High-Resolution Road Vehicle Collision Prediction for the City of Montreal

Road accidents are an important issue of our modern societies, responsible for millions of deaths and injuries every year in the world. In Quebec only, in 2018, road accidents are responsible for 359 deaths and 33 thousands of injuries. In this paper, we show how one can leverage open datasets of a city like Montreal, Canada, to create high-resolution accident prediction models, using big data analytics. Compared to other studies in road accident prediction, we have a much higher prediction resolution, i.e., our models predict the occurrence of an accident within an hour, on road segments defined by intersections. Such models could be used in the context of road accident prevention, but also to identify key factors that can lead to a road accident, and consequently, help elaborate new policies. We tested various machine learning methods to deal with the severe class imbalance inherent to accident prediction problems. In particular, we implemented the Balanced Random Forest algorithm, a variant of the Random Forest machine learning algorithm in Apache Spark. Interestingly, we found that in our case, Balanced Random Forest does not perform significantly better than Random Forest. Experimental results show that 85% of road vehicle collisions are detected by our model with a false positive rate of 13%. The examples identified as positive are likely to correspond to high-risk situations. In addition, we identify the most important predictors of vehicle collisions for the area of Montreal: the count of accidents on the same road segment during previous years, the temperature, the day of the year, the hour and the visibility

Air Traffic Management Safety Challenges

The primary goal of the Air Traffic Management (ATM) system is to control accident risk. ATM safety has improved over the decades for many reasons, from better equipment to additional safety defences. But ATM safety targets, improving on current performance, are now extremely demanding. Safety analysts and aviation decision-makers have to make safety assessments based on statistically incomplete evidence. If future risks cannot be estimated with precision, then how is safety to be assured with traffic growth and operational/technical changes? What are the design implications for the USA’s ‘Next Generation Air Transportation System’ (NextGen) and Europe’s Single European Sky ATM Research Programme (SESAR)? ATM accident precursors arise from (eg) pilot/controller workload, miscommunication, and lack of upto- date information. Can these accident precursors confidently be ‘designed out’ by (eg) better system knowledge across ATM participants, automatic safety checks, and machine rather than voice communication? Future potentially hazardous situations could be as ‘messy’ in system terms as the Überlingen mid-air collision. Are ATM safety regulation policies fit for purpose: is it more and more difficult to innovate, to introduce new technologies and novel operational concepts? Must regulators be more active, eg more inspections and monitoring of real operational and organisational practices

GR-232 - Accident Prediction using Big Data Analysis using Ensemble Learning

Many country wide internal roads and national highways have dim lights or no street lights all over the world. We usually observe some turns on roads more prone to accidents than other places. In our paper we used Logistic Regression and Decision Trees that together build an Ensemble learning for predicting these accident zones. For validating the results, five evaluation metrics such as Accuracy, Precision, f-measures, Re-call and Area under curve are used. State of art model for US accident dataset gives F1 score of 57%. We are implementing using ensemble learning wherein logistic regression gives F1-score of 53%

Air Traffic Safety: continued evolution or a new Paradigm.

The context here is Transport Risk Management. Is the philosophy of Air Traffic Safety different from other modes of transport? – yes, in many ways, it is. The focus is on Air Traffic Management (ATM), covering (eg) air traffic control and airspace structures, which is the part of the aviation system that is most likely to be developed through new paradigms. The primary goal of the ATM system is to control accident risk. ATM safety has improved over the decades for many reasons, from better equipment to additional safety defences. But ATM safety targets, improving on current performance, are now extremely demanding. What are the past and current methodologies for ATM risk assessment; and will they work effectively for the kinds of future systems that people are now imagining and planning? The title contrasts ‘Continued Evolution’ and a ‘New Paradigm’. How will system designers/operators assure safety with traffic growth and operational/technical changes that are more than continued evolution from the current system? What are the design implications for ‘new paradigms’, such as the USA’s ‘Next Generation Air Transportation System’ (NextGen) and Europe’s Single European Sky ATM Research Programme (SESAR)? Achieving and proving safety for NextGen and SESAR is an enormously tough challenge. For example, it will need to cover system resilience, human/automation issues, software/hardware performance/ground/air protection systems. There will be a need for confidence building programmes regarding system design/resilience, eg Human-in-the-Loop simulations with ‘seeded errors’

An Injury Severity Prediction-Driven Accident Prevention System

Traffic accidents are inevitable events that occur unexpectedly and unintentionally. Therefore, analyzing traffic data is essential to prevent fatal accidents. Traffic data analysis provided insights into significant factors and driver behavioral patterns causing accidents. Combining these patterns and the prediction model into an accident prevention system can assist in reducing and preventing traffic accidents. This study applied various machine learning models, including neural network, ordinal regression, decision tree, support vector machines, and logistic regression to have a robust prediction model in injury severity. The trained model provides timely and accurate predictions on accident occurrence and injury severity using real-world traffic accident datasets. We proposed an informative negative data generator using feature weights derived from multinomial logit regression to balance the non-fatal accident data. Our aim is to resolve the bias that happens in the favor of the majority class as well as performance improvement. We evaluated the overall and class-level performance of the machine learning models based on accuracy and mean squared error scores. Three hidden layered neural networks outperformed the other models with 0.254 ± 0.038 and 0.173 ± 0.016 MSE scores for two different datasets. A neural network, which provides more accurate and reliable results, should be integrated into the accident prevention system

Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin

ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?

ChatGPT embarks on a new era of artificial intelligence and will revolutionize the way we approach intelligent traffic safety systems. This paper begins with a brief introduction about the development of large language models (LLMs). Next, we exemplify using ChatGPT to address key traffic safety issues. Furthermore, we discuss the controversies surrounding LLMs, raise critical questions for their deployment, and provide our solutions. Moreover, we propose an idea of multi-modality representation learning for smarter traffic safety decision-making and open more questions for application improvement. We believe that LLM will both shape and potentially facilitate components of traffic safety research.Comment: Submitted to Nature - Machine Intelligence (Revised and Extended