Mobile Phone Data Analysis: A Spatial Exploration Toward Hotspot Detection

Ghahramani, Mohammadhossein; Zhou, MengChu; Hon, Chi Tin

doi:10.1109/tase.2018.2795241

Cited by 51 publications

(23 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [41], we have utilized different correlation analyses to scrutiny the dynamics of a city. A descriptive spatial autocorrelation analysis (a global approach) is carried out to illustrate the relations among different areas.…”

Section: A Spatial-temporal Analysismentioning

confidence: 99%

Urban sensing based on mobile phone data: approaches, applications, and challenges

Ghahramani

Zhou

Wang

2020

IEEE/CAA J. Autom. Sinica

Self Cite

View full text Add to dashboard Cite

Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications. The ability to accurately and extensively monitor and analyze these data is necessary. Much concern in mobile data analysis is related to human beings and their behaviours. Due to the potential value that lies behind these massive data, there have been different proposed approaches for understanding corresponding patterns. To that end, monitoring people's interactions, whether counting them at fixed locations or tracking them by generating origindestination matrices is crucial. The former can be used to determine the utilization of assets like roads and city attractions. The latter is valuable when planning transport infrastructure. Such insights allow a government to predict the adoption of new roads, new public transport routes, modification of existing infrastructure, and detection of congestion zones, resulting in more efficient designs and improvement. Smartphone data exploration can help research in various fields, e.g., urban planning, transportation, health care, and business marketing. It can also help organizations in decision making, policy implementation, monitoring and evaluation at all levels. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data. We classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.

show abstract

Section: A Spatial-temporal Analysismentioning

confidence: 99%

Urban sensing based on mobile phone data: approaches, applications, and challenges

Ghahramani

Zhou

Wang

2020

IEEE/CAA J. Autom. Sinica

Self Cite

View full text Add to dashboard Cite

show abstract

“…To solve route planning problems with big data, intelligent algorithms such as neural networks [74] are beneficial for finding patterns and features. Ghahramani et al (2019) [75] proposed an exploratory spatial data analysis algorithm for urban planning and development. Dai et al (2019) [76] proposed a path travel time model for buses considering the link travel time and station dwell time and applied it to a bus line in Hangzhou.…”

Section: Taxi Service Improvementmentioning

confidence: 99%

Multiple Utility Analyses for Sustainable Public Transport Planning and Management: Evidence from GPS-Equipped Taxi Data in Haikou

Gui

2020

Sustainability

View full text Add to dashboard Cite

The transportation utility values calculated by traditional utility methods are not comprehensive. Some objects and factors are ignored in traditional utility methods, and this narrow perspective is their primary drawback. In intelligent transportation systems, it is necessary to calculate transportation utility for promoting public traffic planning and management. To build a sustainable intelligent transportation system, modified utility methods are essential to analyze transportation utility in a comprehensive way with innovative technologies and efficient communication systems. To solve the disadvantages of traditional utility methods, it is necessary to establish a new method to build sustainable public transport in the future. In this study, the Multiple Utility Method and Transportation Utility Method are proposed for public transport planning and management from multiple perspectives. A sample is presented to provide a better description, and 69,174 GPS-equipped taxi data in Haikou are adopted for the application of the Transportation Utility Method. The results show that the transportation utility values calculated by the Transportation Utility Method are more comprehensive than the transportation utility calculated by traditional utility methods. This indicates that it is necessary to calculate transportation utility from multiple perspectives based on the Transportation Utility Method. Future directions could include improving the methods, considering more factors, expanding the data used, and extrapolating this research to other cities around the world with similar urban metrics and urban form.

show abstract

“…Due to the progress of mobile Internet, Internet, and other technologies, the vehicle-mounted activity data based on individual vehicles (mainly represented by GPS data and vehicle-mounted terminal operation data) has been widely developed and applied in the field of urban transportation and taxi research in recent years. Ghahramani and Zhou et al [10] proposed an exploratory spatial data analysis algorithm based on cloud processing data analysis by cooperating with a telecom company. The spatial analysis method was used to detect the spatial distribution of mobile phones, and the kernel density method was used to determine these distributions.…”

Section: Introductionmentioning

confidence: 99%

Optimization Model of Taxi Fleet Size Based on GPS Tracking Data

Yang

Yuan

et al. 2019

Sustainability

View full text Add to dashboard Cite

A reasonable taxi fleet size has a significant impact on the satisfaction of urban traffic demand, the alleviation of urban traffic congestion, and the stability of taxi business groups. Most existing studies measure the overall scale by using macro indices, and few studies are from the micro level. To meet the transportation demand for taxis, mitigating the mismatch between taxi supply and demand, this research proposes an urban taxi fleet size calculating model based on GPS tracking data. Firstly, on the basis of road network segmentation, the probability model of a passenger taxi-taking a road section as a unit is built to evaluate the difficulty of taxi-taking on a road section. Furthermore, a user queuing model is built for the “difficult to take a taxi” road section in the peak period, and the service mileage required by potential taxi users is calculated. After that, a transportation capacity measurement model is built to estimate the number of taxis required in different time periods, Finally, the income constraint model is used to explain the impact of different vehicle fleet sizes on the income of taxi groups, so as to provide a reference for the determination of the final fleet size. The model is applied to data from Xi’an. The calculation results are based on data from May 2014, and show that the scale of taxi demand is about 654–2237, and after considering the impact of different fleet size increases on income, when the income variation index is limited to 0.10, i.e., the decrease of drivers’ income will not exceed 10%, an increase of 1286 taxis will be able to meet 66% of the unmet demand in the peak period. The conclusion indicates that the model can effectively calculate the required fleet size and formulate the constraint solutions. This method provided can be considered as a support for formulating the regulation strategy of an urban taxi fleet size.

show abstract

Mobile Phone Data Analysis: A Spatial Exploration Toward Hotspot Detection

Cited by 51 publications

References 31 publications

Urban sensing based on mobile phone data: approaches, applications, and challenges

Urban sensing based on mobile phone data: approaches, applications, and challenges

Multiple Utility Analyses for Sustainable Public Transport Planning and Management: Evidence from GPS-Equipped Taxi Data in Haikou

Optimization Model of Taxi Fleet Size Based on GPS Tracking Data

Contact Info

Product

Resources

About