Caracterizaciones de Espacios E.C

The congestion index was calculated for each of the M25 motorway segments to appropriately represent the level of congestion. In the case of fatal and serious injury accidents, this variable revealed the negative sign suggesting that the increased level of congestion is associated with the decreased level of fatal and serious injury accident occurrences. This variable however has been found to be statistically insignificant in all forms of Poisson models for both categories of accidents (Tables 6.2 and 6.3). This means that the level of traffic congestion has no impact on the frequency of road accidents according to the data on the M25. Other measures of traffic congestion such as total vehicle delay per km length of road have also been tested and this variable has also been found to be statistically insignificant. Therefore, spatial differences of traffic congestion among road segments of the M25 cannot explain the variation in road accidents.

This result is in line with the findings of Noland and Quddus (2005) who investigated the association between traffic congestion and road accidents in London based on area-wide data, and did not find conclusive evidence showing that there is any effect of traffic congestion on road accidents.

There may be a number of reasons for the insignificance of traffic congestion in the models. Firstly, this result has been based on a congestion measurement calculated from hourly data averaged over a year so as to match with the dependent variable of the models that have been taken as the annual accident frequency per road segment. In reality, the level of congestion varies over time (such as over a day and throughout a year) and this generalisation may have an impact on the effect of congestion on accidents. As stated below, however, the data have been disaggregated into peak and off-peak periods in order to take into account the different levels of congestion.

Secondly, the effects of congestion might be captured by other factors such as speed variance and traffic flow. Literature suggests that speed variance is an important factor in explaining the occurrence of traffic accidents (Lave, 1985; Aljanahi et al., 1999;

Ossiander and Cummings, 2002). This may also be true in this case since the aggregated data used does not explicitly represent how traffic speed on a specific segment varies at different times. Speed variance therefore was intended to be included in the model. The variation of speed in the literature, however, is measured by

Chapter 6: Results from Accident Frequency Models 123 Acceleration Noise (AN) which is also regarded as a congestion measurement (Taylor et al., 2000b). Moreover, AN requires a considerable amount of data and due to the fact that speed variance is affected not just by traffic conditions (e.g., congestion) but also by driving behaviour AN is not considered in this thesis.

With regards to the effect of traffic flow, it can be speculated that congestion has different effects under different traffic flow conditions. For example, the effect of traffic congestion on the frequency of road accidents may be positive under low traffic flow conditions and negative under high traffic flow conditions. To investigate this, data has been disaggregated into two parts: the peak time period when the traffic flow is high;

and the off-peak time period when the traffic flow is low. The peak and off-peak time periods were determined by the average hourly traffic volume.

The average hourly traffic volume on the M25 has been investigated and found to follow similar variations throughout a day as shown in Figure 5.2 (see Chapter 5). It is noticeable from Figure 5.2 that traffic flow is higher during the period 6:00am–8:00pm on weekdays and 9:00am–8:00pm on weekends. As such, these time periods are considered as peak while the rest of the time periods are considered as off-peak. All four specifications of the model have been estimated for both peak and off-peak periods. The model estimation results are presented in Tables 6.4-6.7.

Chapter 6: Results from Accident Frequency Models 124

Table 6.4 Models for fatal and serious injury accidents on the M25 during the peak time period

Poisson-lognormal Poisson-gamma

Poisson-lognormal CAR

Poisson-lognormal CAR

(1st order neighbour) (2nd order neighbour)

Variables Mean S.D. Mean S.D. Mean S.D. Mean S.D.

Congestion index -0.3544 0.8559 -0.512 0.9461 -0.3163 0.8901 -0.3822 0.8938 log(Traffic volume) 1.407** 0.4534 1.268* 0.7372 1.628** 0.5193 1.443** 0.6951 Segment length (km) 0.1262** 0.0306 0.1393** 0.0395 0.1336** 0.0294 0.1337** 0.0294 log(minimum radius) 0.244 0.2478 0.3319 0.2878 0.2987 0.2970 0.3043 0.243 Maximum gradient (%) 0.2007* 0.1167 0.2097 0.1396 0.2406** 0.1217 0.2356** 0.1174 Number of lanes -0.0518 0.1710 -0.01717 0.2302 0.0483 0.1865 0.0647 0.2045

Direction 0.1469 0.1944 0.1808 0.2278 -0.0044 0.4006 0.0089 0.3833

Intercept -25.65** 7.3800 -24.02** 11.5 -30.21** 8.676 -27.17** 11.23

S.D. (u) 0.1644** 0.1138 0.2748** 0.1946

S.D. (v) 0.3862** 0.1555 0.6015** 0.0972 0.2099** 0.1818 0.1985** 0.182

DIC 256.244 253.674 256.564 277.084

N 70 70 70 70

* Statistically significantly different from zero (90% credible sets show the same sign)

** Statistically significantly different from zero (95% credible sets show the same sign)

Chapter 6: Results from Accident Frequency Models 125

Table 6.5 Models for slight injury accidents on the M25 during the peak time period

Poisson-lognormal Poisson-gamma

Poisson-lognormal CAR

Poisson-lognormal CAR

(1st order neighbour) (2nd order neighbour)

Variables Mean S.D. Mean S.D. Mean S.D. Mean S.D.

