Crash Risk Reduction at Signalized Intersections Using Longitudinal Data

This study extends the previous work of Burkey and Obeng (2004) that examined the impact of red light cameras on the type and severity of crashes at signalized intersections in Greensboro, NC. The extension takes the following form. First, we extend the data to cover 57 months, and to include demographics, technology variables, the condition of a driver at the time of the crash, vehicle characteristics, land use and visual obstruction. Second, instead of examining the impact of red light cameras, we focus on identifying the determinants of crash severity, two-vehicle crashes, and property damage costs. The major findings are that the safety impacts of seatbelt use outweigh the impacts of airbags deploying because the latter tends to increase evident injuries and property damage costs, while the former reduces these injuries. We also find that head-on collisions and under rides increase evident injuries. For two-vehicle crashes, we find that the risk of severe injuries increases in pickup-pickup crashes and SUV-pickup crashes, while the risk of possible injuries increases in car-truck crashes. For property damage costs, we found the condition of the driver at the time of the crash (i.e., illness, impaired, medical condition, driver falling asleep, driver apparently normal) to be important determinants in increasing these costs. The types of accidents that we found to increase property damage costs are running into a fixed object and under rides. Finally, we found that property damage costs of crashes are low where the land uses are commercial and institutional suggesting that the accidents that occur in these areas are minor.longitudinal data; accidents; intersections

Road Safety Audit for the Intersection of US 59 and IA 9 in Osceola County, Iowa Final Report, March 2012

The Iowa Department of Transportation (DOT) requested a road safety audit (RSA) of the US 59/IA 9 intersection in northwestern Iowa, just south of the Minnesota border, to assess intersection environmental issues and crash history and recommend appropriate mitigation to address the identified safety issues at the intersection. Although the number of crashes at the location has not been significantly higher than the statewide average for similar intersections, the severity of these crashes has been of concern. This RSA was unique in that it included intersection video observation and recorded traffic conflict data analysis, along with the daylight and nighttime field reviews. This report outlines the findings and recommendations of the RSA team for addressing the safety concerns at this intersection

Methodology to assess safety effects of future Intelligent Transport Systems on railway level crossings

There is consistent evidence showing that driver behaviour contributes to crashes and near miss incidents at railway level crossings (RLXs). The development of emerging Vehicle-to-Vehicle and Vehicle-to-Infrastructure technologies is a highly promising approach to improve RLX safety. To date, research has not evaluated comprehensively the potential effects of such technologies on driving behaviour at RLXs. This paper presents an on-going research programme assessing the impacts of such new technologies on human factors and drivers’ situational awareness at RLX. Additionally, requirements for the design of such promising technologies and ways to display safety information to drivers were systematically reviewed. Finally, a methodology which comprehensively assesses the effects of in-vehicle and road-based interventions warning the driver of incoming trains at RLXs is discussed, with a focus on both benefits and potential negative behavioural adaptations. The methodology is designed for implementation in a driving simulator and covers compliance, control of the vehicle, distraction, mental workload and drivers’ acceptance. This study has the potential to provide a broad understanding of the effects of deploying new in-vehicle and road-based technologies at RLXs and hence inform policy makers on safety improvements planning for RLX

Deep Predictive Models for Collision Risk Assessment in Autonomous Driving

In this paper, we investigate a predictive approach for collision risk assessment in autonomous and assisted driving. A deep predictive model is trained to anticipate imminent accidents from traditional video streams. In particular, the model learns to identify cues in RGB images that are predictive of hazardous upcoming situations. In contrast to previous work, our approach incorporates (a) temporal information during decision making, (b) multi-modal information about the environment, as well as the proprioceptive state and steering actions of the controlled vehicle, and (c) information about the uncertainty inherent to the task. To this end, we discuss Deep Predictive Models and present an implementation using a Bayesian Convolutional LSTM. Experiments in a simple simulation environment show that the approach can learn to predict impending accidents with reasonable accuracy, especially when multiple cameras are used as input sources.Comment: 8 pages, 4 figure

Optimal Variable Speed Limit Control Strategy on Freeway Segments under Fog Conditions

Fog is a critical external factor that threatens traffic safety on freeways. Variable speed limit (VSL) control can effectively harmonize vehicle speed and improve safety. However, most existing weather-related VSL controllers are limited to adapt to the dynamic traffic environment. This study developed optimal VSL control strategy under fog conditions with fully consideration of factors that affect traffic safety risks. The crash risk under fog conditions was estimated using a crash risk prediction model based on Bayesian logistic regression. The traffic flow with VSL control was simulated by a modified cell transmission model (MCTM). The optimal factors of VSL control were obtained by solving an optimization problem that coordinated safety and mobility with the help of the genetic algorithm. An example of I-405 in California, USA was designed to simulate and evaluate the effects of the proposed VSL control strategy. The optimal VSL control factors under fog conditions were compared with sunny conditions, and different placements of VSL signs were evaluated. Results showed that the optimal VSL control strategy under fog conditions changed the speed limit more cautiously. The VSL control under fog conditions in this study effectively reduced crash risks without significantly increasing travel time, which is up to 37.15% reduction of risks and only 0.48% increase of total travel time. The proposed VSL control strategy is expected to be of great use in the development of VSL systems to enhance freeway safety under fog conditions

