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A Time of Day Analysis of Pedestrian-Involved Crashes in California: Investigation of Injury Severity, a Logistic Regression and Machine Learning Approach Using HSIS Data
| 题名： | A Time of Day Analysis of Pedestrian-Involved Crashes in California: Investigation of Injury Severity, a Logistic Regression and Machine Learning Approach Using HSIS Data |
| 正文语种： | 英文 |
| 作者： | Seyedmirsajad Mokhtarimousavi (S) |
| 关键词： | dest；association；stria；strategies；traffic；operation；logistic；learning；national；analysis |
| 摘要： | According to the National Highway Traffic Safety Association (NHTSA), in 2016, 5,987 pedestrians died in the United States, or 16 people every day, for one year. More specifically, California, USA stands among the top five states for motor vehicle collisions and was ranked first with respect to pedestrian traffic fatalities, with 867 in 2016.1 Compared to vehicle-to-vehicle collisions, pedestrian-involved crashes typically result in more severe injuries and fatalities. In fact, pedestrians are threatened by a higher risk of injuries and death, especially in poorly designed roadways with less consideration for pedestrian safety. Although incorporating safety policies into traffic operations, such as law enforcement strategies, has resulted in some safety improvements, the trendline of pedestrian fatalities still illustrates a steady increase over the past 10 years (from 4,699 fatalities in 2007 to 5,987 fatalities in 2016). |
| 出版年： | 2019 |
| 期刊名称： | ITE Journal |
| 卷： | 89 |
| 期： | 10 |
| 页码： | 25-33 |
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