Monitoring travel patterns in German city regions with the help of mobile phone network data

Fina, Stefan; Joshi, Jigeeshu; Wittowsky, Dirk

doi:10.1080/17538947.2020.1836048

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…One possible data source could be mobile network data, from which travel patterns can be derived (cf. [ 11 ]). However, the spatial accuracy of mobile network data can be low, and, more importantly, separating the commuting trips from the rest of activities is not a trivial task.…”

Section: Discussionmentioning

confidence: 99%

A data-driven analysis of the potential of public transport for German commuters using accessibility indicators

Mocanu

Joshi

Winkler

2021

Eur. Transp. Res. Rev.

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Background A significant mode shift will be required in order to meet the ambitious greenhouse gas emissions reduction targets in Germany and elsewhere. Such a mode shift can only be achieved by a combination of drastic push and pull measures. Getting commuters to switch modes might be particularly difficult and have a negative impact on their access to employment and welfare. Methodology We investigate the potential for a mode shift from car to public transport for German commuters using a data-driven approach based mainly on open data sources that avoids complex transport model runs. Different datasets on the home and workplace location of all employees in Germany are consolidated to create an origin-destination commuter matrix at traffic analysis zone level. The commuter matrix is merged with travel time data for car and public transport to calculate a spatially disaggregated and mode-specific measure of accessibility. The comparison of accessibility by car and public transport is used to derive the potential for a mode shift and identify potential challenges and barriers. Results Public transport accessibility to workplaces is poorer across the country compared to access by car. On average, public transport travel times are almost three times higher than the corresponding car travel times. The differences in accessibility are largely independent of the region type. Results are validated by an independent dataset from a household travel survey. Based on these results, the potential for a mode shift appears to be very low.

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Section: Discussionmentioning

confidence: 99%

A data-driven analysis of the potential of public transport for German commuters using accessibility indicators

Mocanu

Joshi

Winkler

2021

Eur. Transp. Res. Rev.

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“…Mobile big data has considerable advantages in studying crowd mobility and social patterns. For example, Fina S et al compared travel patterns in selected monocentric and polycentric urban areas in Germany using mobile big data to test hypotheses of transit-oriented regional development and congestion risk in transport networks [39]. Many studies have combined the above research methods to achieve more comprehensive data coverage, and this study aims to assist in understanding spatial and temporal mobility patterns in different city types.…”

Section: Application Of Big Data Tools In Urban Spatial Researchmentioning

confidence: 99%

Spatial Patterns and Multi-Dimensional Impact Analysis of Urban Street Quality Perception under Multi-Source Data: A Case Study of Wuchang District in Wuhan, China

Li,

Xu,

Sun

2023

Applied Sciences

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The human spatial perception of urban streets has a high complexity and traditional research methods often focus on access surveys of human perception. Urban streets serve as both a direct conduit for pedestrians’ impressions of a city and a reflection of the spatial quality of that city. Street-view images can provide a large amount of primary data for the image semantic segmentation technique. Deep learning techniques were used in this study to collect the boring, beautiful, depressing, lively, safe, and wealthy perception scores of street spaces based on these images. Then, the spatial pattern of urban street-space quality perception was analyzed by global Moran’s I and GIS hotspot analyses. The findings demonstrate that various urban facilities affect street quality perception in different ways and that the strength of an influencing factor’s influence varies depending on its geographical location. The results of the influencing factors reveal the difference in the degree of influence of positive and negative influencing factors on various perceptions of the visual dimension of pedestrians. The primary contribution of this study is that it reduces the potential bias of a single data source by using multi-dimensional impact analysis to explain the relationship between urban street perception and urban facilities and visual elements. The study’s findings offer direction for high-quality urban development as well as advice for urban planning and enhanced design.

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“…Consequently, a series of standout achievements have also appeared. For example, Ginsberg et al, (2009) used search engine data to predict the influenza epidemic [8]; Fina et al, (2020) used mobile phone network data to monitor the travel patterns of different regions in Germany [10]; and He et al, (2020) used big geographic data to investigate the growth in outlying expansion development zones in 275 Chinese cities [11]. As a typical information-intensive industry, big data is increasingly playing a significant role in the real estate appraisal industry.…”

Section: Introductionmentioning

confidence: 99%

The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data

Wei

Wang

et al. 2022

Land

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In the era of big data, advances in relevant technologies are profoundly impacting the field of real estate appraisal. Many scholars regard the integration of big data technology as an inevitable future trend in the real estate appraisal industry. In this paper, we summarize 124 studies investigating the use of big data technology to optimize real estate appraisal through the hedonic price model (HPM). We also list a variety of big data resources and key methods widely used in the real estate appraisal field. On this basis, the development of real estate appraisal moving forward is analyzed. The results obtained in the current studies are as follows: First, the big data resources currently applied to real estate appraisal include more than a dozen big data types from three data sources; the internet, remote sensing, and the Internet of things (IoT). Additionally, it was determined that web crawler technology represents the most important data acquisition method. Second, methods such as data pre-processing, spatial modeling, Geographic information system (GIS) spatial analysis, and the evolving machine learning methods with higher valuation accuracy were successfully introduced into the HPM due to the features of real estate big data. Finally, although the application of big data has greatly expanded the amount of available data and feature dimensions, this has caused a new problem: uneven data quality. Uneven data quality can reduce the accuracy of appraisal results, and, to date, insufficient attention has been paid to this issue. Future research should pay greater attention to the data integration of multi-source big data and absorb the applications developed in other disciplines. It is also important to combine various methods to form a new united evaluation model based on taking advantage of, and avoiding shortcomings to compensate for, the mechanism defects of a single model.

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Monitoring travel patterns in German city regions with the help of mobile phone network data

Cited by 8 publications

References 18 publications

A data-driven analysis of the potential of public transport for German commuters using accessibility indicators

A data-driven analysis of the potential of public transport for German commuters using accessibility indicators

Spatial Patterns and Multi-Dimensional Impact Analysis of Urban Street Quality Perception under Multi-Source Data: A Case Study of Wuchang District in Wuhan, China

The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data

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