Mode choice and spatial distribution in long-distance passenger transport – Does mobile network data deliver similar results to other transportation models?

Burgdorf, Christian; Mönch, Alexander; Beige, Sigrun

doi:10.1016/j.trip.2020.100254

Cited by 7 publications

(6 citation statements)

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“…They used mobile network data as an alternative to the traditional approach, using official statistical data, and household and roadside surveys. Burgdorf et al found that the mobile network approach was broadly consistent with the reference for the overall values, but there were significant deviations on the level of origin-destination pairs [18].…”

Section: Literature Reviewmentioning

confidence: 77%

“…Since these locations fundamentally determine people's mobility customs, the commuting trends can be analyzed between these locations. Commuting is studied using mobile network data within a city [14,15] or between cities [16][17][18][19] and also examined by social network data, such as Twitter [20][21][22].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Burgdorf et al analyzed long-distance (over 50 km) passenger transport in Germany, including the means of transport [18]. They used mobile network data as an alternative to the traditional approach, using official statistical data, and household and roadside surveys.…”

Section: Literature Reviewmentioning

confidence: 99%

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Commuting Analysis of the Budapest Metropolitan Area Using Mobile Network Data

Pintér

Felde

2022

IJGI

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The analysis of human movement patterns based on mobile network data makes it possible to examine a very large population cost-effectively and has led to several discoveries about human dynamics. However, the application of this data source is still not common practice. The goal of this study was to analyze the commuting tendencies of the Budapest Metropolitan Area using mobile network data as a case study and propose an automatized alternative approach to the current, questionnaire-based method, as commuting is predominantly analyzed by the census, which is performed only once in a decade in Hungary. To analyze commuting, the home and work locations of cell phone subscribers were determined based on their appearances during and outside working hours. The detected home locations of the subscribers were compared to census data at a settlement level. Then, the settlement and district level commuting tendencies were identified and compared to the findings of census-based sociological studies. It was found that the commuting analysis based on mobile network data strongly correlated with the census-based findings, even though home and work locations were estimated by statistical methods. All the examined aspects, including commuting from sectors of the agglomeration to the districts of Budapest and the age-group-based distribution of the commuters, showed that mobile network data could be an automatized, fast, cost-effective, and relatively accurate way of analyzing commuting, that could provide a powerful tool for sociologists interested in commuting.

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Section: Literature Reviewmentioning

confidence: 77%

Section: Literature Reviewmentioning

confidence: 99%

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Commuting Analysis of the Budapest Metropolitan Area Using Mobile Network Data

Pintér

Felde

2022

IJGI

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“…The Beijing model uses data from a multimodal transportation planner application in which the researcher has no control over the acquisition process. Checking and removing bias is becoming more and more relevant as an increasing number of transport models are using mobile application data to model human mobility [78][79][80]. These kind of acquisition technologies have been proven to have an inherent bias due to the different levels of technology adoption across socio-economic groups [81].…”

Section: Discussionmentioning

confidence: 99%

Fair Models for Impartial Policies: Controlling Algorithmic Bias in Transport Behavioural Modelling

Vega-Gonzalo

Christidis

2022

Sustainability

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The increasing use of new data sources and machine learning models in transport modelling raises concerns with regards to potentially unfair model-based decisions that rely on gender, age, ethnicity, nationality, income, education or other socio-economic and demographic data. We demonstrate the impact of such algorithmic bias and explore the best practices to address it using three different representative supervised learning models of varying levels of complexity. We also analyse how the different kinds of data (survey data vs. big data) could be associated with different levels of bias. The methodology we propose detects the model’s bias and implements measures to mitigate it. Specifically, three bias mitigation algorithms are implemented, one at each stage of the model development pipeline—before the classifier is trained (pre-processing), when training the classifier (in-processing) and after the classification (post-processing). As these debiasing techniques have an inevitable impact on the accuracy of predicting the behaviour of individuals, the comparison of different types of models and algorithms allows us to determine which techniques provide the best balance between bias mitigation and accuracy loss for each case. This approach improves model transparency and provides an objective assessment of model fairness. The results reveal that mode choice models are indeed affected by algorithmic bias, and it is proven that the implementation of off-the-shelf mitigation techniques allows us to achieve fairer classification models.

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“…Janzen ( 2019) estimated an activitybased model based on a synthetic population, while Brederode et al (2019) used a multi-proportional gravity model to fuse mobile phone network data with survey data on the OD level before parameter estimation. Burgdorf et al, (2020) validated the behavioural output of an off-the-shelf mobile phone network data provider with the behavioural output from a gravity model based on survey data, and found that the mode split and travel frequencies were similar. Huang et al (2019) reviewed studies classifying modes of trips identified in mobile phone network data.…”

Section: Introductionmentioning

confidence: 90%