using real-time speed data to quantify impacts of weather on travel speeds
Open Access
- Author:
- Yu, Yinghai
- Graduate Program:
- Civil Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- June 14, 2019
- Committee Members:
- Vikash Varun Gayah, Thesis Advisor/Co-Advisor
Martin T Pietrucha, Committee Member
Sukran Ilgin Guler, Committee Member - Keywords:
- Weather
Travel Speed
Linear Regression
Random Forest
Partial Dependence Plot - Abstract:
- Travel speed is an essential criterion for evaluating motorists’ driving experiences and the traffic conditions they encounter when they travel. However, travel speed can be affected by many factors, such as the geometric design of roadways. Travel speed also varies according to different operational conditions, such as whether the roads are congested or uncongested. In this study, the impacts of weather—specifically, precipitation and visibility—on travel speed were studied during uncongested and congested travel conditions. To do so, probe speed data were obtained from the Regional Integrated Transportation Information System website and combined with weather data from the Pennsylvania State Climatologist website. In the uncongested condition, travel speed is specified as free flow travel speed. In the congested condition analysis, the travel speed takes into account the impacts of ambient traffic on vehicle speed. Generally, past studies on travel speed develop simple regression models to quantify the impact of weather on speed. However, beyond these approaches, this paper proposes an advanced model, random forest, to further explore possible factors that will impact travel speed. This study found that hourly precipitation, which is measured by precipitation intensity, had a negative impact on travel speeds in both uncongested and congested conditions. Visibility, which is measured by distance, has slightly positive impact on free flow travel speed in uncongested conditions. If hourly precipitation increases by 0.01 inch/hr, the free flow travel speed will decrease by 1.711 mph. In congested regions, the only continuous variable is hourly precipitation, because visibility is not considered. The results showed that for each increase in increments of 0.01 inch/hr of precipitation, the free flow travel speed on the congested corridor decreases about 8.86 mph. Instead of utilizing linear models, this study also implemented non-linear transformations for hourly precipitation and visibility. Both the uncongested and congested analyses conducted a non-linear transformation for both hourly precipitation and visibility. The study found that, in the uncongested analysis, the square root form of hourly precipitation and linear form of visibility has the highest model performance which is measured using R2 value. In the congested analysis, the cubic form of hourly precipitation and exponential form of visibility has the highest model performance. Given that linear models assume that the relationship between independent and dependent variables is linear, a non-linear model, random forest, is proposed to further explore the relationship between travel speeds and hourly precipitation and visibility. In the analysis, the importance of each independent variable is calculated and ranked. The random forest model indicates that in uncongested condition, 45 mph speed limit has the largest impact on free flow travel speed; in congested condition, hourly precipitation and visibility have comparatively larger impact on travel speed. Moreover, the impact of weather on travel speed will be visualized using Partial Dependence Plot (PDP). An interesting finding is, no matter in which condition, travel speed drops dramatically if hourly precipitation is light. But, when hourly precipitation is greater than a certain value, travel speed will not decrease.