# A Collaborative Monitoring Method for Traffic Situations under Urban Road Emergencies

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Road Traffic Situation Assessment Model

#### 2.1. Urban Road Monitoring Network

#### 2.1.1. Construction of the Urban Road Monitoring Network

#### 2.1.2. Collaborative Monitoring Area Based on Emergencies

#### 2.1.3. Determination of Road Traffic Situation Level

#### 2.1.4. Collaborative Monitoring Model of the Road Traffic Situation

#### 2.2. Traffic Situation Assessment Indicator Analysis

#### 2.2.1. Traffic Situational Awareness Indicator Analysis

#### 2.2.2. Construction of the Traffic Situation Prediction Indicators System

- (1)
- Road factors ${U}_{1}$.

- (2)
- Natural factors ${U}_{2}$.

- (3)
- Human factors ${U}_{3}$.

#### 2.2.3. Analysis of Traffic Situation Prediction Indicators

- (1)
- The values are non-negative integers;
- (2)
- The probability distribution is discrete;
- (3)
- The occurrence of emergencies are independent of each other.

#### 2.3. Road Traffic Situation Assessment

#### 2.3.1. Road Traffic Situational Awareness Calculation

#### 2.3.2. Road Traffic Trend Prediction Calculation

## 3. Collaborative Monitoring Algorithms for Situational Assessment

#### 3.1. Traffic Situational Awareness Approach

#### 3.2. Collaborative Traffic Trend Prediction Approach

#### 3.2.1. Monitoring Data Pre-Processing

#### 3.2.2. Monitoring Indicator Weight Calculation

#### 3.2.3. Traffic Trend Prediction

#### 3.3. Evaluation Indicators for Traffic Trend Prediction

## 4. Experimental Results

#### 4.1. Dataset Production

#### 4.1.1. Traffic Situational Awareness Monitoring Dataset

#### 4.1.2. Traffic Trend Prediction Monitoring Dataset

#### 4.2. Correlation Analysis of Indicators

#### 4.2.1. Parameter Estimation of the Poisson Regression Model

#### 4.2.2. Analysis of Test Results of Poisson Regression Model

#### 4.2.3. Analysis of Urban Road Emergencies and Road Correlation

#### 4.3. Data Pre-Processing

#### 4.4. Analysis of Results

#### 4.4.1. Experiment 1: Traffic Situational Awareness

#### 4.4.2. Experiment 2: Calculation of Indicators’ Weights for Traffic Trend Prediction

#### 4.4.3. Experiment 3: Traffic Trend Prediction

## 5. Conclusions and Future Work

#### 5.1. Conclusions

- Previous studies have more often used individual roads where emergencies occur to build road traffic situation assessment models, and they focus on the completeness and real-time nature of the entire model process. This study proposes a collaborative monitoring method based on the traffic road network. Compared with previous studies, the data input of this method is more diversified, which is reflected in both the types of monitoring indicators and monitoring targets. The previous studies were evaluated by single indicators, so the results were more dependent on the attributes and accuracy of the monitoring indicators. In contrast, the results are more informative by using multiple indicator data fusion calculation. The monitoring target diversification refers to the data input sources from different urban road nodes. The data obtained in this way will be more reliable and comprehensive. This method starts with a single road node monitoring an emergency, goes through collaborative nodes to provide data support and finally calculates the impact of the event on the whole traffic road network. It monitors urban road traffic in a multi-node monitoring way.
- The GT-AHP-EM method is proposed in the process of indicator assignment for situational prediction. It not only reduces the artificial factor of subjective assignment method, but also fully considers the influence of objective data. The method reconciles the contradiction between subjective weights and objective weights and improves the scientific rationality of the assignment to a certain extent. The experimental results show that the method uses traffic flow as the model input for traffic state identification in the process of traffic situational awareness, and the output results are consistent with the objective laws of road traffic. When the method is used for traffic trend prediction, the accuracy of the prediction results is improved by 2.7% and 0.7% compared with the AHP and EM methods, respectively. Therefore, the validity and rationality of the model were proven.
- This study can be continued to dig deeper in terms of data sources, scenario conditions, etc. The experimental data sources are all collected through road sensor devices, and the monitoring data are easily collected and the data quantity is huge. We could consider this feature to carry out other studies in the traffic field for different research targets and scenarios and analyze the road, vehicles and pedestrians under different time periods for multiple roads. Meanwhile, the emergency event selected for the experimental part of this study is an illegal red light running event, which is used as a case to conduct analysis and verification. The experimental results show that this study has some feasibility. However, we need to select other data to verify the applicability of this model to different emergencies.

