Dynamic Replanning for Improved Public Transport Routing
Authors: Abdallah Abuaisha, Bojie Shen, Daniel D. Harabor, Peter J. Stuckey, Mark Wallace
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | All experiments were implemented in C++17 with full optimisation on a 3.20 GHz Apple M1 machine with 16 GB of RAM, running mac OS 14.5 and utilising a single thread. Datasets. Four metropolitan networks of varying sizes, namely Perth, Berlin, Paris, and London, are considered. |
| Researcher Affiliation | Academia | Department of Data Science and AI, Monash University, Australia EMAIL |
| Pseudocode | Yes | Algorithm 1: Baseline Replanning Algorithm (Pull) ... Algorithm 2: Envelope Replanning Algorithm (Push) |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The first two datasets are from an open data platform,5 while the others are sourced from [Phan and Viennot, 2019].6 Footnote 5: https://openmobilitydata.org Footnote 6: https://files.inria.fr/gang/graphs/public_transport |
| Dataset Splits | Yes | We generate a sample of 1,000 unique stop pairs (so, sd) chosen uniformly at random for each dataset. These stop pairs are assigned ten fixed departure times (τq) throughout the day, from midnight to 9 pm. This results in a total of 10,000 queries per dataset. |
| Hardware Specification | Yes | All experiments were implemented in C++17 with full optimisation on a 3.20 GHz Apple M1 machine with 16 GB of RAM, running mac OS 14.5 and utilising a single thread. |
| Software Dependencies | No | The paper mentions C++17 as the implementation language but does not list any specific versioned libraries, solvers, or specialized packages beyond the programming language itself, as required for a positive classification. |
| Experiment Setup | Yes | Delay events are modelled as described in Section 2. The time at which the delay event occurs for each trip is randomly selected. ... we use a synthetic delay model that follows an exponential distribution, as suggested by several studies [Hansen, 2001; Markovi c et al., 2015; Bast et al., 2013]. This distribution has a single parameter λ = 1/ δ, where δ is the mean delay. ... The mean delay values used vary depending on transport mode and time of day, ... For fully-separated modes, such as trains, δ = 2 minutes is used regardless of the time of day. For semi-separated modes, such as trams, δ = 3 minutes is used for off-peak periods and δ = 7 minutes for peak periods. Finally, for mixed-traffic modes, such as buses, δ = 5 minutes is used for off-peak periods and δ = 10 minutes for peak periods. |