• No results found

Characteristics of pedestrian crash : a case study in Louisiana

N/A
N/A
Protected

Academic year: 2021

Share "Characteristics of pedestrian crash : a case study in Louisiana"

Copied!
1
0
0

Loading.... (view fulltext now)

Full text

(1)

1(11)

CHARACTERISTICS OF PEDESTRIAN CRASH: A CASE STUDY IN

LOUISIANA

Xiaoduan Sun University of Louisiana

Department of Civil Engineering, University of Louisiana, Lafayette, LA 70504, United States Phone: 1+337-482-6514 E- mail: xsun@louisiana.edu

Ming Sun University of Louisiana

Department of Civil Engineering, University of Louisiana, Lafayette, LA 70504, United States

ABSTRACT

Pedestrians are the most vulnerable users of the transportation system. While encouraging “Green Transportation”, a sad fact emerges in the United States: Pedestrian deaths are climbing faster than motorist fatalities, reaching nearly 6,000 in 2016 -- the highest in more than two decades. In state of Louisiana, pedestrian fatalities reached 110, 14.6% of total traffic fatalities in 2015. In the same year Louisiana pedestrian fatality rate (pedestrian fatalities per 100k population) is 2.18, much higher than the U.S. average 1.67. To investigate why, what, and how pedestrian crashes occurred in order to effectively reduce the pedestrian crashes, this paper investigates the pedestrian crash problem in Louisiana through the pedestrian crash analysis. It is shocking to know that the 47.5% of pedestrian fatalities occurred on the state rural roadways while the rural population is only 26.8%, which yields a fatal pedestrian crash rate of 4.4 for the rural areas and 1.5 for the urban areas. To achieve the state Destination Zero Deaths, the state must take actions to reduce rural pedestrian fatalities. While working to reduce pedestrian crashes, it is very important to know the difference in pedestrian crash characteristics. For example, while 30% of the total pedestrian fatalities involved pedestrian alcohol or drugs usage, it is 34% in rural and 24% in urban areas. Pedestrian volume is significant in urban pedestrian crashes but not an influential factor for rural pedestrian crashes. In terms of crash locations, only 18.4% rural pedestrian fatalities occurred at intersection while in urban it is 37.8%, which clearly directs where and what type of actions should be taken for pedestrian fatality reduction. The data analysis also show that generally the older pedestrians (50+ in age) have the highest crash risk, almost twice higher than that of younger pedestrians (younger than 30). Similar to all fatal crashes, the “peak hour” for fatal pedestrian crashes is at night when both vehicular and pedestrian volume is the lowest. Few suggestions are made at the end of the paper regarding the selection of potential crash countermeasures for pedestrian crashes at different locations.

References

Related documents

The implementation of the collision avoidance system described in section 3.2.4 leads to simulated pedestrians that walk over streets in order to avoid collisions, a behavior

-Background, health, and earlier experiences of falling accidents during the winter 2007/2008 -Daily diary of walked distance, walking conditions, occurrence of incidences or

When Stora Enso analyzed the success factors and what makes employees "long-term healthy" - in contrast to long-term sick - they found that it was all about having a

Based on evaluation of the parameters mean speed and speeds over 30 km/h, pedestrian compliance, total crossing time and pedestrian perception of the new and previous designs, the

The knee and lumbar spine components were integrated into the dummy by creating different kinematics and joint relations between components in LS-Dyna.. After simulating

In a study of the Indian states it is shown that the states with high public expenditure have a higher economic growth and lower poverty levels (Sasmal and Sasmal,

If we compare the responses to the first three questions with those to the last three questions, we notice a clear shift towards less concern for relative

One might have thought of retrying a Poisson model, but as the posterior overdispersion estimates (Figure 4.7) show the negative binomial is also justified by the data for