Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Incorporating Queueing Dynamics into Schedule-Driven Traffic Control
Authors: Hsu-Chieh Hu, Allen M. Hawkes, Stephen F. Smith
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the potential of this approach through microscopic traffic simulation of a real-world road network, showing a 10 15% reduction in average wait times over the schedule-driven traffic signal control system in heavy traffic scenarios. and 4 Experimental Evaluation In this section, we compare the above described online queue prediction algorithm to three other real-time traffic control methods. |
| Researcher Affiliation | Collaboration | 1Rapid Flow Technologies, Inc., Pittsburgh, PA 2Carnegie Mellon University, Pittsburgh, PA |
| Pseudocode | Yes | Algorithm 1 Calculate qdinter and qdintra of c; Algorithm 2 Calculate (pd, t, d) of Sk; Algorithm 3 Predict Q(Sk) given Sk 1 and ck |
| Open Source Code | No | The paper does not provide any concrete access information (link, explicit statement of release) for the source code of the methodology. |
| Open Datasets | No | The network model we consider for a more complex scenario is based on the St. Albert neighborhood of Canada as shown in Figure 3. The low demand data is extracted from the field data of St. Albert of 6-9am, 1/6/2020 1/8/2020, and ramped up to generate two other demands. No concrete access information for the dataset is provided. |
| Dataset Splits | No | The paper describes simulation runs and averaging results but does not specify explicit training, validation, or test dataset splits in the context of model evaluation. |
| Hardware Specification | No | The paper mentions that the simulation model was developed in VISSIM but does not specify any hardware details like GPU/CPU models, processors, or memory used for running the experiments. |
| Software Dependencies | No | The paper states 'The simulation model was developed in VISSIM, a commercial microscopic traffic simulation software package.' but does not provide specific version numbers for VISSIM or any other software dependencies. |
| Experiment Setup | Yes | The simulation model was developed in VISSIM, a commercial microscopic traffic simulation software package... We use a linear function to approximate the line without the signal delay, and the estimations of a0 and a1 are approximately (5.8s, 2.4s)... All simulations run for 1 hour of simulated time. Results for a given experiment are averaged across all simulation runs with different random seeds. |