NB regression model

IV. Results

4.3 NB regression model

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Table 6 shows the NB regression results for the total crass occurred from 2008-2015 in Seoul metropolitan city in dong-district scale. As expected before in the correlation results part, there is no significant relationship between crash and the crash risk complaints. While, exposure variables (pop + emp, pop density, emp density, children% and senior %) explain the crash with high significance (p<.0.01). In the aspect of the density, crashes occurred in the population dense area while the employment density showed the opposite results. High dense employment area such as the CBD or the districts that commercial facilities located densely show the low possibility of crash occur. The proportion of the children and senior showed the opposite results as we expected before. Because those ages are vulnerable to the crash risk, we expected that it will have positive relationship with the crash, however, the results show the negative effect. Road density also show the opposite result with the expectation. For the facility variables, crosswalk density shows the calming effect to the crash as expected before. The proportion of intersections which installed with crosswalk decreases the number of crashes. In case of the school density, it also shows the same effect with the crosswalk density.

Furthermore, we examined the several types of crashes with each categorized complaint. Among them, three types of crashes (car-car, death and v_signal) show the statistically significant results with the facility complaints. Facility complaints, which is represented as the degree of perceived crash risk of people regarding to the traffic safety facilities, shows positive association with three types of crashes.

Especially, compared to the coefficient values, it affects relatively high influence on v_signal than other kind of crashes.

Regarding to the exposure variables (pop + emp, pop density, emp density), crash risk complaints and landuse diversity show statistically significant result in all models. Exposure variables, the sum of population and the number of employees and population density, are significant positive factors for all kinds of crashes. But the effect of employment density shows the negative association with crashes, which is somewhat different with previous researches. Some researches show that high employment density increase the crash frequency (Clifton, 2007; Priyantha, 2006; Ukkusuri, 2012). But in the neighborhood-scale, it shows the negative association with the crash (Park, 2016; Ouyang, 2014).

Male % variables was not shown the meaningful association with the total crash modeling, however, in Model 3 and 4, it shows the strong association with each type of crashes. Comparing the coefficients, it can be seen that males are vulnerable to the serious collision – death crash. Crosswalk density still has strong association with each type of crash. Landuse variables show the similar results with the Model 1.

33 Table 7. The results of NB model for sub-crash groups and facility complaints

Model 2 Model 3 Model 4

car-car z death z v_signal z

facility complaints 0.0221 ** 1.97 0.0175 * 1.69 0.0227 * 1.75

pop + emp 1.8E-05 *** 11.08 1.4E-05 *** 9.1 1.9E-05 *** 9.79

pop density 9.8E-06 *** 3.04 1.0E-05 *** 2.76 1.2E-05 *** 3.11

emp density -2.5E-06 ** -2.46 -3.3E-06 ** -2.2 -3.7E-06 *** -3.04

male % ^{0.0200 1.47} 0.0543 *** 3.2 0.0331 ** 2.11

children% ^-0.0537 *** -2.6 -0.0319 -1.4 -0.0553 ** -2.34

senior % -0.0665 *** -3.66 -0.0519 ** -2.51 -0.0638 *** -2.97

road density -19.7948 *** -3.52 -7.9707 -1.2 -20.8650 *** -3.1

crosswalk density -0.0067 ** -2 -0.0093 ** -2.34 -0.0079 ** -1.98

school density -5.6E-06 ** -1.96 -5.4E-06 -1.4 -6.1E-06 * -1.71

bus stop density 0.0013 0.9 0.0025 1.49 0.0028 1.52

subway density 0.0053 *** 2.57 0.0035 1.4 0.0057 ** 2.3

parkinglot density -0.0111 ** -2.56 -0.0062 -0.97 -0.0142 *** -2.57

building density 0.1596 0.41 -0.7667 -1.43 0.4730 0.98

residential % 0.0022 0.64 0.0065 1.3 -0.0031 -0.73

commercial % 0.0101 *** 3.1 0.0115 ** 2.27 0.0075 * 1.83

industrial % 0.0005 0.07 0.0104 1.39 -0.0077 -0.95

green open % 0.0068 ** 1.99 0.0131 *** 2.66 0.0018 0.43

HHI -0.0002 *** -6.57 -0.0002 *** -6.35 -0.0001 *** -5.25

_cons ^{6.2298 8.04 -0.0596 -0.06 4.0252 4.7}

number of obs ^{441 441 441}

Pseudo R2 ^{0.0925 0.1658 0.0992}

Log likelihood -2855.0831 -1093.3815 -2131.2234

***: p<0.01 **: 0.01<P<0.05 *: 0.05<P<0.1

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For the vulnerable class, the ratio of population over 65-years old (senior%) decreases the likelihood of crash and its subgroups. In case of the male%, it shows the same results with previous researches.

Regarding to the facilities, crosswalk density, school density, busstop density, parkinglot density are marginal associated with the crash frequency. As we expected, high crosswalk density and school density decreases the probability of crash occurrence because of their calming devices. In case of crosswalk density, it shows the same results with death-crash and pedestrian-car crash. Parkinglot density is negatively associated with crash frequency contrary to the expectation. In case of the busstop density, which represents the use of public transportation, is negatively related with crash.

The mixed landuse (landuse diversity index) also shows significant association with the crash. It indicates that communities with high mixed landuse have lower crashes than ones with a lower land use mix index. Specifically, high commercial ratio increases the probability of crash occurrence and the same for the ratio of green and openspace. Only in the pedestrian-car crash, the ratio of residential area is positively related with crash occurrence.

Next, we analyzed the crash model with the two groups divided by the area 1𝑘𝑚². It shows the different results with the total crash models. First, the crash risk complaints variable has strong association with the number of crash in the under 1𝑘𝑚² group. Second, population density variable loses the significance in the both models. Road density shows the strong association with only under 1𝑘𝑚² group. It doesn’t have significant relationship with the crash in the over 1𝑘𝑚² area group. Landuse variables are also shown the significant relationship only with the small area group.

35 Table 8 The results of the NB regression (groups by area)

over 1𝒌𝒎^𝟐 under 1𝒌𝒎^𝟐

crash z crash z

crash risk complaints 0.0008 0.31 0.0266 *** 2.66

pop + emp 1.1E-05 *** 9.74 7.1E-05 *** 10.37

pop density -4.3E-06 -0.96 -2.5E-06 -0.85

emp density -2.0E-05 *** -2.88 -2.8E-06 *** -3.21

male % -0.0132 -0.33 0.0224 * 1.89

children% -0.0629 *** -2.6 -0.0798 *** -3.81

senior % -0.0307 -1.26 -0.0819 *** -4.81

road density -0.2771 -0.03 -18.0012 *** -3.73

crosswalk density 0.0141 *** 2.6 -0.0079 *** -2.56

school density -1.9E-07 -0.02 -5.7E-07 -0.24

bus stop density 0.0048 1.13 0.0051 *** 3.99

subway density 0.0087 0.56 0.0056 *** 3.43

parkinglot density 0.0468 1.6 -0.0081 ** -2.49

building density -0.2096 -0.19 -0.1256 -0.39

residential % 0.0036 0.36 -0.0016 -0.53

commercial % 0.0106 1.08 0.0067 ** 2.35

industrial % -0.0048 -0.42 0.0177 *** 2.63

green open % -0.0031 -0.33 0.0033 0.94

HHI -0.0001 * -1.95 -2.9E-05 -1.23

_cons 7.9415 3.62 5.7335 8.03

number of obs 139 302

Pseudo R2 0.0796 0.0796

Log likelihood -1064.982 -1745.4124

***: p<0.01 **: 0.01<P<0.05 *: 0.05<P<0.1

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