Review of global practice of using cell phone data to measure traveller data

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Ukrainian State University of Railway Transport, Ukraine

Review of global practice of using cell phone data to measure traveller data

Yurii Yashchuk

post graduate student

Scientific adviser: Tetiana Butko

Dr. Sc. (Tech.), professor, professor

of the Department of management of operational work

JSC "Ukrzaliznytsia" is a national integrated railway company and the largest employer in the country, playing a crucial role in the Ukrainian economy and the labour market. The rail companies faces significant risks, mainly due to prolonged disruptions due to the ongoing armed conflict on Ukrainian territory [1]. In such conditions, the question of the need for a comprehensive solution to the problems of the functioning of passenger railway transport, where the number of travellers serves as a pivotal indicator of the demand for transportation services, offering essential information for planning and evaluations. Advancements in technology have enabled the collection of crowd-sourced traffic data [2]. Crowdsourced smartphone data can collect information about user locations, travel paths, route choices, travel times, and speeds. This analysis explores how the number of passengers in trains is measured, examining technologies and methods that use cell phone data globally to assess their potential application in Ukraine.

When looking at global practices, two significant challenges emerge in the search for ridership data. One challenge is the tendency of train operators to regard such information, especially high-resolution data, as confidential business details [3]. Another challenge lies in the varying quality and coverage of the available data. Previous research, exemplified by [4], has addressed the unreliability of ridership data.

Quantification of rail travel demand typically relies on the number of passengers, with more intricate metrics including the common origin / destination matrix format. Traditionally, there are various approaches to determine the demand between an origin and destination point. The most prevalent method is the O-D matrix, which delineates population transitions between different geographical regions representing the route's origin (O) and destination (D). User surveys are the most commonly used method to populate these matrices. Traditional surveys offer strengths, such as including crucial responses' informa tion such as age and sex, along with details about the purpose of the trip [5]. However, a significant challenge with user surveys is the declining response rates, potentially introducing bias into the samples [6]. These surveys are typically conducted no more than once a year and may lack regularity. Consequently, this method may suffer from low frequency, high cost, variable data quality, low precision, and susceptibility to errors.

Mobile phone data can elucidate people's movement patterns, as demonstrated in [7] study, where the mobile phone records of a million users in Boston were analysed to describe transportation needs. Passenger counting serves as the pivotal measurement parameter associated with ridership. Different types of measurement and ridership estimation techniques are applied for different network levels. The selection of the appropriate network level depends on the specific use and issue being addressed outline the uses of passenger counting and ridership calculation based on data measurement methods. These uses vary for each measurement type, and operators typically employ multiple types to fulfil various purposes.

Considerable research has concentrated on developing methodologies to extract valuable insights into human mobility from mobile phone traces and understanding their limitations [5]. Mobile phone data can be used to estimate commuting patterns and travel times for individuals. Chaudhary et al. [8] discuss the collection of information about occupancy levels in public transportation systems using smartphones, demonstrating the predictive accuracy of patterns in bus occupancy levels up to 92%. Higuchi et al. [9] identify innovative uses based on mobile devices, including technologies commonly found in smartphones such as GPS, Wi-Fi, and Bluetooth. Various approaches involve analysing the exchange of information between the mobile base station and the cellular network to calculate this information. Most studies perform trip extraction from raw cell network data to extract relevant movements for traffic analysis [5 etc.]. The authors use different scaling factors to estimate total travel demand in terms of the number of people travelling.

Mobile phones, through activated apps, are an expanding data source. Apps can track entire journeys, especially when users have allowed GPS tracking. These applications can be supplied by private or public transportation entities or can be used for navigation, health monitoring, or other purposes. Data from these apps are typically managed by the app-issuing organisation, not by the mobile network managers. The use of mobile phone data has both advantages and disadvantages. For example, CDR data provide approximate locations when the phone communicates with a cell phone tower, offering an incomplete and imprecise view of daily trips. Furthermore, mobile phone data cannot provide information about the traveller, such as age, income, or the purpose of the trip, as a survey would [5]. However, mobile phone data are automatically collected, making them more frequent and economical than surveys. Additionally, since mobile phone data can be gathered over a more extended period, they can capture information about variations in travellers' daily travel behaviour.

Thus, there is the possibility to use mobile phone data to depict travel patterns, specifically those involving train travel. A notable positive surge is observed when trains pass and it becomes feasible to integrate mobile data with information on train traffic. This integrated approach can be applied in the organisation of train passenger travel by train using crowd-sourced traffic data.

References

passenger railway transport crowdsourcing

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