Optimizing School Bus Stop Placement in Howard County, Maryland: A GIS-Based Heuristic Approach

Optimizing School Bus Stop Placement in Howard County, Maryland: A GIS-Based Heuristic Approach

Michael Galdi (Baltimore County Department of Planning, Baltimore, MD, USA) and Paporn Thebpanya (Towson University, Towson, MD, USA)
Copyright: © 2016 |Pages: 15
DOI: 10.4018/IJAGR.2016010103
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Abstract

In the current system, school bus stops in Howard County, Maryland are manually placed along the school bus routes based on safety, cost-efficiency, and many other variables. With such liberal placement, bus stops are sometimes placed unnecessarily. This issue is prevalent in many school districts and often results in needlessly close bus stop proximity. In this study, the authors implemented a GIS-based heuristic to assist school officials in optimizing their districts bus stop placement. They also estimated the proportion of county-wide bus stops that could be eliminated by this approach. Following the constraints determined by State and local guidelines, the ArcGIS Network Analyst Extension was used to identify unnecessary bus stops across the study area. The initial output was re-evaluated by school officials in order to determine if those bus stops would be eliminated. The results indicate that approximately 30% of the existing bus stops were marked as “candidates for elimination” by the GIS process. After a review of these candidates, it was determined that at least 15% of the total school bus stops could be eliminated. Statistical estimates lent credence to the benefit of a re-evaluation of these bus stops. The method developed in this study can easily be replicated. Hence, it may inspire other school systems to exercise the same approach. Additionally, the results provide a gateway for future studies in examining more efficient school bus routes with less travel time, as well as investigating how much the carbon footprint of school bus fleets can be reduced.
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School Bus Routing Problem

The School Bus Routing Problem (SBRP) is the objective of a fleet of school buses to efficiently pick up and drop off students to and from school (Li & Fu, 2002). These objectives must abide by specific constraints, such as bus capacity, the number of available buses, school start time, and maximum allowable travel time (Leiva, Muñoz, Giesen, & Larrain, 2010; Spasovic et al., 2001). Several studies have shown that it is almost impossible to efficiently design bus routes that account for all of these desired factors (See Li & Fu, 2002; Spasovic, Chien, Kelnhofer-Feeley, Wang, & Hu, 2001). With the multitude of the SBRP’s variables, it is challenging to balance the objective of the school system in reducing costs and the objective in maintaining an acceptable level of equity for the bus riders (Spasovic et al., 2001).

In some specific case studies, SBRP is also related to other parameters, including the Multiple Traveling Salesman Problem, Vehicle Routing Problem, and urban bus routing (Li & Fu, 2002; Park & Kim, 2010; Schittekat et al., 2013). While the Multiple Traveling Salesman Problem is a classic case for a mathematical solution using Operations Research techniques (Spasovic et al., 2001), the SBRP is not easily optimized via mathematical approaches due to the complexity of the problem, the number of constraints, and sometimes conflicting variables to optimize (Li & Fu, 2002; Spasovic et al., 2001). As a result, heuristic approaches are often used to supplement purely automated algorithms (Angel, Caudle, Noonan, & Whinston, 1972). Many school systems even neglect mathematical and GIS solutions completely. For instance, school systems in Hong Kong simply use transportation official’s intuition to manually design each route, as well as place each bus stop (Li & Fu, 2002).

The SBRP is made up of a number of different parameters. While some researchers have taken holistic approaches to address all of the aspects at once (Desrosiers, Sauvé, & Soumis, 1988), others have attempted to address them somewhat independently (Newton & Thomas, 1969; Park and Kim, 2010), even though they are highly related. Treating the parameters with some degree of independence reduces the complexity and allows for specific local constraints and variables to be factored in (Park & Kim, 2010).

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