Driver injury severity at u.s. highway-rail crossings

There are approximately 240,000 highway-rail grade crossings in the United States and highway-rail grade crossing areas have been considered in this study as these are locations where crashes frequently occur. Existing studies on crash models at highway- rail grade crossings can be classified into two categories: accident frequency prediction models and driver injury severity models. Accident frequency prediction at highway-rail grade crossings have been investigated by previous studies using varied statistical models. Few studies, however, have focused on driver injury severity studies. Three drawbacks will be addressed in this research including limitations in traditional highway- rail grade crossings studies, limited models to study driver injury severity, and the relatively small databases. Three driver injury severity models are developed including overall model, driver injury severity model with respect to control devices, and driver injury severity model with respect to age and gender. Based on the model study, it is found that older drivers are more susceptible than younger drivers to cause an increase in severity, an increase in severity under bad weather condition, and improving highway pavement will significantly reduce driver injury severity at passive control highway-rail grade crossings, etc

Accident prediction using machine learning:analyzing weather conditions, and model performance

Abstract. The primary focus of this study was to investigate the impact of weather and road conditions on the severity of accidents and to determine the feasibility of machine learning models in accurately predicting the likelihood of such incidents. The research was centered on two key research questions. Firstly, the study examined the influence of weather and road conditions on accident severity and identified the most related factors contributing to accidents. We utilized an open-source accident dataset, which was preprocessed using techniques like variable selection, missing data elimination, and data balancing through the Synthetic Minority Over-sampling Technique (SMOTE). Chi-square statistical analysis was performed, suggesting that all weather-related variables are more or less associated with the severity of accidents. Visibility and temperature were found to be the most critical factors affecting the severity of road accidents. Hence, appropriate measures such as implementing effective fog dispersal systems, heatwave alerts, or improved road maintenance during extreme temperatures could help reduce accident severity. Secondly, the research evaluated the ability of machine learning models including decision trees, random forests, naive bayes, extreme gradient boost, and neural networks to predict accident likelihood. The models’ performance was gauged using metrics like accuracy, precision, recall, and F1 score. The Random Forest model emerged as the most reliable and accurate model for predicting accidents, with an overall accuracy of 98.53%. The Decision Tree model also showed high overall accuracy (95.33%), indicating its reliability. However, the Naive Bayes model showed the lowest accuracy (63.31%) and was deemed less reliable in this context. It is concluded that machine learning models can be effectively used to predict the likelihood of accidents, with models like Random Forest and Decision Tree proving the most effective. However, the effectiveness of each model may vary depending on the dataset and context, necessitating further testing and validation for real-world implementation. These findings not only provide insight into the factors affecting accident severity but also open a promising avenue in employing machine learning techniques for proactive accident prediction and mitigation. Future studies can aim to refine the models further and potentially integrate them into traffic management systems to enhance road safety

Modelling of advanced submicron gate InGaAs/InAIAs pHEMTS and RTD devices for very high frequency applications

InP based InAlAs/InGaAs pseudomorphic High Electron Mobility Transistors (pHEMTs) have shown outstanding performances, which makes them prominent in high frequency mm-wave and submillimeter-wave applications. However, conventional InGaAs/InAlAs pHEMTs have major drawbacks, i.e., very low breakdown voltage and high gate leakage current. These disadvantages degrade device performance, especially in Monolithic Microwave Integrated Circuit (MMIC) low noise amplifiers (LNAs). The optimisation of InAlAs/InGaAs epilayer structures through advanced bandgap engineering together with gate length reduction from 1 m into deep sub-μm regime is the key solution to enabled high breakdown and ultra-high speed, low noise pHEMT devices to be fabricated. Concurrently, device modelling plays a vital role in the design and analysis of pHEMT device and circuit performance. Physical modeling becomes essential to fully characterise and understand the underlying physical phenomenon of the device, while empirical modelling is significant in circuit design and predicts device’s characteristic performance. In this research, the main objectives to accurately model the DC and RF characteristics of the two-dimensional (2D) physical modelling for sub-μm gate length for strained channel InAlAs/InGaAs/InP pHEMT has been accomplished and developed in ATLAS Silvaco. All modelled devices were optimised and validated by experimental devices which were fabricated at the University of Manchester; the sub-micrometer devices were developed with T-gate using I-line optical lithography. The underlying device physics insight are gained, i.e, the effects of changes to the device’s physical structure, theoretical concepts and its general operation, hence a reliable pHEMT model is obtained. The kink anomalies in I-V characteristics was reproduced and the 2D simulation results demonstrate an outstanding agreement with measured DC and RF characteristics. The aims to develop linear and nonlinear models for sub-μm transistors and their implementation in MMIC LNA design is achieved with the 0.25 m In0.7Ga0.3As/In0.52Al0.48As/InP pHEMT. An accurate technique for the extraction of empirical models for the fabricated active devices has been developed and optimised using Advance Design System (ADS) software which demonstrate excellent agreement between experimental and modelled DC and RF data. A precise models for MMIC passive devices have also been obtained and incorporated in the proposed design for a single and double stage MMIC LNAs in C- and X-band frequency. The single stage LNA is designed to achieve maximum gain ranging from 9 to 13 dB over the band of operation while the gain is increased between 20 dB and 26 dB for the double stage LNA designs. A noise figure of less than 1.2 dB and 2 dB is expected respectively, for the C- and X-band LNA designed while retaining stability across the entire frequency bands. Although the RF performance of pHEMT is being vigorously pushed towards terahertz region, novel devices such as Resonant Tunnelling Diode (RTD) are needed to support future ultra-high speed, high frequency applications especially when it comes to THz frequencies. Hence, the study of physical modelling is extended to quantum modelling of an advanced In0.8Ga0.2As/AlAs RTD device to effectively model both large size and submicron RTD using Silvaco’s ATLAS software to reproduce the peak current density, peak-to-valley-current ratio (PVCR), and negative differential resistance (NDR) voltage range. The simple one-dimensional physical modelling for the RTD devices is optimised to achieve an excellent match with the fabricated RTD devices with variations in the spacer thickness, barrier thickness, quantum well thickness and doping concentration