Many drivers hate sharing the road with bicyclists, but perhaps they could become allies, if road improvements for bikes made the road safer for cars as well and vice versa.
This exploration looks for commonalities between what causes bike and car accidents in the Boston Area. I found models to predict bike and car accidents, and identified the key features for predicting them, and bike accidents are a predictor of car accidents and vice versa, among many other road attributes.
The data sets for this project come from:
- A database of Boston bike accidents from 2009 to 2012, constructed from police reports during this time: https://dataverse.harvard.edu/dataverse/BARI
- Accident/crash data: US accident data 2017 https://www.kaggle.com/sobhanmoosavi/us-accidents
- The Massachusetts Department of Transportation (Mass DOT) road inventory https://geo-massdot.opendata.arcgis.com/datasets/road-inventory-2018
- The Mass DOT road inventory has a data dictionary: https://github.com/LinneaHarts/BostonRoadAccidents/blob/master/RI-DataDictionary.pdf
- Create clusters of accidents within 50m of one another using shapely and geopandas packages
- Associate road data with those clusters
- Create areas without accidents and associate road data with those
For more information about Data Wrangling, see this report: https://github.com/LinneaHarts/BostonRoadAccidents/blob/master/reports/Bike%20and%20Car%20Accident%20Prediction%20Data%20Wrangling.pdf
And these notebooks: https://github.com/LinneaHarts/BostonRoadAccidents/tree/master/data_wrangling
I tried many different models for regression and classification and found tree and ensemble methods the most effective for classifying an area as being a place that a car or bike accident would take place:
| Model | Vehicle | Accuracy |
|---|---|---|
| DecisionTreeClassifier | car | 0.886 |
| DecisionTreeClassifier | bike | 0.902 |
| RandomForestClassifier | car | 0.885 |
| RandomForestClassifier | bike | 0.908 |
| BaggingClassifier | car | 0.895 |
| BaggingClassifier | bike | 0.913 |
| GradientBoostingClassifier | car | 0.885 |
| GradientBoostingClassifier | bike | 0.895 |
| AdaBoostClassifier | car | 0.859 |
| AdaBoostClassifier | bike | 0.884 |
For more information about machine learning on this data, see this report: https://github.com/LinneaHarts/BostonRoadAccidents/blob/master/reports/Bike%20and%20Car%20Accident%20Machine%20Learning%20Report.pdf
And these notebooks: https://github.com/LinneaHarts/BostonRoadAccidents/tree/master/machine_learning
To Get Started:
- Overview presentation: https://github.com/LinneaHarts/BostonRoadAccidents/blob/master/reports/Bike%20and%20Car%20Accident%20Prediction%20Final%20Report.pdf
- Machine learning report: https://github.com/LinneaHarts/BostonRoadAccidents/blob/master/reports/Bike%20and%20Car%20Accident%20Machine%20Learning%20Report.pdf
- Final Report: https://github.com/LinneaHarts/BostonRoadAccidents/blob/master/reports/Bike%20and%20Car%20Accident%20Final%20Report.pdf