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User-Constrained Multi-Modal Route Planning
"... In the multi-modal route planning problem we are given multiple transportation networks (e. g., pedestrian, road, public transit) and ask for a best integrated journey between two points. The main challenge is that a seemingly optimal journey may have changes between networks that do not reflect the ..."
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In the multi-modal route planning problem we are given multiple transportation networks (e. g., pedestrian, road, public transit) and ask for a best integrated journey between two points. The main challenge is that a seemingly optimal journey may have changes between networks that do not reflect the user’s modal preferences. In fact, quickly computing reasonable multimodal routes remains a challenging problem: Previous approaches either suffer from poor query performance or their available choices of modal preferences during query time is limited. In this work we focus on computing exact multi-modal journeys that can be restricted by specifying arbitrary modal sequences at query time. For example, a user can say whether he wants to only use public transit, or also prefers to use a taxi or walking at the beginning or end of the journey; or if he has no restrictions at all. By carefully adapting node contraction, a common ingredient to many speedup techniques on road networks, we are able to compute point-to-point queries on a continental network combined of cars, railroads and flights several orders of magnitude faster than Dijkstra’s algorithm. Thereby, we require little space overhead and obtain fast preprocessing times.
Parallel Computation of Best Connections in Public Transportation Networks. Journal version. Submitted for publication. Online available at i11www.iti.uni-karlsruhe
, 2011
"... Abstract—Exploiting parallelism in route planning algo-rithms is a challenging algorithmic problem with obvious applications in mobile navigation and timetable information systems. In this work, we present a novel algorithm for the so-called one-to-all profile-search problem in public transportation ..."
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Abstract—Exploiting parallelism in route planning algo-rithms is a challenging algorithmic problem with obvious applications in mobile navigation and timetable information systems. In this work, we present a novel algorithm for the so-called one-to-all profile-search problem in public transportation networks. It answers the question for all fastest connections between a given station S and any other station at any time of the day in a single query. This algorithm allows for a very natural parallelization, yielding excellent speed-ups on standard multi-core servers. Our approach exploits the facts that first, time-dependent travel-time functions in such networks can be represented as a special class of piecewise linear functions, and that second, only few connections from S are useful to travel far away. Introducing the connection-setting property, we are able to extend DIJKSTRA’s algorithm in a sound manner. Furthermore, we also accelerate station-to-station queries by preprocessing important connections within the public transportation network. As a result, we are able to compute all relevant connections between two random stations in a complete public transportation network of a big city (Los Angeles) on a standard multi-core server in less than 55 ms on average. I.
Result Diversity for Multi-Modal Route Planning
- 13th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, volume 33 of OpenAccess Series in Informatics (OASIcs), pages123–136, Dagstuhl
, 2013
"... We study multi-modal route planning allowing arbitrary (meaningful) combinations of public transportation, walking, and taking a car / taxi. In the straightforward model, the number of Pareto-optimal solutions explodes. It turns out that many of them are similar to each other or unreasonable. We int ..."
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We study multi-modal route planning allowing arbitrary (meaningful) combinations of public transportation, walking, and taking a car / taxi. In the straightforward model, the number of Pareto-optimal solutions explodes. It turns out that many of them are similar to each other or unreasonable. We introduce a new filtering procedure, Types aNd Thresholds (TNT), which leads to a small yet representative subset of the reasonable paths. We consider metropolitan areas like New York, where a fast computation of the paths is difficult. To reduce the high compu-tation times, optimality-preserving and heuristic approaches are introduced. We experimentally evaluate our approach with respect to result quality and query time. The experiments confirm that our result sets are indeed small (around 5 results per query) and representative (among the reasonable Pareto-optimal paths), and with average query times of about one second or less.
Efficient Route Planning in Flight Networks
, 2009
"... We present a set of three new time-dependent models with increasing flexibility for realistic route planning in flight networks. By these means, we obtain small graph sizes while modeling airport procedures in a realistic way. With these graphs, we are able to efficiently compute a set of best con ..."
