Real-time Traffic Pattern Analysis and Inference with Sparse Video Surveillance Information
Authors: Yang Wang, Yiwei Xiao, Xike Xie, Ruoyu Chen, Hengchang Liu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experiments on real-world data show the effectiveness of our approach. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Technology, University of Science and Technology of China 2 School of Software Engineering, University of Science and Technology of China |
| Pseudocode | Yes | Algorithm 1 The TISV Algorithm |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | No | The GPS data is collected from 4,303 taxicabs of a year, i.e., from May, 2015 to June, 2016. We also collected all the video surveillance information with 44 camera-equipped road intersections of two-month, including the April and May of 2016. ... We use the dataset of taxicabs from May 1, 2015 to April 30, 2016 to train the transition probabilities, then use the surveillance data of the same period to build the holistic traffic estimation models. The paper uses its own collected data and does not provide information for public access to this dataset. |
| Dataset Splits | Yes | We randomly choose 35% camera-equipped intersections as verifying intersections. |
| Hardware Specification | No | The paper mentions that "taxicabs are equipped with a GPS-based navigation system and 3G/4G network" for data collection, but it does not specify any hardware details (like GPU/CPU models, memory, or cloud instances) used for running the experiments or training the models. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers used for the experiments. |
| Experiment Setup | No | The paper describes the experimental setup in terms of road networks, taxicab systems, verification method, and competitors. However, it does not provide specific experimental setup details such as hyperparameters, optimization settings, or other system-level training configurations. |