32 research outputs found
Analysis of human mobility patterns from GPS trajectories and contextual information
This work was supported by the EU FP7 Marie Curie ITN GEOCROWD grant (FP7- PEOPLE-2010-ITN-264994).Human mobility is important for understanding the evolution of size and structure of urban areas, the spatial distribution of facilities, and the provision of transportation services. Until recently, exploring human mobility in detail was challenging because data collection methods consisted of cumbersome manual travel surveys, space-time diaries or interviews. The development of location-aware sensors has significantly altered the possibilities for acquiring detailed data on human movements. While this has spurred many methodological developments in identifying human movement patterns, many of these methods operate solely from the analytical perspective and ignore the environmental context within which the movement takes place. In this paper we attempt to widen this view and present an integrated approach to the analysis of human mobility using a combination of volunteered GPS trajectories and contextual spatial information. We propose a new framework for the identification of dynamic (travel modes) and static (significant places) behaviour using trajectory segmentation, data mining and spatio-temporal analysis. We are interested in examining if and how travel modes depend on the residential location, age or gender of the tracked individuals. Further, we explore theorised “third places”, which are spaces beyond main locations (home/work) where individuals spend time to socialise. Can these places be identified from GPS traces? We evaluate our framework using a collection of trajectories from 205 volunteers linked to contextual spatial information on the types of places visited and the transport routes they use. The result of this study is a contextually enriched data set that supports new possibilities for modelling human movement behaviour.PostprintPeer reviewe
Analysis of Actual Versus Permitted Driving Speed: a Case Study from Glasgow, Scotland
With a lack of consistent information about actual driving speed on the majority of roads in the UK we propose a method to determine car speeds from a sample of movement data from a GPS-based travel survey. Furthermore, we identify potential road links within a road network where speeding incidents take place. Speed at which a car travels is a strong determinant in the potential risk of a crash as well as the severity of the crash. Using the GPS movement data we can detect areas in the city of Glasgow where the driving speed exceeds the permitted limits and thus identifying possible areas of higher crash risk
Mobility Data Mining for Rural and Urban Map-Matching
Ajalis-ruumiliste andmete kogumine on hoogustunud erinevates rakendustes ja seadmetes. Globaalne positsiooneerimise süsteem (GPS) on kõige populaarsem viis asukoha teave saamiseks. Kaardipunktide vastavusse seadmine on konseptsioon, mis püüab GPS andmeid trajektooris viia vastavusse reaalse teedevõrguga. GPS andmete suurim probleem tuleneb andmete mõõtmis-ja kogumisvigadest ja nende parandamine on suur väljakutse. Käesoleva lõputöö eesmärk on arendada andmete töötlusvoo ja visualiseerimise raamistik muutmaks GPS punktid loogilisteks trajektoorideks ja vigaste GPS punktide asukohtade parandamiseks. Selle eesmärgi saavutamiseks tutvustatakse uut lähenemist trajektooride mustrite leidmiseks.The functionality of gathering spatio-temporal data has seen increasing usage in various applications and devices. The Global Positioning System (GPS) is a satellite navigation system which is mostly used for gathering location information. Map-matching is the procedure of matching trajectories from a sequence of raw GPS data points to the appropriate road networks. GPS data errors are one of the biggest problems and correcting them is a big challenge. The main goal of this thesis work is to build a data pipeline and visualization framework for turning raw GPS data to trajectories and correcting erroneous GPS points by new map-matching approach. For achieving the goal a new approach for trajectory pattern mining is introduced
Hierarchical accompanying and inhibiting patterns on the spatial arrangement of taxis' local hotspots
Due to the large volume of recording, the complete spontaneity, and the
flexible pick-up and drop-off locations, taxi data portrays a realistic and
detailed picture of urban space use to a certain extent. The spatial
arrangement of pick-up and drop-off hotspots reflects the organizational space,
which has received attention in urban structure studies. Previous studies
mainly explore the hotspots at a large scale by visual analysis or some simple
indexes, where the hotspots usually cover the entire central business district,
train stations, or dense residential areas, reaching a radius of hundreds or
even thousands of meters. However, the spatial arrangement patterns of
small-scale hotspots, reflecting the specific popular pick-up and drop-off
locations, have not received much attention. Using two taxi trajectory datasets
in Wuhan and Beijing, China, this study quantitatively explores the spatial
arrangement of fine-grained pick-up and drop-off local hotspots with different
levels of popularity, where the sizes are adaptively set as 90m*90m in Wuhan
and 105m*105m in Beijing according to the local hotspot identification method.
Results show that popular hotspots tend to be surrounded by less popular
hotspots, but the existence of less popular hotspots is inhibited in regions
with a large number of popular hotspots. We use the terms hierarchical
accompany and inhibiting patterns for these two spatial configurations.
