Nested Logit regression model Harvard Case Solution & Analysis

Nested Log it regression model  Case Study Solution 

Introduction

This assignment introduces the census for Motorized vehicle crashes,which represents a significant cause of death annually. However, it was estimated by the world health organization that 20 to 50 million road accidents result in non-fatal injuries which could range from debilitating outcomes to minor injuries. Furthermore, it was also estimated that about 1.24 million people die from fatal accidents on the road. Additionally, it was determined that the increasing road accidents had adverse effects on the economy, which, in turn, raised the medical costs of the victims. Moreover, it decreased the productivity of the organization and also was a major cause of traffic congestion on the road. In addition to this, it could be determined that in the U.S fatalities relating to highways occurred more frequently on rural highways, attributed to the lack of safety measure implement in that area. However, crash severity would remain the primary focus of the analysis as severity levels could have a tremendous impact on the economic condition and emotional state of the people. Therefore, the main object of the study was to estimate crash injury severity outcomes by implementing nested logit and probit Regression models.

Furthermore, KABCO scale directs development of the model towards a discrete choice model. However, this study focuses on binary nested logit Regression models ofseverity outcomes of road accidents.

Data Description

The Data had been collected from four datasets, consisting of details regarding vehicle, crash, roadway and occupant data in dta format, merged together to define specific crash events in the rural areas regarding the highways in Illinois. Furthermore, the dataset defines the level of crash severity for each incident, ranging from the worst injury to any driver or occupant involved in the accident, which could be determined using nested logit and probit regression model.

Furthermore, it can be determined that the studies suggest unreliability of data recorded in the dataset, as it does not account for subjective information of each incident. Moreover, the subjective information, including road defects,weather or driver conditions could also be a relevant cause of crash injuries. Hence, the absence of the subjective data from analysis could cause the model to omit variable bias, which would result in the parameters overestimation or underestimation.

Variable description

Using nested logit and progit regression model to estimate crash severity, where “severity” was taken as the dependent variable. Whereas, the independent variables were distributed into four different groups as per their features namely, Roadway Attributes,Traffic, Driver & Passenger, and Crash Type. However, the variable description is given in Exhibit-1.

 

 

Exihibit-1 (Variable Description)

Class Variable Description
Traffic aadt1 Avg annual traffic per day (veh/day)
comm_vol volume of heavy commercial vehicles per day (trucks/day)
large Indicator variable: 1=large vehicle (i.e., bus or

truck) involved in crash; 0=no large vehicle involved

Roadway Attributes lane width Lane width (ft.)
shoulder type Type of shoulder: 1 = earth/sod; 2 = aggregate; 3 = paved; 4 = composite

shoulder type of aggregate and sod; 5 = composite shoulder type of paved with

either aggregate or sod

shoulder width Shoulder width (ft.)
Crash Type other Indicator variable: 1 = collision type other than specific ones below;

0 = otherwise

fixed_object Indicator variable: 1 = fixed-object collision;

0 = otherwise

animal Indicator variable: 1 = animal collision; 0 = other
overturn Indicator variable: 1 = overturn collision; 0=other
turning_rear end Indicator variable: 1 = turning or rear-end collision; 0=other
side_same Indicator variable: 1 = sideswipe same direction; 0=other
side opp_head_angle Indicator variable: 1 = sideswipe opposite direction, head-on, or

angle collision; 0=other

Driver & Passenger drv_max_age Max age of drivers involved in the crash (years)
back_max_age Max age of the back seat occupants involved in the crash
front_max_age Maxage of front seat occupants involved in the crash
num_male Number of male occupants involved in the crash (other than the drivers)
num_female Number of female occupants involved in the crash (other than the drivers)
backind Back seat occupant indicator: 1=there was at least one back seat occupant involved in the crash; 0=no back seat occupants involved
front ind Indicator variable: 1=there was at least one front seat occupant involved in the crash; 0= no front seat occupant involved
drvsex_male Indicator variable: 1 = driver is male; 0 = driver is female
drv_no_rest Indicator variable: 1 = there was at least one driver involved in the crash that did not use restrain or used restraint improperly; 0 =all drivers used restraint properly
front_no_rest Indicator variable: 1 = there was at least one front seat occupant involved in the crash that did not use restraint or used restraint improperly; 0 =all front seat occupants used restraint properly
back_no_rest Indicator variable: 1 = there was at least one back seat occupant involved in the crash that did not use restraint or used restraint improperly; 0 =all back seat occupants used restraint properly

 

The descriptive statistics of the dependent as well as independent variable is given below in exhibit-2.

Nested Logit regression model Harvard Case Solution & Analysis

 

 

Exhibit-2 (Descriptive Statistics)

 

Descriptive Statistics
Variable                   Observations  Mean   Std. Dev.   Min  Max
severity                    24,622            0.38            0.90                -              4.00
aadt1                    24,622    3,609.63    2,189.94       400.00    14,500.00
comm_vol                    24,622       422.24       301.12           1.00            14.00
shoulderwi~h                    24,622            6.26            2.57           1.00            14.00
drv_max_age                    24,536          41.63          16.73         13.00            98.00
back_max_age                    23,843            1.91            8.45                -            92.00
front_max_~e                    24,467            8.03          17.93                -            97.00
num_male                    24,622            0.17            0.52                -            19.00
num_female                    24,622            0.24            0.58                -            17.00
backind                    24,622            0.09            0.28                -              1.00
drvsex_male                    24,548            0.59            0.49                -              1.00
drv_no_rest                    21,841            0.03            0.76                -              1.00
front_no_r~t                    24,135            0.01            0.08                -              1.00
back_no_rest                    23,797            0.01            0.08                -              1.00
other                    24,622            0.04            0.20                -              1.00
animal                    24,622            0.58            0.49                -              1.00
overturn                    24,622            0.06            0.23                -              1.00
turning_re~d                    24,622            0.78            0.27                -              1.00
side_same                    24,622            0.11            0.11                -              1.00
side opp_he~e                    24,622            0.04            0.20                -              1.00

Exihibit-3 (Regression Analysis)

In Exhibit-3, the p-values of the independent variables are reasonable. However, most of the values are less than 5%. Moreover, under the regression model, the coefficient of AADT and animal are negative, which means that either AADT or the probability of the crash being caused by animals was increasing. However, the accident tends to be in the minor severity category............

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