Automated Identification of Delay-Generating Locations for Bus Priority Intervention Planning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F25%3A00390691" target="_blank" >RIV/68407700:21260/25:00390691 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21340/25:00390691

Result on the web

<a href="https://doi.org/10.14311/NNW.2025.35.006" target="_blank" >https://doi.org/10.14311/NNW.2025.35.006</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2025.35.006" target="_blank" >10.14311/NNW.2025.35.006</a>

Result language

angličtina

Original language name

Automated Identification of Delay-Generating Locations for Bus Priority Intervention Planning

Original language description

Public transport systems face increasing pressure to improve reliability under constrained infrastructure and limited investment capacity. Small-scale bus priority interventions represent a cost-effective tool for improving operational performance, yet their implementation requires reliable identification and prioritisation of delay-generating locations. This paper presents a fully automated method for spatial identification and quantification of delay formation in bus transport systems based exclusively on high-resolution AVL data. The proposed approach reconstructs vehicle trajectories using a high spatial resolution vectorized road network and map-matching, enabling continuous delay estimation along the entire route rather than only at stops. Referential travel times are derived empirically from historical data using a percentile-based approach, allowing delay quantification independently of static timetables and accommodating heterogeneous operating conditions. The method supports aggregation across multiple trips, lines, and corridors, providing a system-wide view of delay accumulation and its infrastructure-related causes. The methodology is demonstrated on regional bus services. An experimental evaluation of machine learning models as substitutes for referential journeys indicates that, given the available data structure, AI-based approaches fail to achieve meaningful predictive performance and cannot reliably replace the proposed statistical reference. The presented method offers a scalable and robust decision-support tool for prioritizing bus priority interventions and improving public transport reliability using operational data already available to most transport authorities.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20104 - Transport engineering

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neural Network World

ISSN

1210-0552

e-ISSN

2336-4335

Volume of the periodical

35

Issue of the periodical within the volume

6

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

22

Pages from-to

101-122

UT code for WoS article

001730356700001

EID of the result in the Scopus database

2-s2.0-105035983295