Long-Distance Travel

Mattioli, Giulio; Adeel, Muhammad

doi:10.1016/b978-0-08-102671-7.10695-5

Cited by 11 publications

(7 citation statements)

References 17 publications

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“…Let first recall that beyond definition and measurement issues (see Mattioli and Adeel, 2021), strong social inequalities generally exist in long-distance travel. At the level of countries, airline passenger-km per capita is significantly correlated with GDP per capita, but even in high-income countries, the flying population is estimated to be only 40%; and even in the US, it is estimated that 53% of adults did not fly in 2018 (Gössling and Humpe, 2020).…”

Section: Social Justice In the High-speed Rail Market: A Reviewmentioning

confidence: 99%

Is high-speed rail socially exclusive? An evidence-based worldwide analysis

Dobruszkes

Chen

Moyano

et al. 2022

Travel Behaviour and Society

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Section: Social Justice In the High-speed Rail Market: A Reviewmentioning

confidence: 99%

Is high-speed rail socially exclusive? An evidence-based worldwide analysis

Dobruszkes

Chen

Moyano

et al. 2022

Travel Behaviour and Society

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“…Third, the emphasis on "typical" mobility of most of today's mobility surveys could be misleading especially with respect to car mobility and long-distance travel is still not well covered in existing surveys (Mattioli & Adeel 2021). Likewise, existing studies on most…”

Section: Discussionmentioning

confidence: 99%

“…Here, we use 50 km, 100 km, and 200 km of daily VKT as thresholds for long-distance driving as is common in many studies of long-distance driving (cf. Mattioli & Adeel 2021). We extract the days with more than 50, 100, or 200 km of daily VKT from the daily VKT data on a vehicle specific level and calculate the share of longdistance days and the share of long-distance km in annual VKT for each individual vehicle.…”

Section: Methodsmentioning

confidence: 99%

“…To the authors' best knowledge, it is the first empirical study to analyze longitudinal car driving data with several hundred days of observation for most users. Thus, our data is likely to include rare but important long-distance travel events such as holidays (Mattioli & Adeel 2021) which are particularly important for limited range electric vehicles. Second, driving distances recorded through onboard diagnostics are more reliable than travel diaries and allow us to capture even rare events such as long-distance driving with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Variability of daily car usage and the frequency of long-distance driving

Plötz

Sprei

2021

Transportation Research Part D: Transport and Environment

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“…However, they also have many shortcomings such as being costly to collect and having low sampling rates, short survey duration, under-reporting of trips, and being out-of-date [12]. Travel surveys also fail to capture most of the infrequent long-distance trips [13].…”

Section: A Related Workmentioning

confidence: 99%

A Mobility Model for Synthetic Travel Demand From Sparse Traces

Liao

Wennerberg

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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Knowing how much people travel is essential for transport planning. Empirical mobility traces collected from call detail records (CDRs), location-based social networks (LBSNs), and social media data have been used widely to study mobility patterns. However, these data suffer from sparsity, an issue that has largely been overlooked. In order to extend the use of these low-cost and accessible data, this study proposes a mobility model that fills the gaps in sparse mobility traces from which one can later synthesise travel demand. The proposed model extends the fundamental mechanisms of exploration and preferential return to synthesise mobility trips. The model is tested on sparse mobility traces from Twitter. We validate our model and find good agreement on origin-destination matrices and trip distance distributions for Sweden, the Netherlands, and São Paulo, Brazil, compared with a benchmark model using a heuristic method, especially for the most frequent trip distance range (1-40 km). Moreover, the learned model parameters are found to be transferable from one region to another. Using the proposed model, reasonable travel demand values can be synthesised from a dataset covering a large enough population of very sparse individual geolocations (around 1.5 geolocations per day covering 100 days on average).

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Long-Distance Travel

Cited by 11 publications

References 17 publications

Is high-speed rail socially exclusive? An evidence-based worldwide analysis

Is high-speed rail socially exclusive? An evidence-based worldwide analysis

Variability of daily car usage and the frequency of long-distance driving

A Mobility Model for Synthetic Travel Demand From Sparse Traces

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