Urban mobility study using taxi traces

Veloso, Marco; Phithakkitnukoon, Santi; Bento, Carlos

doi:10.1145/2030080.2030086

Cited by 92 publications

(57 citation statements)

References 9 publications

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“…They also identify vehicle stop positions and discover single trip length distribution deviates from an exponential and favors a power law gradually when trip length becomes longer. In summary, the results of urban mobility in [13] and [11] are consistent with ours. Thus, it can be conjectured that the phenomenon may not happen accidentally and exist in urban areas of cities widely.…”

Section: Displacementsupporting

confidence: 91%

“…Similar results are also discovered from GPS trackings [9,2,10,11,12,13], wireless network traces [14], check-ins from locationbased services [15,16] and even movements of banking notes [17].…”

Section: Introductionsupporting

confidence: 74%

“…Compared with their dataset, our datasets are more comprehensive and cover the whole city fully. Also, through analyzing GPS traces of taxis in Lisbon, [13] illustrate that trip distance can be well represented by an exponential distribution. It is worthy to note that the exponential parameter they obtained (λ = 0.26) is not far from ours.…”

Section: Displacementmentioning

confidence: 99%

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The scaling of human mobility by taxis is exponential

Xiao

Zheng

Liu

et al. 2012

Physica A: Statistical Mechanics and its Applications

216

124

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As a significant factor in urban planning, traffic forecasting and prediction of epidemics, modeling patterns of human mobility draws intensive attention from researchers for decades. Power-law distribution and its variations are observed from quite a few real-world human mobility datasets such as the movements of banking notes, trackings of cell phone users' locations and trajectories of vehicles. In this paper, we build models for 20 million trajectories with fine granularity collected from more than 10 thousand taxis in Beijing. In contrast to most models observed in human mobility data, the taxis' traveling displacements in urban areas tend to follow an exponential distribution instead of a power-law. Similarly, the elapsed time can also be well approximated by an exponential distribution. Worth mentioning, analysis of the interevent time indicates the bursty nature of human mobility, similar to many other human activities.

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Section: Displacementsupporting

confidence: 91%

Section: Introductionsupporting

confidence: 74%

Section: Displacementmentioning

confidence: 99%

See 1 more Smart Citation

The scaling of human mobility by taxis is exponential

Xiao

Zheng

Liu

et al. 2012

Physica A: Statistical Mechanics and its Applications

216

124

View full text Add to dashboard Cite

show abstract

“…According to references [5][6][7][8][9][10][11][12][13][14][15][16][17][18], the distribution of travelled distance is exponential or follows Lévy-distribution. About travel time distribution, the literature says almost nothing, so we tried to fit various types of distributions by using MATLAB, Wolfram Mathematica and Easy Fit StatAssist [19] (this latter allows calculations for datasets with less than 250000 points, so for the large dataset, it was not used).…”

Section: Fitting Of Distribution Functionsmentioning

confidence: 99%

“…The transition probabilities (i.e. the matrix elements) were extracted from the raw GPS data in the following way: every car got an index of 1 if it was moving in the investigated time interval (based on similar investigations found in the literature [5][6][7][8][9][10][11][12][13][14][15][16][17][18]) a car was considered to be moving if its speed was between 1m/s and 34m/s: values out of this range were rejected) and an index of 0, if it was parking. By counting the 0 → 0, 0 → 1 1 → 0 and 1 → 1 transitions, we could determine the matrix elements for the given time interval.…”

Section: Transition Probabilitiesmentioning

confidence: 99%

Stochastic Modeling of Electric Car Charging Station for a Taxi Fleet

Farkas

Dán

2014

Period. Polytech. Elec. Eng. Comp. Sci.

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IntroductionElectric vehicles (EVs) require recharging, just as cars with internal combustion engines need to be refuelled. However, EVs take longer time to be recharged, so the planning of a charging station requires precise models in order to avoid unwanted and annoying queues and waiting at the very station itself. A deterministic model for such planning purpose does not come into question, as the number of cars is large and they all have different characteristics: driving distances, driving style, consumption, etc. all vary from car to car. Therefore, a stochastic model is required, capable of capturing the relevant features of electric car usage and with which the required number of chargers can be determined.In this paper we present a stochastic model applied to a taxi fleet, based on real traffic data. Car usage characteristics were extracted from measured GPS trajectories [1,2] and Monte Carlo simulation was used to generate synthetic driving cycles. The stochastic model was implemented in MATLAB.

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