Exploratory analysis of injury severity under different levels of driving automation (SAE Levels 2 and 4) using multi-source data.

Vehicles equipped with automated driving capabilities have shown potential to improve safety and operations. Advanced driver assistance systems (ADAS) and automated driving systems (ADS) have been widely developed to support vehicular automation. Although the studies on the injury severity outcomes that involve automated vehicles are ongoing, there is limited research investigating the difference between injury severity outcomes for the ADAS and ADS equipped vehicles. To ensure a comprehensive analysis, a multi-source dataset that includes 1,001 ADAS crashes (SAE Level 2 vehicles) and 548 ADS crashes (SAE Level 4 vehicles) is used. Two random parameters multinomial logit models with heterogeneity in the means of random parameters are considered to gain a better understanding of the variables impacting the crash injury severity outcomes for the ADAS (SAE Level 2) and ADS (SAE Level 4) vehicles. It was found that while 67 percent of crashes involving the ADAS equipped vehicles in the dataset took place on a highway, 94 percent of crashes involving ADS took place in more urban settings. The model estimation results also reveal that the weather indicator, driver type indicator, differences in the system sophistication that are captured by both manufacture year and high/low mileage as well as rear and front contact indicators all play a role in the crash injury severity outcomes. The results offer an exploratory assessment of safety performance of the ADAS and ADS equipped vehicles using the real-world data and can be used by the manufacturers and other stakeholders to dictate the direction of their deployment and usage.

Investigating surrogate safety measures at midblock pedestrian crossings using multivariate models with roadside camera data.

This study aims to evaluate and compare Surrogate Safety Measures (SSMs) at five midblock Rectangular Rapid Flashing Beacons (RRFB) and two midblock Pedestrian Hybrid Beacons (PHB) sites in Florida using extensive video data collected over the study period of July to November 2021. Computer vision and data processing resulted in four pedestrian SSMs, namely spatial gap, temporal gap, relative time to collision (RTTC) and Post Encroachment Time (PET). An initial investigation of the SSMs using Mann-Whitney-Wilcoxon tests revealed significant differences in the SSM values across different treatment types and hours of the day. Additionally, univariate regression of spatial gap, and multivariate regression of temporal gap, RTTC and PET revealed significant differences of SSMs across RRFB and PHB sites. The study considered both linear and non-linear (gamma, inverse Gaussian and lognormal) regression models. After considering various traffic and operational parameters, the data were aggregated for each pedestrian-vehicle interaction on each lane to create a total of 395 observations. The SSMs included average spatial gap, temporal gap, RTTC and PET for each interaction of pedestrian and vehicle on each lane. The results indicated that non-linear models performed better than the linear models. Moreover, the presence of the PHB, weekday, signal activation, lane count, pedestrian speed, vehicle speed, land use mix, morning period and pedestrian starting position from the sidewalk have been found to be significant determinants of the SSMs. Results also suggest temporal SSMs increase at the PHB sites compared to the RRFB sites, indicating an improvement of traffic safety at PHB sites. However, the spatial gap decreased for PHB sites compared to the RRFB sites, which suggests that pedestrians tend to start to cross the RRFB sites when they perceive vehicles to be further away than at the PHB sites.

Advances and applications of computer vision techniques in vehicle trajectory generation and surrogate traffic safety indicators.

The application of Computer Vision (CV) techniques massively stimulates microscopic traffic safety analysis from the perspective of traffic conflicts and near misses, which is usually measured using Surrogate Safety Measures (SSM). However, as video processing and traffic safety modeling are two separate research domains and few research have focused on systematically bridging the gap between them, it is necessary to provide transportation researchers and practitioners with corresponding guidance. With this aim in mind, this paper focuses on reviewing the applications of CV techniques in traffic safety modeling using SSM and suggesting the best way forward. The CV algorithms that are used for vehicle detection and tracking from early approaches to the state-of-the-art models are summarized at a high level. Then, the video pre-processing and post-processing techniques for vehicle trajectory extraction are introduced. A detailed review of SSMs for vehicle trajectory data along with their application on traffic safety analysis is presented. Finally, practical issues in traffic video processing and SSM-based safety analysis are discussed, and the available or potential solutions are provided. This review is expected to assist transportation researchers and engineers with the selection of suitable CV techniques for video processing, and the usage of SSMs for various traffic safety research objectives.

ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model.

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry.

Effect of signal timing on vehicles' near misses at intersections.

Driving characteristics often vary between the different states of the signal. During red and yellow phase, drivers tend to speed up and reduce the following distance which in turn increases the possibility of rear end crashes. Intersection safety, therefore, relies on the correct modelling of signal phasing and timing parameters, and how drivers respond to its changes. This paper aims to identify the relationship between surrogate safety measures and signal phasing. Unmanned aerial vehicle (UAV) video data has been used to study a major intersection. Post encroachment time (PET) between vehicles was calculated from the video data as well as speed, heading and relevant signal timing parameters such as all red time, red clearance time, yellow time, etc. Random parameter ordered logit model was used to model the relationship between PET and signal timing parameters. Overall, the results showed that yellow time and red clearance time is positively related to PETs. The model was also able to identify certain signal phases that could be a potential safety hazard and would need to be retimed by considering the PETs. The odds ratios from the models also indicate that increasing the mean yellow and red clearance times by one second can improve the PET levels by 10% and 3%, respectively.

Explore traffic conflict risks considering motion constraint degree in the diverging area of toll plazas.

In the diverging area of toll plazas, the absence of lane markings, the gradual widening of lanes, and the crossing of vehicles with different tolling methods increase the likelihood of collisions. This study proposed a concept of motion constraint degree to investigate traffic conflict risks in the toll plaza diverging area. On the basis of the motion constraint degree, a two-step method was developed, in which all potentially influencing factors were separated into two parts. The first part was used to analyze the association between the motion constraint degree and some factors, while the remaining factors were utilized for risk regression/prediction together with the motion constraint degree. The random parameters logit model was applied for regression analysis and four prevalent machine learning models were employed for risk prediction. Results indicate that (1) the proposed approach considering motion constraint degree outperforms the conventional direct method, no matter for conflict risk regression or prediction; (2) the motion constraint degree is not monotonically correlated with the risk level of vehicles; (3) due to the layout of the toll plaza, ETC vehicles are less likely to be at risk in the diverging area; and (4) lane-changing behaviors in the restricted space increase the conflict risk.

Enhanced index of risk assessment of lane change on expressway weaving segments: A case study of an expressway in China.

Vehicles frequently change lanes at weaving segments, and there is a high probability of collision. To assess the risk of lane change, this study proposes a novel lane change risk assessment index (LCRAI), which takes both exposure time and conflict severity into consideration. First, the generalized time-to-collision (GTTC) and exposure-to-risk index (ERI) are proposed to evaluate conflicts during lane change, and then a spatiotemporal overlap analysis is applied to exclude the cases when two vehicles arrive at the conflict area at different times. Second, the change of velocity after a crash occurrence is calculated and used to evaluate the severity risk (SRI). Finally, ERI and SRI are combined to compute the LCRAI. A case study of a weaving segment is conducted, and the weaving segment is divided into four sections to compare their risks. Conflict frequency on the section close to the exit is the largest and it is smallest after passing the exit, while conflict severity is most serious just after entering the main line on the middle lane. As for both conflict frequency and severity, LCRAI shows that the middle section is the most dangerous while the last 100 m section is the safest in the weaving segment. The index proposed will contribute to the risk assessment of lane change in connected environments and improve safety in weaving segments of expressway combined with real-time safety countermeasures.

Using closed-circuit television cameras to analyze traffic safety at intersections based on vehicle key points detection.

