Transit Buses as Traffic Probes: Use of Geolocation Data for Empirical Evaluation

Author:

Bertini Robert L.1,Tantiyanugulchai Sutti1

Affiliation:

1. Department of Civil and Environmental Engineering, Portland State University, P.O. Box 751, Portland, OR 97207-0751

Abstract

With the growing availability of data because of the deployment of intelligent transportation systems, methods for assessing and reporting traffic characteristics and conditions have begun to shift. Although previous level-of-service methods were developed for use with limited data, actual performance measures can now be developed and tested. On freeways, performance measures often are estimated directly by using data from inductive loop detectors (e.g., speed, occupancy, vehicle counts). For arterials with numerous signalized intersections, performance measures are more challenging because of more complicated traffic control and many origins and destinations. However, within signalized networks, travel time, speed, and other key performance measures can be obtained both directly and indirectly from sources such as automatic vehicle location (AVL) data. The use of AVL data for characterizing the performance of an arterial is demonstrated. First, data are extracted from the bus dispatch system of the Tri-County Metropolitan Transit District (TriMet), the transit provider for Portland, Oregon. Then, the performance characteristics as described by bus travel on an arterial are compared to ground truth data collected by probe vehicles equipped with Global Positioning System sensors traveling with normal (nontransit) traffic on the same arterial on the same days. Comparisons are made between the two methods, and some conclusions are drawn regarding the utility of the transit AVL data.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Civil and Structural Engineering

Reference9 articles.

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1. Real-time Incident Detection in Public Bus Systems Using Machine Learning;2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC);2023-09-24

2. Use of High-Resolution Signal Controller Data to Measure Transit Signal Priority Performance: A Case Study in the Boston Region;Transportation Research Record: Journal of the Transportation Research Board;2023-09-07

3. Automated Traffic Surveillance Using Existing Cameras on Transit Buses;Sensors;2023-05-26

4. Transit Arrival Time Prediction Using Interaction Networks;IEEE Transactions on Intelligent Transportation Systems;2023-04

5. Speed data collection methods: a review;Transportation Research Procedia;2023

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