| Overall Rating | Silver |
|---|---|
| Overall Score | 61.55 |
| Liaison | Melissa Cadwell |
| Submission Date | Jan. 7, 2022 |
Syracuse University
OP-16: Commute Modal Split
| Status | Score | Responsible Party |
|---|---|---|
|
3.29 / 5.00 |
Melissa
Cadwell Sustainability Coordinator Energy Systems & Sustainability Management |
"---"
indicates that no data was submitted for this field
Total full-time equivalent student enrollment:
19,184
Full-time equivalent of employees:
4,935
Part 1. Student commute modal split
Yes
Total percentage of students that use more sustainable commuting options as their primary mode of transportation:
78.60
A brief description of the method(s) used to gather data about student commuting:
Office of Institutional Research
Calculating Student Travel Mileage
1) Housing data
• Filter to relevant terms (Fall 2018/Spring 2019)
• Remove duplicate entries by filtering to their maximum length of stay
• If student is present in the data frame they’re deemed to be an “On Campus” student
2) Class data (use all classes)
• Filter to relevant terms (Fall 2018/Spring 2019)
• Filter to only classes taken on “Main Campus” or the various study abroad centers (China, England, France, Hong Kong, Italy, Chile, Spain, Turkey)
• Filter to classes taken “in person”
• Filter to status “enrolled”
• Filter to grades that result in some type of grade (Pass/Fail, A-F, etc)
• Count/total the number of classes each student is taking
3) Study Abroad data
• Filter to relevant terms (Fall 2018/Spring 2019)
• Geocode missing lat/lons
• Calculate distance from home address to study abroad destination
o Total Study Abroad Travel Miles – 6,520,148
4) Students who take Classes on Campus
• Bring in Class data frame (with count of the number of classes a student is taking)
• Join (left join) student data containing home address, home state, home country
5) Student Commuting Habits
• Use data frame of student class counts and home address data
• Join (left join) parking data provided by Parking Services
• Join (left join) previously altered housing data (indicating if student lived on campus or not)
• Assume that if student is in the data frame and IS NOT living on campus, they’re living off campus
o Students taking on-line classes and classes not on campus were previously removed
• Assume if a student is in the data frame and DOES NOT have a parking pass, they don’t have a parking pass
• Establish a commuting distance for each student
o If the student’s home to campus distance is less than 50 and they having a parking pass, they travel “x” distance
o If the student’s home to campus distance is more than 50 and they have a parking pass, they live closer than indicated. Assign them a distance of 8 (mean distance from home)
o If the student’s home to campus distance is less than 50 and they do not have a parking pass, assign them a distance of 0 (they commute some other way or are on campus)
o If the student’s home to campus distance is more than 50 and they do not have a parking pass, assign them a distance of 8 (they commute some other way or drive but park elsewhere)
6) Separate students who we KNOW ARE driving
• Filter to students with “Parking Pass” and live “Off Campus”
• If the student doesn’t have a US address assign them a commute distance of 8 miles
• Establish Commuting number - Divide the number of classes they take by 2
o Assuming they don’t make separate trips for each class
• Establish One Way Weekly Miles - Multiply Commuting number (above) by (Commuting Distance)
• Establish One Way Yearly Miles – Multiply Weekly Miles by 32 (number of weeks of classes)
7) Sort students who we don’t know their commuting method
• Filter to students with NO “Parking Pass” and live “Off Campus”
• Calculate a row number for each student in the data frame
• Regardless of where or how far away they live, sort students into these buckets
o 32% Walk to class
o 8% Bike to Class
o 28% take the bus to class
o 34% Drive to class
We can make the above assumption based on all classes are “in person”, “on campus”, classes. They should be getting to campus on a semi-regular basis.
