12,400 research outputs found
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
Semantic Compression for Edge-Assisted Systems
A novel semantic approach to data selection and compression is presented for
the dynamic adaptation of IoT data processing and transmission within "wireless
islands", where a set of sensing devices (sensors) are interconnected through
one-hop wireless links to a computational resource via a local access point.
The core of the proposed technique is a cooperative framework where local
classifiers at the mobile nodes are dynamically crafted and updated based on
the current state of the observed system, the global processing objective and
the characteristics of the sensors and data streams. The edge processor plays a
key role by establishing a link between content and operations within the
distributed system. The local classifiers are designed to filter the data
streams and provide only the needed information to the global classifier at the
edge processor, thus minimizing bandwidth usage. However, the better the
accuracy of these local classifiers, the larger the energy necessary to run
them at the individual sensors. A formulation of the optimization problem for
the dynamic construction of the classifiers under bandwidth and energy
constraints is proposed and demonstrated on a synthetic example.Comment: Presented at the Information Theory and Applications Workshop (ITA),
February 17, 201
Compressed data structures for trajectory representation
Programa Oficial de Doutoramento en Computación . 5009V01[Abstract]
The proliferation of GPS devices in smartphones, vehicles and sport wearables in one
hand, and geolocation mechanisms (such as smart cards in public transportation) in
the other hand, have produced an unprecedented capacity of obtaining and storing
trajectories that people generate by the movements that originate from their daily
schedules. However, no standard data models exist to represent these trajectories,
and besides neither traditional databases nor new NoSQL databases are adequate for
the representation and exploitation of the complex data of spatio-temporal nature
which these trajectories consist of. This general outlook is even more complex once
we consider that whenever we are storing information related to a context of public
transportation passengers, customers inside a mall, or simply vehicles moving in a
city we must deal with a true Big Data scenario in which guaranteeing an efficient
response can be very challenging.
Consequently, in this thesis we address the design of compact data structures
for the representation of the followed trajectories, both in the context of vehicles
and/or people moving in urban or periurban spaces, as in the context of itineraries
of commuters in public transportation. Additionally to designing these compact
data structures that allow us to represent the Big Data scenario usually seen in
this application domain, we have designed the algorithms that allow the efficient
exploitation of said information.
These algorithms, in addition to solving classic spatio-temporal queries, such
as obtaining the position of a moving object at a time instant, reconstructing the
trajectory of an object, or even spatio-temporal window queries (which objects are
inside a spatial range either within a time window or at a time instant), are also
able to solve more specialized queries for the analysis of trajectories that travelers
make. For instance, we have designed algorithms to query the number of travelers
that start (or finish) their trip in a certain place within a determined time interval,
or the number of travelers that switch from one line from the public transportation
network to another using a particular stop, or even the number of travelers that
had started their trip in a certain place (which can be either a stop or a whole
neighborhood) to finish it in another place.
Both the designed structures as the querying algorithms, which are available at https://github.com/dgalaktionov/compact-trip-representation, have been
experimentally evaluated. With these structures we are able to represent, in a
compact space of 100 MiB, a collection of approximately a million and a half of taxi
trajectories, or alternatively ten million trajectories consisting of itineraries over
public transportation networks, given that they are more compact. In both cases, we
can solve most of the considered exploitation queries in the order of microseconds,
with algorithms that scale logarithmically with respect to the increase in the number
of stored trajectories.
Finally, considering the practical quality of this work, it was required for the
performed research to be of a clearly applied nature, which led us to developing a
web application with Geograhic Information Systems technology, which integrates
with our compressed structures and algorithms instead of relying on common spatial
databases. This application, which provides a simple and intuitive user interface
that represents the map of a transportation network, enabled an end user to run the
aforementioned algorithms over a large collection of historic trajectories. Likewise,
this interface presents the query results in a graphical and intuitive way.[Resumen]
La proliferación de por un lado de dispositivos GPS en smartphones, vehÃculos
o pulseras de deporte, y por otro, de otros mecanismos de geolocalización (como
las tarjetas de pago de trasporte público), han generado una capacidad inédita de
