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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21340/25:00390691

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20104 - Transport engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

2336-4335

Svazek periodika

35

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

22

Strana od-do

101-122

Kód UT WoS článku

001730356700001

EID výsledku v databázi Scopus

2-s2.0-105035983295