In a real-time framework, traffic data are constantly collected and processed to generate traffic situations for the last aggregation interval, called the current traffic situation or cTS. Thereafter, cTS is classified into class of NTS or NTS. 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 F a lse alarm rate (%) 1 2 3 4 50 5 10 15 20 25 30 35
Length of risk memory
Missed
a
larm rate (
%
)
Missed alarm rate (% ) False alarm rate (% )
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There can be two cases discussed in sub-sections below.
7.4.3.1. Binary Outputs
If traffic operators are only interested in whether there is crash occurrence during the next traffic situations or not, the outputs of MyTRIM can be binary with two values corresponding to answers “yes” and “no” from MyTRIM to the operators.
In this case, the value of Lrm needs to be defined in advance.
Figure 7-9 presents an example of binary outputs returned by MyTRIM to traffic operators. At current moment (Now), traffic operators receive signal from MyTRIM saying that there will be a crash occurrence during the next traffic situation.
Figure 7‐9: An example series of binary outputs returned by MyTRIM
Binary outputs are more suitable for cases where Lrm value is low, i.e. Lrm =1, 2, or 3 and traffic operators
wait to activate preventive measures.
7.4.3.2. Multiple Level Outputs
If false alarm rate is more sensitive to traffic operators, the five outputs of MyTRIM corresponding to five
Lrm values with respect to cTS can be combined and provided to traffic operators. Five outputs can be
combined because the output value “yes” of higher Lrm also implies the output value “yes” of lower Lrm.
Therefore, the combined output is the output “yes” of the highest Lrm.
Figure 7-10 presents four examples of multi-level outputs. In Case 1, no risk is identified at the beginning. At the second period, risk is identified, the output of MyTRIM is “yes” with Lrm=1, “no” with
other Lrm values, the combined output is “yes” at level 1. At the third period, risk is identified; the output
of MyTRIM is “yes” with Lrm=1 and 2 and “no” with other Lrm values, the combined output is “yes” at
level 2 and so on. At the sixth period, risk is identified; the output of MyTRIM is “yes” with Lrm=1, 2, 3,
Time
Now
Case 1
Case 2
Case 3
Case 4
X
X
Figure 7‐10: Examples of multi‐level outputsSimilarly in Case 2, the risk develops from the second to the fourth period up to level 3. Thereafter, the risk disappears and the combine output become “no”.
Case 3 can never occur as the risk jumps from “no” to “yes” at level 4. Case 4 can never occur either as the risk jumps from “yes” level 1 to “yes” level 4.
7.5. Summary
This chapter discusses about the development of Traffic Regime – based Risk Identification Models – TR-based RIM under highly risky traffic regimes and Motorway Traffic Risk Identification Model - MyTRIM. The obtained RIM allow identifying high percentages of NTS and PTS in validation data sets which indicates the high accuracy of the models in identifying new traffic situations into one of two classes: NTS and PTS. RIM are also refined such that variables having no impact on models’ performance can be eliminated.
The developed RIM also identify variable importance. The most important variables are detected and called Critical Factors. The importance of variables is estimated by several approaches to find the correct Critical Factors. Critical Factors are useful in understanding causality of crashes and provide preliminary idea on developing preventive measures.
Also the performance of RIM is high, the overall false warning rate remain very high. This is why MyTRIM is developed. MyTRIM functions based on a parameter called length of risk memory - Lrm
whose value ranges from 1 to 5. Lrm=5 is the most desirable as the false alarm rate is at acceptable level
whereas the correct alarm rate remains high.
The applicability of MyTRIM is also discussed with respect to the form of MyTRIM’s outputs which can be binary or multi-level. While binary output is more appropriate in term of crash prevention, multi-level output might be better for traffic operators who would like to observe the evolution of traffic risk in real- time.
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Chapter 8 Conclusions
8.1. Summary
The current research investigates the motorway traffic crash risk identification by elaborating a methodology aimed at developing models capable of identifying real-time, called Motorway Traffic Risk
Identification Models - MyTRIM. With a selected study site, the proposed methodology is implemented to
exploit individual vehicle traffic data combined with meteorological as well as crash data, aiming to differentiate traffic conditions leading to crashes from other traffic-related conditions.
The imbalance between pre-crash and non-crash cases is one of the problems addressed in the current research (according to the rarity of crashes on motorways). A methodology for sampling non-crash data relevant to pre-crash data is proposed in the current research, and aims at avoiding an arbitrary selection of non-crash data (in comparison with pre-crash data). Results of data sampling process are clusters called Traffic Regimes.
Different classification and regression approaches were tested in order to choose the most suitable one - Random Forests Regression - that is capable of handling the data imbalance problem and have good performance with data in the current research. Under each Traffic Regime, a TR-based Risk Identification Model is developed to differentiate between NTS and PTS. Critical factors are also identified, which is useful in understanding causality of crashes and provides preliminary idea on developing preventive measures
Also the performance of RIM is high, the overall false warning rate remain very high. This is why MyTRIM is developed. MyTRIM functions based on a parameter called length of risk memory - Lrm
whose value ranges from 1 to 5. Lrm=5 is the most desirable as the false alarm rate is at acceptable level
whereas the correct alarm rate remains high.
The applicability of MyTRIM is also discussed with respect to the form of MyTRIM’s outputs which can be binary or multi-level. While binary output is more appropriate in term of crash prevention, multi-level output might be better for traffic operators who would like to observe the evolution of traffic risk in real- time.
In the next section, the contributions of the current research are presented. Thereafter, the applicability of the obtained results is discussed in section 8.3. Section 0 examines the potential improvements in terms of performance of MyTRIM. Subsequently, possible future research directions are discussed in section 0.