Classification of automobile and transit trips from Smartphone data: Enhancing accuracy using spatial statistics and GIS

Nour, Akram; Hellinga, Bruce; Casello, Jeffrey M.

doi:10.1016/j.jtrangeo.2015.11.005

Cited by 20 publications

(18 citation statements)

References 4 publications

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“…Smartphones have become ubiquitous tools of the human race, as millions of people now go about their days with small GPS-capable computers in their hands or pockets. The majority of current research involving smartphone GPS capabilities focuses on transportation or directional uses [1, 2, 3, 4], patterns of human movement [5, 6, 7], and health tracking [8, 9, 10]. Two interests of society involve (a) whether the accuracy is sufficient to enable them to be a reasonable substitute for more expensive commercially available mapping devices, and similarly (b) to what grade of receiver (consumer or mapping) could smartphones be a reasonable substitute.…”

Section: Introductionmentioning

confidence: 99%

Smartphone GPS accuracy study in an urban environment

2019

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An iPhone 6 using the Avenza software for capturing horizontal positions was employed to understand relative positional accuracy in an urban environment, during two seasons of the year, two times of day, and two perceived WiFi usage periods. On average, time of year did not seem to influence the average error observed in horizontal positions when GPS-only (no WiFi) capability was enabled, nor when WiFi was enabled. Observations of average horizontal position error only seemed to improve with time of day (afternoon) during the leaf-off season. During each season and during each time of day, horizontal position error seemed to improve in general during perceived high WiFi usage periods (when more people were present). Overall average horizontal position accuracy of the iPhone 6 (7–13 m) is consistent with the general accuracy levels observed of recreation-grade GPS receivers in potential high multi-path environments.

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Section: Introductionmentioning

confidence: 99%

Smartphone GPS accuracy study in an urban environment

2019

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“…GPSbased smartphone applications (GPS-apps) allow the recording of a multiple data on an individual's movements in space and time, at a high level of detail (e.g. visited destinations, actual distances, times, and route-paths) with limited effort (Nour et al, 2016;Zhou et al, 2017). Although these applications are becoming more available, the utilization of GPS-apps for collecting mobility demand and patterns data in Global South, is less common.…”

Section: Introductionmentioning

confidence: 99%

Measuring individuals' travel behaviour by use of a GPS-based smartphone application in Dar es Salaam, Tanzania

Joseph

Neven

Martens

et al. 2020

Journal of Transport Geography

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“…GPS (Global Positionning System) data represent an alternative form of mobile phone data largely leveraged in active travel research. Owing to their ubiquity, GPS-enabled mobile phones can be used to collect GPS records (Gonzalez et al 2010;Nitsche et al 2014;Nour et al 2016;Widhalm et al 2012) and largely decrease the data collection cost comparing to the dedicated GPS loggers used in GPS-based surveys (Bohte and Maat 2009;Deutsch et al 2012;Stopher et al 2008;Wolf et al 2004). As for typical GPS data, different methods for different purposes could be adopted (Shen and Stopher 2014).…”

Section: Mobile Phone Data For Travel Behaviour Researchmentioning

confidence: 99%

A data-driven approach for origin–destination matrix construction from cellular network signalling data: a case study of Lyon region (France)

et al. 2020

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Spatiotemporal data, and more specifically origin-destination matrices, are critical inputs to mobility studies for transportation planning and urban management purposes. Traditionally, high-cost and hard-to-update household travel surveys are used to produce large-scale origin-destination flow information of individuals' whereabouts. In this paper, we propose a methodology to estimate Origin-Destination (O-D) matrices based on passively-collected cellular network signalling data of millions of anonymous mobile phone users in the Rhône-Alpes region, France. Unlike Call Detail Record (CDR) data which rely only on phone usage, signalling data include all network-based records providing higher spatiotemporal granularity. The explored dataset, which consists of time-stamped traces from 2G and 3G cellular networks with users' unique identifier and cell tower locations, is used to first analyse the cell phone activity degree indicators of each user in order to qualify the mobility information involved in these records. These indicators serve as filtering criteria to identify users whose device transactions are sufficiently distributed over the analysed period to allow studying their mobility. Trips are then extracted from the spatiotemporal traces of users for whom the home location could be detected. Trips have been derived based on a minimum stationary time assumption that enables to determine activity (stop) zones for each user. As a large, but still partial, fraction of the population is observed, scaling is required to obtain an O-D matrix for the full population. We propose a method to perform this scaling and we show that signalling data-based O-D matrix carries similar estimations as those that can be obtained via travel surveys.

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Classification of automobile and transit trips from Smartphone data: Enhancing accuracy using spatial statistics and GIS

Cited by 20 publications

References 4 publications

Smartphone GPS accuracy study in an urban environment

Smartphone GPS accuracy study in an urban environment

Measuring individuals' travel behaviour by use of a GPS-based smartphone application in Dar es Salaam, Tanzania

A data-driven approach for origin–destination matrix construction from cellular network signalling data: a case study of Lyon region (France)

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