Datasets
Standard Dataset
Traffic V2X
- Citation Author(s):
- Submitted by:
- Apurba Nandi
- Last updated:
- Mon, 01/13/2025 - 12:19
- DOI:
- 10.21227/kz49-tj33
- License:
- Categories:
- Keywords:
Abstract
The dataset we have used, provides hourly traffic counts from four distinct junctions, comprising a total of 48,120 observations. Each entry includes a timestamp (DateTime), a junction identifier (Junction), the observed vehicle count (Vehicles), and a unique identifier (ID). The data highlights real-world complexities, as the sensors at these junctions operated over varying durations. While some junctions offer consistently recorded data, others have sparse or irregular observations. This variability necessitates careful preprocessing and robust feature engineering to ensure accurate analysis and predictions. The traffic data exhibits dynamic patterns shaped by factors such as time of day, location, and specific conditions at each junction. These characteristics create an exciting opportunity to explore intricate temporal relationships and trends. The dataset holds immense potential for applications such as traffic forecasting, urban infrastructure planning, and the analysis of mobility patterns in cities. However, addressing data inconsistencies across junctions is crucial to unlock its full analytical value
The dataset we have used, provides hourly traffic counts from four distinct junctions, comprising a total of 48,120 observations. Each entry includes a timestamp (DateTime), a junction identifier (Junction), the observed vehicle count (Vehicles), and a unique identifier (ID). The data highlights real-world complexities, as the sensors at these junctions operated over varying durations. While some junctions offer consistently recorded data, others have sparse or irregular observations. This variability necessitates careful preprocessing and robust feature engineering to ensure accurate analysis and predictions. The traffic data exhibits dynamic patterns shaped by factors such as time of day, location, and specific conditions at each junction. These characteristics create an exciting opportunity to explore intricate temporal relationships and trends. The dataset holds immense potential for applications such as traffic forecasting, urban infrastructure planning, and the analysis of mobility patterns in cities. However, addressing data inconsistencies across junctions is crucial to unlock its full analytical value
Documentation
Attachment | Size |
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traffic (1).md | 2.71 MB |
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