7 research outputs found
Reverse k Nearest Neighbor Search over Trajectories
GPS enables mobile devices to continuously provide new opportunities to
improve our daily lives. For example, the data collected in applications
created by Uber or Public Transport Authorities can be used to plan
transportation routes, estimate capacities, and proactively identify low
coverage areas. In this paper, we study a new kind of query-Reverse k Nearest
Neighbor Search over Trajectories (RkNNT), which can be used for route planning
and capacity estimation. Given a set of existing routes DR, a set of passenger
transitions DT, and a query route Q, a RkNNT query returns all transitions that
take Q as one of its k nearest travel routes. To solve the problem, we first
develop an index to handle dynamic trajectory updates, so that the most
up-to-date transition data are available for answering a RkNNT query. Then we
introduce a filter refinement framework for processing RkNNT queries using the
proposed indexes. Next, we show how to use RkNNT to solve the optimal route
planning problem MaxRkNNT (MinRkNNT), which is to search for the optimal route
from a start location to an end location that could attract the maximum (or
minimum) number of passengers based on a pre-defined travel distance threshold.
Experiments on real datasets demonstrate the efficiency and scalability of our
approaches. To the best of our best knowledge, this is the first work to study
the RkNNT problem for route planning.Comment: 12 page
Bus Frequency Optimization: When Waiting Time Matters in User Satisfaction
Reorganizing bus frequency to cater for the actual travel demand can save the
cost of the public transport system significantly. Many, if not all, existing
studies formulate this as a bus frequency optimization problem which tries to
minimize passengers' average waiting time. However, many investigations have
confirmed that the user satisfaction drops faster as the waiting time
increases. Consequently, this paper studies the bus frequency optimization
problem considering the user satisfaction. Specifically, for the first time to
our best knowledge, we study how to schedule the buses such that the total
number of passengers who could receive their bus services within the waiting
time threshold is maximized. We prove that this problem is NP-hard, and present
an index-based algorithm with approximation ratio. By exploiting the
locality property of routes in a bus network, we propose a partition-based
greedy method which achieves a approximation ratio. Then we
propose a progressive partition-based greedy method to further improve the
efficiency while achieving a approximation ratio.
Experiments on a real city-wide bus dataset in Singapore verify the efficiency,
effectiveness, and scalability of our methods
Influential Slot and Tag Selection in Billboard Advertisement
The selection of influential billboard slots remains an important problem in
billboard advertisements. Existing studies on this problem have not considered
the case of context-specific influence probability. To bridge this gap, in this
paper, we introduce the Context Dependent Influential Billboard Slot Selection
Problem. First, we show that the problem is NP-hard. We also show that the
influence function holds the bi-monotonicity, bi-submodularity, and
non-negativity properties. We propose an orthant-wise Stochastic Greedy
approach to solve this problem. We show that this method leads to a constant
factor approximation guarantee. Subsequently, we propose an orthant-wise
Incremental and Lazy Greedy approach. In a generic sense, this is a method for
maximizing a bi-submodular function under the cardinality constraint, which may
also be of independent interest. We analyze the performance guarantee of this
algorithm as well as time and space complexity. The proposed solution
approaches have been implemented with real-world billboard and trajectory
datasets. We compare the performance of our method with many baseline methods,
and the results are reported. Our proposed orthant-wise stochastic greedy
approach leads to significant results when the parameters are set properly with
reasonable computational overhead.Comment: 15 page
Revisión sistemática de sistemas inteligentes de transporte (ITS) a través de internet de las cosas (IOT) para problemas de transporte terrestre de pasajeros
Trabajo de InvestigaciónEl desarrollo de este trabajo fue realizar una revisión sistemática de sistemas inteligentes de transporte (ITS) a través de internet de las cosas (IOT) para problemas de transporte terrestre de pasajeros, siguiendo la metodología de revisión sistemática de Barbara Kitchenham, definiendo palabras y frases para generar cadenas de busqueda e ir agregando criterios de inclusión y exclusión, en el proceso de búsqueda en bases de datos científicas, con el fin de realizar un análisis cuantitativo, mostrando una caracterización de términos referentes a la investigación.INTRODUCCIÓN
1. GENERALIDADES
2. PLANIFICACION DE LA REVICION SISTEMATICA.
3. RESULTADOS
CONCLUCIONES
RECOMENDACIONES
BIBLIOGRAFÍA
ANEXOSPregradoIngeniero de Sistema
Fast trajectory search for real-world applications
With the popularity of smartphones equipped with GPS, a vast amount of trajectory data are being produced from location-based services, such as Uber, Google Maps, and Foursquare. We broadly divide trajectory data into three types: 1) commuter trajectories from taxicabs and ride-sharing apps; 2) vehicle trajectories from GPS navigation apps; 3) activity trajectories from social network check-ins and travel blogs. We investigate efficient and effective search on each of the three types of trajectory data, each of which has a real-world application. In particular: 1) commuter trajectory search can serve for the transport capacity estimation and route planning; 2) vehicle trajectory search can help real-time traffic monitoring and trend analysis; 3) activity trajectory search can be used in interactive and personalized trip planning. As the most straightforward trajectory data, a commuter trajectory only contains two points: origin and destination indicating a passenger’s movement, which is valuable for transportation decision making. In this thesis, we propose a novel query RkNNT to estimate the capacity of a bus route in the transport network. Answering RkNNT is challenging due to the high amount of data from commuters. We propose efficient solutions to prune most trajectories which cannot choose a query route as their nearest one. Further, we apply RkNNT to the optimal route planning problem-MaxRkNNT. A vehicle trajectory has more points than a commuter trajectory, as it tracks the whole trace of a vehicle and can further advocate the application of traffic monitoring. We conclude the common queries over trajectory data for monitoring purposes and proposes a search engine Torch to manage and search trajectories with map matching over a road network, instead of storing raw data sampled from GPS with a high cost. Besides improving the efficiency of search, Torch also supports compression, effectiveness evaluation of various existing similarity measures, and large-scale clustering k-paths with a novel similarity measure LORS. Exploring the activity trajectory data which contains textual information can help plan personalized trips for tourists. Based on spatial indexes which we propose for commuter and vehicle trajectory data, we further develop a unified search paradigm to process various top-k queries over activity trajectory and POIs data (hotels, restaurants, and attractions, etc.) at the same time. In particular, a new point-wise similarity measure PATS and an indexing framework with a unified search paradigm are proposed