Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals

Toader, Bogdan; Sprumont, François; Faye, Sébastien; Popescu, Mioara; Viti, Francesco

doi:10.3390/ijgi6030062

Cited by 11 publications

(7 citation statements)

References 34 publications

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“…The algorithm’s adjustable parameters are intuitive and reflect those most often used in health and aging research [41,62,63,64]. Moreover, in contrast to many other algorithms, it is capable of dealing with temporal gaps in GPS data [65]. Montoliu’s algorithm is a time-based clustering algorithm using the three above-introduced input parameters: Dmax represents the maximum allowed distance of a GPS fix from the first GPS fix of a stop cluster; Tmin represents the minimum duration of a group of identified GPS fixes in order to count as a stop; and finally, Tmax represents the maximum allowed time gap between consecutive location points to be considered as a part of the same stop cluster (see Figure 1).…”

Section: Methodsmentioning

confidence: 99%

Assessing Older Adults’ Daily Mobility: A Comparison of GPS-Derived and Self-Reported Mobility Indicators

Fillekes

Kim

Trumpf

et al. 2019

Sensors

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Interest in global positioning system (GPS)-based mobility assessment for health and aging research is growing, and with it the demand for validated GPS-based mobility indicators. Time out of home (TOH) and number of activity locations (#ALs) are two indicators that are often derived from GPS data, despite lacking consensus regarding thresholds to be used to extract those as well as limited knowledge about their validity. Using 7 days of GPS and diary data of 35 older adults, we make the following three main contributions. First, we perform a sensitivity analysis to investigate how using spatial and temporal thresholds to compute TOH and #ALs affects the agreement between self-reported and GPS-based indicators. Second, we show how daily self-reported and GPS-derived mobility indicators are compared. Third, we explore whether the type and duration of self-reported activity events are related to the degree of correspondence between reported and GPS event. Highest indicator agreement was found for temporal interpolation (Tmax) of up to 5 h for both indicators, a radius (Dmax) to delineate home between 100 and 200 m for TOH, and for #ALs a spatial extent (Dmax) between 125 and 200 m, and temporal extent (Tmin) between 5 and 6 min to define an activity location. High agreement between self-reported and GPS-based indicators is obtained for TOH and moderate agreement for #ALs. While reported event type and duration impact on whether a reported event has a matching GPS event, indoor and outdoor events are detected at equal proportions. This work will help future studies to choose optimal threshold settings and will provide knowledge about the validity of mobility indicators.

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Section: Methodsmentioning

confidence: 99%

Assessing Older Adults’ Daily Mobility: A Comparison of GPS-Derived and Self-Reported Mobility Indicators

Fillekes

Kim

Trumpf

et al. 2019

Sensors

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“…e results denote a good example of how the presented profiling methodology can be used to assess the compatibility for long-term parking sharing of two or more users using, e.g., specific indicators for collaborative mobility between individuals [22]. More precisely, the profiling can be used to search in a specific region and users that have profiles that match other users for specific applications/ sharing services.…”

Section: Parking Sharingmentioning

confidence: 97%

“…e very first step in this process is the extraction of the duration and location of activities from raw data. A detailed review and comparison of the methodologies from literature is presented in [22]. However, all mentioned methodologies suffer from limitations when applied to dynamic and live profiling on large datasets.…”

Section: Travel Behaviour Analyticsmentioning

confidence: 99%

“…ere are some conditions that should be met in order to organise a ride sharing between two or more users, such as the departure and arrival position to be suitable for all the participants and the departure and arrival time to synchronise matching at best their schedule. e latter condition can be assessed by analysing the probability to be in a specific location, by the days and hours of the week, and can be used in a collaborative mobility system [22].…”

Section: Ride Sharingmentioning

confidence: 99%

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A Data-Driven Scalable Method for Profiling and Dynamic Analysis of Shared Mobility Solutions

Toader

Moawad²,

Hartmann³

et al. 2021

Journal of Advanced Transportation

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The advent of Internet of Things will revolutionise the sharing mobility by enabling high connectivity between passengers and means of transport. This generates enormous quantity of data which can reveal valuable knowledge and help understand complex travel behaviour. At the same time, it challenges analytics platforms to discover knowledge from data in motion (i.e., the analytics occur in real time as the event happens), extract travel habits, and provide reliable and faster sharing mobility services in dynamic contexts. In this paper, a scalable method for dynamic profiling is introduced, which allows the extraction of users’ travel behaviour and valuable knowledge about visited locations, using only geolocation data collected from mobile devices. The methodology makes use of a compact representation of time-evolving graphs that can be used to analyse complex data in motion. In particular, we demonstrate that using a combination of state-of-the-art technologies from data science domain coupled with methodologies from the transportation domain, it is possible to implement, with the minimum of resources, the next generation of autonomous sharing mobility services (i.e., long-term and on-demand parking sharing and combinations of car sharing and ride sharing) and extract from raw data, without any user input and in near real time, valuable knowledge (i.e., location labelling and activity classification).

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“…In Reference [9], Toader et al proposed an indicator for collaborative mobility between individuals based on the use of smartphone data. In Reference [10], a carpooling prototype system was developed to match passengers with drivers based on their trajectories.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Several Metaheuristic Algorithms to Optimize Monetary Incentive in Ridesharing Systems

Hsieh

2020

IJGI

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The strong demand on human mobility leads to excessive numbers of cars and raises the problems of serious traffic congestion, large amounts of greenhouse gas emissions, air pollution and insufficient parking space in cities. Although ridesharing is a potential transport mode to solve the above problems through car-sharing, it is still not widely adopted. Most studies consider non-monetary incentive performance indices such as travel distance and successful matches in ridesharing systems. These performance indices fail to provide a strong incentive for ridesharing. The goal of this paper is to address this issue by proposing a monetary incentive performance indicator to improve the incentives for ridesharing. The objectives are to improve the incentive for ridesharing through a monetary incentive optimization problem formulation, development of a solution methodology and comparison of different solution algorithms. A non-linear integer programming optimization problem is formulated to optimize monetary incentive in ridesharing systems. Several discrete metaheuristic algorithms are developed to cope with computational complexity for solving the above problem. These include several discrete variants of particle swarm optimization algorithms, differential evolution algorithms and the firefly algorithm. The effectiveness of applying the above algorithms to solve the monetary incentive optimization problem is compared based on experimental results.

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Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals

Cited by 11 publications

References 34 publications

Assessing Older Adults’ Daily Mobility: A Comparison of GPS-Derived and Self-Reported Mobility Indicators

Assessing Older Adults’ Daily Mobility: A Comparison of GPS-Derived and Self-Reported Mobility Indicators

A Data-Driven Scalable Method for Profiling and Dynamic Analysis of Shared Mobility Solutions

A Comparative Study of Several Metaheuristic Algorithms to Optimize Monetary Incentive in Ridesharing Systems

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