Each year, around 6 million car accidents occur in the U.S. on average. Road safety features (e.g., concrete barriers, metal crash barriers, rumble strips) play an important role in preventing or mitigating vehicle crashes. Accurate maps of road safety features is an important component of safety management systems for…
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48 results for “road safety”
System predicts ice formation to improve road safety.
problem Improving road safety through accurate ice formation prediction.
method Combining physical models and machine learning for data correction.
result Machine learning effectively corrects incorrect data in forecasts.
Time-Aware Gated Recurrent Unit Networks for Road Surface Friction Prediction Using Historical Datacs.LG
An accurate road surface friction prediction algorithm can enable intelligent transportation systems to share timely road surface condition to the public for increasing the safety of the road users. Previously, scholars developed multiple prediction models for forecasting road surface conditions using historical data. …
Unified approach detects traffic conflicts across various interactions.
problem Inconsistent detection of traffic conflicts across different interactions.
method Unified probabilistic approach decomposes conflicts into statistical learning tasks.
result Effective collision warnings across diverse datasets and environments.
Model forecasts motor vehicle collision rates with high accuracy.
problem Forecasting motor vehicle collision rates with high accuracy.
method Adopted Heston Stochastic Volatility model and extended it to account for seasonality and accelerated safety periods.
result Short-term forecasts show high accuracy (over 95%) and outperform existing models.
Study improves prediction of UK road accidents' severity using AI.
problem Improving prediction of UK road traffic accident severity.
method Combination of machine learning, econometric, and statistical methods on historical data.
result XGBoost model with RMSE of 0.176 and MAE of 0.087 outperforms naive forecasting.
Study uses LCRN to detect driver distraction from EEG signals.
problem Improving road safety by detecting driver distraction.
method Used a Long-term Recurrent Convolutional Network (LCRN) for EEG-based driver distraction detection.
result LCRN model outperformed state-of-the-art TSC models in detecting driver distraction.
Automated road infrastructure mapping using connected vehicle data and deep learning.
problem Manual identification of intersections is laborious and time-consuming.
method Geohashing, YOLOv5 algorithm for classification of road segments and intersections.
result Overall classification accuracy of 95%, with high F1 scores for straight roads and intersections.
Research studies have shown that a large proportion of hazards remain unrecognized, which expose construction workers to unanticipated safety risks. Recent studies have also found that a strong correlation exists between viewing patterns of workers, captured using eye-tracking devices, and their hazard recognition perf…
Road transportation is of critical importance for a nation, having profound effects in the economy, the health and life style of its people. With the growth of cities and populations come bigger demands for mobility and safety, creating new problems and magnifying those of the past. New tools are needed to face the cha…
This review explores ML and DL techniques for detecting distracted driving across various modalities.
problem Improving detection of complex distraction patterns, especially cognitive distractions.
method Categorizes and evaluates studies based on modality, data accessibility, and methodology.
result Multimodal systems outperform single-modal systems in detecting complex distraction patterns.
Study uses vehicle trajectory data to predict traffic incidents on highways.
problem Early detection of traffic incidents to reduce secondary crashes.
method Machine learning algorithms (Logistic Regression, Random Forest, Extreme Gradient Boost, Artificial Neural Network) applied to vehicle trajectory data.
result Random Forest model performs best for incident prediction.
In recent years, car makers and tech companies have been racing towards self driving cars. It seems that the main parameter in this race is who will have the first car on the road. The goal of this paper is to add to the equation two additional crucial parameters. The first is standardization of safety assurance --- wh…
Transportation agencies have an opportunity to leverage increasingly-available trajectory datasets to improve their analyses and decision-making processes. However, this data is typically purchased from vendors, which means agencies must understand its potential benefits beforehand in order to properly assess its value…
Affective states have a critical role in driving performance and safety. They can degrade driver situation awareness and negatively impact cognitive processes, severely diminishing road safety. Therefore, detecting and assessing drivers' affective states is crucial in order to help improve the driving experience, and i…
Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions. Latest studies mainly focus on modeling the spatial dependency by utilizi…
CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twinscs.CV
CitySim dataset captures vehicle trajectories for safety research.
problem Lack of fine-grain vehicle trajectories for safety-oriented research.
method Five-step procedure: video stabilization, object filtering, stitching, detection, and error filtering.
result CitySim dataset improves safety evaluations and facilitates digital-twin research.
