Sentence examples for mobile phone trajectory from inspiring English sources

The cost of up-scaling a mobile phone trajectory system is only the cost of incremental computational power.

According to studies on probabilistic models for mobile phone trajectory estimation from Thiagarajan [ 39], the misplaced road segment error is approximately only 45 metres.

We conclude that there is an opportunity to calculate traffic air pollution and its public health impact by mobile phone trajectory mapping.

Figure  2 illustrates the potential to characterize traffic-related microenvironments, e.g., in-vehicle exposure in congestion as opposed to non-congestion: (a) mapping a mobile phone trajectory; (b) associating mobile phone with vehicles, and linking vehicles to air pollution; and (c) estimating exposure for people travelling through the pollution fields [ 63].

While banknote sightings have been useful in establishing the predictability of movement patterns on broader scales, mobile phone trajectories operating in space time provide detailed insights on individual mobility patterns (González et al. 2008).

The cost is mainly in data power to compute mobile phone trajectories and emission dispersion modelling in real time.

For operational modeling, it would be helpful to adapt the approaches used by these models in translating massive data sets (e.g., bank notes, mobile phone user trajectories, air and commuting travel networks) into model parameter values.

There are several companies providing individual trajectory mobile phone based traffic monitoring services including: Cellint [ 40], Intelligent Mechatronic Systems Inc IMSS) [ 41], ITIS [ 42], and Urban Informatics (UI) [ 43], etc.

On the other hand, mobile phone data, where spatio-temporal trajectories of millions of users are passively recorded has recently emerged as a promising input for transport analyses.

We propose a new approach to assess the impact of traffic-related air pollution on public health by mapping personal trajectories using mobile phone tracking technology in an urban environment.

In this study, we propose a conceptual framework to improve exposure assessment by using existing, low cost mobile phone data to obtain individual trajectories to further estimate the concentration of the traffic emission in the roadway network and its contribution to exposure for the urban population.