An Incremental Probabilistic Model to Predict Bus Bunching in Real-Time

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Luis Moreira Matias (Author) Other; Joao Gama (Author) FEP; Joao Mendes Moreira (Author) FEUP; Jorge Freire de Sousa (Author) FEUP

Conference proceedings International

Title: ADVANCES IN INTELLIGENT DATA ANALYSIS XIII

Pages: 227-238

13th International Symposium on Intelligent Data Analysis (IDA)

Leuven, BELGIUM, OCT 30-NOV 01, 2014

Indexing

ISI Proceedings

ISI Web of Knowledge - 20 Citations

Scopus - 22 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

Other information

Authenticus ID: P-009-Z2A

DOI: 10.1007/978-3-319-12571-8_20

Abstract (EN): In this paper, we presented a probabilistic framework to predict Bus Bunching (BB) occurrences in real-time. It uses both historical and real-time data to approximate the headway distributions on the further stops of a given route by employing both offline and online supervised learning techniques. Such approximations are incrementally calculated by reusing the latest prediction residuals to update the further ones. These update rules extend the Perceptron's delta rule by assuming an adaptive beta value based on the current context. These distributions are then used to compute the likelihood of forming a bus platoon on a further stop - which may trigger an threshold-based BB alarm. This framework was evaluated using real-world data about the trips of 3 bus lines throughout an year running on the city of Porto, Portugal. The results are promising.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12