scholarly journals Can We Map-Match Individual Cellular Network Signaling Trajectories in Urban Environments? Data-Driven Study

2019 ◽  
Vol 2673 (7) ◽  
pp. 74-88 ◽  
Author(s):  
Loïc Bonnetain ◽  
Angelo Furno ◽  
Jean Krug ◽  
Nour-Eddin El Faouzi
Keyword(s):  
Mobile Phone ◽  
Cellular Network ◽  
Travel Demand ◽  
Transportation Network ◽  
Urban Environments ◽  
Network Control ◽  
Mobile Phone Data ◽  
Transport Systems ◽  
Spatiotemporal Scales ◽  
Temporal Dimensions

Mobile phone data collected by network operators can provide fundamental insights into individual and aggregate mobility of people, at unprecedented spatiotemporal scales. However, traditional call detail records (CDR) have fundamental issues because of low accuracy in both spatial and temporal dimensions, which limits their applicability for detailed studies on mobility, especially in urban scenarios. This paper focuses on a new generation of mobile phone passive data, individual cellular network signaling data, characterized by higher spatiotemporal resolutions than traditional CDR. A framework based on unsupervised hidden Markov model is designed for map-matching such data on a multimodal transportation network, aimed at accurately inferring the complex multimodal travel itineraries and popular paths people follow in their urban daily mobility. This information, especially if computed at large spatiotemporal scales, can represent a solid basis for studying actual and dynamic travel demand, to properly dimension multimodal transport systems and even perform anomaly detection and adaptive network control. The approach is evaluated in a case study based on real cellular traces collected by a major French operator in the city of Lyon, and a validation study at both microscopic and macroscopic levels proposed. The results show that this approach can properly handle sparse and noisy cell phone trajectories in complex urban environments. Moreover, the results are promising concerning popular paths detection and reconstruction of origin–destination matrices.

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

Profile based location update for cellular network using mobile phone data

2019 ◽  
Author(s):  
Smita Parija ◽  
Swati Swayamsiddha ◽  
Prasanna Kumar Sahu ◽  
Sudhansu Sekhar Singh
Keyword(s):  
Mobile Phone ◽  
Cellular Network ◽  
Mobile Phone Data ◽  
Location Update

scholarly journals Validation of MCMC-Based Travel Simulation Framework Using Mobile Phone Data

2021 ◽  
Vol 2 ◽  
Author(s):  
Suxia Gong ◽  
Ismaïl Saadi ◽  
Jacques Teller ◽  
Mario Cools
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Mobile Phone Data ◽  
Simulation Framework ◽  
Modeling Framework ◽  
Practical Applications ◽  
Mobility Data ◽  
Demand Models ◽  
The Difference ◽  
Using Data

An essential step in agent-based travel demand models is the characterization of the population, including transport-related attributes. This study looks deep into various mobility data in the province of Liège, Belgium. Based on the data stemming from the 2010 Belgian HTS, that is, BELDAM, a Markov chain Monte Carlo (MCMC) sampling method combined with a cross-validation process is used to generate sociodemographic attributes and trip-based variables. Besides, representative micro-samples are calibrated using data about the population structure. As a critical part of travel demand modeling for practical applications in the real-world context, validation using various data sources can contribute to the modeling framework in different ways. The innovation in this study lies in the comparison of outputs of MCMC with mobile phone data. The difference between modeled and observed trip length distributions is studied to validate the simulation framework. The proposed framework infers trips with multiple attributes while preserving the traveler’s sociodemographics. We show that the framework effectively captures the behavioral complexity of travel choices. Moreover, we demonstrate mobile phone data’s potential to contribute to the reliability of travel demand models.

The Cellular Network as a Sensor: From Mobile Phone Data to Real-Time Road Traffic Monitoring

2015 ◽  
Vol 16 (5) ◽  
pp. 2551-2572 ◽  
Author(s):  
Andreas Janecek ◽  
Danilo Valerio ◽  
Karin Anna Hummel ◽  
Fabio Ricciato ◽  
Helmut Hlavacs
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Cellular Network ◽  
Road Traffic ◽  
Traffic Monitoring ◽  
Mobile Phone Data

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.

scholarly journals Modelling urban vibrancy with mobile phone and OpenStreetMap data