#### 5.2. Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Traffic Situation Level | Situation Level Description |
---|---|

unobstructed state | When in an unobstructed state, vehicles are widely spaced and traffic moves in an orderly manner, without interference from road events. Traffic managers do not need to manually direct traffic on city roads, and a high level of traffic safety and efficiency can be achieved. |

slightly unobstructed state | When it is in a slightly unobstructed state relative to the unobstructed state, the number of vehicles on that city road increases, but it does not cause congestion. The traffic order is good, and if a road incident occurs, it will not cause disorder in the city road traffic. Traffic efficiency is at a high level at this time. |

mild congestion | When in mild congestion, the traffic road is sensitive to sudden disruptions and affected by emergencies. When disturbed, the road traffic is less efficient. At this time, the normal operation of traffic can be ensured with the intervention of traffic managers. |

moderate congestion | In the state of moderate congestion, the traffic road is more sensitive to the interference of emergencies and more affected by emergencies. When disturbed, road traffic is inefficient. As the complexity of urban roads increases, the chance of conflicts between vehicles increases, and most vehicles are delayed at intersections, resulting in low traffic efficiency. At this time, the traffic operation can be maintained under the intervention of the traffic manager. |

severe congestion | In the state of severe congestion, the traffic road is very sensitive to the disturbance of emergencies, and it is easy to produce great fluctuations. When disturbed, most vehicles will be greatly affected, and the road traffic efficiency is very low. Urban roads are becoming more complex. At this time, the traffic manager needs to arrange multiple traffic departments to conduct traffic dredging and maintain traffic order. |

Target layer | Guideline Layer | Indicator Layer |
---|---|---|

Road traffic situation assessment under emergencies V | Road Factors ${U}_{1}$ | road length ${U}_{11}$/km |

one-way road width ${U}_{12}$/m | ||

traffic lights ${U}_{13}$ | ||

intersections ${U}_{14}$ | ||

Natural Factors ${U}_{2}$ | rainfall intensity ${U}_{21}$/(mm·min${}^{-1})$ | |

temperature ${U}_{22}$/(${}^{\circ}$C) | ||

visibility ${U}_{23}$/m | ||

Human Factors ${U}_{3}$ | law enforcement officers ${U}_{31}$ | |

traffic flow ${U}_{32}$/(Vehicles·min${}^{-1})$ | ||

road section construction ${U}_{33}$ |

Orders n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

$RI$ | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | 1.51 | 1.54 | 1.56 |

Number | Time | Traffic Flow (pcu/15 min) |
---|---|---|

1 | 00:00 | 90 |

2 | 00:15 | 60 |

3 | 00:30 | 114 |

4 | 00:45 | 120 |

5 | 01:00 | 180 |

⋯ | ⋯ | ⋯ |

Number | Road Length (km) | Road Width (m) | Traffic Lights | Intersections | Road Enforcement Officers | Traffic Flow (pcu/15 min) | Road Construction and Maintenance | Rainfall (mm/min) | Temperature (${}^{\circ}$C) | Visibility (m) |
---|---|---|---|---|---|---|---|---|---|---|

1 | 0.981 | 15 | 2 | 7 | 6 | 52 | 0 | 1.1 | 8 | 400 |

2 | 1.141 | 14 | 5 | 2 | 4 | 48 | 1 | 0.5 | 8 | 600 |

3 | 0.766 | 15 | 2 | 2 | 2 | 72 | 0 | 0.5 | 11 | 600 |

4 | 1.236 | 10 | 3 | 11 | 2 | 98 | 1 | 0.3 | 9 | 750 |

5 | 1.374 | 7 | 2 | 3 | 0 | 26 | 3 | 0.8 | 8 | 400 |

6 | 2.279 | 9 | 3 | 14 | 3 | 46 | 2 | 0.6 | 8 | 600 |

⋯ | ⋯ | ⋯ | ⋯ | ⋯ | ⋯ | ⋯ | ⋯ | ⋯ | ⋯ | ⋯ |

Indicator | Standard Error | Coefficient | Z | p |
---|---|---|---|---|

Constant | 0.297 | 3.195 | 10.741 | 0.000 *** |

Road length (km) | 0.034 | 0.136 | 4.048 | 0.009 *** |

Road width (m) | 0.012 | 0.008 | 0.658 | 0.511 |

Traffic lights | 0.016 | −0.026 | −1.608 | 0.108 |

Intersections | 0.017 | 0.045 | 2.663 | 0.008 *** |

Road enforcement officers | 0.014 | −0.005 | −0.353 | 0.724 |

Traffic flow (pcu/15 min) | 0.001 | −0.001 | −2.739 | 0.023 ** |

Road construction and maintenance | 0.024 | −0.023 | −4.961 | 0.006 *** |

Rainfall (mm/min) | 0.17 | 0.429 | 2.53 | 0.011 ** |

Temperature (${}^{\circ}$C) | 0.009 | −0.012 | −1.345 | 0.179 |

Visibility (m) | 0.0 | −0.001 | −2.443 | 0.015 ** |

Inspection Index | Poisson Regression Model |
---|---|

${\chi}^{2}$ | ${\chi}^{2}=566.58,p\left({\chi}^{2}\right)<0.001$ |

$L{L}_{C}$ | −1091.695 |

$LL$ | −808.405 |

$AIC$ | 1654.811 |

$BIC$ | 1725.928 |

Road Number | $+{\mathit{U}}_{11}$ | $+{\mathit{U}}_{12}$ | $-{\mathit{U}}_{13}$ | $+{\mathit{U}}_{14}$ | $+{\mathit{U}}_{21}$ | $-{\mathit{U}}_{22}$ | $-{\mathit{U}}_{23}$ | $-{\mathit{U}}_{31}$ | $-{\mathit{U}}_{32}$ | $-{\mathit{U}}_{33}$ |
---|---|---|---|---|---|---|---|---|---|---|

