Optimization of School Bus Routes for Crystal Lake South High School
Utilizing ARCLogistics
By
Tyler Nelsen
An Undergraduate Thesis
Submitted in Partial Fulfillment for the Requirements of
Bachelor of Arts
in
Geography and Earth Science
Carthage College
Kenosha, WI
April, 2010
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Optimization of School Bus Routes for Crystal Lake South High School Utilizing
ARCLogistics
Tyler Nelsen
Abstract
The ability to efficiently and safely transport their students to and from school on
a daily basis is an important part of every school district’s responsibilities. Like any
organization, school districts want to cut down on the cost of transportation. With
technological advances, this task is becoming more of a science every year. The School
Bus Routing problem continues to be difficult one to solve. ARCLogistcs, a routing
software from ESRI, was tested in against District 155 Crystal Lake South High School’s
current routes by entering the current routes into the ARCLogistics interface and
comparing them to a set of routes generated by ARCLogistics itself. Despite the high
reputation of ESRI in the Geographic Information Systems world, the routes generated
by ARCLogistics were less cost effective, taking a combined total of 42 minutes longer
than the current ten routes, over four extra minutes per bus route. These results,
combined with several design failures, in terms of bus routing, in the ARCLogistics
software, make it difficult to use ARCLogistics for School Bus Routing purposes.
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Table of Contents
Introduction…………………………………………………………………….Pg 4
Routing…………………………………………………………………Pg 5
Vehicle Routing Problem…………………………………………….Pg 7
ARCLogistics………………………………………………………….Pg 9
Study Area…………………………………………………………….Pg 12
Methodology…………………………………………………………………..Pg 14
Data Acquisition………………………………………………………Pg 14
Current Routes………………………………………………………..Pg 15
Geocoding……………………………………………………………..Pg 16
New Routes……………………………………………………………Pg 17
Results…………………………………………………………………………Pg 19
Discussion……………………………………………………………………..Pg 23
Software Failures……………………………………………………..Pg 24
References…………………………………………………………………….Pg 29
Acknowledgements..………………………………………………………….Pg 30
List of Figures
Figure 1, Boundaries Map…………………………………………………..Pg 13
Figure 2, Sample Dispatcher Summary…………………………………...Pg 18
Figure 3, New Route Number 1 Error……………………………………...Pg 25
Figure 4, Current Route Number 2 Error…………………………………..Pg 26
Figure 5, One House, Two Buses…………………………………………..Pg 27
List of Tables
Table 1, Cost of Routes……………………………………………………...Pg 19
Table 2, Number of Stops……………………………………………………Pg 20
Table 3, Miles Traveled………………………………………………………Pg 21
Table 4, Drive Time (Hours)…………………………………………………Pg 22
Table 5, Time Spent at Stops (Hours)……………………………………...Pg 22
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Introduction
Schools are an essential part of this country. They act as a primary means through
which children grow socially and academically, and set them up for their lives once
schooling is no longer necessary. It is, then, of great importance that children arrive at
school safely and efficiently. One of the most common ways to accomplish this is
through school buses. Over 23 million students ride school buses each year (National
Highway Traffic Safety Administration, 1998). The process of forming consistent school
bus routes is obviously essential to the school systems. The main goals of school bus
routing are to maximize the efficiency in terms of travel time for students to the bus
stops, fuel usage for the buses, and the overall number of buses needed to accomplish
the task. Combining and optimizing these factors is part of what geographers call the
“Vehicle Routing Problem”. There are many methods of solving this problem, though
there is no one solution or algorithm to calculating the most efficient routes in a given
situation, as each situation provides its own set of variables. With increasing
technology, however, it is becoming increasingly easier to account for more and more of
these variables. ARCLogistics, software developed by ESRI to help solve the Vehicle
Routing Problem, provides the next technological advance toward adapting to each
individual vehicle routing problem and meeting the needs of Crystal Lake South High
School’s bus routing problem.