In the within-intersection area, vehicles from different approaches make turning movements resulting in many conflict points. Hence, drivers are more prone to make mistakes in that area, which leads to severe crash outcomes. In the current roadway system, the Closed-Circuit Television (CCTV) cameras could be a cost-effective sensor to monitor the safety condition in the within-intersection area. This study proposed a framework named "Near Miss Event Detection System (NMEDS)" for road safety diagnostics using video data collected from CCTV cameras. The proposed framework combined the Mask-RCNN bounding box detection and Occlusion-Net detection algorithm to reconstruct vehicles' key points in a 3D view. Vehicles' key points including right-front headlight, left-front headlight, right-back taillight, and left-back taillight could be identified and transformed into a 2D bird's-eye view (i.e., real-world coordinate system) for safety analysis. A method was proposed to modify the occluded key points, which could not be observed by cameras due the turning movements in the within-intersection area. The post-encroachment time (PET) was calculated by using the trajectory data in the 2D view. The proposed framework was compared with two counterparts (i.e., bounding box detection only and key point detection only) by conducting an empirical study at a 4-leg intersection. The results suggested that the proposed framework could obtain more accurate vehicle trajectory and better autocorrelation analytics was conducted to identify the significantly dangerous locations in the within-intersection area. It is expected that the proposed methods could help diagnose road safety problems using CCTV cameras. Moreover, the proposed method could be incorporated with Connected Vehicle Systems and provide information to nearby drivers based on Infrastructure-to-Vehicle (I2V) technologies.

Evaluation of automated emergency braking system's avoidance of pedestrian crashes at intersections under occluded conditions within a virtual simulator.

Pedestrians' red-light crossing can present a threat to themselves and the safety at intersections, especially for the through vehicles since their speeds are higher compared to the turning vehicles. The automated emergency braking (AEB) system could actively detect pedestrians and react to avoid potential conflicts. This study contributes to evaluate the effectiveness of the AEB system under occlusion conditions. The braking algorithm was developed in the virtual simulator CARLA to control the ego vehicle. Three occlusion scenarios in which the sensor of the AEB system could not detect the pedestrian if the pedestrian is occluded by a stopping vehicle. The evaluation experiments were conducted at a typical 4-leg intersection considering different motion statuses of the ego vehicle and pedestrian. The effects of field of view (FOV) of the sensor and activation threshold of the AEB system were also explored. The study indicated that the effectiveness of the AEB system could be reduced by the occlusion time. A longer activation threshold is recommended if the pedestrian is potentially occluded for a long time. The effects of other factors such as the speed of the ego vehicle and pedestrian and scenarios were also identified.

Vulnerable road users' crash hotspot identification on multi-lane arterial roads using estimated exposure and considering context classification.

This research develops safety performance functions and identifies the crash hotspots based on estimated vulnerable road users' exposure at intersections and along the roadway segments. The study utilized big data including Automated Traffic Signal Performance Measures (ATSPM) data, crowdsourced data (Strava), Closed Circuit Television (CCTV) surveillance camera videos, crash data, traffic information, roadway features, land use attributes, and socio-demographic characteristics. It comprises an extensive comparison between a wide array of statistical and machine learning models that were developed to estimate pedestrian and bike exposure. The results indicated that the XGBoost approach was the best to estimate vulnerable road users' exposure at intersections as well as bike exposure along the roadway segments. Afterwards, the estimated exposure was utilized as input variables to develop crash prediction models that relate different crash types to potential explanatory variables. Negative Binomial approach was followed to develop crash prediction models to be consistent with the Highway Safety Manual. The results show that the exposure variables (i.e., AADT, bike exposure, and the interaction between them) have significant influences on the two types of crashes (i.e., crashes of vulnerable road users at intersections and bike crashes along the segments). Further, the results indicated that the context classification is significantly related to crashes. Based on the developed models, the PSIs were calculated and the hotspots were identified for the two crash types. It was found that hotspots were more likely to be located near the city of Orlando. Coastal roadways were classified as cold categories regarding bike crashes. Further, C4 roadway segments were found to be significantly related to the increase of vulnerable road users' crashes at intersections and bike crashes along the segments.

Modeling pedestrians' near-accident events at signalized intersections using gated recurrent unit (GRU).

Pedestrian safety plays an important role in the transportation system. Intersections are dangerous locations for pedestrians with mixed traffic. This paper aims to predict the near-accident events between pedestrians and vehicles at signalized intersections using PET (Post Encroachment Time) and TTC (Time to Collision). With automated computer vision techniques, mobility features of pedestrians and vehicles are generated. Extreme Value Theory (EVT) is used to model PET and minimum TTC values to select the most appropriate threshold values to label pedestrians' near-accident events. A Gated Recurrent Unit (GRU) neural network is further used to predict these events. The established model reaches an AUC (Area Under the Curve) value of 0.865 on the test data set. Moreover, the proposed model can also be applied to develop collision warning systems under the Connected Vehicle environment.

