15,747 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
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
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
Timely Data Delivery in a Realistic Bus Network
Abstract—WiFi-enabled buses and stops may form the backbone of a metropolitan delay tolerant network, that exploits nearby communications, temporary storage at stops, and predictable bus mobility to deliver non-real time information. This paper studies the problem of how to route data from its source to its destination in order to maximize the delivery probability by a given deadline. We assume to know the bus schedule, but we take into account that randomness, due to road traffic conditions or passengers boarding and alighting, affects bus mobility. We propose a simple stochastic model for bus arrivals at stops, supported by a study of real-life traces collected in a large urban network. A succinct graph representation of this model allows us to devise an optimal (under our model) single-copy routing algorithm and then extend it to cases where several copies of the same data are permitted. Through an extensive simulation study, we compare the optimal routing algorithm with three other approaches: minimizing the expected traversal time over our graph, minimizing the number of hops a packet can travel, and a recently-proposed heuristic based on bus frequencies. Our optimal algorithm outperforms all of them, but most of the times it essentially reduces to minimizing the expected traversal time. For values of deadlines close to the expected delivery time, the multi-copy extension requires only 10 copies to reach almost the performance of the costly flooding approach. I
The Quest for a Killer App for Opportunistic and Delay Tolerant Networks (Invited Paper)
Delay Tolerant Networking (DTN) has attracted a lot of attention from the research community in recent years. Much work have been done regarding network architectures and algorithms for routing and forwarding in such networks. At the same time as many show enthusiasm for this exciting new research area there are also many sceptics, who question the usefulness of research in this area. In the past, we have seen other research areas become over-hyped and later die out as there was no killer app for them that made them useful in real scenarios. Real deployments of DTN systems have so far mostly been limited to a few niche scenarios, where they have been done as proof-of-concept field tests in research projects. In this paper, we embark upon a quest to find out what characterizes a potential killer applications for DTNs.
Are there applications and situations where DTNs provide
services that could not be achieved otherwise, or have potential to do it in a better way than other techniques? Further, we highlight some of the main challenges that needs to be solved to realize these applications and make DTNs a part of the mainstream network landscape
Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks
We study the system-level effects of the introduction of large populations of
Electric Vehicles on the power and transportation networks. We assume that each
EV owner solves a decision problem to pick a cost-minimizing charge and travel
plan. This individual decision takes into account traffic congestion in the
transportation network, affecting travel times, as well as as congestion in the
power grid, resulting in spatial variations in electricity prices for battery
charging. We show that this decision problem is equivalent to finding the
shortest path on an "extended" transportation graph, with virtual arcs that
represent charging options. Using this extended graph, we study the collective
effects of a large number of EV owners individually solving this path planning
problem. We propose a scheme in which independent power and transportation
system operators can collaborate to manage each network towards a socially
optimum operating point while keeping the operational data of each system
private. We further study the optimal reserve capacity requirements for pricing
in the absence of such collaboration. We showcase numerically that a lack of
attention to interdependencies between the two infrastructures can have adverse
operational effects.Comment: Submitted to IEEE Transactions on Control of Network Systems on June
1st 201
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