Crash prediction is a critical component of road safety analyses. A widely adopted approach to crash prediction is application of regression based techniques. The underlying calibration process is often time-consuming, requiring significant domain knowledge and expertise and cannot be easily automated. This paper intro…
Deep Learning detects cyclists' orientation for safer roads.
problem Ensuring safety for cyclists in intelligent transportation systems.
method Transfer Learning with pre-trained models, multi-class detection, new dataset creation.
result Faster R-CNN with ResNet50 proved precise but slower; SSD with InceptionV2 provided good trade-off.
Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods. This paper proposes novel machine learning models for intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often as…
Mobile app development in recent years has resulted in new products and features to improve human life. Mobile telematics is one such development that encompasses multidisciplinary fields for transportation safety. The application of mobile telematics has been explored in many areas, such as insurance and road safety. …
CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarioscs.LG
Naturalistic driving trajectories are crucial for the performance of autonomous driving algorithms. However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by currently developed algorithms. When considering safety, testing algorithms in near-miss…
Weather is an important factor affecting transportation and road safety. In this paper, we leverage state-of-the-art convolutional neural networks in labelling images taken by street and highway cameras located across across North America. Road camera snapshots were used in experiments with multiple deep learning frame…
In Autonomous Vehicles (AVs), one fundamental pillar is perception, which leverages sensors like cameras and LiDARs (Light Detection and Ranging) to understand the driving environment. Due to its direct impact on road safety, multiple prior efforts have been made to study its the security of perception systems. In cont…
This paper suggests claim history will be deprecated in future auto insurance rates.
problem The role of historical claim records in auto insurance rates.
method Proposes a new risk variable elimination method and real-time road risk model design.
result Claim history will be considered a 'noise' factor and deprecated in Pay-How-You-Drive models.
Paper presents LSTMMDN for hourly bike flow estimation in Copenhagen.
problem Sparse or unavailable hourly bike flow data for safety analysis.
method Hybrid LSTM MDN model for hourly bike flow estimation.
result 66-77% more accurate bike flow estimates compared to calibration factors.
Study improves crash rate forecasting in Washington, D.C. using stochastic volatility model.
problem Forecasting crash rates in areas with irregular traffic patterns and exogenous events.
method Adopted a stochastic volatility model to capture heterogeneity and temporal instability.
result The stochastic volatility model outperforms conventional models in forecasting crash rates in Washington, D.C.
The paper explores new risk models for autonomous driving.
problem Risk management and actuarial modeling for autonomous vehicles.
method Examines technical difficulties and proposes a novel risk model.
result The new model better reflects real-world driving safety.
Machine learning techniques for road networks hold the potential to facilitate many important transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a road network by utilizing information of, e.g., adjacent road segments. While state-of-the-…
Safety is an essential aspect in the facilitation of automated vehicle deployment. Current testing practices are not enough, and going beyond them leads to infeasible testing requirements, such as needing to drive billions of kilometres on public roads. Automated vehicles are exposed to an indefinite number of scenario…
In this paper we adopted state-of-the-art machine learning algorithms, namely: random forest (RF) and least squares boosting, to model crash data and identify the optimum model to study the impact of narrow lanes on the safety of arterial roads. Using a ten-year crash dataset in four cities in Nebraska, two machine lea…
RFN improves GCNs for road networks, outperforming state-of-the-art by 21%-40%.
problem Leveraging the structure of road networks effectively in machine learning tasks.
method Introducing RFN, a novel GCN specifically designed for road networks.
result RFN outperforms state-of-the-art GCNs by 21%-40% on road network tasks.
