Performance Evaluation of Bus Lines with Data Envelopment Analysisand Geographic Information Systems<br />Yong Lao, Profes...
Project Background<br />Currently public transit agencies are under increasing pressure to operate more efficiently as the...
The Goal<br />To combine Data Envelopment Analysis (DEA) and Geographic Information Systems (GIS) to examine the performan...
Questions Raised<br />What are the operational costs and benefits associated with a bus line?<br />How to identify and cre...
The Use of GIS<br />Overlay and analyze demographic variables at census tract level.<br />Population density (population p...
Using the Weighted Linear Combination Method to Model the Level of Demand <br />
The Level of Service<br />Calculate the number of bus stops per census tract.<br />Calculate the level of service by divid...
Data Envelopment Analysis<br />Data Envelopment Analysis (DEA) is a widely used optimization technique to evaluate efficie...
Using DEA to Evaluate MST Bus Lines<br />Monterey Salinas Transit (MST)<br />Currently the MST transit system serves a 280...
DEA Model Variables<br />j: decision making units, j = 1,…,n<br />i: input, i = 1,…,m<br />r: output, r = 1,…,s<br />xij: ...
The DEA Model<br />
Input and Output Variables for the DEA Model<br />
The Service Corridor Of A Bus Line<br />
DEA Model Results and Recommendations<br />
Comparison of Operational Efficiency and Spatial Effectiveness<br />
Conclusions<br />By combining GIS and DEA, we are able to closely monitor the commuting pattern, demographic information, ...
Thank you<br />
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LTC, Jack R. Widmeyer Transportation Research Conference, 11/04/2011, Yong Lao

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LTC, Jack R. Widmeyer Transportation Research Conference, 11/04/2011, Yong Lao

  1. 1. Performance Evaluation of Bus Lines with Data Envelopment Analysisand Geographic Information Systems<br />Yong Lao, Professor<br />Division of Social, Behavioral and Global Studies<br />California State University Monterey Bay<br />November 4, 2010<br />
  2. 2. Project Background<br />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.<br />The majority of the existing research focuses on the operations of the public transit system, attempting to evaluate performances from the management perspective.<br />The characteristics of local population and commuting pattern largely determine the passenger demand as well as operational scale for the public transit system.<br />
  3. 3. The Goal<br />To combine Data Envelopment Analysis (DEA) and Geographic Information Systems (GIS) to examine the performance of the public transit system in Monterey-Salinas area.<br />Operational efficiency: measures the productivity of a public transit agency, focusing on the input elements controlled by the management.<br />Spatial effectiveness: measures how well the general public is being served, focusing on the environmental elements often beyond the control of the management.<br />
  4. 4. Questions Raised<br />What are the operational costs and benefits associated with a bus line?<br />How to identify and create the service corridor, demographic profile and travel pattern associated with a bus line?<br />How to measure and compare the operational efficiency and spatial effectiveness of bus lines?<br />
  5. 5. The Use of GIS<br />Overlay and analyze demographic variables at census tract level.<br />Population density (population per sq miles)<br />Population 65 years and over<br />Journey to work by bus<br />Private vehicle occupancy<br />Total disabilities<br />Median household income<br />
  6. 6. Using the Weighted Linear Combination Method to Model the Level of Demand <br />
  7. 7. The Level of Service<br />Calculate the number of bus stops per census tract.<br />Calculate the level of service by dividing the number of bus stops by the level of demand at each census tract.<br />
  8. 8. Data Envelopment Analysis<br />Data Envelopment Analysis (DEA) is a widely used optimization technique to evaluate efficiencies of decision making units<br />First introduced by Charnes and Cooper in 1978<br />Examples: banks, schools, libraries, government agencies, etc.<br />
  9. 9. Using DEA to Evaluate MST Bus Lines<br />Monterey Salinas Transit (MST)<br />Currently the MST transit system serves a 280 square-mile area of Monterey County and Southern Santa Cruz County<br />With an annual budget of $20.2 million, MST employs more than 2100 people, operating 86 vehicles and 50 routes. <br />Each bus line is treated as a DMU in the DEA model.<br />There are 24 fixed bus lines<br />
  10. 10.
  11. 11. DEA Model Variables<br />j: decision making units, j = 1,…,n<br />i: input, i = 1,…,m<br />r: output, r = 1,…,s<br />xij: The i th input for DMU j<br />yrj: the r th output for DMU j<br />λj: the weight parameter for DMU j<br />µ: the level of output<br />θ: relative efficiency score, θ = 1/µ <br />
  12. 12. The DEA Model<br />
  13. 13. Input and Output Variables for the DEA Model<br />
  14. 14. The Service Corridor Of A Bus Line<br />
  15. 15. DEA Model Results and Recommendations<br />
  16. 16.
  17. 17.
  18. 18.
  19. 19.
  20. 20. Comparison of Operational Efficiency and Spatial Effectiveness<br />
  21. 21. Conclusions<br />By combining GIS and DEA, we are able to closely monitor the commuting pattern, demographic information, and performance related to each bus line.<br />GIS is mainly used for preparing and analyzing data for the DEA model. <br />The DEA approach can help us to better understand the impact of socio-economic environment on business operations.<br />The results of the study provide useful information for improving MST operations and services.<br />
  22. 22. Thank you<br />
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