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We present a set of three new time-dependent models with increasing flexibility for realistic route planning in flight networks. By these means, we obtain small graph sizes while modeling airport procedures in a realistic way. With these graphs, we are able to efficiently compute a set of best connections with multiple criteria over a full day. It even turns out that due to the very limited graph sizes it is feasible to precompute full distance tables between all airports. As a result, best connections can be retrieved in a few microseconds on real world data.
UniALT for Regular Language Constrained Shortest Paths on a Multi-Modal Transportation Network
- In Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS), A. Caprara
"... Shortest paths on road networks can be efficiently calculated using Dijkstra’s algorithm (D). In addition to roads, multi-modal transportation networks include public transportation, bicycle lanes, etc. For paths on this type of network, further constraints, e.g., preferences in using certain modes ..."
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Shortest paths on road networks can be efficiently calculated using Dijkstra’s algorithm (D). In addition to roads, multi-modal transportation networks include public transportation, bicycle lanes, etc. For paths on this type of network, further constraints, e.g., preferences in using certain modes of transportation, may arise. The regular language constrained shortest path problem deals with this kind of problem. It uses a regular language to model the constraints. The problem can be solved efficiently by using a generalization of Dijkstra’s algorithm (DRegLC). In this paper we propose an adaption of the speed-up technique uniALT, in order to accelerate DRegLC. We call our algorithm SDALT. We provide experimental results on a realistic multi-modal public transportation network including time-dependent cost functions on arcs. The experiments show that our algorithm performs well, with speed-ups of a factor 2 to 20.
Generalised time-dependent graphs for fully multimodal journey planning
- In Proceedings of 15th International IEEE Conference on Intelligent Transportation Systems. IEEE
, 2013
"... Abstract — We solve the fully multimodal journey planning problem, in which journey plans can employ any combination of scheduled public transport (e.g., bus, tram and underground), individual (e.g., walk, bike, shared bike and car), and ondemand (e.g., taxi) transport modes. Our solution is based o ..."
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Abstract — We solve the fully multimodal journey planning problem, in which journey plans can employ any combination of scheduled public transport (e.g., bus, tram and underground), individual (e.g., walk, bike, shared bike and car), and ondemand (e.g., taxi) transport modes. Our solution is based on a generalised time-dependent graph that allows representing the fully multimodal earliest arrival problem as a standard graph search problem and consequently using general shortest path algorithms to solve it. In addition, to allow users to express their journey planning preferences and to speed up the search process, flexible journey plan templates can be used in our approach to restrict the transport modes and mode combinations permitted in generated journey plans. We have evaluated our solution on a real-world transport network of the city of Helsinki and achieved practically usable search runtimes in the range of hundreds of milliseconds. I.
Efficient Computation of Shortest Paths in Time-Dependent Multi-Modal Networks
"... We consider shortest paths on time-dependent multi-modal transportation networks where restrictions or preferences on the use of certain modes of transportation may arise. We model restrictions and preferences by means of regular languages. Methods for solving the corresponding problem (called the r ..."
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We consider shortest paths on time-dependent multi-modal transportation networks where restrictions or preferences on the use of certain modes of transportation may arise. We model restrictions and preferences by means of regular languages. Methods for solving the corresponding problem (called the regular language constrained shortest path problem) already exist. We propose a new algorithm, called State Dependent ALT (SDALT), which runs considerably faster in many scenarios. Speed-up magnitude depends on the type of constraints. We present different versions of SDALT including uni-directional and bi-directional search. We also provide extensive experimental results on realistic multi-modal transportation networks.
Fully Realistic Multi-Criteria Multi-Modal Routing∗
, 2014
"... We report on a multi-criteria search system, in which the German long- and short-distance trains, local public transport, walking, private car, private bike, and taxi are incorporated. The system is fully realistic. Three optimization criteria are addressed: travel time, travel cost, and convenience ..."
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We report on a multi-criteria search system, in which the German long- and short-distance trains, local public transport, walking, private car, private bike, and taxi are incorporated. The system is fully realistic. Three optimization criteria are addressed: travel time, travel cost, and convenience. Our algorithmic approach computes a complete Pareto set of reasonable con-nections. The computational study demonstrates that, even in such a large-scale, highly complex scenario, appropriate speed-up techniques yield an acceptable query response time. 1
