The correlation between human mobility and socio-demographic in megacity

Xie, Kaiqiang; Xiong, Hui; Li, Chunyan

doi:10.1109/isc2.2016.7580733

Cited by 5 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 2 shows a positive and moderately strong correlation between attitudes with subjective norms. It also shows a positive and moderately strong correlation between attitude and strong intention; which is in line with findings in previous research [16]. This research found a positive and moderately strong correlation between subjective norms and intention.…”

Section: Data Processingsupporting

confidence: 92%

“…Measurement on the travel mode behavior could be beneficial in effort to influence people to travel in a sustainable manner [14]. In addition, socio-demographic factors and personal characteristics of commuters, such as age and physical condition, are known to influence travel mode choice [16]. In Indonesian context, particularly in Jakarta, online transportation has grown popularity amongst other transportation mode.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pro-Environmental Behaviour in Travel Mode Choice of Graduate Students: Case Study in Jakarta

et al. 2018

View full text Add to dashboard Cite

Rapid urban growth requires an established and dynamic transportation system to connect places and facilitate economic, social, cultural and human political activities. The choice of public transportation is considered a pro-environmental behavior in regards to emissions mitigation, therefore leads to sustainability. This paper attempts to understand graduate students’ pro-environmental behavior in travel mode choice towards public transportation, and to observe correlation between socio-demographic factors and pro- environmental behavior. This research was conducted in University X in Jakarta with 82 respondents, analyzed in quantitative approach (SPSS) and mix method. Using Theory of Planned Behavior (TPB), this research concludes that attitude is the most dominant sub-variable of behavior, seen from correlation between attitude and subjective norm, intention, and respondents’ age. Subjective norms and intention has positive correlation, but no correlation to socio-demographics.

show abstract

Section: Data Processingsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Pro-Environmental Behaviour in Travel Mode Choice of Graduate Students: Case Study in Jakarta

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Current methods assume that the travel mode depends only on features such as speed, acceleration and characteristics of the transport network and built environment in the form of GIS data. However, socio-demographic factors and personal characteristics of travellers (such as age and disability) are known to influence the choice of transportation modes to a large extent (Tyler 2006;Xie et al 2016). For example, past research in London has shown that elderly and disabled people are more inclined to use the bus for their everyday journeys as opposed to the Underground (Transport for London 2012).…”

Section: Introductionmentioning

confidence: 99%

Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics

Bantis

Haworth

2017

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

With the increasing prevalence of geo-enabled mobile phone applications, researchers can collect mobility data at a relatively high spatial and temporal resolution. Such data, however, lack semantic information such as the interaction of individuals with the transportation modes available. On the other hand, traditional mobility surveys provide detailed snapshots of the relation between socio-demographic characteristics and choice of transportation modes. Transportation mode detection is currently approached using features such as speed, acceleration and direction either on their own or in combination with GIS data. Combining such information with socio-demographic characteristics of travellers has the potential of offering a richer modelling framework that could facilitate better transportation mode detection using variables such as age and disability. In this paper, we explore the possibility to include both elements of the environment and individual characteristics of travellers in the task of transportation mode detection. Using dynamic Bayesian Networks, we model the transition matrix to account for such auxiliary data by using an informative Dirichlet prior constructed using data from traditional mobility surveys. Results have shown that it is possible to achieve comparable accuracy with the most widely used classification algorithms while having a rich modelling framework, even in the case of sparse mobility data.

show abstract

“…Other works, such as [ 55 , 58 ] focus on relating the demographic features of people with visiting patterns, spatial trajectories, and the difference between time spent at home and outside. Interestingly enough, Markov models were used in [ 56 ] to predict the next location of a user based on trajectory patterns tracked by the GPS information of its smartphone.…”

Section: User Profilingmentioning

confidence: 99%

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Hernández–Álvarez

Fuentes

González-Manzano

et al. 2020

Sensors

View full text Add to dashboard Cite

Ensuring the confidentiality of private data stored in our technological devices is a fundamental aspect for protecting our personal and professional information. Authentication procedures are among the main methods used to achieve this protection and, typically, are implemented only when accessing the device. Nevertheless, in many occasions it is necessary to carry out user authentication in a continuous manner to guarantee an allowed use of the device while protecting authentication data. In this work, we first review the state of the art of Continuous Authentication (CA), User Profiling (UP), and related biometric databases. Secondly, we summarize the privacy-preserving methods employed to protect the security of sensor-based data used to conduct user authentication, and some practical examples of their utilization. The analysis of the literature of these topics reveals the importance of sensor-based data to protect personal and professional information, as well as the need for exploring a combination of more biometric features with privacy-preserving approaches.

show abstract

The correlation between human mobility and socio-demographic in megacity

Cited by 5 publications

References 24 publications

Pro-Environmental Behaviour in Travel Mode Choice of Graduate Students: Case Study in Jakarta

Pro-Environmental Behaviour in Travel Mode Choice of Graduate Students: Case Study in Jakarta

Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Contact Info

Product

Resources

About