Approaches, technologies and importance of analysis of the number of train travellers

Sørensen, Anette Østbø; Olsson, Nils O.E.; Akhtar, Muhammad Mohsin; Bull-Berg, Heidi

doi:10.1080/21650020.2019.1566022

Cited by 10 publications

(6 citation statements)

References 50 publications

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“…This research adopted a simple approach in the form of a manual survey with the technicality of distributing questionnaires using the Google application survey form. Furthermore, this is consistent to the studies carried out by Sorensen et al [17], and Cerna et al [18], which stated that in estimating the potential use of logistic modes of transportation namely road, rail, water and air transportation in Slovakia, a subjective approach based on the distribution of questionnaires designed to contain various service attributes that affect freight forwarding companies was adopted.…”

Section: Methodssupporting

confidence: 86%

“…The method used to determine the potential users of railroad transportation modes across Sumatra, was operated according to new service attributes in the form of seamless integration with online transportation is a simple approach that involves the distribution of questionnaires using the Google form survey application. Sorensen et al [17] reported that several alternative methods are used in calculating the number of railway trips, namely manually (simple), sensor measurement tools, ticket systems and mobile phones. This research adopted a simple approach in the form of a manual survey with the technicality of distributing questionnaires using the Google application survey form.…”

Section: Methodsmentioning

confidence: 99%

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Potential Use of Trans-Sumatra Railway Through Seamless Integration Service with Online Transportation

Miro¹,

Purnawan²,

Adji³

et al. 2023

IJTDI

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This research aims to determine the initial stage of the process series and estimate the level of travelers' desire from Padang to other cities on the Sumatra Island using the trans-Sumatra railway transportation mode planned by the government to operate in 2024. Due to the enormous cost of development investment, data was obtained by observing potential users through a preliminary survey and by distributing questionnaires to residents of Padang city. This was carried out to determine the public's interest in the railway mode, which has been served by flexible transportation modes, such as Inter-City Inter-Provincial buses and planes. Furthermore, the analytical method was used to determine whether the community wants to use the trans Sumatra rail mode and the implementation of new services to integrate with a Seamless system in train operations. Studies have not been previously conducted on a Seamless door-to-door service. The result showed a need for new service attributes in the rail transportation mode by making the train flexible with door-to-door service and the implementation of a Seamless Service with 1 ticket.

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Section: Methodssupporting

confidence: 86%

Section: Methodsmentioning

confidence: 99%

Potential Use of Trans-Sumatra Railway Through Seamless Integration Service with Online Transportation

Miro¹,

Purnawan²,

Adji³

et al. 2023

IJTDI

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“…Access to passenger information can be difficult when evaluating the value of rail infrastructure investments. There is currently a digital transformation focus on railways in terms of utilizing big data for better management of resources including passenger utilization of assets [169]. The recent development of European standard (EN12896) for a reference data model for public transport information has provided for sharing of timetabling, fares, operational management, real-time data, and journey planning across railways [170].…”

Section: E Big Data Analytics Tool Selection and Challengesmentioning

confidence: 99%

Requirements for Big Data Adoption for Railway Asset Management

2020

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Nowadays, huge amounts of data have been captured along with the day-today operation of assets including railway systems. Hence, we have come to the era of big data. The utilization of big data technologies for asset condition information management is becoming indispensable for improving asset management decision making. The vital information such as precursor information collected on failure modes and knowledge that may be available for analysis is hidden within the large extent of data. There are analysis tools incorporated with techniques such as multiple regression analysis and machine learning that are facilitated by the availability of big data. Therefore, the utilization of big data technologies for asset condition information management is becoming indispensable for improving asset management decision making. This paper provides a review of the requirements and challenges for big data analytics applications to railway asset management. The review focuses on railway asset data collection, data management, data applications with the implementation of Blockchain technology as well as big data analytics technologies. The need for, and the importance of big data analytics in railway asset management; and the requirement for the asset condition data collection in the railway industry are highlighted. Research challenges in railway asset management via application of big data analytics are identified and the future research directions are presented. INDEX TERMS Big data analytics, asset management, railway, data management, blockchain.

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“…The methodology was applied to a case study in Netherlands, and was shown to be valuable in supporting tactical transit planning and decision making. Sørensen et al (2017) identify several technologies for measuring ridership on trains. The technologies and approaches include (1) manual counts and surveys, (2) on-board sensors, such as door passing, weight, CCTV and Wi-Fi-use, (3) ticketing systems, ticket sales or ticket validation, and (4) tracking of travellers for larger part of the journey, such as tracking of mobile phones and payments.…”

Section: Research On Train Ridership Is Importantmentioning

confidence: 99%

Use of mobile phone data for analysis of number of train travellers

Sørensen

Bjelland

Bull-Berg

et al. 2018

Journal of Rail Transport Planning & Management

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Several studies have pointed to the difficulties of obtaining good data on train ridership. There are at least two challenges regarding these data. First, train operators consider such data confidential business information, especially in high resolution. Second, the data that actually are available vary in quality and coverage. This paper studies mobile phone data as an alternative measure to obtain data about train ridership.Handset counts were obtained from one telecom operator for selected mobile phone base stations and compared with timetable data and APC. The selected base stations are located so that it is likely that a large share of the mobile phone traffic is generated by train passengers. The number of units connected to a base station is found to correspond relatively well with the trains that pass close to the base stations. A ratio between the handset count and APC data appear as promising in utilising handset count to calculate train ridership, with ratios around one in the rush hours. We discuss preliminary results as well as methodological and technical challenges.To make sure that we do not violate privacy concerns, the data used in the study have been approved by personal privacy representatives.

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Approaches, technologies and importance of analysis of the number of train travellers

Cited by 10 publications

References 50 publications

Potential Use of Trans-Sumatra Railway Through Seamless Integration Service with Online Transportation

Potential Use of Trans-Sumatra Railway Through Seamless Integration Service with Online Transportation

Requirements for Big Data Adoption for Railway Asset Management

Use of mobile phone data for analysis of number of train travellers

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