Safety evaluation of traffic conflict is a very important and challenging issue in evaluating intersection safety under incomplete traffic accident data conditions and is also one of the main safety surrogate measures of analyzing accident data recently. It helps to analyze and solve intersection problems comprehensively and deeply. From there, it helps to improve traffic safety as well as reduce the risk of traffic accidents at intersections. Various evaluation methods based on traffic conflict have been proposed to make conflict safety levels at intersections more consistent and objective. However, a major concern is that many existing measurements are still subjective and are not easy to obtain uniformly. This study aimed to develop a model for safety evaluation at intersections in a comprehensive way that may be expected to directly link to the severity of the accident from different evaluation indicators. First, the three factors, including time to collision (TTC), conflicting speed (CS), and deceleration rate (DR) to avoid a crash, are introduced into safety evaluation of conflicts as the indicators. And then, as regards the fuzziness and randomness of the evaluation indicators, the qualitative concept has to be converted into a quantitative one utilizing cloud model, which implements the natural transformation between the qualitative concept of the safety level of traffic conflict and the membership degree of the evaluation indicators corresponding to the different safety levels. Finally, an indicator weight model is built based on the information entropy and the AHP method to determine the safety level. We illustrate the practical implementation of the proposed method using actual data of a typical signalized intersection from Hanoi City of Vietnam. The results indicate that traffic conflict analyzed by the proposed method was appropriate with actual state of the intersection, and the proposed method is simple, effective, and feasible, so it has a certain application value.
The probability that a severe and/or fatal accident occurs at an intersection is higher than elsewhere, so the intersection safety is a crucial component of traffic safety. According to the statistics of accidents, traffic accidents at intersections account for a range of 10% to 40% of the total accidents in the world every year. For example, the fatalities at intersections accounted for around 20% of the total fatalities in the EU in the period 2001–2010 [
The main indicators used in the safety evaluation using traffic conflicts are time to collision (TTC), postencroachment time (PET), deceleration rate (DR) to avoid a crash, and gap time (GT). Of these, the TTC is commonly used, and lower its value indicates a higher likelihood of accidents, but cannot be directly linked to the severity of the accident [
There are many factors affecting the severity of traffic conflicts, and it is difficult to accurately quantify the model. Moreover, the traffic conflict is a complex process, the conflict situation is different, and the severity of the conflict is different. The safety evaluation based on conflict severity requires a comprehensive consideration of multiple indicators, which are the random and fuzzy attributes with their critical values; i.e., the conflict severity is also uncertain. Hence, there are two types of uncertainty that should be considered in the safety evaluation using conflict severity: (1) randomness, which is often exhibited in the generated event of a traffic conflict; and (2) fuzziness, which is often reflected in the classification standard. For engineering uncertainty, Li et al. [
From the above point of view, this study uses the three basic factors, including TTC, CS, and DR as evaluation indicators, where the TTC represents the possibility of traffic accidents and the CS and DR represent the severity of traffic accident. The safety evaluation model is proposed using a cloud model and traffic conflicts to evaluate the intersections in the context of Vietnamese transport where mixed traffic flow with the interaction and conflicts between motorcycles themselves and different types of vehicles becomes very complicated. It is also one of the main causes of traffic safety problems in urban areas in Vietnam [
The cloud model, which was proposed by professor Deyi [
The distribution of
Forward cloud generator.
In addition to the normal cloud, half-down cloud, half-up cloud, and trapezoidal cloud are used. The specific concept can be referred to as Li et al. [ Step 1: according to the three digital characteristics of cloud, a normally distributed random number Step 2: a normally distributed random number Step 3: according to Step 1 and Step 2, Step 4:
Digital features of normal cloud.
From the “3
According to the principle of
Safety evaluation of intersections using cloud model and traffic conflicts can be summarized as follows: Step 1: construction of indicator system using indicator of the traffic conflicts at intersections Step 2: discretization of continuous attributes, i.e., determination of criteria of indicators Step 3: determination of parameters ( Step 4: calculation of the indicator weights Step 5: substitution of the observed data into cloud models repeatedly to obtain the distributions of certainty degrees, and further final outcomes corresponding to all levels Step 6: determination of safety level based on the level with correlation coefficient calculated from the cloud model and the indicator weight model Finally, determination of safety index of the intersection
The indicator system of safety evaluation at intersection should be constructed based on some parameters of traffic conflict, including TTC, CS, and DR. TTC is “the expected time for two road users to collide if they remain at their present speed, direction, and on the same trajectory” [
Let
Currently, the safety levels are not uniform, usually from 3 to 5 levels. Some scholars used 4 levels to divide safety levels of the intersection; for example, Yu and Wang [
In reviewing previous studies, it can be seen that the thresholds of the indicator are inconsistent. Generally, the TTC [
In Vietnam, previous studies showed that most of the road traffic accidents at signalized intersections occur more often during nonpeak hours of traffic flow [ Traffic conflict is collected on nonpeak hours of traffic flow when accidents are more likely The models in this study do not consider the conflict separately for each type of vehicle; i.e., there is no weight of conflict between motorbikes and other vehicles This study also does not consider the conflict of different types of movement separately
On the basis of the above assumptions, we use cumulative frequency curves of TTC, CS, and DR under a total of 2669 conflict points during nonpeak hours, including 319 conflict points of 2 intersections in Hanoi City [
Cumulative frequency curve for the TTC.
Cumulative frequency curve for the CS.
Cumulative frequency curve for the DR.
Domain of the indicators.
