Developing a Machine-Learning-Based Automatic Incident Detection System for Traffic Safety: Promises and Limitations
Abstract
:1. Introduction
2. Literature Review
2.1. Comparative Incident Detection Algorithms
- Pattern Recognition (PATREG): Expanding on the California algorithm, PATREG incorporates historical traffic patterns into its detection logic [51]. By comparing current data with established patterns, it identifies deviations that might signal an incident. However, its dependence on historical data can limit its adaptability to dynamic traffic conditions or novel incident types.
- All-Purpose Incident Detection (APID): Utilizes multiple detection routines tailored for various traffic conditions, incorporating additional tests such as compression wave and persistence tests to enhance accuracy [52].
- Limited Adaptability: Algorithms relying solely on predefined thresholds or historical patterns might struggle to adapt to dynamic traffic conditions or novel incident types [46].
- Data Dependence: Their performance heavily relies on the quality and accuracy of input data from detectors or other sources. Issues with data collection or transmission can negatively impact their detection accuracy [46].
2.2. Statistical AID Algorithms
- Bayesian Algorithms: These algorithms employ Bayesian statistics to continuously update the probability of an incident based on incoming traffic data [24,43]. They offer flexibility in incorporating prior knowledge and adapting to changing conditions but require careful model design and parameter selection.
- High Occupancy, Low Speed, and Congestion Criterion (HIOCC): Identifies potential incidents by detecting the concurrence of high occupancy, low speed, and significant congestion, surpassing predefined thresholds [51]. While robust to isolated anomalies, its dependence on multiple criteria can reduce sensitivity to certain incident types. These models analyze historical traffic data as time series, identifying patterns and trends [26,40,41,55,56,57,58,59,60]. Statistical methods such as Autoregressive Integrated Moving Average (ARIMA) can then be used to predict future traffic flow [55,56,57,58,59,60]. Significant deviations from these predictions might indicate an incident. While offering adaptability, their effectiveness relies on the quality and representativeness of historical data.
- Adaptability to Changing Conditions: By analyzing patterns and trends, they can potentially adapt to dynamic traffic conditions better than methods relying solely on fixed thresholds.
- Incorporation of Prior Knowledge: Bayesian approaches allow for incorporating historical data and domain knowledge, potentially improving detection accuracy.
- Computational Efficiency: Some methods, such as SND, are computationally efficient and suitable for real-time applications.
2.3. AI-Based AID Models
- Artificial Neural Networks (ANNs): Inspired by the biological structure of the brain, ANNs consist of interconnected layers of processing units that learn complex relationships within the data [79,80,81,82,83]. They excel at identifying non-linear patterns in traffic flow, making them well-suited for incident detection [5,77,84,85,86,87,88,89,90,91,92,93].
- Random Forests (RFs): Random Forests are ensemble methods that combine multiple decision trees to improve accuracy and reduce the impact of noisy data [94,95,96,97,98]. Furthermore, they offer some level of interpretability, allowing researchers to understand which features are most important for incident detection [39,98,99,100].
- Fuzzy Logic (FL): This technique incorporates the concept of partial truths [101,102,103,104], allowing for nuanced evaluation of traffic data that might not fall strictly into predefined categories. This flexibility can enhance the sensitivity of incident detection, especially in scenarios with ambiguous data [105,106,107,108,109].
- Hybrid Models: Researchers are increasingly exploring the potential of combining different AI algorithms or AI with other techniques such as statistical methods. This fusion approach can leverage the strengths of each individual technique to create more comprehensive and robust AID models [114].
- Learning Ability: They can continuously learn and improve their detection accuracy with exposure to new data.
- Real-time Processing: ML algorithms can analyze traffic data in real-time, enabling prompt incident detection.
- Adaptability: They can be adapted to different types of roadways and traffic conditions.
- Data Dependence: The performance heavily relies on the quality and quantity of training data.
- Computational Cost: Training complex ML models can be computationally expensive.
