Mobility Sequence Extraction and Labeling Using Sparse Cell Phone Data
Authors: Yingxiang Yang, Peter Widhalm, Shounak Athavale, Marta Gonzalez
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this study we reconstruct trip sequences from sparse cell phone records. Next we propose a Bayesian trip purpose classification method and compare it to a Markov random field based trip purpose clustering method, representing scenarios with and without labeled training data respectively. Preliminary Results To quantitatively show how the clusters from the MRF method can be matched to the five survey defined classes, we use a travel survey data as input to the trained MRF cluster model and observe the confusion matrix in Table 1. |
| Researcher Affiliation | Collaboration | Yingxiang Yang Massachusetts Inst. of Technology 77 Massachusetts Ave. Cambridge, USA Peter Widhalm Austrian Institute of Technology Giefinggasse 2, Vienna, Austria Shounak Athavale Ford Motor Company Palo Alto, CA Marta C. Gonz alez Massachusetts Inst. of Technology 77 Massachusetts Ave. Cambridge, USA |
| Pseudocode | No | The paper describes methods in text but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code, such as a repository link or an explicit statement about code release. |
| Open Datasets | No | The paper states "we use a travel survey data as input to the trained MRF cluster model" and mentions "labeled training data", but does not provide concrete access information (link, DOI, repository, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | No | The paper mentions setting "spatial and temporal thresholds on the stay extraction" which relates to data processing, but it does not specify concrete hyperparameter values, training configurations, or system-level settings for the models themselves (e.g., learning rate, batch size, optimizer settings). |