2 research outputs found
New structures to solve aggregated queries for trips over public transportation networks
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 a 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 or aggregate queries regarding
the load of any line of the network at different times.Comment: This research has received funding from the European Union's Horizon
2020 research and innovation programme under the Marie Sk{\l}odowska-Curie
Actions H2020-MSCA-RISE-2015 BIRDS GA No. 69094