Unconventional Public Transport Anonymous Data Collection employing Artificial Intelligence

Marius G. Minea · Catalin Marian Dumitrescu · Ionuț-Cosmin CHIVA

8 citations0/0 files storedInternational Conference on Electronics, Computers and Artificial Intelligence (ECAI)

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

Human Mobility and Location-Based Analysis Transportation Planning and Optimization Urban Transport and Accessibility

Abstract

This paper presents results of a research concerning the possibility of collecting anonymous data regarding public transport level of service, employing wireless technologies (Bluetooth and Wi-Fi), big data and statistical filtering. Machine learning algorithms are used to detect, locate, create a map and record presence of travelers in public transport vehicles and stations, along with regular traffic data. The solution could also serve as back-up system for locating vehicles on their path.

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 — 8 citations exist upstream.

References

2017A probabilistic Passenger-to-Train Assignment Model based on automated data 2015Transit passenger origin–destination flow estimation: Efficiently combining onboard survey and large automatic passenger count datasets

Cited by

2022Predicting public transport arrival time and congestion based on BLE beacon crowdsourced data

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.