Poisson Log-Linear Regression Model for Rural Signalized Intersection

2012 ◽  
Vol 594-597 ◽  
pp. 1391-1394
Author(s):  
Zhi Ping Ren
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Traffic Safety ◽  
Linear Regression Model ◽  
Signalized Intersections ◽  
Signalized Intersection ◽  
Left Turn ◽  
Traffic Volumes ◽  
Input Variables ◽  
Log Linear

Safety performance of rural signalized intersections is critical for identifying high-risk sites and predicting the hazardousness. This paper aims to develop a predictive model that will describe the safety of rural signalized intersections based on various input variables. Data are examined from 124 rural signalized intersections over three states, and Poisson log-linear regression model is presented, which connected traffic number and the average traffic volumes, geometric characteristics and signalization characteristics variables together. The model and associated data analysis reveal that average daily traffic, media width, speed limit, degree of horizontal curvature and left-turn lane are the factors that have greatest overall effect on safety. The results show that the Poisson log-linear regression model is able to describe the rural signalized intersection safety accurately. Using this model, effective countermeasures can be applied for improving traffic safety.

A new bounded log-linear regression model

Metrika ◽  
2013 ◽  
Vol 77 (5) ◽  
pp. 695-720 ◽  
Author(s):  
HaiYing Wang ◽  
Nancy Flournoy ◽  
Eloi Kpamegan
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Log Linear

Estimating severity of sideways fall using a generic multi linear regression model based on kinematic input variables

2017 ◽  
Vol 54 ◽  
pp. 19-25 ◽  
Author(s):  
A.M. van der Zijden ◽  
B.E. Groen ◽  
E. Tanck ◽  
B. Nienhuis ◽  
N. Verdonschot ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Model Based ◽  
Multi Linear Regression ◽  
Input Variables ◽  
Sideways Fall

Effects of U-Turns on Capacities of Signalized Intersections

2005 ◽  
Vol 1920 (1) ◽  
pp. 74-80 ◽  
Author(s):  
Pan Liu ◽  
Jian John Lu ◽  
Jingjing Fan ◽  
Juan C. Pernia ◽  
Gary Sokolow
Keyword(s):  
Regression Model ◽  
Signalized Intersections ◽  
Signalized Intersection ◽  
Left Turn ◽  
Highway Capacity Manual ◽  
Bay Area ◽  
Study Team ◽  
Adjustment Factors ◽  
Left Turns ◽  
Saturation Flow

In Florida, the increased use of restrictive medians and directional median openings has generated many U-turns at signalized intersections. There is no widely accepted procedure for estimating the effects of U-turning vehicles on signalized intersection capacity. In the 2000 edition of the Highway Capacity Manual, U-turns are treated as left turns for estimation of saturation flow rates. However, the operational effects of U-turns and left turns are different. This study analyzed the effects of U-turning vehicles on the left-turn saturation flow rate. Data were collected at three signalized intersections in the Tampa Bay area in Florida. In total, the study team recorded the queue discharge times for 260 queues, including 571 U-turning vehicles and 1,441 left-turning vehicles. On the basis of the data collected in the field, a regression model was developed to estimate the relationship between the average queue discharge time for each turning vehicle and the various percentages of U-turning vehicles in the left-turn traffic stream. Adjustment factors for various percentages of U-turning vehicles were also developed by using the regression model. The adjustment factors developed in this study can be directly used to estimate the capacity reduction due to the presence of various percentages of U-turning vehicles at a signalized intersection.

scholarly journals ANALISIS JUMLAH KENDARAAN BERMOTOR DI DAERAH ISTIMEWA YOGYAKARTA (1990–2012)

KINERJA ◽  
2017 ◽  
Vol 19 (1) ◽  
pp. 68
Author(s):  
I Agus Wantara
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Traffic Congestion ◽  
Ordinary Least Squares ◽  
T Test ◽  
Estimation Technique ◽  
The People ◽  
Log Linear

In the last few years, traffic congestions are often occurred in Yogyakarta. This situation is caused by the increasing number of vehicles in Yogyakarta.This study evaluates the effect of the gross regional domesticproduct (PDRB), the people of Daerah Istimewa Yogyakarta (JP), and region income (PD) to the number of vehicles in Daerah Istimewa Yogyakarta (JKB). The model consists of one behavioral equation: the number of vehicles equation. The estimation technique uses Ordinary Least Squares (OLS). MacKinnon, White, and Davidson test (MWD test) is used to choose between the two models: linear regression model or log-linearregression model.The sample covers observations for 23 years (1990 - 2012). The data are obtained from (1) Bank Indonesia (2) Badan Pusat Statistik DIY and various other sources. It is found that individually lnJP andlnPD are statistically significant (positive) except ln PDRB on the basis of (separate) t test. It is also found that on the basis of the F test collectively all the regressors have a significant effect on the regressand lnJKB.Keywords: the number of vehicles, traffic congestion, linear regression model, log-linear regression model.

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