Dientes tratados endodónticamente

Figure 6-19 presents the evolution of traffic under traffic regimes before crashes used in this study. Each cell column represents a crash, each cell row represents the moment of the PTS before crashes. Each cell is a PTS and the color within each cell represents the traffic regime of that PTS. The top row represents the frequencies of corresponding patterns.

The PTS pattern HHHHHH repeats the most - eight times – which means that even the traffic is stable under regime H, the crash risk is still high. There are 64 crashes (i.e. 53.3%) for which the traffic comes only to regimes G and H such as the pattern 23, 25, 27-29, 36-38, 41, 46, 47, 56, 57, 58-64, and 66-72. PTS patterns are also observed within NTS data. Figure 6-20 presents the repetition frequencies of PTS patterns and the pre-crash rate which is the ratio between the frequencies of PTS patterns in pre-crash data and PTS pattern in NTS data. The pattern indices in Figure 6-20 are the same to pattern indices in Figure 6-19. The PTS patterns BBBBBB and CCCCCC are the most popular in NTS data. After that, the frequencies of PTS patterns GBBBBB and HHHHHH are also high. It means that the chance for the traffic to end up with a crash after these patterns is not high.

There are patterns that can lead to higher pre-crash rate such as HGBCHH (one of four cases ended up with a crash), HHHBHB (two of ten cases ended up with a crash), CGHCHC (one of seven cases ended up with a crash), and GDDGHB (one of eight cases ended up with a crash).

Figure 6‐19: PTS Pattern repetitions  5 10 15 20 25 30 35 40 45 50 55 60 65 70 00' -05' -10' -15' -20' -25' -30' Patterns Ti m e B C D E F G H Crash Occurrence Freq 1 2 3 4 578 5 10 15 20 25 30 35 40 45 50 55 60 65 70 0 1 2 3 4 5 6 7 Pattern Index

PTS Pattern Counts in NTS data (x1'000) PTS Pattern Counts in NTS data divided by PTS Pattern Counts (x0.1)

16'171 23'913

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6.5. Summary

This chapter presents the decisions to sample NTS that are relevant to PTS and then concentrates on uncovering the characteristics of eight traffic regimes. The fundamental Speed/Flow diagrams on two lanes cannot properly characterize each of traffic regimes as only four variables can be presented and different regimes overlap in the diagrams.

Three groups of traffic regimes are observed: group of low traffic, group of medium traffic and group of high traffic. The typical one-weekday traffic evolution starts from midnight with the first group, then changes to the second group at around 5:00 to 6:00 AM before reaching the third group and remain in the third group during day time. The traffic in the evening changes to the second group and then the first group until midnight. Weekend traffic evolution is slightly different with the traffic coming at the third groups at some time near noon.

Within the first group, traffic under regime E is denser than under regimes A and F. Regime F represents special traffic conditions when there is only traffic on one lane.

Regimes C and D are classified into the second group and are similar in traffic characteristics. The only difference between C and D is that regime C occurs under rainy conditions whereas regime D occurs under normal weather conditions.

In the third group, regime H represents high flow or congested traffic conditions whereas regimes B and G represents traffic conditions leading to and following regime H, respectively. There is high fluctuation between regimes H and G and between regimes G and B.

Regarding to PTS evolution, it is observed that before almost of crashes, the traffic falls into regime H at least once (Regime H represents high flow or congested conditions).

In the next chapter, the development of risk identification models under regimes B, C, G, and H will be presented. No model is developed under regimes A, D, E, and F and whenever, a new traffic situation is classified into one of these regimes, it will be automatically declared as NTS.

Chapter 7 Real‐time Risk Identification

This chapter dedicates to the identification of real-time traffic crash risks. The issues addressed here include selecting the most appropriate method for developing models capable of identifying traffic risk, improving the performance of developed models, interpreting results.

7.1. Overview

Relating to RIM development, Table 7-1 summarizes the results obtained from previous chapters.

Table 7‐1: Results from previous chapters 

Index Parameter Value

1 Number of Traffic Regimes considered 8, namely from A to H. 2 Traffic regimes with high Risk Chance B, C, G, and H.

3 Number of variables used 22 (see Table 5-1 in Chapter 5) According Figure 3-1, after defining NTS and PTS and sampling NTS to obtain Traffic regimes, RIM can be developed under each traffic regime. As presented in 3.4.2, the technique employed for RIM development should be a supervised learning technique. This is because the classes of outputs are known: NTS and PTS. Besides, the selected supervised learning method needs to match the following criteria:

1) Accept categorical variables such as time of the day and day of the week in inputs. 2) Should be resistant to the imbalance between NTS and PTS.

3) Should facilitate the interpretation of results

In Chapter 6, NTS and PTS are matched under Traffic Regimes such that NTS and PTS under each regime are the most comparable to each other. As Risk Chance under regimes A, D, E, and F is zero or almost zero, there is no need to develop RIM under such regimes. Under each of four regimes B, C, G, and H, a RIM is developing to differentiate between NTS and PTS. Given a new traffic situation, the role of RIM is to classify that traffic situation into one of two classes: NTS or PTS. It means that the traffic situation must have occurred for RIM to make the classification. It also means that RIM do not make any prediction yet just identify what has happened.

Based RIM, real-time prediction model is also established to provide short-term prediction

In the next sections, the selection of a supervised learning method is presented with respect to three criteria 1), 2), and 3) above. Thereafter, results of the application of the selected method are discussed. The result interpretation is also discussed with regard to the causality of crashes. Consequently, short- term crash risk prediction is discussed with applicability of the developed framework in reality.

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7.2. Supervised Learning Method