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Routing
School bus routing, like any other service, is a business. As such, it is essential to the
business that costs be as low as possible without affecting performance. In the case of
school bus routing, the optimal route would be one that uses fuel with the highest
efficiency, and utilizes the least number of buses without disrupting the comfort of the
students on the bus and minimizing the distance that students must walk from their
houses to the bus stops (Boulder Valley School District, 2009). The issue of combining
these three factors into a useable route is one faced by every school district. Districts
are also facing the continual problem of attempting to cut costs under the current
economic stresses while maintaining the safety of students under their care. A Madison,
Wisconsin district recently made the headlines of the Portland Press Herald in an article
by Erin Rhoda after it had to cut down on the number of buses it provided and expand
the radius from the schools in which it would no longer provide bus services to keep up
with its budget. In this strained economy, it is becoming more and more important to
exercise the highest efficiency route management in order to use the money the districts
have in the best way, and often times, the inefficiency begins with the creation of the
routes.
The process of configuring routes is often done manually in the country’s school
districts. In these districts where routing is handled manually, the process requires
paper maps,
lists of addresses, and a lot of guessing. For school districts that can
afford it, bus stops are not only separated by geographic standards, but also by the age
level of the students at each stop an on each bus, complicating the situation even
further. This can take weeks of planning and must be done yearly to account for the
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change in the student body as students graduate out and younger students graduate
into the school district. Additionally, throughout every year there are an influx of
students transferring in and an exodus of students transferring out. These changes in
the student body affect the school bus routing systems as well. The district is forced to
either require the new students to find their own way to and from school each day, or
rearrange bus routes, unless the students happen to fit into the current routing system.
This entire process, when done manually, falls to the discretion of individuals and teams
who often have little training in the school bus routing problem. Fortunately,
geographers have systematically organized the problem of routing these buses in order
to aid the process.
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Vehicle Routing Problem
The routing of school buses falls under the broader topic called the “Vehicle
Routing Problem” which derives from the “Traveling Salesman Problem”. The Traveling
Salesman Problem is a problem which attempts to identify the shortest route which will
pass through a given number of points (Dantzig & Ramser, 1959). The Vehicle Routing
Problem refers to a more precise form of the Traveling Salesman Problem whose
design is to service “x” number of customers using a fleet of vehicles. This problem is
used in numerous businesses worldwide to determine the optimal means of transporting
goods to customers. It is often used under the assumption of a central depot from which
customers have placed orders for goods. The fleet is to pick up the goods from the
depot and distribute them to the customers with minimal costs. The problem may,
however, involve various pickup locations while in the process of distribution, each trip
involving a different route. The varying nature of the vehicle routing problem has made it
difficult to formulate one single algorithm or solution to the problem. The School Bus
Routing Problem is one which initially inverts the common Vehicle Routing Problem in
that the buses pick up the students in their individual stops first and then delivers them
all to the central destination, the school, and then reverts back to the common form by
picking up all the students at the school and dropping them off at individual stops.
Today, technology helps to bring the Vehicle Routing Problem closer to being
solved. Using GIS (Geographic Information Systems), companies and transportation
services are finding a means to solve the vehicle routing problem to their specific
situations and variables. ESRI, the largest GIS software provider in the United States,
has come up with the leading means to solve the Vehicle Routing Problem:
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ARCLogistics. ARCLogistics is software used in conjunction with ESRI’s ARCMap,
which is ESRI’s standard software for data analysis using maps. ARCLogistics receives
data input from ARCMap and allows the user to focus on creating routes to meet the
orders given.
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ARCLogistics
Morey Seafood International LLC is one of ARCLogistics’s success stories in
terms of the vehicle routing problem (Boyles 2002). Morey Seafood is one of the United
States’ largest distributors of seafood. The company delivers thousands of pounds of
fresh fish to restaurants across the United States daily. In order to distribute this
massive amount of fish in the Detroit area, Morey’s began utilizing GIS in 2000. Prior to
incorporating GIS into the process, Morey’s had its trucks cover individual zones of
Detroit. The main issue with this was that one zone could easily be flooded with orders
while another could receive only a few on any given day. Once the drivers received their
orders, they would determine their own routes to complete the orders. In addition to the
obvious inefficiency of the zone system, all twenty-four trucks arrived at the same time
each morning to be loaded by only four loading docks; this caused a great amount of
wasted time. There was very little communication between the drivers and the sales
personnel, so the salesmen could not give the clients an accurate estimation of when
the shipment would arrive.
