Zero-Inflated Poisson Regression - regression

Question

I want to predict the number of visits of a place of interest, i.e. restaurants, and my data has the following characteristics: my visits data is count data, it is highly skewed, there are a high amount of zeros present and there are some numbers acting like outliers. I cannot remove the zeros because they are indicating that either there are not visits at that specific time or there are no places of interest in that area. I can't remove the outliers either because it indicates the maximum number of visits during that time period. My dependent variable is the number of visits per census tract, and my independent variables involve census data such as total population, age groups (15-24, 25-34, 35-44, 45-44, 55-64), household size, household income (less than $10K...$149.9K), population by gender, transportation (total vehicles, total public, total walked, total other), and the number of places of interests per tract. Below the distribution of the number of visits:
Since the distribution is not normal, I worked the generalized linear model using a Poisson distribution. However, the variance is greater than the mean, indicating over-dispersion. Since there is a high number of zeros, my intuition tells me to use the zero-inflated poisson or the negative binomial. When I ran the zero-inflated poisson model using the zeroinfl() function in R, I got the following results:
Call:
zeroinfl(formula = Vsts_p_ ~ Ttl_ppl + A_15__2 + A_25__3 + A_35__4 + A_45__5 +
A_55__6 + Hshlds_ + L__1000 + X10000_ + X15000_ + X25000_ + X35000_ +
X50000_ + X75000_ + X100000 + Totl_Ml + Ttl_vhc + Ttl_pbl + Ttl_wlk +
Ttl____ + POIs, data = rest_train, family = "poisson")
Pearson residuals:
Min 1Q Median 3Q Max
-9.6015 -0.6256 -0.2359 -0.1049 82.1648
Count model coefficients (poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 5.7918192 NA NA NA
Ttl_ppl -0.0007789 NA NA NA
A_15__2 0.0004508 NA NA NA
A_25__3 -0.0009964 NA NA NA
A_35__4 0.0008019 NA NA NA
A_45__5 -0.0031752 NA NA NA
A_55__6 -0.0006723 NA NA NA
Hshlds_ 0.0019657 NA NA NA
L__1000 -0.0499632 NA NA NA
X10000_ 0.1235269 NA NA NA
X15000_ -0.1172473 NA NA NA
X25000_ 0.0497449 NA NA NA
X35000_ -0.0297751 NA NA NA
X50000_ -0.1157051 NA NA NA
X75000_ -0.0102363 NA NA NA
X100000 -0.0120035 NA NA NA
Totl_Ml 0.0011945 NA NA NA
Ttl_vhc -0.0004261 NA NA NA
Ttl_pbl -0.0091919 NA NA NA
Ttl_wlk -0.0042964 NA NA NA
Ttl____ 0.0073077 NA NA NA
POIs 0.0583964 NA NA NA
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.834e+00 NA NA NA
Ttl_ppl 1.235e-03 NA NA NA
A_15__2 -1.707e-03 NA NA NA
A_25__3 -4.341e-03 NA NA NA
A_35__4 -6.557e-04 NA NA NA
A_45__5 -5.858e-03 NA NA NA
A_55__6 -2.595e-03 NA NA NA
Hshlds_ -1.411e-04 NA NA NA
L__1000 -5.746e-02 NA NA NA
X10000_ 6.327e-02 NA NA NA
X15000_ 2.452e-02 NA NA NA
X25000_ -2.395e-02 NA NA NA
X35000_ 7.081e-03 NA NA NA
X50000_ -5.557e-03 NA NA NA
X75000_ 8.765e-03 NA NA NA
X100000 2.724e-03 NA NA NA
Totl_Ml 9.600e-04 NA NA NA
Ttl_vhc 7.418e-04 NA NA NA
Ttl_pbl -1.734e-05 NA NA NA
Ttl_wlk 1.022e-02 NA NA NA
Ttl____ -9.307e-04 NA NA NA
POIs -1.009e+00 NA NA NA
Number of iterations in BFGS optimization: 50
Log-likelihood: -6258 on 44 Df
Why I'm getting NA results? I ran the glm poisson model and it ran without any issues. My intention is to predict the number of visits using the generalized geographical weighted regression (GWR) model but first, I want to see which of the independent variables influence the dependent variable, I cannot use the correlation plot to do that because of the distribution not being normal. I need to select the appropriate independent variables to run the ggwr model.