- Black Box Phenomenon: Their internal workings can be opaque, making it challenging to understand and interpret their decision-making processes.
2.4. Image Processing AID Algorithms
- Direct Observation: These models directly observe the traffic scene, potentially providing richer information compared to models solely relying on sensor data.
- Versatility: They can be adapted to various camera configurations and environmental conditions, offering flexibility in deployment.
- Identification of Specific Incidents: Analyzing visual cues enables the identification of specific types of incidents, such as car accidents or disabled vehicles, which might be challenging for other methods.
- Computational Demands: Processing video data can be computationally expensive, requiring powerful hardware and optimized algorithms.
- Weather Dependence: Visibility limitations due to rain, snow, or fog can hinder performance and lead to false alarms [121].
- Privacy Concerns: The use of video data raises privacy concerns that require careful consideration through anonymization techniques and responsible data management practices.
2.5. Evaluating the Performance of AID Models
3. Methodology
3.1. Study Area Selection
3.2. Data Generation and Development of the Simulation Model
3.3. Simulated Traffic Data Collection Parameters
3.4. Characteristics of the Generated Dataset
3.5. Development of the AID Model Using Multi-Layer Feedforward Artificial Neural Network (MLFANN)
4. Results
4.1. Cross-Validation and Testing Phases Results
4.2. Investigating the Influence of Traffic Congestion Level (D/C Ratio) on Model Performance
4.3. Quantifying the Impact of Incident Severity on Model Performance
4.4. Sensitivity Analysis of Model Performance to Detector Spacing
4.5. Evaluating the Effect of Incident Location on Model Performance
5. Discussion
6. Summary and Conclusions
6.1. Summary
6.2. Conclusions
- Mitigating Low-Impact Incidents: During periods of low traffic volume, minor incidents can be challenging to detect due to their minimal impact on traffic flow. This aligns with previous research [32,106,131,146,147]. The model relies on significant deviations in traffic patterns to identify incidents, and minor events during low traffic may not cause sufficient disruption to trigger an alarm.
- The Duality of Congestion: Congestion levels (D/C ratio) exhibit a two-fold effect. While high congestion contributes to a decrease in FAR, it can also lead to longer MTTD values. During peak hours, consistent traffic patterns make it easier for the model to identify abnormal behavior indicative of incidents (lower FAR) [106,108,146].However, queues forming at blocked sections can delay the overall impact on traffic flow, resulting in higher MTTD.
- Severity’s Impact on Detection Speed: The severity of an incident plays a significant role in detection times. Incidents with more severe lane blockages exert a greater influence on traffic flow, acting as readily detectable signals for the model. This translates to shorter MTTD values, as these incidents are easier to identify [87,109].
- Distance and Detection Time: The distance between the incident location and the upstream detector significantly impacts detection time. As this distance increases, the incident’s impact takes longer to propagate upstream, leading to higher MTTD values [106,148]. Conversely, incidents further from the detector can experience a decrease in FAR as their delayed impact reduces the likelihood of false detections.
- Balancing Detector Spacing: Detector spacing necessitates a balancing act. Larger spacings, while potentially offering cost-effectiveness, can contribute to longer MTTD due to delays in incident detection, as observed in previous research [106,148]. Conversely, smaller spacings may lead to an increase in FAR due to fluctuations in traffic measurements caused by longer travel times between detectors.
- Optimizing Persistence Testing: The research emphasizes the importance of persistence testing to mitigate false alarms. While treating consecutive false alarms as a single event (if they persist for a short time) helps reduce FAR, it is crucial to acknowledge the potential impact on incident detection time. This is particularly relevant if an incident occurs during the ignored period.
6.3. Recommendations for Future Research
- Real-World Testing: Validate the model’s performance using extensive real-world traffic data to assess its effectiveness in practical settings.
- Model Generalizability: Evaluate the model’s performance across various freeways, highway systems, and traffic conditions to determine its generalizability.