In order to properly utilize ARCLogistics’s capabilities, Morey’s Seafood had to
load the names and addresses of its customers into the software, as well as start and
stop times for the trucks, time needed to make a delivery, and the weights of the
products the trucks would carry. With this information successfully loaded into
ARCLogistics, the routes can be configured. The ARCLogistics interface utilizes a map
showing the unassigned orders as black dots. This visual designation is an important
upgrade for Morey’s, since previously the dispatcher would have to look through a pile
of papers to determine how close two orders were to each other. With the visual display
9
on a map, the dispatcher can easily see where the orders are located. After checking to
make sure that the daily orders were input correctly, the dispatcher directs the software
to built the routes for the day and ARCLogistics will generate the highest efficiency
routes to reach every order, complete with directions and estimated times of arrival at
each order, factoring in time for the actual delivery of each package.
In addition to the easy use of ARCLogistics, the software can also save company
money in the long run. The Star Tribune Newspaper of Minneapolis-St. Paul made the
switch over to ARCLogistics from manual routing and paid back the cost of the software
in 2.5 Months (ESRI 2008). Additionally the Newspaper projects a five year profit of
$672,740 due to savings on fuel and extra time available to the employees who used to
handle routing manually. The company saw a 3% reduction in the number of vans
needed to accomplish the distribution, 4% fewer miles traveled, 3% fewer labor hours
needed for routing, and a 6% overall cost reduction.
Apex Office Supply utilized ARCLogistics to help carry the load of an ever expanding
business. When the company began, managing routes was a simple enough problem to
handle manually, but with expansion and growth in both the area they supplied and the
density of the population covered, more efficiency was needed. Changes in the number
of orders or the desired arrival time of specific orders caused major setbacks and forced
many orders to arrive late. In addition, the company drivers began to get pulled over for
speeding in order to catch up on orders (ESRI 2006). When ARCLogistics was
incorporated into the process, Apex was able to optimize their routes for the highest
efficiency so that orders would arrive on time without speeding tickets becoming an
issue. Additionally, the interface allowed Apex to add, remove, or change the time of
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arrival for any package on short notice without disrupting the day’s deliveries.
ARCLogistics enabled Apex Office Supply to not only cut down on its costs, but to
provide a much better service to its customers by ensuring safe and timely arrival of
orders.
Ivan Smith Furniture experienced many of the same results that Apex Office
Supply, Star Tribune, and Morey’s Seafood saw (ESRI 2010). These companies all saw
a decrease in fuel usage and number of vehicles required, while increasing the
efficiency in terms of time spent on building routes, the projected arrival time of orders,
and overall profits when they incorporated ARCLogistics into their own route problems.
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Study Area
In this study, the bus routes of Illinois’ School district 155, specifically Crystal Lake
South High School, will be investigated and compared against ARCLogistics. Crystal
Lake is one of Chicago’s northwestern suburbs in McHenry County and as of 2009
hosted a population of 42,180. Crystal Lake South High School’s boundaries cover the
smallest area of the city’s three high schools (shown in green on Figure 1), but contain
the highest population of the three with 1922 students. In order to qualify for South High
School’s bus service, a student must live outside of a mile-and-a-half radius of the
school. Of those 1922 students, 1019 qualify for buses. While many of these students
walk, ride bikes, drive or carpool to school each day, South is required to provide busing
for all of the 1019 students. Each bus has a maximum capacity of 71 students.
Therefore, in order to accommodate all the students who require bus service availability,
a minimum of 15 buses must be provided. However, the school district attempts to
maximize efficiency by accounting for the fact that the majority of students drive a car to
school or get a ride from someone with a car. Therefore, the district only provides 10
buses for the students. No data was given for how close to capacity these buses are on
a regular basis or on how exactly the use of ten buses was determined.