obtener y almacenar las trayectorias que generan las personas al moverse durante
sus quehaceres diarios. Sin embargo, no existen modelos de datos estándar para
representar dichas trayectorias, además de que ni las bases de datos tradicionales,
ni para las nuevas bases de datos NoSQL se adecúan bien a la representación y
explotación de esos datos complejos de naturaleza espacio-temporal que son las
trayectorias. Para hacer más complejo aún el panorama, se constata además que
cuando se quieren almacenar trayectorias de viajeros de transporte público, o de
clientes en centros comerciales, o simplemente de personas o vehÃculos moviéndose
por la ciudad hay que enfrentarse a un verdadero escenario Big Data en el que la
eficiencia en la respuesta a las consultas se hace muy difÃcil. Por todo ello, en esta
tesis se aborda el diseño de estructuras de datos compactas para la representación
de las trayectorias seguidas, por un lado, por vehÃculos y/o personas que se mueven
por las calles de un entorno urbano o periurbano acotado, y por otro los itinerarios
de viajeros de transporte público. Además de diseñar esas estructuras de datos
compactas, que permiten representar ese escenario Big Data habitual en estos
dominios de aplicación, se han diseñado los algoritmos que permiten la explotación
eficiente de dichos datos. Dichos algoritmos, además de resolver las consultas
espacio-temporales clásicas, tanto las de posición de un objeto en un tiempo, o
trayectoria de un objeto durante un intervalo temporal, como las consultas de rango
espacio-temporal (qué objetos están en una ventana del espacio en un instante o
intervalo temporal) resuelven también consultas más especializadas para el análisis
de trayectorias de viajeros. Por ejemplo, hemos diseñado algoritmos para consultar
el número de viajeros que inician (o terminan) su viaje en cierto lugar dentro
de un cierto intervalo temporal, o el número de viajeros que conmutan de una
lÃnea a otra de la red de transporte público en una cierta parada, o incluso el
número de viajeros que inicia su viaje en cierto lugar (parada o barrio) y lo
termina en otra parada o barrio determinados. Tanto las estructuras de datos
diseñadas como todos los algoritmos de consulta, que están disponibles en https://github.com/dgalaktionov/compact-trip-representation, han sido evaluados
experimentalmente. Con estas estructuras es posible representar en un espacio de 100
MiB una colección de aproximadamente un millón y medio de trayectorias de taxis, o
alternativamente diez millones de trayectorias consistentes de itinerarios sobre redes
de transporte público, al ser éstas últimas más compactas. En ambos casos, podemos
resolver la mayor parte de las consultas de explotación planteadas en el orden de
microsegundos, con algoritmos que escalan de forma logarÃtmica con respecto al
incremento en el número de trayectorias almacenadas. Por último y dado el carácter
de tesis industrial de este trabajo, era necesario que la investigación realizada tuviese
un carácter claramente aplicado, por ello se implementó una aplicación web con
tecnologÃa de Sistemas de Información Geográfica que en vez de trabajar sobre una
base de datos espacial convencional utiliza la estructura comprimida y los algoritmos
para su explotación diseñados en la tesis. Esa aplicación facilita, mediante una
sencilla e intuitiva interfaz de usuario que representa el mapa de la red de transporte,
el lanzamiento de los algoritmos diseñados sobre un amplio conjunto de trayectorias
de viajeros. Del mismo modo esa interfaz presenta los resultados de las consultas de
modo gráfico e intuitivo.[Resumo]
A proliferación de por un lado os dispositivos GPS en smartphones, vehÃculos ou
brazaletes deportivos e por outro lado os mecanismos de xeolocalización (como as
tarxetas de pago do transporte público), xeraron unha capacidade sen precedentes
para obter e almacenar as traxectorias que a xente xera ao moverse durante as súas
tarefas diarias. Non obstante, non hai modelos de datos estándar para representar
tales traxectorias, ademais de que nin as bases de datos tradicionais nin para as
novas bases de datos NoSQL son adecuadas para a representación e explotación de
datos tan complexos de natureza espazo-temporal que son as traxectorias. Para facer
o panorama aÃnda máis complexo, tamén se comproba que cando se quere almacenar
traxectorias de viaxeiros de transporte público, ou clientes en centros comerciais, ou
simplemente de persoas ou vehÃculos que se desprazan pola cidade, se ten que afrontar
un verdadeiro escenario de Big Data no que a eficiencia na resposta ás consultas faise
moi difÃcil. Por iso, esta tese trata do deseño de estruturas compactas de datos para
a representación dos camiños seguidos, por un lado, por vehÃculos e/ou persoas que
se desprazan polas rúas dun contorno urbano ou periurbano delimitado, e por outros
itinerarios de viaxeiros en transporte público. Ademais de deseñar estas estruturas
compactas de datos, que permiten representar ese escenario Big Data habitual neste
dominios de aplicación, deseñáronse algoritmos que permitan a explotación eficiente
dos devanditos datos. Estes algoritmos, ademais de resolver as clásicas consultas
espazo-temporais, tanto a posición dun obxecto á vez, como a traxectoria dun obxecto
durante un intervalo de tempo, asà como as consultas de rango espazo-temporal (qué
obxectos están nun rango do espazo nun intre ou nun intervalo temporal) tamén
resolver consultas máis especializadas para a análise de traxectorias de viaxeiros.
