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

TL;DR

This paper introduces new data structures and models for efficiently representing and querying trips over public transportation networks, reducing redundancy and improving query performance.

Contribution

The paper presents a novel conceptual model and two compact data representations tailored for public transit trip data, enhancing data management and query efficiency.

Findings

Experimental results demonstrate space/time trade-offs of the proposed approaches.

The models effectively handle various aggregate and counting queries.

Redundancy reduction improves storage efficiency.

Abstract

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.