Finally, to uncover the underlying mechanism, a KNN-based model is proposed to
reproduce the spatial distribution of other less popular hotspots according to
the most popular ones. These findings help decision-makers construct reasonable
urban minimum units for precise traffic and disease control, as well as plan a
more humane spatial arrangement of points of interest
Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models
Water demand forecasting is a crucial task in the efficient management of the water supply system. This paper compares classical and adapted machine learning algorithms used for water usage predictions including ARIMA, support vector regression, random forests and extremely randomized trees. These models were enriched with human mobility data to improve the predictive power of water demand forecasting. Furthermore, a framework for processing mobility data into time-series correlated with water usage data is proposed. This study uses 51 days of water consumption readings and over 7 million geolocated mobility records from urban areas. Results show that using human mobility data improves water demand prediction. The best forecasting algorithm employing a random forest method achieved 90.4% accuracy (measured by the mean absolute percentage error) and is better by 1% than the same algorithm using only water data, while classic ARIMA approach achieved 90.0%. The Blind (copying) prediction achieved 85.1% of accuracy
Revealing intra-urban spatial structure through an exploratory analysis by combining road network abstraction model and taxi trajectory data
The unprecedented urbanization in China has dramatically changed the urban
spatial structure of cities. With the proliferation of individual-level
geospatial big data, previous studies have widely used the network abstraction
model to reveal the underlying urban spatial structure. However, the
construction of network abstraction models primarily focuses on the topology of
the road network without considering individual travel flows along with the
road networks. Individual travel flows reflect the urban dynamics, which can
further help understand the underlying spatial structure. This study therefore
aims to reveal the intra-urban spatial structure by integrating the road
network abstraction model and individual travel flows. To achieve this goal, we
1) quantify the spatial interaction relatedness of road segments based on the
Word2Vec model using large volumes of taxi trip data, then 2) characterize the
road abstraction network model according to the identified spatial interaction
relatedness, and 3) implement a community detection algorithm to reveal
sub-regions of a city. Our results reveal three levels of hierarchical spatial
structures in the Wuhan metropolitan area. This study provides a data-driven
approach to the investigation of urban spatial structure via identifying
traffic interaction patterns on the road network, offering insights to urban
planning practice and transportation management
Community Time-Activity Trajectory Modelling based on Markov Chain Simulation and Dirichlet Regression
Accurate modeling of human time-activity trajectory is essential to support
community resilience and emergency response strategies such as daily energy
planning and urban seismic vulnerability assessment. However, existing modeling
of time-activity trajectory is only driven by socio-demographic information
with identical activity trajectories shared among the same group of people and
neglects the influence of the environment. To further improve human
time-activity trajectory modeling, this paper constructs community
time-activity trajectory and analyzes how social-demographic and built
environment influence people s activity trajectory based on Markov Chains and
Dirichlet Regression. We use the New York area as a case study and gather data
from American Time Use Survey, Policy Map, and the New York City Energy & Water
Performance Map to evaluate the proposed method. To validate the regression
model, Box s M Test and T-test are performed with 80% data training the model
and the left 20% as the test sample. The modeling results align well with the
actual human behavior trajectories, demonstrating the effectiveness of the
proposed method. It also shows that both social-demographic and built
environment factors will significantly impact a community's time-activity
trajectory. Specifically, 1) Diversity and median age both have a significant
influence on the proportion of time people assign to education activity. 2)
Transportation condition affects people s activity trajectory in the way that
longer commute time decreases the proportion of biological activity (eg.
sleeping and eating) and increases people s working time. 3) Residential
density affects almost all activities with a significant p-value for all
biological needs, household management, working, education, and personal
preference.Comment: to be published in Computers, Environment and Urban Syste
Context-aware movement analysis in ecology : a systematic review
This work was supported by the Coordination for the Improvement of Higher Education Personnel (BEX:13438/13-1), the Leverhulme Trust Research Project Grant (RPG-2018-258); the Discovery grant from the Natural Sciences and Engineering Research Council of Canada the Polish National Science Centre (UMO-2019/35/O/ST6/04127).Research on movement has increased over the past two decades, particularly in movement ecology, which studies animal movement. Taking context into consideration when analysing movement can contribute towards the understanding and prediction of behaviour. The only way for studying animal movement decision-making and their responses to environmental conditions is through analysis of ancillary data that represent conditions where the animal moves. In GIScience this is called Context-Aware Movement Analysis (CAMA). As ecology becomes more data-oriented, we believe that there is a need to both review what CAMA means for ecology in methodological terms and to provide reliable definitions that will bridge the divide between the content-centric and data-centric analytical frameworks. We reviewed the literature and proposed a definition for context, develop a taxonomy for contextual variables in movement ecology and discuss research gaps and open challenges in the science of movement more broadly. We found that the main research for CAMA in the coming years should focus on: 1) integration of contextual data and movement data in space and time, 2) tools that account for the temporal dynamics of contextual data, 3) ways to represent contextualized movement data, and 4) approaches to extract meaningful information from contextualized data.Publisher PDFPeer reviewe