Congestion index -0.0856 0.4662 -0.0375 0.5058 0.1664 0.4660 0.4248 0.4534 log(Traffic volume) 1.245** 0.2708 1.325** 0.3669 1.343** 0.3283 1.767** 0.2057 Segment length (km) 0.1474** 0.0225 0.1491** 0.0273 0.1503** 0.0214 0.1466** 0.0220 log(minimum radius) 0.1053 0.1432 0.0535 0.1299 0.1622 0.1690 0.0619 0.1482 Maximum gradient (%) 0.2314** 0.0758 0.2049** 0.0850 0.244** 0.0751 0.2499** 0.0752 Number of lanes 0.2496** 0.1098 0.2032* 0.1187 0.2423** 0.1205 0.1637 0.1075 Direction 0.0080 0.1330 0.0045 0.1445 -0.0894 0.2529 -0.0114 0.2057 Intercept -20.74** 3.9560 -21.42** 5.9060 -22.79** 4.8460 -29.07** 3.0760

S.D. (u) 0.0965** 0.0888 0.0903** 0.1073

S.D. (v) 0.4754** 0.0617 0.5281** 0.0580 0.413** 0.0801 0.4366** 0.0723

DIC 474.027 468.151 474.542 474.949

N 70 70 70 70

* Statistically significantly different from zero (90% credible sets show the same sign)

** Statistically significantly different from zero (95% credible sets show the same sign)

Chapter 6: Results from Accident Frequency Models 126

Table 6.6 Models for fatal and serious injury accidents on the M25 during the off-peak time period

Poisson-lognormal Poisson-gamma

Poisson-lognormal CAR

Poisson-lognormal CAR

(1st order neighbour) (2nd order neighbour)

Variables Mean S.D. Mean S.D. Mean S.D. Mean S.D.

Congestion index -0.1946 0.6714 -0.0583 0.7412 -0.1708 0.7381 -0.1681 0.7240 log(Traffic volume) 1.24** 0.4693 1.762** 0.7319 1.365** 0.6978 1.369* 0.6625 Segment length (km) 0.1438** 0.0323 0.1699** 0.0467 0.1448** 0.0339 0.1437** 0.0338 log(minimum radius) 0.1556 0.3083 0.0975 0.3478 0.161 0.3038 0.1553 0.3029 Maximum gradient (%) 0.1723 0.1396 0.1573 0.1667 0.17 0.1407 0.1713 0.1405 Number of lanes -0.2636 0.2186 -0.3739 0.2709 -0.2869 0.2429 -0.2970 0.2392 Direction -0.1331 0.2212 -0.1152 0.2788 -0.2605 0.2975 -0.2226 0.2728 Intercept -20.34** 6.8320 -27.73 10.97 -22.18** 10.09 -22.17** 9.61

S.D. (u) 0.0522** 0.0424 0.0628** 0.0610

S.D. (v) 0.0607** 0.05778 0.5454** 0.1029 0.0641** 0.0608 0.0586** 0.0553

DIC 175.083 185.187 179.572 178.668

N 70 70 70 70

* Statistically significantly different from zero (90% credible sets show the same sign)

** Statistically significantly different from zero (95% credible sets show the same sign)

Chapter 6: Results from Accident Frequency Models 127

Table 6.7 Models for slight injury accidents on the M25 during the off-peak time period

Poisson-lognormal Poisson-gamma

Poisson-lognormal CAR

Poisson-lognormal CAR

(1st order neighbour) (2nd order neighbour)

Variables Mean S.D. Mean S.D. Mean S.D. Mean S.D.

Congestion index -0.2619 0.4068 -0.2558 0.4335 0.0927 0.4122 0.0998 0.4214 log(Traffic volume) 0.8131** 0.2149 0.8331** 0.2739 0.6662** 0.2775 0.776** 0.3342 Segment length (km) 0.1442** 0.0262 0.1492** 0.0315 0.1555** 0.0239 0.155** 0.0243 log(minimum radius) -0.0435 0.1668 -0.0390 0.1956 0.0991 0.1723 0.0634 0.1933 Maximum gradient (%) 0.246** 0.0873 0.2483** 0.1030 0.2918** 0.0868 0.2782** 0.0896 Number of lanes 0.2952** 0.1230 0.2808** 0.1382 0.3631** 0.1332 0.3285** 0.1417 Direction 0.0192 0.1523 0.0109 0.1723 -0.1550 0.3325 -0.1290 0.3048 Intercept -13.07** 2.9520 -13.3** 3.6920 -12.19** 3.8340 -13.49** 4.5260

S.D. (u) 0.1706** 0.1303 0.2633** 0.2171

S.D. (v) 0.4235** 0.0792 0.5301** 0.0715 0.2473** 0.1608 0.2462** 0.1684

DIC 334.021 332.995 334.559 335.156

N 70 70 70 70

* Statistically significantly different from zero (90% credible sets show the same sign)

** Statistically significantly different from zero (95% credible sets show the same sign)

Chapter 6: Results from Accident Frequency Models 128 As can be seen, the results in Tables 6.4-6.7 are consistent with the results presented in Tables 6.2 and 6.3 in terms of the set of statistically significant variables. The congestion index continues to be statistically insignificant across all models, both in the peak and off-peak time periods. This further confirms that congestion has no impact on the frequency of traffic accidents according to the data on the M25.