12
Figure 1. Map of District 155. This map displays the boundaries of each of the four schools in District
155. The green area represents Crystal Lake South High School’s boundaries.
School Bus Routing is and will continue to be a challenging problem whose nearly
limitless number of variables make it a difficult and time consuming issue, with no allencompassing solution or algorithm. However, as technology progresses, compensating
for these variations will be created and made simpler. Currently, the leading software in
the realm of routing is ESRI’s ARCLogistics. The goal of this research is to provide
Crystal Lake South High School with new bus routes using ARCLogistics. These routes
will hopefully provide greater efficiency in terms of travel time for students to the bus
stops, fuel usage for the buses, and the overall number of buses needed for the school.
13
Methodology
Data Acquisition
In order to accomplish the task of using ARCLogistics to improve Crystal Lake South
High School’s bus routes, I first needed to acquire the necessary data. This included
both student home address information and the current bus routes used by Crystal Lake
South High School. The student data was given to me by the District 155 School Board
and included the house number and street name, city, state, and zip code (each in its
own column) of each student enrolled at Crystal Lake South in the 2010/2011 school
year who qualified for a bus route. Crystal Lake South High School students only qualify
for the bus service if they live outside of a mile-and-a-half radius of the school. The
current bus routes of Crystal Lake South were given to me by the school district as well
and have a total of ten buses. The number of students allocated per bus ranges from 72
students to 131 students including students who don’t actually use the bus service.
14
Current Routes
In order to test a new model against the high school’s current routes, the current
buses and routes must be placed manually into the ARCLogistics interface. The start
and end locations of the buses, which are the garage and the school, must be entered
first manually as points on the map view. Buses, called trucks in ARCLogistics, can be
added and edited from the list view of the ARCLogistics interface. The buses can be
adjusted in terms of capacity, start/end time and location, and bus number. The current
stops must be manually entered as points on the map view and specified as the stops of
their respective buses in order to construct the exact routes used by Crystal Lake South
High School. These routes can then be built and the mileage and time spent en route
can be analyzed for efficiency in terms of mileage and travel time.
15
Geocoding
In order to utilize the student data given to me by the school district for ARCLogistics
to create new routes, I had to geocode the addresses into the street map provided by
ARCLogistics. Geocoding is a form of data preprocessing which takes written data such
as, in this case, address information and transforms it into point data which can be
viewed as a layer on a map in a GIS. In this case, the point data is shown in the map
view of ARCLogistics. ARCLogistics contains a street map layer together with a
reference points layer that includes millions of actual address points which allows it to
use “Rooftop Geocoding” as opposed to the more common “Address Interpolation
Geocoding”. Address Interpolation assigns sections of street segments of addresses
and places the addresses that are geocoded into the segment based on numerical
order instead of where the address is actually located. Thus, an address number of 50
would be placed on the center of a segment of 0-100, even though it may not actually lie
there in reality. Rooftop Geocoding assigns a longitude and latitude to each individual
address so that the highest degree of accuracy can be achieved when geocoding
addresses.
16
New Routes
With the addresses geocoded, the number of buses must again be specified,
along with start and end locations for each bus. Once the desired number of buses has
been specified, the routes will be built using the individual addresses. These routes will
obviously not be the true routes, since stopping at each house would be very inefficient,
but they serve as the framework for the target routes to be created. Once the pre-routes
are made, the addresses within each route can be clustered into points along the
individual routes at the nearest intersections. These manually-entered points will then
be used as the stops for the real routes, which can now be built by ARCLogistics after
removing the individual addresses from the map view. From there, any necessary
alterations can be made to the routes in order to cut down transit time and increase the
efficiency of the routes and convenience for the students. These final routes can then
be compared to the school’s current routes in terms of travel time, mileage, number of
buses used, and the travel time for the students to the stops. Additionally, new routes
can be made by varying the number of buses utilized and repeating the process of
forming the new routes. The routes will be compared to the old routes by a printout
given by ARCLogistics which will display costs of fuel consumption for each bus on both
the old routes and the new routes. Figure 2 displays an example report.
