University Bus Systems: Network Flow Demand Analysis By Craig Yannes University of Connecticut October 21, 2008

Introduction  University bus systems are an important form of transportation around university campuses  Research has shown that these systems generate more ridership than their counterparts (Daggett and Gutkowski, 2005)  University enrollment has also been expanding

Problem  The sharp increase in demand has put a strain on university resources to provide an effective bus system  Many routing design decisions have been made subjectively  This results in an inefficient system, which leaves demand centers unidentified, underserved or unserved

Solution  Network flow theory, in conjunction with spatial analysis (GIS), can help remove the subjectivity from university bus system design  Focus of this project is the selection of optimal stopping locations considering operational cost while serving the maximum passengers (demand)

Goals  Create a simple and efficient model framework to analyze the coverage of a university bus system  Analyze the effects of stopping service areas (walk distance to stop) on the selection of optimal stopping locations

Objectives  Collect and generate transportation link and transit demand data  Generate and analyze demand data to create centers which will serve as potential bus stops  Create a network flow model representation and use an appropriate solution technique, yielding the optimal demand centers to be served by the bus system at differing service areas

Background  The following have utilized network flow algorithms and theory to solve transit design (routing, scheduling, frequency, etc.) problems: Ceder and Wilson (1986) Chu and Hobeika (1979) Ranjithan, Singh and Van Oudheusden (1987) LeBlanc (1988) Kocur and Hendrickson (1982) Kuah and Perl (1985)

Background  Overall transit design involves two interest groups (passenger and operator)  Depending on the component being designed, the focus shifts between these groups  This proposed research will focus solely on operator costs while attempting to serve the maximum amount of passengers

Background  Furth, Mekuria and SanClemente (2007) created and used GIS applications to analyze the spacing of transit stops based on the street network and parcel data  The study evaluated walking, riding and operating costs when stops were reconfigured from the existing placement  Similar to this research except that no network flow theory was used

Network Representation The following design was used to create a network representation of the bus system: Arcs leading to accessible nodes based on current location Cost on Links is a function of distance and demand Nodes representing potential stopping locations Supply Node (Bus Depot) Supply = Number of demand nodes Destination nodes which are spread throughout the exterior of the network to pull flow in all directions Demand = 1 for each node

Data  Connecticut Department of Environmental Protection: Connecticut Street Network Shapefile (1:100,000)  2006 UConn personal geodatabase which included street, building and parking lot feature classes

Transportation Network

Demand  Calculated using ITE Trip Generation Manual  Produces auto trips based on the building/area purpose and attributes such as area, number of seats and number of units  Although this research focuses on transit trips, the auto trips can be used to determined relative demand between locations

Building and Parking Lot Demand

Potential Stopping Locations  32 Locations were selected at points along the roadway near key intersections and large generators  The demand at each one of these stops is equal to the sum of demand of the buildings and parking lots within a particular distance (1/8, 1/4, 1/2 mile) of the stopping location  5 destination locations were also selected such that the flow would be spread around the network evenly

Potential Stopping Locations

Link Generation between Stops  Links represent the transportation path between two stops though it does not follow street layout directly  The following rules were used to create the links between the stops: Links must proceed in a forward progression (must be getting closer to a destination) Links cannot pass through stops Stops on the exterior must connect with sink locations

Link Generation

Link Cost  Combination of two factors Distance between stops Demand at the ending stop  Because higher demand should incur less cost the inverse demand was used  This requires a scaling factor so that demand values are comparable in magnitude to distance 

Network Representation

Solution Technique  A geometric network was created in GIS weighted with the calculated link cost  The Network analyst toolset in ArcGIS was used to find the shortest path between the source node and each of the 5 sink nodes  The nodes that lie on these shortest paths are the optimal stopping locations

1/8 Mile Service Area

1/4 Mile Service Area

1/2 Mile Service Area

Results Path 1/8 mile1/4 mile1/2 mile Stops Distance (miles) Stops Distance (miles) Stops Distance (miles) Total 17 stops (12 unique) stops (11 unique) stops (13 unique) 6.63

Conclusions  A model framework has been created which analyzes a bus network yielding optimal stopping locations  Increasing the service area, reduces the distance traveled and increases that amount of unique stops served by the system  Planners must be cautious when trying to balance ridership and efficiency  Analyzing the system for different service areas can help quantify this relationship and create a more efficient system

Future Research  Expand the analysis area to include other locations surrounding the campus  Acquire or generate more accurate demand data  Comparison / application to the existing bus system  Incorporate the effect of larger service areas on demand  Create similar network frameworks that focus on the other aspects of bus system design

Comments / Questions?

References  Daggett J. and Gutkowski R. (2003). University Transportation Survey: Transportation in University Communities. Colorado State University.  Sutton J. C. GIS Applications in Transit Planning and Operations: A Review of Current Practice, Effective Applications and Challeneges in the USA. Transportation Planning and Technology, Vol. 28, 2005, pp  Furth P. G., Mekuria M. and SanClemente J. Stop-spacing Analysis Using GIS Tools with Parcel and Street Network Data. Presented at 86th Annual Meeting of the Transportation Research Board, Washington, D.C.,  Ceder A. and Wilson N. H. M.. Bus Network Design. Transportation Research Part B, Vol. 20B, 1986, pp  Chu C. and A. G. Hobeika. In Transportation Research Record: Journal of the Transportation Research Board, No.730, Transportation Research Board of the National Academies, Washington, D.C., 1979, pp  Institute of Transportation Engineers (ITE). Trip Generation, 6th ed., Washington, D.C.,  Kocur G. and Hendrickson C. Design of Local Bus Service with Demand Equilibrium. Transportation Science, Vol. 16, 1982, pp  Kuah G. K. and Perl J. A methodology for feeder bus network design. In Transportation Research Record: Journal of the Transportation Research Board, No.1120, Transportation Research Board of the National Academies, Washington, D.C., 1985, pp. 40–51.  LeBlanc L. J. (1988). Transit system network design. Transportation Research Part B, Vol. 22B, 1988, pp  Ranjithan S., Singh K. N., and Van Oudheusden D. L. The Design of Bus Route Systems – An Interactive Location-Allocation Approach. Transportation, Vol. 14, 1987, pp

References  GIS Data Connecticut Department of Environmental Protection Richard Mrozinski, University of Connecticut Department of Geography  Images Title: Problem: ,00.html ,00.html Background: Questions: