# Queensland University of Technology Transport Data Analysis and Modeling Methodologies

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1 1 Queensland University of Technology Transport Data Analysis and Modeling Methodologies Lab Session #11 (Mixed Logit Analysis II) You are given accident, evirnomental, traffic, and roadway geometric data from 275 segments of highway in Washington State. The data are from The injury data consist of three possible outcomes: no injury, possible injury, injury. Your task is to estimate a mixed logit model of these three possible discrete outcomes. The mixed logit model allows for parameter variations across roadway segments (i.e. variations in β), a mixing distribution is introduced giving injury-severity proportions (see Train 2003), in EXP[ βix in] = EXP[ β X ] I i In ( β ϕ ) P f dβ where f (β φ) is the density function of β with φ referring to a vector of parameters of the density function (mean and variance), and all other terms are as previously defined. Equation 3 is the formulation for the mixed logit model. For model estimation, β can now account for segment-specific variations of the effect of X on injury-severity proportions, with the density function f (β φ) used to determine β. Mixed logit proportions are then a weighted average for different values of β across roadway segments where some elements of the vector β may be fixed and some may randomly distributed. If the parameters are random, the mixed logit weights are determined by the density function f(β φ). Most studies have used a continuous form of this density function in model estimation (such as a normal distribution) and this is what you are to use. In your specification, consider random variable possibilities including constant or fixed (C), normally distributed (N) and log-normally distributed (L). 1. The results of your best model specification. 2. A discussion of the logical process that led you to the selection of your final specification (the theory behind the inclusion of your selected variables). Include t-statistics and justify the signs of your variables.

2 Variables available for your specification are (in file Ex16-1.txt): 2 Variable Number ID FREQ ROUTE LENGTH INCLANES DECLANES WIDTH MIMEDSH MXMEDSH SPEED URB FC AADT SINGLE DOUBLE TRAIN PEAKHR GRADEBR MIGRADE MXGRADE MXGRDIFF TANGENT CURVES Explanation Segment ID number Number of accidents Route Number Segment length in miles Number of lanes in increasing milepost direction Number of lanes in decreasing milepost direction Total combined width of all lanes Minimum median shoulder in feet Maximum median shoulder in feet Speed limit (mi/h) Indicates urban area (1=yes, 0=no) Functional class (1=local, 2=collector, 3=arterial, 4=principal arterial, 5=interstate) Average Annual Daily Traffic Daily percentage of single unit trucks Daily percentage of tractor and trailer trucks Daily percentage of tractor and two-trailer trucks Percent of daily traffic in the peak hour Number of grade breaks in the segment Minimum grade in the segment Maximum grade in the segment Maximum grade difference in the segment Tangent length in the segment Number of cureves in the segment

3 3 MINRAD ACCESS MEDWIDTH FRICTION ADTLANE SLOPE INTECHAG AVEPRE AVESNOW Minimum radius in feet Segment access control (0=none, 1=partial, 3=full) Median width (1=less than 30ft; 2=30 to 40ft; 3=40 to 50ft; 4=50 to 60ft to 5=high) Friction value (0 to 100 with 100 being high) Average daily travel per lane Segment slope (0=flat, 1=slight, 2=medium, 3=high) Indicates number of interganges in the segment Average precipitation per month in inches Average snowfall per month in inches

5 --> nlogit;lhs=injfreq; choices=pdo,pinj,inj; model: U(pdo)=a0+a1*laneadt+a3*minrad/ U(pinj)=b0+b2*truck/ U(inj)=c3*friction+c2*intmi+c1*gbmile ;fcn=a0(c),a1(c),a3(n), b0(c),b2(n),c2(n),c3(c),c1(n);rpl;frequencies;parameter;pts=200,halton\$ Normal exit from iterations. Exit status=0. 5 Start values obtained using nonnested model Maximum Likelihood Estimates Model estimated: Sep 14, 2010 at 11:06:53AM. Dependent variable Choice Weighting variable None Number of observations 258 Iterations completed 5 Log likelihood function R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj No coefficients Constants only. Must be computed directly. Use NLOGIT ;...; RHS=ONE \$ Chi-squared[ 6] = Prob [ chi squared > value ] = Response data are given as frequencies. Number of obs.= 275, skipped 17 bad obs. Variable Coefficient Standard Error b/st.er. P[ Z >z] A A D D A D D B B C C C Normal exit from iterations. Exit status=0. Random Parameters Logit Model Maximum Likelihood Estimates Model estimated: Sep 14, 2010 at 11:09:57AM. Dependent variable INJFREQ Weighting variable Number of observations None 825 Iterations completed 30 Log likelihood function Restricted log likelihood Chi squared Degrees of freedom 12 Prob[ChiSqd > value] = R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj No coefficients Constants only. Must be computed directly. Use NLOGIT ;...; RHS=ONE \$ At start values Response data are given as frequencies.

6 Random Parameters Logit Model Replications for simulated probs. = 200 Number of obs.= 275, skipped 17 bad obs. Variable Coefficient Standard Error b/st.er. P[ Z >z] Random parameters in utility functions A A D D A D D B B C C C Derived standard deviations of parameter distributions CsA (Fixed Parameter)... CsA (Fixed Parameter)... NsA CsB (Fixed Parameter)... NsB NsC CsC (Fixed Parameter)... NsC

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