Analysis of public transit trip chain of commuters based on mobile phone data and GPS data

2017 ◽  
Author(s):  
Li Zhou ◽  
Yuxiong Ji ◽  
Yizhe Wang
Keyword(s):  
Mobile Phone ◽  
Public Transit ◽  
Mobile Phone Data ◽  
Gps Data ◽  
Trip Chain

Using Longitudinal Mobile Phone Data to Understand the Stability of Individual Travel Patterns

2017 ◽  
Vol 2643 (1) ◽  
pp. 166-177 ◽  
Author(s):  
Zhengyu Duan ◽  
Chun Wang ◽  
H. Michael Zhang ◽  
Zengxiang Lei ◽  
Haifeng Li ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Activity Space ◽  
Mobile Phone Data ◽  
Travel Patterns ◽  
Trip Chain ◽  
Demand Models ◽  
The Stability ◽  
Daily Trip ◽  
Daily Travel

Most travel demand models assume that individuals’ daily travel patterns are stable or follow a fixed routine. This hypothesis is being questioned by more and more researchers. In this study, longitudinal mobile phone data were used to study the stability of individual daily travel patterns from three aspects, including activity space, activity points, and daily trip-chain patterns. The activity space was represented by the number of nonhome activity points, the radius of nonhome activity points, and the distance from home. The visitation pattern of activity points was analyzed by entropy and predictability measures. The stability of trip-chain patterns was described by the number of distinct trip chains, the typical trip chain, and the typical trip-chain ratio. Analysis of 21 days of mobile phone data from three communities in Shanghai, China, revealed that individuals’ daily travel patterns showed considerable variation. Although individuals’ visitation patterns to activity points were very regular, the day-to-day variations of individual trip-chain patterns were quite significant. On average, an individual exhibited about eight types of daily trip chains during the 21-day period. The daily travel patterns of residents in the outskirts were more stable than those of residents in the city center. Individuals’ travel patterns on weekdays were more complex than those on weekends. As individuals’ activity spaces increased, the stability of their travel patterns decreased.

The Potential GTX Travel Demand using Mobile Phone Data

2019 ◽  
Vol 7 (1) ◽  
pp. 77-84
Author(s):  
Jin Ki Eom ◽  
Kwang-Sub Lee ◽  
Ho-Chan Kwak ◽  
Ji Young Song ◽  
Myeong-Eon Seong
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Mobile Phone Data

scholarly journals Effects of social distancing on the spreading of COVID-19 inferred from mobile phone data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hamid Khataee ◽  
Istvan Scheuring ◽  
Andras Czirok ◽  
Zoltan Neufeld
Keyword(s):  
Mobile Phone ◽  
Basic Reproductive Number ◽  
Mobile Phone Data ◽  
Reproductive Number ◽  
Expansion Phase ◽  
Social Distancing ◽  
Exponential Expansion ◽  
Total Death ◽  
Mobility Reduction ◽  
Epidemiological Parameters

AbstractA better understanding of how the COVID-19 pandemic responds to social distancing efforts is required for the control of future outbreaks and to calibrate partial lock-downs. We present quantitative relationships between key parameters characterizing the COVID-19 epidemiology and social distancing efforts of nine selected European countries. Epidemiological parameters were extracted from the number of daily deaths data, while mitigation efforts are estimated from mobile phone tracking data. The decrease of the basic reproductive number ($$R_0$$ R 0 ) as well as the duration of the initial exponential expansion phase of the epidemic strongly correlates with the magnitude of mobility reduction. Utilizing these relationships we decipher the relative impact of the timing and the extent of social distancing on the total death burden of the pandemic.

scholarly journals Did the NSA and GCHQ Diminish Our Privacy? What the Control Account Should Say

2020 ◽  
Vol 7 (1) ◽  
pp. 29-48 ◽  
Author(s):  
Leonhard Menges
Keyword(s):  
Mobile Phone ◽  
Personal Information ◽  
Real Life ◽  
Mobile Phone Data ◽  
Standard Account ◽  
Do So

AbstractA standard account of privacy says that it is essentially a kind of control over personal information. Many privacy scholars have argued against this claim by relying on so-called threatened loss cases. In these cases, personal information about an agent is easily available to another person, but not accessed. Critics contend that control accounts have the implausible implication that the privacy of the relevant agent is diminished in threatened loss cases. Recently, threatened loss cases have become important because Edward Snowden’s revelation of how the NSA and GCHQ collected Internet and mobile phone data presents us with a gigantic, real-life threatened loss case. In this paper, I will defend the control account of privacy against the argument that is based on threatened loss cases. I will do so by developing a new version of the control account that implies that the agents’ privacy is not diminished in threatened loss cases.

Vehicle Travel Path Recognition in Urban Dense Road Network Environments by Using Mobile Phone Data

2021 ◽  
pp. 1-29
Author(s):  
Yudong Guo ◽  
Fei Yang ◽  
Peter Jing Jin ◽  
Haode Liu ◽  
Sai Ma ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Road Network ◽  
Mobile Phone Data ◽  
Travel Path ◽  
Network Environments

Analysis of urban agglomeration structure through spatial network and mobile phone data

2021 ◽  
Author(s):  
Xintao Liu ◽  
Jianwei Huang ◽  
Jianhui Lai ◽  
Junwei Zhang ◽  
Ahmad M. Senousi ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Agglomeration ◽  
Mobile Phone Data ◽  
Spatial Network

Travel mode recognition of urban residents using mobile phone data and MapAPI

2021 ◽  
pp. 239980832098300
Author(s):  
Zhenghong Peng ◽  
Guikai Bai ◽  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu
Keyword(s):  
Urban Planning ◽  
Mobile Phone ◽  
Traffic Flow ◽  
Road Traffic ◽  
Urban Traffic ◽  
Phone Call ◽  
Traffic Information ◽  
Mobile Phone Data ◽  
Urban Residents ◽  
Travel Mode

Obtaining the time and space features of the travel of urban residents can facilitate urban traffic optimization and urban planning. As traditional methods often have limited sample coverage and lack timeliness, the application of big data such as mobile phone data in urban studies makes it possible to rapidly acquire the features of residents’ travel. However, few studies have attempted to use them to recognize the travel modes of residents. Based on mobile phone call detail records and the Web MapAPI, the present study proposes a method to recognize the travel mode of urban residents. The main processes include: (a) using DBSCAN clustering to analyze each user’s important location points and identify their main travel trajectories; (b) using an online map API to analyze user’s means of travel; (c) comparing the two to recognize the travel mode of residents. Applying this method in a GIS platform can further help obtain the traffic flow of various means, such as walking, driving, and public transit, on different roads during peak hours on weekdays. Results are cross-checked with other data sources and are proven effective. Besides recognizing travel modes of residents, the proposed method can also be applied for studies such as travel costs, housing–job balance, and road traffic pressure. The study acquires about 6 million residents’ travel modes, working place and residence information, and analyzes the means of travel and traffic flow in the commuting of 3 million residents using the proposed method. The findings not only provide new ideas for the collection and application of urban traffic information, but also provide data support for urban planning and traffic management.

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