Parallel computation of best connections in public transportation networks

Delling, Daniel; Katz, Bastian; Pajor, Thomas

doi:10.1145/2133803.2345678

Cited by 20 publications

(11 citation statements)

References 20 publications

(14 reference statements)

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“…If w ′ = u, then SketchGen adds lv into Suv as a path sketch, and moves on to the next label in Lin(v) (Lines [12][13][14].…”

Section: Candidate Generation and Refinementmentioning

confidence: 99%

“…In particular, any labels with starting time before t or ending time after t ′ are omitted (Lines 4-5 and 10-11), while any remaining lu ∈ Lout(u) that concerns v and any remaining lv ∈ Lin(u) that concerns u are inserted into Suv as path sketches (Lines 6-8 and [12][13][14]. For the other labels, SketchGen carefully decides (i) whether a pair of labels can form a path sketch, and (ii) whether it should proceed to the next label in Lout(v) or Lin(v) (Lines [15][16][17][18][19][20][21][22][23][24][25].…”

Section: Candidate Generation and Refinementmentioning

confidence: 99%

“…However, as pointed out in [6,8], such techniques are generally not incomparable to the state-of-the-art methods that process queries on G instead of G ′ . On the other hand, techniques in the second category [11,13,15,18,30,31,33,36] considers query types that are more sophisticated than EAP, LDP, SDP, in that they take into account advanced query constraints, such as the cost of making a transfer, the fare of a ride, etc.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Route Planning on Public Transportation Networks

Wang

Lin

Yang

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

104

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A public transportation network can often be modeled as a timetable graph where (i) each node represents a station; and (ii) each directed edge u, v is associated with a timetable that records the departure (resp. arrival) time of each vehicle at station u (resp. v). Several techniques have been proposed for various types of route planning on timetable graphs, e.g., retrieving the route from a node to another with the shortest travel time. These techniques, however, either provide insufficient query efficiency or incur significant space overheads.This paper presents Timetable Labelling (TTL), an efficient indexing technique for route planning on timetable graphs. The basic idea of TTL is to associate each node u with a set of labels, each of which records the shortest travel time from u to some other node v given a certain departure time from u; such labels would then be used during query processing to improve efficiency. In addition, we propose query algorithms that enable TTL to support three popular types of route planning queries, and investigate how we reduce the space consumption of TTL with advanced preprocessing and label compression methods. By conducting an extensive set of experiments on real world datasets, we demonstrate that TTL significantly outperforms the states of the art in terms of query efficiency, while incurring moderate preprocessing and space overheads.

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“…If w ′ = u, then SketchGen adds lv into Suv as a path sketch, and moves on to the next label in Lin(v) (Lines [12][13][14].…”

Section: Candidate Generation and Refinementmentioning

confidence: 99%

Section: Candidate Generation and Refinementmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Route Planning on Public Transportation Networks

Wang

Lin

Yang

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

104

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“…Finally, each footpath f = (p1, p2, ) ∈ F enables walking from stop p1 to stop p2 in time . Footpaths are typically provided by the public transit agency, or they are heuristically generated [8].…”

Section: Timetables and Journeysmentioning

confidence: 99%

Better transit routing by exploiting vehicle GPS data

Delling

Italiano²,

Pajor

et al. 2014

Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science

Self Cite

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Current public transport journey planners are mostly based on timetables, i.e., their planning assumes implicitly that all transit vehicles run on schedule. Unfortunately, this is not always the case, as unpredictable delays may occur frequently and for many unplanned reasons. In this scenario, deviations from the original schedule may have quite a negative impact on the quality of the journeys provided by timetable-based planners, as they increase the probability of missed connections and thus result in longer and unpredicted waiting times for the passengers. In this paper, we try to find effective solutions for mitigating this problem and evaluate them empirically on the metropolitan public transportation network of Rome (Italy), where delays are frequent. We first try to assess whether the availability of dynamic information on the geo-location of transit vehicles (via GPS data) may help to improve the quality of the journeys offered by a planner. The main findings of our experiments are that GSP data provides substantial benefits for short journeys, while it does not seem to provide enough insight to improve long journeys. In particular, even with the use of GPS data, a journey planner can still be rather unreliable for long journeys, as there can be large fluctuations between the times predicted by the planner and the actual travel times incurred by passengers. As a second contribution of our work, we provide and evaluate experimentally new routing algorithms to improve the reliability of public transport journey planners.

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“…This edge is time dependent, and its function reflects a travel time of arr t p 2 − dep t p 1 at departure time dep t p 1 . Edge costs can be modeled as special piecewise linear functions that can be efficiently evaluated, as shown by Delling (2011) and Delling, Katz, and Pajor (2012a). To incorporate footpaths, for each p i p j ∈ a time-independent edge is added between the corresponding stop vertices, weighted by l p i p j .…”

Section: Existing Graph-based Approachesmentioning

confidence: 99%

Round-Based Public Transit Routing

Delling

Pajor

Werneck

2015

Transportation Science

Self Cite

113

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W e study the problem of computing all Pareto-optimal journeys in a dynamic public transit network for multiple criteria, such as arrival time and number of transfers. Existing algorithms consider this as a graph problem and solve it using various graph search algorithms. Unfortunately, this leads to either high query times or suboptimal solutions. We take a different approach. We introduce RAPTOR, our novel round-based public transit router. Unlike previous algorithms, it is not Dijkstra-based, looks at each route (such as a bus line) in the network at most once per round, and can be made even faster with simple pruning rules and parallelization using multiple cores. Because it does not rely on preprocessing, RAPTOR works in fully dynamic scenarios. Starting from arrival time and number of transfers as criteria, it can be easily extended to handle flexible departure times or arbitrary additional criteria. As practical examples we consider fare zones and reliability of transfers. When run on complex public transportation networks (such as London), RAPTOR computes all Pareto-optimal journeys between two random locations an order of magnitude faster than previous approaches, which easily enables interactive applications.

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Parallel computation of best connections in public transportation networks

Cited by 20 publications

References 20 publications

Efficient Route Planning on Public Transportation Networks

Efficient Route Planning on Public Transportation Networks

Better transit routing by exploiting vehicle GPS data

Round-Based Public Transit Routing

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