• Based on the above, calculate a new Commuting Distance for them as follows
o Walk – 1mile
o Bike – 1mile
o Bus – 3miles
o Driving – 8 Miles
• Establish Commuting number - Divide the number of classes they take by 2
o Assuming they don’t make separate trips for each class
• Establish One Way Weekly Miles - Multiply Commuting number (above) by (Commuting Distance)
• Establish One Way Yearly Miles – Multiply Weekly Miles by 32 (number of weeks of classes)
8) Calculate different student commuting numbers
• Bind data from data frame 6 and data frame 7 to rejoin all students into one data frame
• Total Yearly miles per method of Transportation
o Driving – 3,206,272
o Bus – 740,640
o Walk – 252,544
o Bike – 54,944
9) Calculate all student Holiday/Vacation Travel
• Note: This data frame should contain all students
• Calculate miles students travel to school for the first day of class (DO not calculate this distance for students who are “off Campus” and live less than 50 miles from campus)
• Calculate miles for students traveling to school and back for thanksgiving (DO not calculate this distance for students who are “off Campus” and live less than 50 miles from campus and for international students)
• Calculate miles for students traveling to school and back for Christmas (DO not calculate this distance for students who are “off Campus” and live less than 50 miles)
• Calculate miles for students traveling to school and back for Spring Break (DO not calculate this distance for students who are “off Campus” and live less than 50 miles from campus and for international students)
• Calculate miles students travel home on the last day of class (DO not calculate this distance for students who are “off Campus” and live less than 50 miles from campus)
10) Bring Study Abroad Miles back into the data frame
• Note: Use data frame from 9
• Join (left join) data from previously calculate study abroad data frame
• If a student studied abroad in the Fall make their holiday travel numbers 0 for Thanksgiving and Christmas
• If a student studied abroad in the Spring make their holiday travel number 0 for spring break
o To School Miles – 29,122,207
o Thanksgiving – 13,133,076
o Christmas – 56,979,996
o Spring Break – 13,339,786
o Back Home – 29,122,207
11) Calculate Total Miles Per Student
• Note: Use data frame from 10
• Sum all the holiday miles (To school, Thanksgiving, Christmas, Spring Break, Back home) for each student
• Sum study abroad miles for each student
• If a student has “Study Abroad Miles” that are > 0, cut their One Way Yearly Commute Miles in half (they weren’t commuting to campus for half the year)
o Total miles attributed to students – 151,220,635
Calculating Student Travel Mileage
1) Housing data
• Filter to relevant terms (Fall 2018/Spring 2019)
• Remove duplicate entries by filtering to their maximum length of stay
• If student is present in the data frame they’re deemed to be an “On Campus” student
2) Class data (use all classes)
• Filter to relevant terms (Fall 2018/Spring 2019)
• Filter to only classes taken on “Main Campus” or the various study abroad centers (China, England, France, Hong Kong, Italy, Chile, Spain, Turkey)
• Filter to classes taken “in person”
• Filter to status “enrolled”
• Filter to grades that result in some type of grade (Pass/Fail, A-F, etc)
• Count/total the number of classes each student is taking
3) Study Abroad data
• Filter to relevant terms (Fall 2018/Spring 2019)
• Geocode missing lat/lons
• Calculate distance from home address to study abroad destination
o Total Study Abroad Travel Miles – 6,520,148
4) Students who take Classes on Campus
• Bring in Class data frame (with count of the number of classes a student is taking)
• Join (left join) student data containing home address, home state, home country
5) Student Commuting Habits
• Use data frame of student class counts and home address data
• Join (left join) parking data provided by Parking Services
• Join (left join) previously altered housing data (indicating if student lived on campus or not)
• Assume that if student is in the data frame and IS NOT living on campus, they’re living off campus
o Students taking on-line classes and classes not on campus were previously removed
• Assume if a student is in the data frame and DOES NOT have a parking pass, they don’t have a parking pass
• Establish a commuting distance for each student
o If the student’s home to campus distance is less than 50 and they having a parking pass, they travel “x” distance
o If the student’s home to campus distance is more than 50 and they have a parking pass, they live closer than indicated. Assign them a distance of 8 (mean distance from home)
o If the student’s home to campus distance is less than 50 and they do not have a parking pass, assign them a distance of 0 (they commute some other way or are on campus)
o If the student’s home to campus distance is more than 50 and they do not have a parking pass, assign them a distance of 8 (they commute some other way or drive but park elsewhere)
6) Separate students who we KNOW ARE driving
• Filter to students with “Parking Pass” and live “Off Campus”
• If the student doesn’t have a US address assign them a commute distance of 8 miles
• Establish Commuting number - Divide the number of classes they take by 2
o Assuming they don’t make separate trips for each class
• Establish One Way Weekly Miles - Multiply Commuting number (above) by (Commuting Distance)
• Establish One Way Yearly Miles – Multiply Weekly Miles by 32 (number of weeks of classes)
7) Sort students who we don’t know their commuting method
• Filter to students with NO “Parking Pass” and live “Off Campus”
• Calculate a row number for each student in the data frame
• Regardless of where or how far away they live, sort students into these buckets
o 32% Walk to class
o 8% Bike to Class
o 28% take the bus to class
o 34% Drive to class
We can make the above assumption based on all classes are “in person”, “on campus”, classes. They should be getting to campus on a semi-regular basis.
