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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DMU (Week_RouteDirection)

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Score PosRT. Met r i c NagRT. Met r i c PosHW. Met r i c NagHW. Met r i c

Illustration of Relationship between Performance Scores and Metric Values