling unit. ITE Parking Generation does not have student housing as a land use category. Land use category 221: Low/Mid-Rise Apartments for suburban land use has an 85 th percentile parking demand ratio of 1.94 vehicles/du, which is below the observed demand ratios for this site. Parking Occupancy 340 320 300 280 260 Wednesday Thursday Saturday Average 240 220 200 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Time of Day Figure 13: Parking lot occupancy for each hour of study. Table 5: Maximum parking supply ratio for each day. Date Peak Parking Observed Parking Supply (# of spaces) Parking Occupancy % Parking Demand Ratio (Per Dwelling Unit) Wednesday 346 515 67% 2.7 Thursday 316 515 61% 2.4 Saturday 309 515 60% 2.4 * Parking Generation for Land Use Code 225 not found Below in Table 6 is the Vehicle Trip Generation Peak Hour Generator values using the 10 th Edition of the ITE Trip Generation Manual. This was done based Land Use Code 225: Off-Campus Student Apartment. Unfortunately there is no data from ITE that provides person trips for this specific land use. Therefore, only comparison between vehicle trips was done. 11
Conclusion Table 6: Vehicle Trip Generation Rate Vehicle Trips per Vehicle Trips per % Vehicles % Vehicles Exiting Bedroom Resident Entering ITE ITE ITE ITE Obs. Obs. Obs. Obs. Manual Manual Manual Manual AM Peak Hour 0.12 0.17 0.13 0.17 41% 35% 59% 65% PM Peak Hour 0.25 0.20 0.28 0.20 50% 46% 50% 54% Weekday Daily Rate 3.15 1.27 3.65 1.27 50% 43% 50% 57% Chapter 4. Conclusion To conclude, the data collection resulted in relatively comparable results when compared to ITE Trip Generation rates in the 10th Edition of the Trip Generation Manual. The observed weekday trip rates varied quite a bit from the ITE manual rates. This is because the observed weekday daily rates do not represent a 24-hour count. While the vehicle traffic observed from 7:00 to 18:00 represents a majority of the traffic, it is likely there were also students that did not return to the apartment complex within the study period of 7:00 to 18:00. More trips to and from the apartment complex beyond this time frame is very likely as well. Percent rates for vehicles entering and exiting during peak hours were all within 7% of the ITE Trip Generation rates for vehicle trips, showing little variance. ITE Parking generation rates for Land Use Code 225 were not found, but our values provided may help to produce rates for future editions of the manual. The data compiled and summarized may be used for future analysis for planners. 12
Conclusion 4.1. Special Thanks ITE @ MSU would like to give special thanks to the following individuals for their valuable help in completing this project. Danae Giannetti - Project Mentor, Montana Dept. of Transportation-Bozeman Design Unit ITE @ MSU Members Joey Beran Matthew Campbell Matthew Bell Alia Peterson Bryce Grame Madeline Pernat Jaden Stewart Aldo Videa Wilson D Souza Faculty Mentors MSU Ahmed Al-Kaisy Yiyi Wang Doug Smith - MSU Surveying Instructor Sanderson Stewart Bozeman, MT office ITE Professionals Lisa Fischer - Montana Chapter Vice President Cameron Waite - Intermountain District Past President MSU Students Sarah Forseth Abram French Daniel Smithgall Nathan Ellis Audrey Stoltzfus 13
Trip Generation Forms Appendix A. Trip Generation Forms 14
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Parking Forms Appendix B. Parking Forms 27
Parking Forms 28