In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention.

INTRODUCTION: A pedestrian crash occurs due to a series of contributing factors taking effect in an antecedent-consequent order. One specific type of antecedent-consequent order is called a crash causation pattern. Understanding crash causation patterns is important for clarifying the complicated growth of a pedestrian crash, which ultimately helps recommend corresponding countermeasures. However, previous studies lack an in-depth investigation of pedestrian crash cases, and are insufficient to propose a representative picture of causation patterns. METHOD: In this study, pedestrian crash causation patterns were discerned by using the Driving Reliability and Error Analysis Method (DREAM). One hundred and forty-two pedestrian crashes were investigated, and five pedestrian pre-crash scenarios were extracted. Then, the crash causation patterns in each pre-crash scenario were analyzed; and finally, six distinct patterns were identified. Accordingly, 17 typical situations corresponding to these causation patterns were specified as well. RESULTS: Among these patterns, the pattern related to distracted driving and the pattern related to an unexpected change of pedestrian trajectory contributed to a large portion of the total crashes (i.e., 27% and 24%, respectively). Other patterns also played an important role in inducing a pedestrian crash; these patterns include the pattern related to an obstructed line of sight caused by outside objects (9%), the pattern that involves reduced visibility (13%), and the pattern related to an improper estimation of the gap distance between the vehicle and the pedestrian (10%). The results further demonstrated the inter-heterogeneity of a crash causation pattern, as well as the intra-heterogeneity of pattern features between different pedestrian pre-crash scenarios. Conclusions and practical applications: Essentially, a crash causation pattern might involve different contributing factors by nature or dependent on specific scenarios. Finally, this study proposed suggestions for roadway facility design, roadway safety education and pedestrian crash prevention system development.

Examining traffic conflicts of up stream toll plaza area using vehicles' trajectory data.

Despite the recognized benefits of electronic toll collection (ETC) system as an important part of toll plaza area, the mixed traffic of electronic toll collection (ETC) vehicles and manual toll collection (MTC) vehicles in the toll plaza diverging area are considered risky to vehicles, in which complex diverging and crossing behavior of vehicles would increase the collision risks. Therefore, it is vitally important to investigate the vehicle collision risk in the up stream toll plaza area. Video data are collected from a typical toll plaza in Nanjing, China, and vehicle trajectory data are extracted using an automated analysis system based on OpenCV. An extended Time-To-Collision (TTC) is proposed to evaluate the vehicle collision risk. Subsequently, the different effects on vehicle collision risk of vehicles with different toll collection types, target lanes and locations are compared. Furthermore, the random parameters logistic model is developed to investigate the effects of explanatory factors on the collision risk of vehicles diverging or adjusting their lane position. The results suggested that the MTC vehicles have the highest collision risk in the toll plaza diverging area and there are significant different effects on collision risk among vehicles with different target toll collection lanes. Further, more dangerous situations could be found for a vehicle if it is closer to the toll collection lanes and surrounded by heavy traffic. It is also confirmed that mixed traffic with MTC and ETC vehicles could increase the crash risk in the toll plaza diverging area. It is expected that the findings could help engineers and operators select the appropriate engineering and traffic control solutions to enhance the safety at the toll plaza diverging area.

Fluorometric method for inorganic pyrophosphatase activity detection and inhibitor screening based on click chemistry.

A fluorometric method for pyrophosphatase (PPase) activity detection was developed based on click chemistry. Cu(II) can coordinate with pyrophosphate (PPi), the addition of pyrophosphatase (PPase) into the above system can destroy the coordinate compound because PPase catalyzes the hydrolysis of PPi into inorganic phosphate and produces free Cu(II), and free Cu(II) can be reduced by sodium ascorbate (SA) to form Cu(I), which in turn initiates the ligating reaction between nonfluorescent 3-azidocoumarins and terminal alkynes to produce a highly fluorescent triazole complex, based on which, a simple and sensitive turn on fluorometric method for PPase can be developed. The fluorescence intensity of the system has a linear relationship with the logarithm of the PPase concentration in the range of 0.5 and 10 mU with a detection limit down to 0.2 mU (S/N = 3). This method is cost-effective and convenient without any labels or complicated operations. The proposed system was applied to screen the potential PPase inhibitor with high efficiency. The proposed method can be applied to diagnosis of PPase-related diseases.

