A compact representation for trips over networks built on self-indexes

Brisaboa, Nieves R.; Fariña, Antonio; Galaktionov, Daniil; Rodríguez, M. Andrea

doi:10.1016/j.is.2018.06.010

Cited by 6 publications

(3 citation statements)

References 59 publications

(155 reference statements)

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“…A previous representation for user trips along a transportation network, named CTR [1], basically associates an integer s i to each stop in the network, and represents a user trip t i as the sequence of the stops traversed plus a terminator $ (t i = s 1 , s 2 , . .…”

Section: Lines Including Each Stop (Inverted Index For Stops)mentioning

confidence: 99%

“…As in CTR [1], we sort the terminators considering that each trip is a cyclical string. For instance in Figure 3, S[18] = 0 would traditionally be followed by S[19] = 0, but for sorting purposes we consider it is instead followed by S[15] = 11.…”

Section: Jcodesmentioning

confidence: 99%

“…In [1], a representation for user trips along a transportation network referred to as CTR was presented. The different stops from bus lines were given a node-ID.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

New structures to solve aggregated queries for trips over public transportation networks

Brisaboa,

Fariña,

Galaktionov

et al. 2019

Preprint

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Representing the trajectories of mobile objects is a hot topic from the widespread use of smartphones and other GPS devices. However, few works have focused on representing trips over public transportation networks (buses, subway, and trains) where user's trips can be seen as a sequence of stages performed within a vehicle shared with many other users. In this context, representing vehicle journeys reduces the redundancy because all the passengers inside a vehicle share the same arrival time for each stop. In addition, each vehicle journey follows exactly the sequence of stops corresponding to its line, which makes it unnecessary to represent that sequence for each journey. To solve data management for transportation systems, we designed a conceptual model that gave us a better insight into this data domain and allowed us the definition of relevant terms and the detection of redundancy sources among those data. Then, we designed two compact representations focused on users' trips (TTCTR) and on vehicle trips (AcumM), respectively. Each approach owns some strengths and is able to answer some queries efficiently. We include experimental results over synthetic trips generated from accurate schedules obtained from a real network description (from the bus transportation system of Madrid) to show the space/time trade-off of both approaches. We considered a wide range of different queries about the use of the transportation network such as counting-based/aggregate queries regarding the load of any line of the network at different times.

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Section: Lines Including Each Stop (Inverted Index For Stops)mentioning

confidence: 99%

Section: Jcodesmentioning

confidence: 99%