${A}_{1}$ | 0.981 | 15 | 2 | 7 | 1.1 | 8 | 400 | 6 | 52 | 0 |

${A}_{2}$ | 1.141 | 14 | 5 | 2 | 0.5 | 8 | 600 | 4 | 48 | 1 |

${A}_{3}$ | 0.766 | 15 | 2 | 2 | 0.5 | 11 | 600 | 2 | 72 | 0 |

${A}_{4}$ | 1.236 | 10 | 3 | 11 | 0.3 | 9 | 750 | 2 | 98 | 1 |

${A}_{5}$ | 1.374 | 7 | 2 | 3 | 0.8 | 8 | 400 | 0 | 26 | 3 |

${A}_{6}$ | 2.279 | 9 | 3 | 14 | 0.6 | 8 | 600 | 3 | 46 | 2 |

Guideline Layer U | Guideline Layer Weights | Indicator Layer | AHP Weights |
---|---|---|---|

Road Factors ${U}_{1}$ | 0.072 | ${U}_{11}$ | 0.00381 |

${U}_{12}$ | 0.00763 | ||

${U}_{13}$ | 0.01836 | ||

${U}_{14}$ | 0.04219 | ||

Natural Factors ${U}_{2}$ | 0.279 | ${U}_{21}$ | 0.07337 |

${U}_{22}$ | 0.02204 | ||

${U}_{23}$ | 0.18386 | ||

Human Factors ${U}_{3}$ | 0.649 | ${U}_{31}$ | 0.504 |

${U}_{32}$ | 0.0993 | ||

${U}_{33}$ | 0.0454 |

Guideline Layer U | Guideline Layer Weights | Indicator Layer | EM Weights |
---|---|---|---|

Road Factors ${U}_{1}$ | 0.524 | ${U}_{11}$ | 0.053 |

${U}_{12}$ | 0.080 | ||

${U}_{13}$ | 0.223 | ||

${U}_{14}$ | 0.168 | ||

Natural Factors ${U}_{2}$ | 0.183 | ${U}_{21}$ | 0.060 |

${U}_{22}$ | 0.051 | ||

${U}_{23}$ | 0.072 | ||

Human Factors ${U}_{3}$ | 0.293 | ${U}_{31}$ | 0.073 |

${U}_{32}$ | 0.083 | ||

${U}_{33}$ | 0.137 |

Guideline Layer U | Guideline Layer Weights | Indicator Layer | GT-AHP-EM Weights |
---|---|---|---|

Road Factors ${U}_{1}$ | 0.2225 | ${U}_{11}$ | 0.0202 |

${U}_{12}$ | 0.0317 | ||

${U}_{13}$ | 0.0865 | ||

${U}_{14}$ | 0.0841 | ||

Natural Factors ${U}_{2}$ | 0.2472 | ${U}_{21}$ | 0.0689 |

${U}_{22}$ | 0.0317 | ||

${U}_{23}$ | 0.1466 | ||

Human Factors ${U}_{3}$ | 0.5303 | ${U}_{31}$ | 0.3605 |

${U}_{32}$ | 0.0939 | ||

${U}_{33}$ | 0.0759 |

Method | $\mathit{Accuracy}$ | $\mathit{Precision}$ | $\mathit{Recall}$ | ${\mathit{F}}_{1}$$\mathit{Score}$ |
---|---|---|---|---|

AHP | 0.7336 | 0.7336 | 0.6487 | 0.6874 |

EM | 0.7539 | 0.7539 | 0.7491 | 0.7510 |

EM-VC | 0.7808 | 0.7808 | 0.8213 | 0.8005 |

BN | 0.7654 | 0.7654 | 0.8065 | 0.7872 |

GT-AHP-EM | 0.7831 | 0.7831 | 0.8172 | 0.7998 |

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**MDPI and ACS Style**

Xiang, M.; An, Y.
A Collaborative Monitoring Method for Traffic Situations under Urban Road Emergencies. *Appl. Sci.* **2023**, *13*, 1311.
https://doi.org/10.3390/app13031311

**AMA Style**

Xiang M, An Y.
A Collaborative Monitoring Method for Traffic Situations under Urban Road Emergencies. *Applied Sciences*. 2023; 13(3):1311.
https://doi.org/10.3390/app13031311

**Chicago/Turabian Style**

Xiang, Min, and Yulin An.
2023. "A Collaborative Monitoring Method for Traffic Situations under Urban Road Emergencies" *Applied Sciences* 13, no. 3: 1311.
https://doi.org/10.3390/app13031311