Bus Route Schedule Adherence Assessment Using Automatic Vehicle Location (AVL) Data

Problem Statement and Motivation Key Achievements and Future Goals Technical Approach Bus Route Schedule Adherence Assessment Using Automatic Vehicle Location (AVL) Data Master’s thesis: Peng Wanga, Advisors: Jie (Jane) Lin, Darold Barnumc Department of Civil and Materials Engineering & Institute for Environmental Science and Policy, Department of Management, Funded Chicago Transit Authority (through Urban Transportation Center) • Transit service reliability has been the top 1 factor that influences customers’ satisfaction with transit service. • Reliability performance measures (e.g. running time adherence, headway regularity, etc.) often show contradicting results separately. • Objective: To demonstrate an optimization method that develops a composite performance index of bus route schedule adherence by combining two elementary metrics together. • The research demonstrates that a linear program method is able to generate one single composite measure that accounts for all input measures properly. The method is testd on 48 CTA bus route- directions over 6 months in 2006, using the archived continuous Automatic Vehicle Location (AVL) data collected by on-board devices on CTA buses. • Future direction: to expand the study to including more performance measures and the entire CTA bus system. • Development of elementary reliability performance measures using archived panel AVL data obtained from CTA • Using a linear program model based on Data Envelopment Analysis (DEA) to combine the above four individual measures into a single composite index • Using panel data analysis technique to estimate the confidence intervals of the obtained performance scores • Conducting DEA-based sensitivity analysis to investigate the influence of input variations on the generated performance scores 0 50 100 150 200 250 16_C 17_U 30_f 8_f 30_H 4_f 11_U 7_f 8_c 15_c 1_c 5_O 30_o 26_X 30_X 30_O DM U (W eek_R outeD irection) Percentage Score PosRT.Metric NagRT.Metric PosHW.Metric NagHW.Metric Illustration of Relationship between Performance Scores and Metric Values

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Bus Route Schedule Adherence Assessment Using Automatic Vehicle Location (AVL) Data. Master’s thesis: Peng Wanga , Advisors: Jie (Jane) Lin, Darold Barnumc Department of Civil and Materials Engineering & Institute for Environmental Science and Policy, - PowerPoint PPT Presentation

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Page 1: Bus Route Schedule Adherence Assessment Using Automatic Vehicle Location (AVL) Data

Problem Statement and Motivation

Key Achievements and Future GoalsTechnical Approach

Bus Route Schedule Adherence Assessment Using Automatic Vehicle Location (AVL) Data

Master’s thesis: Peng Wanga, Advisors: Jie (Jane) Lin, Darold Barnumc Department of Civil and Materials Engineering & Institute for Environmental Science and Policy,

Department of Management, Funded Chicago Transit Authority (through Urban Transportation Center)

• Transit service reliability has been the top 1 factor that influences customers’ satisfaction with transit service.

• Reliability performance measures (e.g. running time adherence, headway regularity, etc.) often show contradicting results separately.

• Objective: To demonstrate an optimization method that develops a composite performance index of bus route schedule adherence by combining two elementary metrics together.

• The research demonstrates that a linear program method is able to generate one single composite measure that accounts for all input measures properly. The method is testd on 48 CTA bus route-directions over 6 months in 2006, using the archived continuous Automatic Vehicle Location (AVL) data collected by on-board devices on CTA buses.

• Future direction: to expand the study to including more performance measures and the entire CTA bus system.

• Development of elementary reliability performance measures using archived panel AVL data obtained from CTA

• Using a linear program model based on Data Envelopment Analysis (DEA) to combine the above four individual measures into a single composite index

• Using panel data analysis technique to estimate the confidence intervals of the obtained performance scores

• Conducting DEA-based sensitivity analysis to investigate the influence of input variations on the generated performance scores

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16_C 17_U 30_f 8_f 30_H 4_f 11_U 7_f 8_c 15_c 1_c 5_O 30_o 26_X 30_X 30_O

DMU (Week_RouteDirection)

Per