17
Figure 2. Dispatcher Summary. This figure shows a copy of the report made by ARCLogistics displaying
the cost of these bus routes and the various factors that go into computing this cost.
18
Results
The new and old routes both used 10 buses to accomplish the task of delivering
the students to the school with the highest efficiency. ARCLogistics computes the
monetary cost of these routes by summing up the driver’s wages and the fuel
consumption during the route. The drivers’ wages were left at the default setting of 16
dollars/hour for consistency. This was added to the distance traveled to compute the
cost of the routes. The end costs were compared and the newer routes showed a higher
cost at $108.93 compared to the cost of the current routes at $95.61. Table 1 shows the
costs of each individual bus route for both the original and new routes.
Bus
Number
1
2
3
4
5
6
7
8
9
10
Total
Cost of Routes
Current
New
Routes
Routes
$9.67
$9.35
$10.31
$7.78
$8.70
$10.55
$9.21
$9.81
$10.03
$10.83
$9.93
$10.90
$13.50
$13.01
$7.70
$11.01
$6.90
$14.46
$9.65
$11.22
$95.61
$108.93
Table 1
The number of stops for each route contributed greatly to the efficiency of the two
sets of routes. The new routes generated by ARCLogistics used nearly twice as many
stops as the current routes. Table 2 displays the breakdown of the stops per route.
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Bus
Number
1
2
3
4
5
6
7
8
9
10
Total
Number of Stops
Current
Routes
8
8
3
5
7
5
11
5
3
7
62
New
Routes
14
8
10
12
10
12
12
11
13
12
114
Table 2
Due in large part to the greater number of stops, the new set of routes also has
more mileage than the current routes. The difference is small, at only 4.4 more total
miles traveled in the new routes, but this does factor into the higher cost for the new
routes. Table 3 shows the mileage for each individual route for both the current and new
routes generated by ARCLogistics.
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Bus
Number
1
2
3
4
5
6
7
8
9
10
Total
Miles Traveled
Current
Routes
5.2
6.6
5.6
6.0
5.5
7.1
8.2
3.9
4.2
5.5
57.6
New
Routes
4.8
4.1
6.6
5.1
6.0
5.7
7.8
6.0
9.6
6.4
62.0
Table 3
The time spent in transit and the time spent at the individual stops has a great
affect on the cost of the routes. For both of these factors, the new routes spend
significantly more time than the new routes. This is, again, due in large part to the new
routes including several more stops than the current routes. Tables 4 and 5 show the
drive time and the time at the stops, respectively, for both sets of routes. When the total
difference for between the buses’ time spent on the routes is computed, the new routes
take 42 minutes longer than the old routes.
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Drive Time (Hours)
Bus
Current
New
Number
Routes
Routes
1
0.18
0.17
2
0.22
0.14
3
0.20
0.24
4
0.22
0.17
5
0.19
0.21
6
0.23
0.20
7
0.27
0.27
8
0.14
0.22
9
0.15
0.31
10
0.18
0.22
Total
1.99
2.14
Time Spent at Stops (Hours)
Bus
Current
New
Number
Routes
Routes
1
0.27
0.27
2
0.22
0.22
3
0.17
0.22
4
0.17
0.28
5
0.27
0.28
6
0.17
0.30
7
0.32
0.30
8
0.22
0.28
9
0.15
0.30
10
0.25
0.28
Total
2.18
2.73
Table 4
Table 5
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Discussion and Conclusion
While ARCLogistics may provide a very efficient means of routing pick-up/dropoff expeditions, it was not capable of making any great improvements upon the routes
previously established by Crystal Lake South High School. Overall, the routes took
longer and were more costly. The only real benefit to the new routes was that because
of the higher number of stops, it can be assumed that the students’ walks to the bus
stops from their houses would be shorter. However, ARCLogistics provides no means of
measuring this, so no hard data can confirm or quantify this improvement. In the future,
it would be beneficial to utilize another of ESRI’s programs called Network Analyst. The
new routes could be imported into Network Analyst along with the address information
as points on the map. Network Analyst would possess the ability to measure the
distance from the students’ houses to the bus stops and come up with an average
distance to the stops. The same process could be done with the current routes and
compared to the new routes. However, with no concrete improvements, it cannot be
recommended that ARCLogistics be used to optimize bus route efficiency.
