PARecommender: A Pattern-Based System for Route Recommendation
Authors: Feiyi Tang, Jia Zhu, Yang Cao, Sanli Ma, Yulong Chen, Jing He, Changqin Huang, Gansen Zhao, Yong Tang
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this demo, we present a system called PARecommender, which predicts traffic conditions and provides route recommendation based on generated traffic patterns. We first introduce the technical details of PARecommender, and then show several real cases that how PARecommender works.During the demonstration, the audience could interact with PARecommender by specifying the place they want to go, and let the system generate a route plan with ETA as shown in Figure 2 based on the traffic pattern. The pattern data used in the demo is derived from the trajectories of Taxis and UGC over six months (more than 10 million GPS points). |
| Researcher Affiliation | Collaboration | Feiyi Tang Victoria University Melbourne, Australia feiyi.tang@live.vu.edu.au Jia Zhu* South China Normal University Guangzhou, China jzhu@m.scnu.edu.cn Yang Cao South China Normal University Guangzhou, China caoyang@m.scnu.edu.cn Sanli Ma Cen Navi Technologies Co.,Ltd Beijing, China masanlig@cennavi.com.cn Yulong Chen Cen Navi Technologies Co.,Ltd Beijing, China chenyulong@cennavi.com.cn Jing He Victoria University Melbourne, Australia jing.he@vu.edu.au Changqin Huang South China Normal University Guangzhou, China cqhuang@m.scnu.edu.cn Gansen Zhao South China Normal University Guangzhou, China gzhao@m.scnu.edu.cn Yong Tang South China Normal University Guangzhou, China ytang@m.scnu.edu.cn |
| Pseudocode | No | The paper describes the method using text and mathematical formulas but does not provide a distinct pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions 'The pattern data used in the demo is derived from the trajectories of Taxis and UGC over six months (more than 10 million GPS points)' and lists sources like 'Baidu' and 'Tecent', but does not provide concrete access information (link, DOI, specific citation with authors/year for a public dataset) for the specific dataset used. |
| Dataset Splits | No | The paper describes a 'train stage' and 'classifier training flow' but does not provide specific details on dataset splits (e.g., percentages or sample counts for training, validation, or testing). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running experiments or the system. |
| Software Dependencies | No | The paper refers to an 'multiple classifiers system (MCS)' and adaptations from other works but does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | No | The paper outlines the training phases, including traffic data clustering and classifier training, but does not specify concrete experimental setup details such as hyperparameter values, optimizer settings, or other training configurations. |