Truncating Shortest Path Search for Efficient Map-Matching
Authors: Takashi Imamichi, Takayuki Osogami, Rudy Raymond
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Computational experiments show that the proposed approaches can reduce the computational cost by a factor of at least 5.4. and We conduct two types of experiments. The first is to evaluate the quality of the results of our map-matching, while the second is to compare the computational time against the baseline algorithm. |
| Researcher Affiliation | Industry | Takashi Imamichi IBM Research Brazil Av. Pasteur 138/146, Botafogo, Rio de Janeiro, 22290-240, Brazil tima@br.ibm.com Takayuki Osogami and Rudy Raymond IBM Research Tokyo 19-21 Nihonbashi Hakozaki-cho, Chuo-ku, Tokyo, 103-8510, Japan {osogami,rudyhar}@jp.ibm.com |
| Pseudocode | No | The paper describes a procedure for exact truncation with equations and logical steps, but it is not formatted as a distinct pseudocode or algorithm block with a title like 'Algorithm 1' or 'Pseudocode'. |
| Open Source Code | No | The paper states, 'We implemented our algorithm in Python as a single threaded program', but does not provide any link or explicit statement about the availability of the source code. |
| Open Datasets | Yes | We apply our map-matching algorithm to a benchmark set that is used and made publicly available by Newson and Krumm [2009]. and We use the first 24 GPS traces from the T-drive data set [Yuan et al., 2010; 2011] |
| Dataset Splits | No | The paper describes using a 'benchmark set' and 'T-drive data set' and how they were divided for different experiments (e.g., 'divide the data set into 12 data sets'), but it does not specify explicit training, validation, and testing splits with percentages or sample counts, nor does it refer to standard splits for these datasets. |
| Hardware Specification | Yes | We implemented our algorithm in Python as a single threaded program and run it on Py Py runtime version 2.6.1 on a PC with 3.3 GHz Intel Xeon E5-2643 CPU. |
| Software Dependencies | Yes | We implemented our algorithm in Python as a single threaded program and run it on Py Py runtime version 2.6.1 on a PC with 3.3 GHz Intel Xeon E5-2643 CPU. |
| Experiment Setup | Yes | Throughout we fix σ = 10.0 meters and β = 0.1 meters in (1) and (3) and we use k = 5 hidden states at every time step in accordance with Osogami and Raymond [2013]. and We fix σ = 10.0 meters, β = 0.1 meters and wturn = 1000.0 in (1), (3) and (2), respectively. |