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252015
Author(s):  
Federico Botta ◽  
Mario Gutiérrez-Roig
Keyword(s):  
Quantitative Analysis ◽  
Mobile Phone ◽  
Social Interactions ◽  
Age Groups ◽  
Spatial Models ◽  
Strong Relationship ◽  
Urban Environments ◽  
Mobile Phone Data ◽  
Third Places ◽  
Urban Places

The concept of urban vibrancy has become increasingly important in the study of cities. A vibrant urban environment is an area of a city with high levels of human activity and interactions. Traditionally, studying our cities and what makes them vibrant has been very difficult, due to challenges in data collection on urban environments and people’s location and interactions. Here, we rely on novel sources of data to investigate how different features of our cities may relate to urban vibrancy. In particular, we explore whether there are any differences in which urban features make an environment vibrant for different age groups. We perform this quantitative analysis by extracting urban features from OpenStreetMap and the Italian census, and using them in spatial models to describe urban vibrancy. Our analysis shows a strong relationship between urban features and urban vibrancy, and particularly highlights the importance of third places, which are urban places offering opportunities for social interactions. Our findings provide evidence that a combination of mobile phone data with crowdsourced urban features can be used to better understand urban vibrancy.

scholarly journals Validating Activity-Based Travel Demand Models Using Mobile Phone Data

2018 ◽  
Author(s):  
Feng Liu ◽  
Ziyou Gao ◽  
Bin Jia ◽  
Xuedong Yan ◽  
Davy Janssens ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Mobile Phone Data ◽  
Demand Models

Analyzing Passenger Travel Demand Related to the Transportation Hub inside a City Area using Mobile Phone Data

2018 ◽  
Vol 2672 (50) ◽  
pp. 23-34 ◽  
Author(s):  
Gang Zhong ◽  
Jian Zhang ◽  
Linchao Li ◽  
Xiaoxuan Chen ◽  
Fan Yang ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Transportation Planning ◽  
Travel Demand ◽  
Urban Transportation ◽  
Mobile Phone Data ◽  
City Area ◽  
Planning And Management ◽  
Passenger Travel ◽  
Transportation Corridors ◽  
The City

The passenger transportation hub plays a crucial role in the urban transportation system. Analyzing transportation hub related travel demand is necessary to support urban transportation planning and management. However, it is difficult to use the traditional travel survey methods to study travel demand because tracking passenger travel trajectories is a near impossible task. The location information from the cellular system provides a feasible way to solve the problem. This paper concentrates on applying mobile phone data to study passenger travel demand related to the Hongqiao transportation hub in Shanghai, China. First, a method is introduced to collect passenger travel information related to the hub from mobile phone data. Then, travel demand indexes are presented to characterize the travel demand in a visual way. Finally, transportation corridors, which connect the hub and other urban areas, are identified to analyze the distribution of travel demand more thoroughly. The results illustrate that the passenger travel demand shows an obvious tide pattern in the city area with the Hongqiao transportation hub as the center. Moreover, there are two identified transportation corridors which reveal the major distribution directions of the passengers, that is, the city center and the Zizhu industrial development zone. The approach in this study testifies that mobile phone data has great potential for transportation planning and management related to transportation hubs.

scholarly journals Assessing the interplay between human mobility and mosquito borne diseases in urban environments

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Emanuele Massaro ◽  
Daniel Kondor ◽  
Carlo Ratti
Keyword(s):  
Mobile Phone ◽  
Human Mobility ◽  
Urban Environments ◽  
System Level ◽  
Mobile Phone Data ◽  
Transmission Model ◽  
Mobility Patterns ◽  
Vector Borne Diseases ◽  
Vector Borne ◽  
City State

AbstractUrbanization drives the epidemiology of infectious diseases to many threats and new challenges. In this research, we study the interplay between human mobility and dengue outbreaks in the complex urban environment of the city-state of Singapore. We integrate both stylized and mobile phone data-driven mobility patterns in an agent-based transmission model in which humans and mosquitoes are represented as agents that go through the epidemic states of dengue. We monitor with numerical simulations the system-level response to the epidemic by comparing our results with the observed cases reported during the 2013 and 2014 outbreaks. Our results show that human mobility is a major factor in the spread of vector-borne diseases such as dengue even on the short scale corresponding to intra-city distances. We finally discuss the advantages and the limits of mobile phone data and potential alternatives for assessing valuable mobility patterns for modeling vector-borne diseases outbreaks in cities.

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.

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