Travel estimation using Control Signal Records in cellular networks and geographical information

Yamada, Yuma; Uchiyama, Akira; Hiromori, Akihito; Yamaguchi, Hirozumi; Higashino, Teruo

doi:10.1109/wmnc.2016.7543981

Cited by 18 publications

(18 citation statements)

References 12 publications

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“…By collecting and analyzing data at the cellular positioning level, it is possible to obtain valuable origination-destination matrices [254]. With the help of geographical information, we can estimate travels and identify transport modes [255]. is insight is at another level of behavior understanding than other types of mobile data can deliver.…”

Section: Critical Discussionmentioning

confidence: 99%

Mobile Technology and Studies on Transport Behavior: A Literature Analysis, Integrated Research Model, and Future Research Agenda

Sun

Liu

Zhang

2021

Mobile Information Systems

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The prevalence of mobile technology has been significant in transport research. Despite a growing application spectrum of smartphone uses and interests in mobility inference, little effort has been put into discussing theories, models, and research topics based on a systematic study of scholarly sources rooted in the interdisciplinary area of mobile technology and transport. Therefore, a timely and comprehensive synthesis of the current state of research is deemed to be required. A literature analysis, following PRISMA guidelines, aims to identify the successful development and implementation of the mobile technology that can be employed for behavior studies in transport. A review of the Web of Science Core Collections, JSTOR and SAGE databases, is performed. A rigorous screening process is used to collect key articles to construct the general image of existing knowledge. In addition, this study suggests an integrated research model to summarize how previous studies attain behavioral outcomes and a research agenda to identify unresolved research questions that future research can address. Two hundred fourty-eight papers meet the inclusion criteria. This study demonstrates that mobile technology is helpful for a better understanding of the various types of transport behaviors. They can be categorized according to their system designs and research topics: (1) Smartphone apps in sustainable transport and travel planning were studied in a remarkable collection of articles. (2) As individual’s mobility was under question, cellular signaling data were prominent for the formulation of analytical models. (3) CDRs, WiFi, and GPS data have increasingly been used, but the share of the modeling techniques for all mobile information systems has remained low. It shows that system designers could supply more desirable and appealing features in most areas. However, applications for the movement of goods are limited, although freighting has moved toward digitalization.

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Section: Critical Discussionmentioning

confidence: 99%

Mobile Technology and Studies on Transport Behavior: A Literature Analysis, Integrated Research Model, and Future Research Agenda

Sun

Liu

Zhang

2021

Mobile Information Systems

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“…A summary of the data used in the eligible papers is presented in Table 1. 2010), Calabrese et al (2011), Xu et al (2011, Horn and Kern (2015), Larijani et al (2015), Holleczek et al (2015), Asgari (2016), Poonawala et al (2016), Yamada et al (2016), Danafar et al (2017), Li et al (2017), Hui et al (2017), Hui (2017), Horn et al (2017) As can be seen from Table 1, 15 out of the 22 studies (about 70%) used network-driven data, while the other 7 studies used event-driven data, particularly CDRs. In terms of location estimation, cell of origin method was often mentioned.…”

Section: Data and Their Characteristicsmentioning

confidence: 98%

Transport mode detection based on mobile phone network data: A systematic review

Huang

Cheng

Weibel

2019

Transportation Research Part C: Emerging Technologies

141

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“…The main principle of the probability-based algorithm is to define a possible matching area as the center of the matching point according to the positioning accuracy of the track points, take the road section within the area as the possible matching road section and calculate its matching probability to determine the best matching road. In addition to the above map-matching algorithms, matching algorithms using complex mathematical theories have emerged in recent years, such as the Bayesian inference matching algorithm, Kalman filtering matching algorithm, fuzzy logic matching algorithm, and matching algorithm based on convex optimization [29][30][31][32].…”

Section: Point-to-point Map Matching Based On Probability Statisticsmentioning

confidence: 99%

A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data

Zhang

Xia

et al. 2019

Sustainability

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Identifying and detecting the travel mode and pattern of individual travelers is an important problem in transportation planning and policy making. Mobile-phone Signaling Data (MSD) have numerous advantages, including wide coverage and low acquisition cost, data stability and reliability, and strong real-time performance. However, due to their noisy and temporally irregular nature, extracting mobility information such as transport modes from these data is particularly challenging. This paper establishes a travel mode identification model based on the MSD combined with residents' travel survey data, Geographic Information System (GIS) data, and navigation data. Using the data obtained from Kunshan, China in 2017, enriched with variables on the travel mode identification, the model achieved a high accuracy of 90%. The accuracy is satisfactory for all of the transport modes other than buses. Furthermore, among the explanatory variables such as the built environment factors (e.g., the coverage rate of a bus stop) are in general more significant, in contrast with other attributes. This indicates that the land use functions are more influential on the travel mode selection as well as the level of travel demand.

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Travel estimation using Control Signal Records in cellular networks and geographical information

Cited by 18 publications

References 12 publications

Mobile Technology and Studies on Transport Behavior: A Literature Analysis, Integrated Research Model, and Future Research Agenda

Mobile Technology and Studies on Transport Behavior: A Literature Analysis, Integrated Research Model, and Future Research Agenda

Transport mode detection based on mobile phone network data: A systematic review

A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data

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