- Advanced Persistence Algorithms: Develop and evaluate more sophisticated persistence tests or algorithms to further reduce False Alarm Rates (FAR) and improve the model’s overall reliability.
- Connected Vehicles: Leverage real-time data from connected vehicles to gain deeper insights into traffic flow, vehicle health, and driver behavior.
- Advanced Sensors: Utilize advanced sensors such as LiDAR and high-resolution cameras to improve detection accuracy and identify various incident types.
- Big Data Analytics: Employ big data analytics to analyze vast datasets and uncover hidden patterns that can aid in incident prediction and prevention.
- Multi-Source Data: Consider incorporating data beyond traditional traffic flow parameters. Explore integrating weather data, road condition reports, and social media feeds to capture a more holistic view of the traffic environment.
- Transfer Learning: Investigate transfer learning techniques to leverage pre-trained models on related tasks, reducing training time and effort.
- Explainable AI: Develop models that provide explanations for their decisions. This transparency can enhance trust and facilitate improvements.
- Cost-Effectiveness: Balance model complexity with cost. Explore cost-effective sensor deployment strategies and efficient computational resources for real-world implementation.
- Scalability: Design models that are scalable to accommodate diverse road networks and traffic patterns.
- Real-World Validation: Rigorously test models with real-world traffic data to ensure their effectiveness and generalizability.
- Potential challenges of model deployment: While the developed model shows promising results, deploying it in real-world traffic management systems may present certain challenges. These include hardware requirements, such as ensuring sufficient computational power for real-time data processing, particularly in systems that rely on edge computing for rapid incident detection. Additionally, reliable data transmission is crucial, especially in regions with limited network infrastructure where sensor data must be consistently transmitted to control centers. Finally, managing processing time is essential for timely incident detection and response, which may require optimizing the model’s complexity to balance accuracy and computational efficiency. Addressing these challenges will be important for the practical implementation of the model in traffic management systems.
- Vehicle Inspections: Implement mandatory and regular vehicle inspections to identify potential mechanical issues before they cause breakdowns or accidents.
- Road Maintenance: Prioritize regular road inspections and maintenance to address infrastructure deficiencies that contribute to accidents (e.g., potholes, inadequate signage).
- Driver Education: Promote driver education programs to enhance awareness of traffic safety rules, defensive driving techniques, and the importance of responsible driving behavior.
- Severe Weather Alerts: Disseminate timely and clear public alerts through various channels (e.g., media, mobile apps) to warn drivers about severe weather conditions and advise on safe driving practices.
- Incident Rerouting: Utilize real-time traffic data to provide drivers with dynamic rerouting alerts, minimizing congestion and reducing the likelihood of secondary incidents.
- Leverage data from connected vehicles, advanced sensors, and big data analytics to gain deeper insights and improve detection accuracy.
- Consider incorporating multi-source data such as weather, road conditions, and social media feeds for a more holistic view.
- Explore transfer learning and explainable AI techniques to improve model efficiency and trust.
- Focus on cost-effective sensor deployment, efficient computational resources, and model scalability for real-world implementation.
- Validate models with extensive real-world data to ensure their effectiveness and generalizability.
- Real-World Validation: Extensive testing with real-world traffic data is essential to comprehensively assess the model’s effectiveness and reliability in practical settings.
- Model Generalizability: Investigating the model’s performance across diverse freeways and highway systems will evaluate its generalizability to different traffic conditions and incident scenarios.
- Advanced Persistence Algorithms: Developing and evaluating more sophisticated persistence tests or algorithms can further reduce FAR and improve the overall reliability of the incident detection model.
- Integration with Traffic Management Strategies: Exploring the integration of incident detection models with advanced Intelligent Transportation systems, which can optimize traffic flow and alleviate congestion, leading to improved overall transportation efficiency.
- Collaboration with Stakeholders: Close collaboration with transportation agencies and stakeholders will ensure the model aligns with operational requirements and can be seamlessly integrated into existing infrastructure.
