Data-Driven Public Transport Routes and Timetables Based on Anonymized Telecom Data

Nikolay Netov () and Radoslav Rizov ()
Additional contact information
Nikolay Netov: St. Kliment Ohridski Sofia University
Radoslav Rizov: St. Kliment Ohridski Sofia University

A chapter in Eurasian Business and Economics Perspectives, 2024, pp 219-231 from Springer

Abstract: Abstract Human activities, including daily urban commuting, usually demonstrate a mix of sustainable and changing behavioral patterns. The development and continuous update of optimal routes and schedules for public transport is a key element in satisfying the transport demands of dwellers. The data available demonstrates a perceived private car dominance over public transport use in Bulgaria. The limited capabilities of cities to develop and introduce optimal routes and schedules of public transport services based on citizens’ actual demands and needs is considered a major shortcoming. The aim of the article is to validate an approach for public transport optimization based on anonymous and statistically aggregated mobile phone positioning data. Our results, which focus exclusively on this novel kind of data, supplement earlier studies on the use of Seasonal ARIMA models in short-term passenger demand forecasting. This allowed us to make a statistical assessment and forecast potential future passenger flow for our chosen routes at different time ranges of the day. Data-driven public transport routes and timetables would make public transport more attractive and useful. Reducing the share of private car trips will improve the air quality and will help lower the level of noise pollution and CO2 emissions in our cities.

Keywords: Public transport; Passenger flow; Bulgaria; Travel behavior; SARIMA (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:eurchp:978-3-031-62719-4_12

Ordering information: This item can be ordered from
http://www.springer.com/9783031627194

DOI: 10.1007/978-3-031-62719-4_12

Access Statistics for this chapter

More chapters in Eurasian Studies in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().