An Intelligent System for Taxi Service Monitoring, Analytics and Visualization
Authors: Yu Lu, Gim Guan Chua, Huayu Wu, Clement Shi Qi Ong
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By utilizing both of the buffered streaming and the large-size historical taxi data, the system focuses on wait time estimation (for both passengers and taxi drivers), citywide taxi pickup/dropoff hotspots, as well as the taxi trip distributions. The three-dimensional (3D) visualization is designed for users to access the analytics results and understand the characteristics of the taxi service. We also adopt the data analytics algorithms reported in [Lu et al., 2015]. |
| Researcher Affiliation | Collaboration | Yu Lu1, Gim Guan Chua1, Huayu Wu1, Clement Shi Qi Ong2 1Institute for Infocomm Research (I2R), A*STAR, Singapore 2Nanyang Polytechnic, Singapore |
| Pseudocode | No | The paper describes the process steps in narrative text, for example: 'Firstly, it extracts all the taxi pickup locations using the taxi state transition. Secondly, it conducts the density based clustering, such as DBSCAN, on the extracted pickup locations...' |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The inputs for the three modules are the collected data from individual taxis. The taxi data collection is mainly leveraging on a special device, called mobile data terminal (MDT), which has been installed on nearly all 26 thousand taxis. The paper refers to 'collected data from individual taxis' and 'large-size offline historical data' which are implied to be proprietary or custom, and does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes using 'buffered streaming' and 'large-size historical taxi data' but does not provide specific details on train/validation/test splits. |
| Hardware Specification | No | The paper states, 'This demonstration does not need the special arrangement and device, but a large-size screen would be preferred.' No specific hardware details like GPU/CPU models or memory are provided for running experiments. |
| Software Dependencies | No | The paper mentions algorithms like DBSCAN and queuing theory but does not list any specific software dependencies or their version numbers. |
| Experiment Setup | No | The paper describes the functionalities of its modules and features used ('FTT probability', 'taxi booking ratio'), but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings for its models (e.g., DBSCAN parameters). |