Nested Logit regression model Case Solution And Analysis, HBR Case Study Solution & Analysis of Harvard Case Studies

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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