36,146 research outputs found
Maximizing the Probability of Arriving on Time: A Practical Q-Learning Method
The stochastic shortest path problem is of crucial importance for the development of sustainable transportation systems. Existing methods based on the probability tail model seek for the path that maximizes the probability of arriving at the destination before a deadline. However, they suffer from low accuracy and/or high computational cost. We design a novel Q-learning method where the converged Q-values have the practical meaning as the actual probabilities of arriving on time so as to improve accuracy. By further adopting dynamic neural networks to learn the value function, our method can scale well to large road networks with arbitrary deadlines. Experimental results on real road networks demonstrate the significant advantages of our method over other counterparts
A Framework for Data-Driven Explainability in Mathematical Optimization
Advancements in mathematical programming have made it possible to efficiently
tackle large-scale real-world problems that were deemed intractable just a few
decades ago. However, provably optimal solutions may not be accepted due to the
perception of optimization software as a black box. Although well understood by
scientists, this lacks easy accessibility for practitioners. Hence, we advocate
for introducing the explainability of a solution as another evaluation
criterion, next to its objective value, which enables us to find trade-off
solutions between these two criteria. Explainability is attained by comparing
against (not necessarily optimal) solutions that were implemented in similar
situations in the past. Thus, solutions are preferred that exhibit similar
features. Although we prove that already in simple cases the explainable model
is NP-hard, we characterize relevant polynomially solvable cases such as the
explainable shortest-path problem. Our numerical experiments on both artificial
as well as real-world road networks show the resulting Pareto front. It turns
out that the cost of enforcing explainability can be very small
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
Tractable Pathfinding for the Stochastic On-Time Arrival Problem
We present a new and more efficient technique for computing the route that
maximizes the probability of on-time arrival in stochastic networks, also known
as the path-based stochastic on-time arrival (SOTA) problem. Our primary
contribution is a pathfinding algorithm that uses the solution to the
policy-based SOTA problem---which is of pseudo-polynomial-time complexity in
the time budget of the journey---as a search heuristic for the optimal path. In
particular, we show that this heuristic can be exceptionally efficient in
practice, effectively making it possible to solve the path-based SOTA problem
as quickly as the policy-based SOTA problem. Our secondary contribution is the
extension of policy-based preprocessing to path-based preprocessing for the
SOTA problem. In the process, we also introduce Arc-Potentials, a more
efficient generalization of Stochastic Arc-Flags that can be used for both
policy- and path-based SOTA. After developing the pathfinding and preprocessing
algorithms, we evaluate their performance on two different real-world networks.
To the best of our knowledge, these techniques provide the most efficient
computation strategy for the path-based SOTA problem for general probability
distributions, both with and without preprocessing.Comment: Submission accepted by the International Symposium on Experimental
Algorithms 2016 and published by Springer in the Lecture Notes in Computer
Science series on June 1, 2016. Includes typographical corrections and
modifications to pre-processing made after the initial submission to SODA'15
(July 7, 2014
Effects of Data Resolution and Human Behavior on Large Scale Evacuation Simulations
Traffic Analysis Zones (TAZ) based macroscopic simulation studies are mostly
applied in evacuation planning and operation areas. The large size in TAZ and
aggregated information of macroscopic simulation underestimate the real
evacuation performance. To take advantage of the high resolution demographic
data LandScan USA (the zone size is much smaller than TAZ) and agent-based
microscopic traffic simulation models, many new problems appeared and novel
solutions are needed. A series of studies are conducted using LandScan USA
Population Cells (LPC) data for evacuation assignments with different network
configurations, travel demand models, and travelers compliance behavior.
First, a new Multiple Source Nearest Destination Shortest Path (MSNDSP)
problem is defined for generating Origin Destination matrix in evacuation
assignments when using LandScan dataset. Second, a new agent-based traffic
assignment framework using LandScan and TRANSIMS modules is proposed for
evacuation planning and operation study. Impact analysis on traffic analysis
area resolutions (TAZ vs LPC), evacuation start times (daytime vs nighttime),
and departure time choice models (normal S shape model vs location based model)
are studied. Third, based on the proposed framework, multi-scale network
configurations (two levels of road networks and two scales of zone sizes) and
three routing schemes (shortest network distance, highway biased, and shortest
straight-line distance routes) are implemented for the evacuation performance
comparison studies. Fourth, to study the impact of human behavior under
evacuation operations, travelers compliance behavior with compliance levels
from total complied to total non-complied are analyzed.Comment: PhD dissertation. UT Knoxville. 130 pages, 37 figures, 8 tables.
University of Tennessee, 2013. http://trace.tennessee.edu/utk_graddiss/259
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