Extracting trips from multi-sourced data for mobility pattern analysis: An app-based data example

Wang, Feilong; Wang, Jingxing; Cao, Jinzhou; Chen, Cynthia; Ban, Xuegang

doi:10.1016/j.trc.2019.05.028

Cited by 56 publications

(40 citation statements)

References 27 publications

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“…App-based data (Wang et al, 2019 ) refers to data captured from a specific app, in this case the “myVodafone” app, in which customers consent to capturing of multiple signals from their phones, including networks speeds to monitor network quality. Some of these signals come with a cell tower position as well, which makes them somewhat equivalent to the other two datasets.…”

Section: Methodsmentioning

confidence: 99%

Data sharing and collaborations with Telco data during the COVID-19 pandemic: A Vodafone case study

et al. 2021

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With the outbreak of COVID-19 across Europe, anonymised telecommunications data provides a key insight into population level mobility and assessing the impact and effectiveness of containment measures. Vodafone's response across its global footprint was fast and delivered key new metrics for the pandemic that have proven to be useful for a number of external entities.Cooperation with national governments and supra-national entities to help fight the COVID-19 pandemic was a key part of Vodafone's response, and in this article the different methodologies developed are analysed, as well as the key collaborations established in this context. In this article we also analyse the regulatory challenges found, and how these can pose a risk of the full benefits of these insights not being harnessed, despite clear and efficient Privacy and Ethics assessments to ensure individual safety and data privacy. Policy Significance StatementVodafone encourages sharing and reuse of data through voluntary, market-driven mechanisms.Data sharing should only take place where it is legally compliant, ethical and socially acceptable, in line with the principles of trustworthiness and privacy-by-design. To be sustainable, sharing and reuse of data should be subject to fair remuneration that recognises the significant investment required to collect, curate and maintain data, as well as produce meaningful and accurate insights.It is of particular importance that policy fosters innovation through data, and this can only exist in a sustainable market for data analytics and insights, that levels the playing field across the world and between the different players, and that at the same time extracts real value for society.

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Section: Methodsmentioning

confidence: 99%

Data sharing and collaborations with Telco data during the COVID-19 pandemic: A Vodafone case study

et al. 2021

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“…for more details). The incenTrip dataset has the best spatial accuracy among the three datasets, with more than 80% of the data’s spatial accuracy less than 50 m, whereas the two LBS datasets show a bimodal distribution, with the second peak locates around 70 m. In general, the spatial accuracy of these three datasets is of high quality because, for all three datasets, around 90% of the data has a spatial accuracy of less than 100 m. Figure 7 b only shows the LRI distribution for the two large-scale LBS datasets, since the incenTrip dataset is collected with pre-defined LRI, where a bimodal distribution can be observed (Wang et al 2019 ). For each dataset, more than 75% of the data has LRI less than 15-s.…”

Section: Case Studiesmentioning

confidence: 99%

“…Another source of MDLD is the Location-based Service (LBS) data, in which spatial information is generated when a mobile application updates the device’s location with the most accurate sources, based on the existing location sensors such as Wi-Fi, Bluetooth, cellular tower, and GPS (Chen et al 2016 ; Wang and Chen 2018 ). Compared to the CDR data, the LBS data can reflect the exact location of mobile devices and thus provide invaluable location information describing individual-level mobility patterns (Chen et al 2016 ; Gonzalez et al 2008 ; Kang et al 2012a , b ; Wang and Chen 2018 ; Wang et al 2019 ). Lots of applications have been developed using the LBS data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, there is also a special focus on deriving the trip ends from LBS data. A “Divide, Conquer and Integrate” (DCI) framework was proposed to process the LBS data to extract mobility patterns in the Puget Sound region (Wang et al 2019 ). The proposed framework combined a rule-based method and incremental clustering method to handle the bi-modally distributed LBS data.…”

Section: Literature Reviewmentioning

confidence: 99%

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A data-driven travel mode share estimation framework based on mobile device location data

et al. 2021

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Mobile device location data (MDLD) contains abundant travel behavior information to support travel demand analysis. Compared to traditional travel surveys, MDLD has larger spatiotemporal coverage of the population and its mobility. However, ground truth information such as trip origins and destinations, travel modes, and trip purposes are not included by default. Such important attributes must be imputed to maximize the usefulness of the data. This paper targets at studying the capability of MDLD on estimating travel mode share at aggregated levels. A data-driven framework is proposed to extract travel behavior information from MDLD. The proposed framework first identifies trip ends with a modified Spatiotemporal Density-based Spatial Clustering of Applications with Noise algorithm. Then three types of features are extracted for each trip to impute travel modes using machine learning models. A labeled MDLD dataset with ground truth information is used to train the proposed models, resulting in a 95% recall rate in identifying trip ends and over 93% tenfold cross-validation accuracy in imputing the five travel modes (drive, rail, bus, bike and walk) with a random forest (RF) classifier. The proposed framework is then applied to two large-scale MDLD datasets, covering the Baltimore-Washington metropolitan area and the United States, respectively. The estimated trip distance, trip time, trip rate distribution, and travel mode share are compared against travel surveys at different geographies. The results suggest that the proposed framework can be readily applied in different states and metropolitan regions with low cost in order to study multimodal travel demand, understand mobility trends, and support decision making.

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“…Intra-city trips, either a single trip or trip chains, can easily be reconstructed from LBS data as long as the locations where activities occur are identified [6]. On this basis, trip purpose is generally inferred by utilizing the detailed spatial and temporal information of stay points [4], [7].…”

Section: Introductionmentioning

confidence: 99%

Inferring Inter-City Trip Purpose From the Perspective of the Group

et al. 2021

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Although trip purpose inference based on passively collected data has long been investigated, less attention has been paid to inter-city trips. The reason is , except using ticket sales data, only limited trips can be extracted due to the lower frequency of inter-city trips during daily life. However, for ticket sales data, only limited features can be explored due to the lower spatial resolution of trajectories. Therefore, this paper endeavoured to exploit the potential of ticket sales data from the perspective of the group. Theoretically, by introducing concepts of text mining, the trip purpose of a group can be viewed as analogous to the topics of a document. Trip purpose was characterized by a time topic model (TTM) that incorporates start time, in contrast to latent Dirichlet allocation (LDA). This approach was implemented via a three-step method. First, groups were reconstructed from tickets. Second, three types of features, i.e., demographic, experience and co-travel network features, were extracted as a series of words to describe passengers. Third, trip purposes were automatically clustered based on the co-occurrence of words in the same group using a TTM. This paper presents comparison experiments to evaluate feature sets and the model performance based on a web -based travel survey, including the ground truth. Moreover, this paper highlights the practical use of a TTM to detect anomalies beyond anticipated trip purpose based on large-scale ticket sales data collected from Beijing, China. The full feature set was found to be preferable since both precision and recall increased when demographic and co-travel network features were considered. Meanwhile, the TTM produced robust and balanced predictions and exhibited additional power to recognize personal business compared with baseline methods.INDEX TERMS Inter-city trip, ticket sales data, topic model, trip purpose inference.

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Extracting trips from multi-sourced data for mobility pattern analysis: An app-based data example

Cited by 56 publications

References 27 publications

Data sharing and collaborations with Telco data during the COVID-19 pandemic: A Vodafone case study

Data sharing and collaborations with Telco data during the COVID-19 pandemic: A Vodafone case study

A data-driven travel mode share estimation framework based on mobile device location data

Inferring Inter-City Trip Purpose From the Perspective of the Group

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