2,750 research outputs found
Dynamic Time-Dependent Route Planning in Road Networks with User Preferences
There has been tremendous progress in algorithmic methods for computing
driving directions on road networks. Most of that work focuses on
time-independent route planning, where it is assumed that the cost on each arc
is constant per query. In practice, the current traffic situation significantly
influences the travel time on large parts of the road network, and it changes
over the day. One can distinguish between traffic congestion that can be
predicted using historical traffic data, and congestion due to unpredictable
events, e.g., accidents. In this work, we study the \emph{dynamic and
time-dependent} route planning problem, which takes both prediction (based on
historical data) and live traffic into account. To this end, we propose a
practical algorithm that, while robust to user preferences, is able to
integrate global changes of the time-dependent metric~(e.g., due to traffic
updates or user restrictions) faster than previous approaches, while allowing
subsequent queries that enable interactive applications
Shortest Path and Distance Queries on Road Networks: An Experimental Evaluation
Computing the shortest path between two given locations in a road network is
an important problem that finds applications in various map services and
commercial navigation products. The state-of-the-art solutions for the problem
can be divided into two categories: spatial-coherence-based methods and
vertex-importance-based approaches. The two categories of techniques, however,
have not been compared systematically under the same experimental framework, as
they were developed from two independent lines of research that do not refer to
each other. This renders it difficult for a practitioner to decide which
technique should be adopted for a specific application. Furthermore, the
experimental evaluation of the existing techniques, as presented in previous
work, falls short in several aspects. Some methods were tested only on small
road networks with up to one hundred thousand vertices; some approaches were
evaluated using distance queries (instead of shortest path queries), namely,
queries that ask only for the length of the shortest path; a state-of-the-art
technique was examined based on a faulty implementation that led to incorrect
query results. To address the above issues, this paper presents a comprehensive
comparison of the most advanced spatial-coherence-based and
vertex-importance-based approaches. Using a variety of real road networks with
up to twenty million vertices, we evaluated each technique in terms of its
preprocessing time, space consumption, and query efficiency (for both shortest
path and distance queries). Our experimental results reveal the characteristics
of different techniques, based on which we provide guidelines on selecting
appropriate methods for various scenarios.Comment: VLDB201
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
Origin-Destination Travel Time Oracle for Map-based Services
Given an origin (O), a destination (D), and a departure time (T), an
Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of
the time it takes to travel from O to D when departing at T. ODT-Oracles serve
important purposes in map-based services. To enable the construction of such
oracles, we provide a travel-time estimation (TTE) solution that leverages
historical trajectories to estimate time-varying travel times for OD pairs.
The problem is complicated by the fact that multiple historical trajectories
with different travel times may connect an OD pair, while trajectories may vary
from one another. To solve the problem, it is crucial to remove outlier
trajectories when doing travel time estimation for future queries.
We propose a novel, two-stage framework called Diffusion-based
Origin-destination Travel Time Estimation (DOT), that solves the problem.
First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that
enables building a diffusion-based PiT inference process by learning
correlations between OD pairs and historical trajectories. Specifically, given
an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a
Masked Vision Transformer~(MViT) that effectively and efficiently estimates a
travel time based on the inferred PiT. We report on extensive experiments on
two real-world datasets that offer evidence that DOT is capable of
outperforming baseline methods in terms of accuracy, scalability, and
explainability.Comment: 15 pages, 12 figures, accepted by SIGMOD International Conference on
Management of Data 202
Embodied Question Answering
We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where
an agent is spawned at a random location in a 3D environment and asked a
question ("What color is the car?"). In order to answer, the agent must first
intelligently navigate to explore the environment, gather information through
first-person (egocentric) vision, and then answer the question ("orange").
This challenging task requires a range of AI skills -- active perception,
language understanding, goal-driven navigation, commonsense reasoning, and
grounding of language into actions. In this work, we develop the environments,
end-to-end-trained reinforcement learning agents, and evaluation protocols for
EmbodiedQA.Comment: 20 pages, 13 figures, Webpage: https://embodiedqa.org
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