• Based on the above, calculate a new Commuting Distance for them as follows
o Walk – 1mile
o Bike – 1mile
o Bus – 3miles
o Driving – 8 Miles
• Establish Commuting number - Divide the number of classes they take by 2
o Assuming they don’t make separate trips for each class
• Establish One Way Weekly Miles - Multiply Commuting number (above) by (Commuting Distance)
• Establish One Way Yearly Miles – Multiply Weekly Miles by 32 (number of weeks of classes)
8) Calculate different student commuting numbers
• Bind data from data frame 6 and data frame 7 to rejoin all students into one data frame
• Total Yearly miles per method of Transportation
o Driving – 3,206,272
o Bus – 740,640
o Walk – 252,544
o Bike – 54,944
9) Calculate all student Holiday/Vacation Travel
• Note: This data frame should contain all students
• Calculate miles students travel to school for the first day of class (DO not calculate this distance for students who are “off Campus” and live less than 50 miles from campus)
• Calculate miles for students traveling to school and back for thanksgiving (DO not calculate this distance for students who are “off Campus” and live less than 50 miles from campus and for international students)
• Calculate miles for students traveling to school and back for Christmas (DO not calculate this distance for students who are “off Campus” and live less than 50 miles)
• Calculate miles for students traveling to school and back for Spring Break (DO not calculate this distance for students who are “off Campus” and live less than 50 miles from campus and for international students)
• Calculate miles students travel home on the last day of class (DO not calculate this distance for students who are “off Campus” and live less than 50 miles from campus)
10) Bring Study Abroad Miles back into the data frame
• Note: Use data frame from 9
• Join (left join) data from previously calculate study abroad data frame
• If a student studied abroad in the Fall make their holiday travel numbers 0 for Thanksgiving and Christmas
• If a student studied abroad in the Spring make their holiday travel number 0 for spring break
o To School Miles – 29,122,207
o Thanksgiving – 13,133,076
o Christmas – 56,979,996
o Spring Break – 13,339,786
o Back Home – 29,122,207
11) Calculate Total Miles Per Student
• Note: Use data frame from 10
• Sum all the holiday miles (To school, Thanksgiving, Christmas, Spring Break, Back home) for each student
• Sum study abroad miles for each student
• If a student has “Study Abroad Miles” that are > 0, cut their One Way Yearly Commute Miles in half (they weren’t commuting to campus for half the year)
o Total miles attributed to students – 151,220,635
Part 2. Employee commute modal split
Yes
Total percentage of employees that use more sustainable commuting options as their primary mode of transportation:
16.50
A brief description of the method(s) used to gather data about employee commuting:
Calculating Faculty Travel Mileage
1) Base Faculty/Staff data
• Filter Faculty/Staff that have the same EMPLID and Same City (removing duplicates)
• Calculate distance from “home” to campus
• Join (left join) Parking Pass data provided by Parking Services
2) Separate Faculty/Staff we KNOW ARE driving
• Filter to Faculty/Staff that have “Parking Pass”
• If a Faculty/Staff member doesn’t have a distance from home to campus, or if that distance is greater than 100 miles, assign them a Commuting Distance of 7
o We’re assuming Faculty/Staff are not commuting >100 miles multiple times a week
• Establish a Commuting number for each Faculty/Staff member
o Faculty and Full Time – 4
o Faculty and Part Time – 2
o Staff and Full Time – 5
o Staff and Part Time – 3
• Calculate One Way Miles Per Week by multiplying Commuting Distance by Commuting Number
3) Sort Faculty/Staff whose commuting method we’re unsure of
• Filter to Faculty/Staff that have “Parking Pass”
• Regardless of Distance from campus, sort Faculty/Staff into these buckets
o 36% Walk to work
o 32% take the bus to work
o 34% Drive to work
• Based on the above, calculate a new Commuting Distance for them as follows
o Walk – 1mile
o Bus – 3miles
o Driving – 8 Miles
• Establish a Commuting number for each Faculty/Staff member
o Faculty and Full Time – 4
o Faculty and Part Time – 2
o Staff and Full Time – 5
o Staff and Part Time – 3
• Calculate One Way Miles Per Week by multiplying Commuting Distance by Commuting Number
4) Combine the “Driving” and “Non Driving” Faculty/Staff data frames
• If the employee is faculty assume they commute for 32 weeks a year
o If the employee is staff, they commute for 52 weeks a year
