Traffic Light Scheduling, Value of Time, and Incentives
Authors: Argyrios Deligkas, Erez Karpas, Ron Lavi, Rann Smorodinsky
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In order to empirically evaluate our mechanism, we implemented it in Python, and performed a series of simulated runs. We used two types of intersections in our experiments: a simple four way intersection (with four incoming lanes, which all continue straight), and a more complex four way intersection with separate lanes for continuing straight and for turning left (for a total of 8 incoming lanes). |
| Researcher Affiliation | Academia | Argyrios Deligkas1, Erez Karpas2, Ron Lavi2, Rann Smorodinsky2 1 Leverhulme Research Centre, University of Liverpool 2 Industrial Engineering and Management, Technion |
| Pseudocode | No | The paper describes algorithms and methods in textual paragraphs and mathematical formulations but does not include any explicitly labeled pseudocode blocks or algorithm figures. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code for the methodology or include a link to a code repository. |
| Open Datasets | Yes | We sampled Vo T according to the empirical study of [Abou-Zeid et al., 2010], which estimated the Vo T of real drivers. |
| Dataset Splits | No | The paper describes the simulation setup and parameters but does not specify explicit training, validation, and test dataset splits as would be typical for a machine learning task using a pre-existing dataset. |
| Hardware Specification | Yes | These experiments were run on a laptop with an Intel i7-6700HQ processor running at 2.6 GHz. |
| Software Dependencies | No | The paper states that the mechanism was 'implemented in Python' but does not specify a Python version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We performed simulations starting from a random initial configuration of the intersection with 10 cars, and simulating forward for 100 time steps. At each time step, cars arrive with a Poisson distribution, where we vary the arrival rate between 0 and 1. |