Bus travel time variability: some experimental evidences

Antonio Comi · Agostino Nuzzolo · Stefano Brinchi · Renata Verghini

40 citations0/0 files storedTransportation research procedia

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Transportation Planning and Optimization Traffic control and management Traffic Prediction and Management Techniques

Abstract

Bus travel time analysis is essential for transit operation planning. Then, this topic obtained large attention in transport engineering literature and several methods have been proposed for investigating its variability. Nowadays, the availability of large data quantities through automated monitoring allows more in-depth this phenomenon to be pointed out with new experimental evidence. The paper presents the results of some analyses carried out using automatic vehicle location (AVL) data of bus lines and automated vehicle counter (AVC) data on some corridors in the urban area of Rome where the bus services are mixed with other traffic and travel times are subject to high degrees of variability. The results show the effect of temporal dimension and similarity between travel time and traffic temporal patterns, and could open the road for the improvement of the short-term forecasting methods, too.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 40 citations exist upstream.

References

2004A Dynamic Bus‐Arrival Time Prediction Model Based on APC Data 2015Improving Mass Transit Operations by Using AVL-Based Systems: A Survey 2016An online learning approach to eliminate Bus Bunching in real-time 2016Using survival models to estimate bus travel times and associated uncertainties 2014Regularity-driven bus operation: Principles, implementation and business models 2016Advanced public transport and intelligent transport systems: new modelling challenges 2004The prediction of bus arrival time using automatic vehicle location systems data 2012Public bus transit travel-time variability in Ankara (Turkey)

Cited by

2022Predicting the probability distribution of bus travel time to measure the reliability of public transport services 2019Bus Travel Time Prediction Model Based on Profile Similarity 2021Dynamic Optimal Travel Strategies in Intelligent Stochastic Transit Networks 2018Determinants of Bus Riding Time Deviations: Relationship between Driving Patterns and Transit Performance 2023Acceptability of transportation demand management policy packages considering interactions and socio-economic heterogeneity

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.