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Software Failures
ARCLogistics’s failure to optimize bus routes is largely a software design issue.
The ARCLogistics interface is designed simply to take in a set of points and find the
fastest way to reach each point using a set number of vehicles. In the case of designing
Crystal Lake South High School’s new bus routes, these points were individual student
addresses. ARCLogistics provides no way to organize these addresses into stops, so
the organization of the stops was largely a manual process. Since the stops were then
organized and designed by a human, there is no way to know if ARCLogistics is actually
a more or less efficient way of designing bus routes than any other system that
computes the traveling salesman problem.
In addition to this, ARCLogistics does not take into consideration the sheer size
of a school bus. The system seems to see each bus stop as a point to start over and will
regularly backtrack down the same street it is driving on. This would be impossible in a
real bus routing situation since no full sized school bus could turn around on a
residential road. This error is displayed in Figure 3. The stops can be moved so that
they align better with the route, but if once the program builds the routes again, it does
not follow the same route because the stops are now changed and the system starts
over.
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Figure 3. New Route Number 1 Error. This image displays the route traveled by Bus # 1 in the routes
created by ARCLogistics. The bus will be directed to make a U-Turn at both stops 7 and 10 in order to
finish the route. This obviously cannot be done by a bus and compromises the efficiency of the routes.
This same problem arose even when recreating Crystal Lake South’s current
Routes. Figure 4 displays this error on Route # 2.
25
Figure 4. Current Route Number 2 Error. Figure 4 displays the route traveled by Bus # 2 in the routes
set by Crystal Lake South High School. This route was created by the stops used currently by the school,
but even with the current, useable routes, ARCLogistics still made the same error of making a U-Turn on
a residential road in a bus.
The final software failure that is made most evident is a lack of logic in designing
the routes to the points that ARCLogistics is given. When making the initial set of new
26
routes, ARCLogistics was directed to make routes from the student address data, which
was to be the framework from which new routes would be made. When ARCLogistics
made these routes, there seemed to be very little logic behind the routes. At times
multiple buses would stop at the same house to pick up one of the multiple students at
an address. Figure 5 displays this error.
Figure 5. One House, Two Buses. The grey circle with the 3 inside of it marks an address in which three
students going to school live. It is colored grey because one student is being picked up by the maroon
route, while the other two are being picked up by the blue route. This is completely inefficient on a
theoretical level and just as absurd on a practical level.
27
These numerous errors combined with the manual input of the stops, makes
ARCLogistics a very poor choice for organizing School Bus Routes. This system is
designed for the simple traveling salesman problem, not for organization and clustering
of points into stops. Crystal Lake South High School’s current method of organizing and
routing their buses remains much more efficient and effective than ARCLogistics’
routing method.
28
References
Rhoda, Erin “Parents protest youngsters walking to school bus stop.” The Portland
Press Herald, October 17, 2010, Editorials Section.
Michaels, Peter. “Optimizing School Bus Routes in Bangor, Maine.” Poster Presented at
Bangor High School in Bangor, Maine, U.S.A., 2010
National Association of State Directors of Pupil Transportation Services. “Identification
and Evaluation of School Bus Route and Hazard Marking Systems” Work Performed
Under a Grant from the National Highway Traffic Safety Administration U.S. Department
of Transportation, June, 1998
U.S. Computing, Inc. “Compass – School Bus Routing Software” U.S. Computing, Inc.
http://www.uscomputinginc.com/products/compass-faqs.aspx
Onboard Informatics. “Crystal Lake, Illinois” City-Data.com. http://www.citydata.com/city/Crystal-Lake-Illinois.html
29
Acknowledgements
-
Carthage College Geography and Earth Science Department Students and Faculty
-
Illinois School District 155
-
Professor Wenjie Sun
-
Alexandra Matzinger
30