- Cost-Benefit Analysis: A comprehensive cost-benefit analysis is crucial to evaluating the economic feasibility of implementing the developed model. This analysis should consider initial investments, operational costs, potential savings from reduced congestion and improved safety, and the overall impact on the transportation network.
- Emerging Technologies: Leveraging data from autonomous and connected vehicles offers valuable insights and enables more accurate and timely incident detection, leading to proactive traffic management strategies.
- Minor Incident Reporting Systems: Implementing user-friendly mobile applications or dedicated hotlines for reporting minor incidents during low traffic volume periods will aid in their detection and response.
- Detector Placement Optimization: Studies to determine the ideal detector spacing that balances detection accuracy and cost-effectiveness are recommended. This optimization will enhance incident detectability and response time, contributing to improved overall traffic management.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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True Normal | True Incident | Class Precision | |
pred. normal | 10,381 | 553 | 94.94% |
pred. incident | 179 | 3407 | 95.01% |
class recall | 98.30% | 86.04% | |
Accuracy | 94.96% | F-score | 90.30% |
AID Model | Authors | DR (%) | FAR (%) | MTTD (min) |
Developed model | 95.96 | 1.01 | 0.89 | |
SND | Parkany and XIE [25] | 92 | 1.3 | 1.1 |
SVM_L | Motamed [38] | 87 | 0.07 | 4.3 |
SVM_RB | Motamed [38] | 91.3 | 0.07 | 5.45 |
SVM_P | Motamed [38] | 91.3 | 0.01 | 2.25 |
ANN | Motamed [38] | 82.6 | 0.06 | 3.25 |
PNN | Motamed [38] | 95.6 | 0.3 | 3.84 |
Hybrid model | XIE et al. [39] | 97.3 | 0.061 | - |
ANN | Cheu and Ritchie [87] | 80 | 1.5 | 4.95 |
GPS-based AID | D’Andrea and Marcelloni [30] | 91.6 | 8.3 | 7 |
IQD_Speed | Ahuja [56] | 94 | 5.4 | - |
IQD_Speed and Occupancy | Ahuja [56] | 92 | 4 | - |
Decision Tree | Ahuja [56] | 97 | 3 | - |
RF | Chakraborty et al. [61,132] | 97 | 3 | - |
IQD | Zyryanov [5] | 97 | 4.8 | 12.4 |
ANN | Rossi et al. [106] | 97.6 | - | - |
FL | Dogru and Subsa [99] | 93.09 | 0.445 | 2.95 |
ANN | Dogru and Subsa [99] | 86.1 | 8 | - |
RF | Dogru and Subsa [99] | 94 | 0.203 | - |
SVM | Dogru and Subsa [99] | 88 | 4.2 | - |
Video-based AID | Ren et al. [121] | 96.6 | 0.72 | 1.16 |
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ElSahly, O.; Abdelfatah, A. Developing a Machine-Learning-Based Automatic Incident Detection System for Traffic Safety: Promises and Limitations. Infrastructures 2024, 9, 170. https://doi.org/10.3390/infrastructures9100170
ElSahly O, Abdelfatah A. Developing a Machine-Learning-Based Automatic Incident Detection System for Traffic Safety: Promises and Limitations. Infrastructures. 2024; 9(10):170. https://doi.org/10.3390/infrastructures9100170
Chicago/Turabian StyleElSahly, Osama, and Akmal Abdelfatah. 2024. "Developing a Machine-Learning-Based Automatic Incident Detection System for Traffic Safety: Promises and Limitations" Infrastructures 9, no. 10: 170. https://doi.org/10.3390/infrastructures9100170
APA StyleElSahly, O., & Abdelfatah, A. (2024). Developing a Machine-Learning-Based Automatic Incident Detection System for Traffic Safety: Promises and Limitations. Infrastructures, 9(10), 170. https://doi.org/10.3390/infrastructures9100170