9,537 research outputs found
Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks
A fundamental problem of interest to policy makers, urban planners, and other
stakeholders involved in urban development projects is assessing the impact of
planning and construction activities on mobility flows. This is a challenging
task due to the different spatial, temporal, social, and economic factors
influencing urban mobility flows. These flows, along with the influencing
factors, can be modelled as attributed graphs with both node and edge features
characterising locations in a city and the various types of relationships
between them. In this paper, we address the problem of assessing
origin-destination (OD) car flows between a location of interest and every
other location in a city, given their features and the structural
characteristics of the graph. We propose three neural network architectures,
including graph neural networks (GNN), and conduct a systematic comparison
between the proposed methods and state-of-the-art spatial interaction models,
their modifications, and machine learning approaches. The objective of the
paper is to address the practical problem of estimating potential flow between
an urban development project location and other locations in the city, where
the features of the project location are known in advance. We evaluate the
performance of the models on a regression task using a custom data set of
attributed car OD flows in London. We also visualise the model performance by
showing the spatial distribution of flow residuals across London.Comment: 9 pages, 5 figures, to be published in the Proceedings of 2020 IEEE
International Conference on Smart Computing (SMARTCOMP 2020
An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques
Origin-destination~(OD) flow modeling is an extensively researched subject
across multiple disciplines, such as the investigation of travel demand in
transportation and spatial interaction modeling in geography. However,
researchers from different fields tend to employ their own unique research
paradigms and lack interdisciplinary communication, preventing the
cross-fertilization of knowledge and the development of novel solutions to
challenges. This article presents a systematic interdisciplinary survey that
comprehensively and holistically scrutinizes OD flows from utilizing
fundamental theory to studying the mechanism of population mobility and solving
practical problems with engineering techniques, such as computational models.
Specifically, regional economics, urban geography, and sociophysics are adept
at employing theoretical research methods to explore the underlying mechanisms
of OD flows. They have developed three influential theoretical models: the
gravity model, the intervening opportunities model, and the radiation model.
These models specifically focus on examining the fundamental influences of
distance, opportunities, and population on OD flows, respectively. In the
meantime, fields such as transportation, urban planning, and computer science
primarily focus on addressing four practical problems: OD prediction, OD
construction, OD estimation, and OD forecasting. Advanced computational models,
such as deep learning models, have gradually been introduced to address these
problems more effectively. Finally, based on the existing research, this survey
summarizes current challenges and outlines future directions for this topic.
Through this survey, we aim to break down the barriers between disciplines in
OD flow-related research, fostering interdisciplinary perspectives and modes of
thinking.Comment: 49 pages, 6 figure
An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service
In this paper, we present machine learning approaches for characterizing and
forecasting the short-term demand for on-demand ride-hailing services. We
propose the spatio-temporal estimation of the demand that is a function of
variable effects related to traffic, pricing and weather conditions. With
respect to the methodology, a single decision tree, bootstrap-aggregated
(bagged) decision trees, random forest, boosted decision trees, and artificial
neural network for regression have been adapted and systematically compared
using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and
slope. To better assess the quality of the models, they have been tested on a
real case study using the data of DiDi Chuxing, the main on-demand ride hailing
service provider in China. In the current study, 199,584 time-slots describing
the spatio-temporal ride-hailing demand has been extracted with an
aggregated-time interval of 10 mins. All the methods are trained and validated
on the basis of two independent samples from this dataset. The results revealed
that boosted decision trees provide the best prediction accuracy (RMSE=16.41),
while avoiding the risk of over-fitting, followed by artificial neural network
(20.09), random forest (23.50), bagged decision trees (24.29) and single
decision tree (33.55).Comment: Currently under review for journal publicatio
Geo-Spotting: Mining Online Location-based Services for Optimal Retail Store Placement
The problem of identifying the optimal location for a new retail store has
been the focus of past research, especially in the field of land economy, due
to its importance in the success of a business. Traditional approaches to the
problem have factored in demographics, revenue and aggregated human flow
statistics from nearby or remote areas. However, the acquisition of relevant
data is usually expensive. With the growth of location-based social networks,
fine grained data describing user mobility and popularity of places has
recently become attainable.
In this paper we study the predictive power of various machine learning
features on the popularity of retail stores in the city through the use of a
dataset collected from Foursquare in New York. The features we mine are based
on two general signals: geographic, where features are formulated according to
the types and density of nearby places, and user mobility, which includes
transitions between venues or the incoming flow of mobile users from distant
areas. Our evaluation suggests that the best performing features are common
across the three different commercial chains considered in the analysis,
although variations may exist too, as explained by heterogeneities in the way
retail facilities attract users. We also show that performance improves
significantly when combining multiple features in supervised learning
algorithms, suggesting that the retail success of a business may depend on
multiple factors.Comment: Proceedings of the 19th ACM SIGKDD international conference on
Knowledge discovery and data mining, Chicago, 2013, Pages 793-80
UrbanFM: Inferring Fine-Grained Urban Flows
Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devices, while preventing the degeneration of data accuracy and
granularity. In this paper, we aim to infer the real-time and fine-grained
crowd flows throughout a city based on coarse-grained observations. This task
is challenging due to two reasons: the spatial correlations between coarse- and
fine-grained urban flows, and the complexities of external impacts. To tackle
these issues, we develop a method entitled UrbanFM based on deep neural
networks. Our model consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs by using a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influences of different external factors. Extensive experiments on two
real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness
and efficiency of our method compared to seven baselines, demonstrating the
state-of-the-art performance of our approach on the fine-grained urban flow
inference problem
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
On the Relation Between Mobile Encounters and Web Traffic Patterns: A Data-driven Study
Mobility and network traffic have been traditionally studied separately.
Their interaction is vital for generations of future mobile services and
effective caching, but has not been studied in depth with real-world big data.
In this paper, we characterize mobility encounters and study the correlation
between encounters and web traffic profiles using large-scale datasets (30TB in
size) of WiFi and NetFlow traces. The analysis quantifies these correlations
for the first time, across spatio-temporal dimensions, for device types grouped
into on-the-go Flutes and sit-to-use Cellos. The results consistently show a
clear relation between mobility encounters and traffic across different
buildings over multiple days, with encountered pairs showing higher traffic
similarity than non-encountered pairs, and long encounters being associated
with the highest similarity. We also investigate the feasibility of learning
encounters through web traffic profiles, with implications for dissemination
protocols, and contact tracing. This provides a compelling case to integrate
both mobility and web traffic dimensions in future models, not only at an
individual level, but also at pairwise and collective levels. We have released
samples of code and data used in this study on GitHub, to support
reproducibility and encourage further research
(https://github.com/BabakAp/encounter-traffic).Comment: Technical report with details for conference paper at MSWiM 2018, v3
adds GitHub lin
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