• Calculate “One Way Yearly Commute” by multiplying one way weekly miles by Fac/Staff Weeks
o Driving – 8,120,796
o Bus – 232,740
o Walking – 89,032
5) Calculate Holiday commuting for Faculty/Staff
• Use original unduplicated Faculty/Staff data frame
• Filter to all faculty/staff who commute more than 100 miles (this number can be changed)
• Calculate “To School” miles as miles (distance from home)
• Calculate Thanksgiving miles as 2 x miles (distance from home)
• Calculate Christmas miles as 2 x miles (distance from home)
• Calculate Spring break miles as 2 x miles (distance from home)
• Calculate “From School” miles as miles (distance from home)
o 3,146,824 miles traveled for Holidays and Breaks
6) Calculate all staff miles
• Join (left join) Commuting and Holiday Travel data frames
• Calculate total miles per year per faculty/staff member
o Total Miles Traveled – 11,390,944
1) Base Faculty/Staff data
• Filter Faculty/Staff that have the same EMPLID and Same City (removing duplicates)
• Calculate distance from “home” to campus
• Join (left join) Parking Pass data provided by Parking Services
2) Separate Faculty/Staff we KNOW ARE driving
• Filter to Faculty/Staff that have “Parking Pass”
• If a Faculty/Staff member doesn’t have a distance from home to campus, or if that distance is greater than 100 miles, assign them a Commuting Distance of 7
o We’re assuming Faculty/Staff are not commuting >100 miles multiple times a week
• Establish a Commuting number for each Faculty/Staff member
o Faculty and Full Time – 4
o Faculty and Part Time – 2
o Staff and Full Time – 5
o Staff and Part Time – 3
• Calculate One Way Miles Per Week by multiplying Commuting Distance by Commuting Number
3) Sort Faculty/Staff whose commuting method we’re unsure of
• Filter to Faculty/Staff that have “Parking Pass”
• Regardless of Distance from campus, sort Faculty/Staff into these buckets
o 36% Walk to work
o 32% take the bus to work
o 34% Drive to work
• Based on the above, calculate a new Commuting Distance for them as follows
o Walk – 1mile
o Bus – 3miles
o Driving – 8 Miles
• Establish a Commuting number for each Faculty/Staff member
o Faculty and Full Time – 4
o Faculty and Part Time – 2
o Staff and Full Time – 5
o Staff and Part Time – 3
• Calculate One Way Miles Per Week by multiplying Commuting Distance by Commuting Number
4) Combine the “Driving” and “Non Driving” Faculty/Staff data frames
• If the employee is faculty assume they commute for 32 weeks a year
o If the employee is staff, they commute for 52 weeks a year
• Calculate “One Way Yearly Commute” by multiplying one way weekly miles by Fac/Staff Weeks
o Driving – 8,120,796
o Bus – 232,740
o Walking – 89,032
5) Calculate Holiday commuting for Faculty/Staff
• Use original unduplicated Faculty/Staff data frame
• Filter to all faculty/staff who commute more than 100 miles (this number can be changed)
• Calculate “To School” miles as miles (distance from home)
• Calculate Thanksgiving miles as 2 x miles (distance from home)
• Calculate Christmas miles as 2 x miles (distance from home)
• Calculate Spring break miles as 2 x miles (distance from home)
• Calculate “From School” miles as miles (distance from home)
o 3,146,824 miles traveled for Holidays and Breaks
6) Calculate all staff miles
• Join (left join) Commuting and Holiday Travel data frames
• Calculate total miles per year per faculty/staff member
o Total Miles Traveled – 11,390,944
Optional Fields
| Percentage of students (0-100) | Percentage of employees (0-100) | |
| Single-occupancy vehicle | --- | --- |
| Zero-emissions vehicle | --- | --- |
| Walk, cycle, or other non-motorized mode | --- | --- |
| Vanpool or carpool | 0 | 1 |
| Public transport or campus shuttle | --- | --- |
| Motorcycle, motorized scooter/bike, or moped | 0 | 0 |
| Distance education / telecommute | --- | 0 |
Website URL where information about student or employee commuting is available:
Additional documentation to support the submission:
---
Data source(s) and notes about the submission:
We lowered the campus shuttle 7 points due to the fact that some of the students we area assuming are riding the bus, could be walking or biking.
The information presented here is self-reported. While AASHE staff review portions of all STARS reports and institutions are welcome to seek additional forms of review, the data in STARS reports are not verified by AASHE. If you believe any of this information is erroneous or inconsistent with credit criteria, please review the process for inquiring about the information reported by an institution or simply email your inquiry to stars@aashe.org.