Paper presents a self-supervised method to infer road lane networks.
problem Difficult and costly to create lane maps for autonomous vehicles.
method Self-supervised learning using neural and search-based model.
result Model can generalize to new road layouts, unlike previous approaches.
On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Networkcs.LG
Road networks are a type of spatial network, where edges may be associated with qualitative information such as road type and speed limit. Unfortunately, such information is often incomplete; for instance, OpenStreetMap only has speed limits for 13% of all Danish road segments. This is problematic for analysis tasks th…
Driving behaviour has a great impact on road safety. A popular way of analysing driving behaviour is to move the focus to the manoeuvres as they give useful information about the driver who is performing them. In this paper, we investigate a new way of identifying manoeuvres from vehicle telematics data, through motif …
MIDAS learns to adaptively control other cars in urban driving scenarios.
problem Autonomous vehicles need to interact with other agents on the road.
method Reinforcement learning with attention mechanism to handle multiple agents.
result MIDAS policies are adaptive and robust to external changes.
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, …
Addressing the Invisible: Street Address Generation for Developing Countries with Deep Learningcs.CV
More than half of the world's roads lack adequate street addressing systems. Lack of addresses is even more visible in daily lives of people in developing countries. We would like to object to the assumption that having an address is a luxury, by proposing a generative address design that maps the world in accordance w…
Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management. One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning. Comprehensively, the majo…
Study shows 'Belt and Road' node cities boost digital finance in China.
problem Impact of 'Belt and Road' node cities on digital inclusive finance.
method Descriptive analysis, literature review, theoretical model, differential differential method.
result Establishment of 'Belt and Road' node cities promotes digital inclusive finance in China, with Internet development as a mediating variable.
Deep generative models for graphs have shown great promise in the area of drug design, but have so far found little application beyond generating graph-structured molecules. In this work, we demonstrate a proof of concept for the challenging task of road network extraction from image data. This task can be framed as im…
Navigating complex urban environments safely is a key to realize fully autonomous systems. Predicting future locations of vulnerable road users, such as pedestrians and cyclists, thus, has received a lot of attention in the recent years. While previous works have addressed modeling interactions with the static (obstacl…
Road Network Metric Learning improves ETA prediction accuracy by addressing data sparsity.
problem Data sparsity in road network embedding vectors affects ETA prediction accuracy.
method Proposes Road Network Metric Learning (RNML-ETA) framework with an auxiliary metric learning task and triangle loss.
result RNML-ETA outperforms state-of-the-art models and improves prediction accuracy for cold links.
Driver drowsiness increases crash risk, leading to substantial road trauma each year. Drowsiness detection methods have received considerable attention, but few studies have investigated the implementation of a detection approach on a mobile phone. Phone applications reduce the need for specialised hardware and hence, …
Roads are critically important infrastructure to societal and economic development, with huge investments made by governments every year. However, methods for monitoring those investments tend to be time-consuming, laborious, and expensive, placing them out of reach for many developing regions. In this work, we develop…
Methodology to analyze traffic accidents using microscopic models.
problem Understanding and predicting traffic accidents and their impact.
method Developed a statistical approach using microscopic traffic models and SUMO.
result Approximate distribution of total losses as a mean-variance mixture.
Study improves traffic prediction intervals for minor roads.
problem Uncertainty in traffic data for underrepresented minor roads.
method Quantile Random Forest with PCA for interval prediction.
result Achieved 88.22% interval coverage and Winkler Score of 7,468.47.
China's rapid economic growth resulted in serious air pollution, which caused substantial losses to economic development and residents' health. In particular, the road transport sector has been blamed to be one of the major emitters. During the past decades, fluctuation in the international oil prices has imposed signi…