The promises of big data and small data for travel behavior (aka human mobility) analysis

Chen, Cynthia; Ma, Jie; Susilo, Yusak O.; Liu, Yu; Wang, Menglin

doi:10.1016/j.trc.2016.04.005

Cited by 462 publications

(308 citation statements)

References 115 publications

(137 reference statements)

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“…Also, the structure of the similarity matrix helps to expedite the computation process because almost 90% of the cells in the matrix are zero that can be transferred to null cells for reducing the size of the matrix and computing more efficiently. Therefore, according to the structure of the similarity matrix and the computational complexity, some techniques such as cloud or parallel computing can easily handle the computations [34].…”

Section: Resultsmentioning

confidence: 99%

Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data

Faroqi

Mesbah

Kim

2017

Journal of Advanced Transportation

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The increasing availability of public transit smart card data has enabled several studies to focus on identifying passengers with similar spatial and/or temporal trip characteristics. However, this paper goes one step further by investigating the relationship between passengers' spatial and temporal characteristics. For the first time, this paper investigates the correlation of the spatial similarity with the temporal similarity between public transit passengers by developing spatial similarity and temporal similarity measures for the public transit network with a novel passenger-based perspective. The perspective considers the passengers as agents who can make multiple trips in the network. The spatial similarity measure takes into account direction as well as the distance between the trips of the passengers. The temporal similarity measure considers both the boarding and alighting time in a continuous linear space. The spatial-temporal similarity correlation between passengers is analysed using histograms, Pearson correlation coefficients, and hexagonal binning. Also, relations between the spatial and temporal similarity values with the trip time and length are examined. The proposed methodology is implemented for four-day smart card data including 80,000 passengers in Brisbane, Australia. The results show a nonlinear spatial-temporal similarity correlation among the passengers.

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Section: Resultsmentioning

confidence: 99%

Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data

Faroqi

Mesbah

Kim

2017

Journal of Advanced Transportation

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“…The paper provides a way to formalize traveler's travel behavior variability pattern by analyzing long-term raw GPS data and to predict The study applies to Puget Sound Regional Council data set, which includes a long-term (18-month) GPS trajectory data set and a particular individual social-demographic data set. The variability derived from the data set indicates that, (1) for HN tours, the full-time employees have tighter departure time restrictions on home to other places tours, for example, the morning home-to-work commute; (2) they are more dedicated to their trips and do not stop frequently; (3) for HH tours, the full-time employee individuals have more departure time flexibility. According to the travel behavior variability properties, the prediction accuracy rates for social-demographic features, including employment status, income, age, and gender, are discovered.…”

Section: Discussionmentioning

confidence: 99%

“…The number of tours threshold ranges as [1,2,5,10,15,20,30,40,50,60,70,80,90,100,120,150,180]. For example, a value of 5 indicates that any travelers with less than five tours are disqualified and discarded, while the travelers with 5 or more are qualified and collected.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…It is one of the most fundamental research topics to many real-world applications, including Active Traffic and Demand Management (ATDM), Mobility-as-a-Service, and transportation demand management. The activity-travel behavior pattern is derived from either manually collected traveler activity diaries in travel surveys or passively obtained data, like global positioning system (GPS) trajectory data [1][2][3][4][5], geolocation data [6,7], and transit smart card data [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

Zhu

Gonder

Lin

2017

Journal of Advanced Transportation

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With the development of and advances in smartphones and global positioning system (GPS) devices, travelers' long-term travel behaviors are not impossible to obtain. This study investigates the pattern of individual travel behavior and its correlation with social-demographic features. For different social-demographic groups (e.g., full-time employees and students), the individual travel behavior may have specific temporal-spatial-mobile constraints. The study first extracts the home-based tours, including Home-toHome and Home-to-Non-Home, from long-term raw GPS data. The travel behavior pattern is then delineated by home-based tour features, such as departure time, destination location entropy, travel time, and driving time ratio. The travel behavior variability describes the variances of travelers' activity behavior features for an extended period. After that, the variability pattern of an individual's travel behavior is used for estimating the individual's social-demographic information, such as social-demographic role, by a supervised learning approach, support vector machine. In this study, a long-term (18-month) recorded GPS data set from Puget Sound Regional Council is used. The experiment's result is very promising. The sensitivity analysis shows that as the number of tours thresholds increases, the variability of most travel behavior features converges, while the prediction performance may not change for the fixed test data.

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“…At the same time researchers are actively using dedicated apps to monitor and learn about dynamic spatiotemporal behaviours. Nevertheless neither passive big data nor apps are a panacea and many obstacles remain in their application (Chen et al 2016). In this regard, with the rise of the Internet City and the implementation of smart city strategy , China has shown promise for providing wide access to many Big Data sources including mobile phone records, smartcard and local social networks some of which is demonstrated in our special issue.…”

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confidence: 98%

ICT, activity space–time and mobility: new insights, new models, new methodologies

Ben-Elia

Zhu

2018

Transportation

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Over three decades have passed since research on the complex interrelationships between Information and Communication Technologies (ICT) and travel behaviour began. Since then much has changed. Broadly speaking researchers have been chasing a complex and bi-directional moving target with both the development of digital technologies (what we refer to as three Internet generations) and changes to human behaviours coinciding side by side. As both transportation technologies and ICT contribute to remote activities, the impact of ICT on individuals' activities and related travel behaviour have been discussed. It is not surprising because untangling this issue is the basis of understanding travel behaviour in information age. In the mid 80 s and early 90 s scholars were trying to disentangle how telecommunications (as they were then called) would likely influence activity and travel patterns and an initial set of hypotheses were established by Salomon and Mokhtarian (Mokhtarian 1990;Salomon 1986). According to their framework telecommunications could substitute, complement, modify travel (route, mode, timing or destination) or have no effect at all. This simple and direct framework fitted well to the rather simple information technology of the time. The 1st Internet generation can traced to the opening of the World Wide Web (www) initially as a unidirectional protocol for passing on information content from producers to the users on sedentary end devices (desktops). Research focused mainly on the substitution/complementary relationships between virtual and physical activities especially e-working (teleworking/telecommuting-Mokhtarian and Salomon 1994) and e-shopping and how they affect the demand for travel (Cao 2012;Zhai et al. 2017). Another strand of research involved the influence of information on modification of travel choices based on the abundance of travel planners available on the web

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The promises of big data and small data for travel behavior (aka human mobility) analysis

Cited by 462 publications

References 115 publications

Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data

Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data

Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

ICT, activity space–time and mobility: new insights, new models, new methodologies

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