LTC, Jack R. Widmeyer Transportation Research Conference, 11/04/2011, Yong LaoWebinar Transcript
Performance Evaluation of Bus Lines with Data Envelopment Analysisand Geographic Information Systems Yong Lao, Professor Division of Social, Behavioral and Global Studies California State University Monterey Bay November 4, 2010
Project Background Currently public transit agencies are under increasing pressure to operate more efficiently as the level of government funding reduces, or as a result of changing ownerships or regulations. The majority of the existing research focuses on the operations of the public transit system, attempting to evaluate performances from the management perspective. The characteristics of local population and commuting pattern largely determine the passenger demand as well as operational scale for the public transit system.
The Goal To combine Data Envelopment Analysis (DEA) and Geographic Information Systems (GIS) to examine the performance of the public transit system in Monterey-Salinas area. Operational efficiency: measures the productivity of a public transit agency, focusing on the input elements controlled by the management. Spatial effectiveness: measures how well the general public is being served, focusing on the environmental elements often beyond the control of the management.
Questions Raised What are the operational costs and benefits associated with a bus line? How to identify and create the service corridor, demographic profile and travel pattern associated with a bus line? How to measure and compare the operational efficiency and spatial effectiveness of bus lines?
The Use of GIS Overlay and analyze demographic variables at census tract level. Population density (population per sq miles) Population 65 years and over Journey to work by bus Private vehicle occupancy Total disabilities Median household income
Using the Weighted Linear Combination Method to Model the Level of Demand
The Level of Service Calculate the number of bus stops per census tract. Calculate the level of service by dividing the number of bus stops by the level of demand at each census tract.
Data Envelopment Analysis Data Envelopment Analysis (DEA) is a widely used optimization technique to evaluate efficiencies of decision making units First introduced by Charnes and Cooper in 1978 Examples: banks, schools, libraries, government agencies, etc.
Using DEA to Evaluate MST Bus Lines Monterey Salinas Transit (MST) Currently the MST transit system serves a 280 square-mile area of Monterey County and Southern Santa Cruz County With an annual budget of $20.2 million, MST employs more than 2100 people, operating 86 vehicles and 50 routes. Each bus line is treated as a DMU in the DEA model. There are 24 fixed bus lines
DEA Model Variables j: decision making units, j = 1,…,n i: input， i = 1,…,m r: output， r = 1,…,s xij: The i th input for DMU j yrj: the r th output for DMU j λj: the weight parameter for DMU j µ: the level of output θ: relative efficiency score, θ = 1/µ
The DEA Model
Input and Output Variables for the DEA Model
The Service Corridor Of A Bus Line
DEA Model Results and Recommendations
Comparison of Operational Efficiency and Spatial Effectiveness
Conclusions By combining GIS and DEA, we are able to closely monitor the commuting pattern, demographic information, and performance related to each bus line. GIS is mainly used for preparing and analyzing data for the DEA model. The DEA approach can help us to better understand the impact of socio-economic environment on business operations. The results of the study provide useful information for improving MST operations and services.