Safety level | (I) | (II) | (III) | (IV) |
---|---|---|---|---|
TTC (s) | [0.800, 2] | [0.607, 1.095] | [0.311, 0.800] | [0, 0.607] |
CS (m/s) | [0, 6.706] | [4.646, 8.056] | [6.706, 10.116] | [8.056 16] |
DR (m/s2) | [0, 2.125] | [0.710, 3.045] | [2.125, 4.467] | [3.045, 8] |
Based on Table
Digital features (
Safety level | TTC | CS | DR |
---|---|---|---|
(I) | (1.095, 0.098, 0.01) | (4.646, 0.687, 0.05) | (0.710, 0.472, 0.05) |
(II) | (0.851, 0.081, 0.01) | (6.351, 0.568, 0.05) | (1.878, 0.389, 0.05) |
(III) | (0.556, 0.082, 0.01) | (8.411, 0.568, 0.05) | (3.296, 0.39, 0.05) |
(IV) | (0.311, 0.099, 0.01) | (10.116, 0.687, 0.05) | (4.467, 0.474, 0.05) |
From the parameters in Table
Cloud model for the TTC.
Cloud model for the CS.
Cloud model for the DR.
When evaluating the system, the importance of each indicator may be quite different. Therefore, it is a basic part of an evaluation model to set suitable weights of evaluation indicator to achieve more accuracy and effectiveness. In this study, we use the analytic hierarchy process (AHP) method [
In the actual project, some indicators are smaller and better, and some indicators are larger and better. The judgment matrix
For smaller and better, such as TTC,
For larger and better, such as CS and DR,
The entropy weight of the jth indicator (
The weights of all indicators are determined, and we can compute the normalized correlation coefficient
Safety levels of conflict points exert different influences on the intersection safety conditions. The influence should be embodied by weights. Therefore, safety index of the intersection could be built by weighted summation of relative value of conflict points as shown in the following equation:
A typical signalized intersection in Hanoi City, the capital of Vietnam, namely, Ham Nghi-Nguyen Dong Chi intersection (coordinates: longitude = 21.035194, latitude = 105.763771), is selected for an empirical study. The traffic flow data are gathered using an unmanned aerial vehicle (UAV) video. All traffic movements are captured under dry-weather conditions and during 30 minutes of nonpeak hours. This technique required two corresponding persons in the field to observe proper positions for video recording and mark 4 base points as ground coordinate system, which is used to convert between image coordinate system and real coordinate system when analyzing video on traffic laboratory.
Video file with resolution of 1920 × 1080 and fps of 23 is replayed in a computer and interpreted until entire necessary data are accomplished in the laboratory. The video is reviewed at either slow speed to scan full-required observation of selected intersection or high speed to skip unnecessary data. Also, the recorder allowed the film to stop at any time and any point to a single traffic scene for detailed observation and analysis. In this study, the location and speed of the vehicles regarding time events from the image video file are determined according to image coordinates and then are converted into ground coordinates using Tracker [
Diagram of calculating indicators.
A dataset of 126 conflict points at selected intersection in a specific time period is established to evaluate safety levels, as shown in Table
The dataset of conflict points.
Conflict points | TTC (s) | CS (m/s) | DR (m/s2) |
---|---|---|---|
1 | 0.373 | 2.246 | 3.013 |
2 | 0.459 | 2.955 | 3.221 |
3 | 0.638 | 5.079 | 3.983 |
4 | 0.746 | 3.039 | 2.036 |
5 | 1.565 | 5.542 | 1.771 |
6 | 0.779 | 5.521 | 3.545 |
7 | 0.373 | 3.295 | 4.418 |
… | … | … | … |
… | … | … | … |
126 | 0.597 | 3.096 | 2.591 |
According to 2669 conflict points of 12 intersections, the entropy-based weights of the indicators are calculated by equations (
Correlation coefficients and safety level of conflict points.
Conflict points | Safety level ( | |||||
---|---|---|---|---|---|---|
(I) | (II) | (III) | (IV) | |||
1 | 0.304 | 0.010 | 0.410 | 0.276 | 2.658 | 3 |
2 | 0.281 | 0.002 | 0.600 | 0.116 | 2.552 | 3 |
3 | 0.281 | 0.043 | 0.347 | 0.329 | 2.724 | 3 |
4 | 0.336 | 0.633 | 0.031 | 0.000 | 1.695 | 2 |
5 | 0.462 | 0.537 | 0.001 | 0.000 | 1.539 | 2 |
6 | 0.145 | 0.341 | 0.435 | 0.079 | 2.448 | 3 |
7 | 0.274 | 0.000 | 0.036 | 0.690 | 3.142 | 4 |
… | … | … | … | … | … | … |
…. | …. | …. | …. | …. | …. | …. |
126 | 0.382 | 0.123 | 0.486 | 0.008 | 2.120 | 3 |
Distribution of safety levels of conflict points at the intersection.
Plane of the distribution of conflict points at the intersection.
The 126 conflict points at the intersection after evaluation include 31 points of level I, 44 points of level II, 42 points of level III, and 9 points of level IV, as shown in Figures
The analysis of traffic conflict is one of the main safety surrogate measures of analyzing accident data. It has the advantages of rapid and quantitative analysis and is widely used at present. In this study, different safety levels of conflict points at intersections were considered in the developing method to evaluate urban intersection safety. The TTC, CS, and DR were taken as evaluation indicators to distinguish conflicts of different safety levels. The method based on cloud model had been developed to calculate the safety levels of conflict points at intersections. The results of the example analysis indicated that the proposed method is intuitive, simple, effective, and feasible. It not only can be useful to estimate the safety condition of intersections but also is the foundation of proposals for safety improvements of intersections.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that they have no conflicts of interest.
This research has been supported by the Ministry of Education and Training of Vietnam, under Grant no. CT2019.05.03.