35,779 research outputs found
Forecasting transport mode use with support vector machines based approach
The paper explores potential to forecast what transport mode one will use for his/her next trip. The support vector machines based approach learns from individual's behavior (validated GPS tracks) to support smart city transport planning services. The overall success rate, in forecasting the transport mode, is 82 %, with lower confusion for private car, bike and walking
A Generalisable Data Fusion Framework to Infer Mode of Transport Using Mobile Phone Data
Cities often lack up-to-date data analytics to evaluate and implement
transport planning interventions to achieve sustainability goals, as
traditional data sources are expensive, infrequent, and suffer from data
latency. Mobile phone data provide an inexpensive source of geospatial
information to capture human mobility at unprecedented geographic and temporal
granularity. This paper proposes a method to estimate updated mode of
transportation usage in a city, with novel usage of mobile phone application
traces to infer previously hard to detect modes, such as bikes and
ride-hailing/taxi. By using data fusion and matrix factorisation, we integrate
socioeconomic and demographic attributes of the local resident population into
the model. We tested the method in a case study of Santiago (Chile), and found
that changes from 2012 to 2020 in mode of transportation inferred by the method
are coherent with expectations from domain knowledge and the literature, such
as ride-hailing trips replacing mass transport.Comment: 19 pages, 8 figure
Comparing and modeling land use organization in cities
The advent of geolocated ICT technologies opens the possibility of exploring
how people use space in cities, bringing an important new tool for urban
scientists and planners, especially for regions where data is scarce or not
available. Here we apply a functional network approach to determine land use
patterns from mobile phone records. The versatility of the method allows us to
run a systematic comparison between Spanish cities of various sizes. The method
detects four major land use types that correspond to different temporal
patterns. The proportion of these types, their spatial organization and scaling
show a strong similarity between all cities that breaks down at a very local
scale, where land use mixing is specific to each urban area. Finally, we
introduce a model inspired by Schelling's segregation, able to explain and
reproduce these results with simple interaction rules between different land
uses.Comment: 9 pages, 6 figures + Supplementary informatio
A universal model for mobility and migration patterns
Introduced in its contemporary form by George Kingsley Zipf in 1946, but with
roots that go back to the work of Gaspard Monge in the 18th century, the
gravity law is the prevailing framework to predict population movement, cargo
shipping volume, inter-city phone calls, as well as bilateral trade flows
between nations. Despite its widespread use, it relies on adjustable parameters
that vary from region to region and suffers from known analytic
inconsistencies. Here we introduce a stochastic process capturing local
mobility decisions that helps us analytically derive commuting and mobility
fluxes that require as input only information on the population distribution.
The resulting radiation model predicts mobility patterns in good agreement with
mobility and transport patterns observed in a wide range of phenomena, from
long-term migration patterns to communication volume between different regions.
Given its parameter-free nature, the model can be applied in areas where we
lack previous mobility measurements, significantly improving the predictive
accuracy of most of phenomena affected by mobility and transport processes.Comment: Main text and supplementary informatio
Exploring universal patterns in human home-work commuting from mobile phone data
Home-work commuting has always attracted significant research attention
because of its impact on human mobility. One of the key assumptions in this
domain of study is the universal uniformity of commute times. However, a true
comparison of commute patterns has often been hindered by the intrinsic
differences in data collection methods, which make observation from different
countries potentially biased and unreliable. In the present work, we approach
this problem through the use of mobile phone call detail records (CDRs), which
offers a consistent method for investigating mobility patterns in wholly
different parts of the world. We apply our analysis to a broad range of
datasets, at both the country and city scale. Additionally, we compare these
results with those obtained from vehicle GPS traces in Milan. While different
regions have some unique commute time characteristics, we show that the
home-work time distributions and average values within a single region are
indeed largely independent of commute distance or country (Portugal, Ivory
Coast, and Boston)--despite substantial spatial and infrastructural
differences. Furthermore, a comparative analysis demonstrates that such
distance-independence holds true only if we consider multimodal commute
behaviors--as consistent with previous studies. In car-only (Milan GPS traces)
and car-heavy (Saudi Arabia) commute datasets, we see that commute time is
indeed influenced by commute distance
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