Por exemplo, deseñamos algoritmos para comprobar o número de viaxeiros que
inician (ou terminan) a súa viaxe nun determinado lugar nun determinado intervalo
de tempo, ou o número de viaxeiros que cambian dunha liña a outra da rede
de transporte público nun certa parada, ou incluso o número de viaxeiros que
comezan a súa viaxe nun determinado lugar (parada ou barrio) e rematan noutra
parada ou barrio especÃfico. Tanto as estruturas de datos deseñadas como todos
os algoritmos de consulta, dispoñibles en https://github.com/dgalaktionov/
compact-trip-representation, foron evaluados experimentalmente. Con estas estruturas é posible representar nun espazo de 100 MiB unha colección de
aproximadamente un millón e medio de traxectos de taxi ou, alternativamente,
dez millóns de traxectos consistentes en itinerarios en redes de transporte público,
sendo estes últimos máis compactos. Nos dous casos, podemos resolver a maiorÃa
das consultas de explotación plantexadas na orde de microsegundos, con algoritmos
que escalan logarÃtmicamente con respecto ao aumento do número de traxectorias
almacenadas. Finalmente, dado o carácter de tese industrial deste traballo, foi
necesario que a investigación realizada tivese un carácter claramente aplicado, polo
que se implementou unha aplicación web con tecnoloxÃa de Sistemas de Información
Xeográfica que no canto de traballar nunha base de datos espacial convencional usa a
estrutura comprimida e algoritmos de explotación deseñados na tese. Esta aplicación
facilita, mediante unha interface de usuario sinxela e intuitiva que representa o mapa
da rede de transporte, o lanzamento dos algoritmos deseñados nun amplo conxunto
de rutas de pasaxeiros. Do mesmo xeito que a interface presenta os resultados das
consultas dun xeito gráfico e intuitivo.Xunta de Galicia; IN848D 2017 2350417Xunta de Galicia; IN852A 2018/14Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2017/58Ministerio de EconomÃa y Competitividad;
TIN2016-78011-C4-1-RMinisterio de EconomÃa y Competitividad;
TIN2015-69951-RMinisterio de Ciencia e Innovación;
RTI-2018-098309-B-C3
An Overview of Moving Object Trajectory Compression Algorithms
Compression technology is an efficient way to reserve useful and valuable data as well as remove redundant and inessential data from datasets. With the development of RFID and GPS devices, more and more moving objects can be traced and their trajectories can be recorded. However, the exponential increase in the amount of such trajectory data has caused a series of problems in the storage, processing, and analysis of data. Therefore, moving object trajectory compression undoubtedly becomes one of the hotspots in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object compression and analyze typical moving object compression algorithms presented in recent years. In this paper, we firstly summarize the strategies and implementation processes of classical moving object compression algorithms. Secondly, the related definitions about moving objects and their trajectories are discussed. Thirdly, the validation criteria are introduced for evaluating the performance and efficiency of compression algorithms. Finally, some application scenarios are also summarized to point out the potential application in the future. It is hoped that this research will serve as the steppingstone for those interested in advancing moving objects mining
Adaptive data synchronization algorithm for IoT-oriented low-power wide-area networks
The Internet of Things (IoT) is by now very close to be realized, leading the world towards a new technological era where people’s lives and habits will be definitively revolutionized. Furthermore, the incoming 5G technology promises significant enhancements concerning the Quality of Service (QoS) in mobile communications. Having billions of devices simultaneously connected has opened new challenges about network management and data exchange rules that need to be tailored to the characteristics of the considered scenario. A large part of the IoT market is pointing to Low-Power Wide-Area Networks (LPWANs) representing the infrastructure for several applications having energy saving as a mandatory goal besides other aspects of QoS. In this context, we propose a low-power IoT-oriented file synchronization protocol that, by dynamically optimizing the amount of data to be transferred, limits the device level of interaction within the network, therefore extending the battery life. This protocol can be adopted with different Layer 2 technologies and provides energy savings at the IoT device level that can be exploited by different applications
3D oceanographic data compression using 3D-ODETLAP
This paper describes a 3D environmental data compression technique for oceanographic datasets. With proper point selection, our method approximates uncompressed marine data using an over-determined system of linear equations based on, but essentially different from, the Laplacian partial differential equation. Then this approximation is refined via an error metric. These two steps work alternatively until a predefined satisfying approximation is found. Using several different datasets and metrics, we demonstrate that our method has an excellent compression ratio. To further evaluate our method, we compare it with 3D-SPIHT. 3D-ODETLAP averages 20% better compression than 3D-SPIHT on our eight test datasets, from World Ocean Atlas 2005. Our method provides up to approximately six times better compression on datasets with relatively small variance. Meanwhile, with the same approximate mean error, we demonstrate a significantly smaller maximum error compared to 3D-SPIHT and provide a feature to keep the maximum error under a user-defined limit
MobilityMirror: Bias-Adjusted Transportation Datasets
We describe customized synthetic datasets for publishing mobility data.
Private companies are providing new transportation modalities, and their data
is of high value for integrative transportation research, policy enforcement,
and public accountability. However, these companies are disincentivized from
sharing data not only to protect the privacy of individuals (drivers and/or
passengers), but also to protect their own competitive advantage. Moreover,
demographic biases arising from how the services are delivered may be amplified
if released data is used in other contexts.
We describe a model and algorithm for releasing origin-destination histograms
that removes selected biases in the data using causality-based methods. We
compute the origin-destination histogram of the original dataset then adjust
the counts to remove undesirable causal relationships that can lead to
discrimination or violate contractual obligations with data owners. We evaluate
the utility of the algorithm on real data from a dockless bike share program in
Seattle and taxi data in New York, and show that these adjusted transportation
datasets can retain utility while removing bias in the underlying data.Comment: Presented at BIDU 2018 workshop and published in Springer
Communications in Computer and Information Science vol 92
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