Summary of Trajectory Data Mining and Trip Travel Time Prediction on Specific Roads, by Muhammad Awais Amin et al.
Trajectory Data Mining and Trip Travel Time Prediction on Specific Roads
by Muhammad Awais Amin, Jawad-Ur-Rehman Chughtai, Waqar Ahmad, Waqas Haider Bangyal, Irfan Ul Haq
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of predicting travel time for route planning and navigation applications, particularly in Pakistan’s unique road conditions. The authors design a comprehensive pipeline for mining trajectories from sensor data, leveraging state-of-the-art approaches like shallow artificial neural networks, deep multi-layered perceptrons, and long-short-term memories to explore travel time prediction on frequent routes. Experimental results show an average prediction error ranging from 30 seconds to 1.2 minutes on trips lasting 10 minutes to 60 minutes, focusing on six most frequent routes in Islamabad’s regions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us predict how long it will take to get somewhere by analyzing sensor data and using special computer programs. Right now, most research is based on data from other countries that might not work well for Pakistan’s roads. The authors create a system to collect this data and use different types of artificial intelligence to predict travel times. They test their system on six common routes in Islamabad and find that it can accurately predict travel times within 30 seconds to 1 minute. |
