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 1 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. 2 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 3 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. 4 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 5 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. 6 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: 7 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. 8 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 10 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. 11 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. 19 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. 20 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. 21 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 22 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. 23 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. 24 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