01128597

Component

https://doi.org/10.3141/2105-01

http://scholar.google.com...nier&publication_year=2009

A new concept in transit travel surveys, called the driver-assisted bus interview, is proposed. The survey uses data that are passively gathered by smart card automatic fare collection systems on public transit. Its superiority lies in the resolution of the data as well as the continuous geographic and temporal coverage of the network and cardholders. The paper first discusses the quality of survey data. It then describes a totally disaggregate object-oriented approach as a method to understand, validate, correct, and enrich the data. The study uses one month of archived smart card boarding data from a medium-size transit agency. The data go through a validation and correction process that makes use of planned service and cardholders’ historic travel pattern. Trip data not collected by the survey are obtained through enrichment techniques. The anchor points of a cardholder can be inferred from the derived employment status, multiday travel pattern, and a trip-generator database. The procedure that infers trip destination and trip purpose for the student subgroup is explained. Advanced analysis and visualization techniques demonstrate the versatility of the data, which can be scrutinized as a travel demand survey, a special trip generator survey, a resource allocation and consumption survey, and a multiday survey.

01141653

09-0822

English

pp 1-10

2009

Transportation Research Record: Journal of the Transportation Research Board

9780309126205

Print

Figures (8) ; References (19)

Automatic fare collection; Bus transit operations; Data collection; Data quality; Public transit; Smart cards; Transit operating agencies; Travel demand; Travel patterns; Travel surveys; Trip generation; Trip purpose

Data enrichment; Trip data

Data and Information Technology; Highways; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Jan 30 2009 5:03PM

• A Semidirective Interview Method to Analyze Behavioral Changes: A Focus on Two Case Studies
• Bayesian Training and Committees of State-Space Neural Networks for Online Travel Time Prediction
• Development of Geolocation Data Compression for Transportation Target Identification
• Distributed Approach for Estimation of Dynamic Origin–Destination Demand
• Efficiency and Effectiveness of Cell Phone Samples in Transportation Surveys
• Forecasting Network Data: Spatial Interpolation of Traffic Counts from Texas Data
• Generating Internationally Comparable Figures on Long-Distance Travel for Europe
• Household Travel Surveys with GPS: An Experiment
• Improving the Usability of a Complex Household Travel Survey: An Evaluation of User Requirements
• Kinematic Terrestrial Light-Detection and Ranging System for Scanning
• Measuring and Estimating Suppressed Travel with Enhanced Activity–Travel Diaries
• Model-Based Synthesis of Household Travel Survey Data in Small and Midsize Metropolitan Areas
• Participatory Geographic Information Systems and Active Transportation: Collecting Data and Creating Change
• Processing Raw Data from Global Positioning Systems Without Additional Information
• Transport and Surveillance Aspects of Location-Based Services
• Vehicle Detection from Satellite Images: Intensity-, Chromaticity-, and Lane-Based Method