337 research outputs found
Trip-Based Public Transit Routing
We study the problem of computing all Pareto-optimal journeys in a public
transit network regarding the two criteria of arrival time and number of
transfers taken. We take a novel approach, focusing on trips and transfers
between them, allowing fine-grained modeling. Our experiments on the
metropolitan network of London show that the algorithm computes full 24-hour
profiles in 70 ms after a preprocessing phase of 30 s, allowing fast queries in
dynamic scenarios.Comment: Minor corrections, no substantial changes. To be presented at ESA
201
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
Trip-Based Public Transit Routing Using Condensed Search Trees
We study the problem of planning Pareto-optimal journeys in public transit
networks. Most existing algorithms and speed-up techniques work by computing
subjourneys to intermediary stops until the destination is reached. In
contrast, the trip-based model focuses on trips and transfers between them,
constructing journeys as a sequence of trips. In this paper, we develop a
speed-up technique for this model inspired by principles behind existing
state-of-the-art speed-up techniques, Transfer Pattern and Hub Labelling. The
resulting algorithm allows us to compute Pareto-optimal (with respect to
arrival time and number of transfers) 24-hour profiles on very large real-world
networks in less than half a millisecond. Compared to the current state of the
art for bicriteria queries on public transit networks, this is up to two orders
of magnitude faster, while increasing preprocessing overhead by at most one
order of magnitude
Dynamic Time-Dependent Routing in Road Networks Through Sampling
We study the earliest arrival and profile problems in road networks with time-dependent functions as arc weights and dynamic updates. We present and experimentally evaluate simple, sampling-based, heuristic algorithms. Our evaluation is performed on large, current, production-grade road graph data with time-dependent arc weights. It clearly shows that the proposed algorithms are fast and compute paths with a sufficiently small error for most practical applications. We experimentally compare our algorithm against the current state-of-the-art. Our experiments reveal, that the memory consumption of existing algorithms is prohibitive on large instances. Our approach does not suffer from this limitation. Further, our algorithm is the only competitor able to answer profile queries on all test instances below 50ms. As our algorithm is simple to implement, we believe that it is a good fit for many realworld applications
General scores for accessibility and inequality measures in urban areas
In the last decades, the acceleration of urban growth has led to an
unprecedented level of urban interactions and interdependence. This situation
calls for a significant effort among the scientific community to come up with
engaging and meaningful visualizations and accessible scenario simulation
engines. The present paper gives a contribution in this direction by providing
general methods to evaluate accessibility in cities based on public
transportation data. Through the notion of isochrones, the accessibility
quantities proposed measure the performance of transport systems at connecting
places and people in urban systems. Then we introduce scores rank cities
according to their overall accessibility. We highlight significant inequalities
in the distribution of these measures across the population, which are found to
be strikingly similar across various urban environments. Our results are
released through the interactive platform: www.citychrone.org, aimed at
providing the community at large with a useful tool for awareness and
decision-making
Integrating ULTRA and trip-based routing
We study a bi-modal journey planning scenario consisting of a public transit network and a transfer graph representing a secondary transportation mode (e.g., walking or cycling). Given a pair of source and target locations, the objective is to find a Pareto set of journeys optimizing arrival time and the number of required transfers. For public transit networks with a restricted, transitively closed transfer graph, one of the fastest known algorithms solving this bi-criteria problem is Trip-Based Routing [Witt, 2015]. However, this algorithm cannot be trivially extended to unrestricted transfer graphs. In this work, we combine Trip-Based Routing with ULTRA [Baum et al., 2019], a preprocessing technique that allows any public transit algorithm that requires transitive transfers to handle an unrestricted transfer graph. Since both ULTRA and Trip-Based Routing precompute transfer shortcuts in a preprocessing phase, a naive combination of the two leads to a three-phase algorithm that performs redundant work and produces superfluous shortcuts. We therefore propose a new, integrated preprocessing phase that combines the advantages of both and reduces the number of computed shortcuts by up to a factor of 9 compared to a naive combination. The resulting query algorithm, ULTRA-Trip-Based is the fastest known algorithm for the considered problem setting, achieving a speedup of up to 4 compared to the fastest previously known approach, ULTRA-RAPTOR
Public Transit Routing with Unrestricted Walking
We study the problem of answering profile queries in public transportation networks that allow unrestricted walking. That is, finding all Pareto-optimal journeys regarding travel time and number of transfers in a given time interval. We introduce a novel algorithm that, unlike most state-of-the-art algorithms, can compute profiles efficiently in a setting that allows arbitrary walking. Using our algorithm, we show in an extensive experimental study that allowing unrestricted walking, significantly reduces travel times, compared to settings where walking is restricted. Beyond that, we publish the transportation networks of Switzerland that we used in our study, in order to encourage further research on this topic
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