Electrochemiluminescence biosensor for folate receptor based on terminal protection of small-molecule-linked DNA.

Owning to the characteristics such as high sensitivity and simplicity of apparatus, electrochemiluminescence (ECL) has become a powerful analytical technique and has been widely used. Ru(phen)3(2+) can be intercalated into the grooves of dsDNA and act as an ECL probe efficiently, which has been applied to develop a sensitive ECL biosensor for folate receptor in this study. One ssDNA with a thiol group at its 3' termini had been modified on the Au electrode first, and the other ssDNA with folic acid at its 3' termini hybridized with the former one being modified on the electrode surface to form a dsDNA. In the absence of folate receptor, the 3'-terminus in the dsDNA region can be specificity hydrolyzed into mononucleotides by ExoIII and on dsDNA presents on the electrode surface, leading to the lower of ECL intensity detected. However, in the presence of the target (folate receptor), ExoIII failed to hydrolyze the dsDNA since the one 3'-terminus had been protected by the target and the other protected by the Au electrode, resulting in the enhancement of ECL intensity. The enhanced ECL intensity has a linear relationship with the logarithm of folate receptor concentration in the range of 0.66nmol/L and 26.31nmol/L with a detection limit of 0.1204nmol/L. The proposed biosensor had been applied to detect HeLa cells concentration with satisfied results.

Tonabersat for migraine prophylaxis: a systematic review.

BACKGROUND: Randomized clinical trials assessing the efficacy and tolerability of tonabersat compared with placebo as prophylaxis for migraine were systematically reviewed in this study. By analyzing all available data, we aimed to establish an overall estimate of any association in order to more accurately inform clinicians and care-givers about how to prevent migraines. OBJECTIVE: To evaluate the efficacy and tolerability of tonabersat when it is used for migraine prevention. STUDY DESIGN: Systematic review of tonabersat for migraine prophylaxis. METHODS: Computerized database search of The Cochrane Pain, Palliative & Supportive Care Trials Register, The Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library), Pubmed, and EMBASE for randomized, double-blind, placebo-controlled trials on tonabersat for migraine until January, 2013. We also searched the ongoing trials. We did not impose any language restrictions.The quality assessment and clinical relevance criteria utilized were the Cochrane Pain, Palliative & Supportive Care review group criteria as utilized for randomized trials. OUTCOME MEASURES: The primary outcome measure was the change in mean number of migraine headache days. The secondary outcome measures were change in attacks, responder rates, the reduction of the consumption of rescue medication, and adverse events. RESULTS: For this systematic review, 133 studies were identified. Of these, 131 studies were excluded, and a total of 2 studies (after removal of duplicate publications) met inclusion criteria for methodological quality assessment with the randomized trial study. The evidence for tonabersat for migration prophylaxis failed to demonstrate a reduction when compared to placebo because of a lack of evidence. But the good tolerability supports further exploration of tonabersat in the prevention of migraine attacks. LIMITATIONS: The limitation of this systematic review was a lack of available evidence. CONCLUSION: There is fair evidence for migraine prophylaxis, but a lack of available evidence for tonabersat for migraine prophylaxis. Although tonabersat failed to demonstrate a significantly greater reduction of migraine headache days than placebo, it was well tolerated. Future work should further investigate the utility of tonabersat in the preventive management of migraine.

Aptamer-based portable biosensor for platelet-derived growth factor-BB (PDGF-BB) with personal glucose meter readout.

A novel portable biosensor for sensitive and selective detection of platelet-derived growth factor-BB (PDGF-BB) had been developed based on the specific recognition between aptamer and protein using a personal glucose meter (PGM) as readout. In the presence of PDGF-BB, the primary aptamer of PDGF-BB which is bound to the surface of streptavidin magnespheres paramagnetic particles (SA-PMPs) reacts quantitatively with invertase-functionalized secondary aptamer of PDGF-BB to form a stable complex, resulting in the attachment of invertase on the SA-PMPs. Subsequently, the invertase catalyzes the hydrolysis of sucrose to produce a large amount of glucose and quantitative readout by the PGM. The enhanced signal of the PGM has a relationship with the concentration of PDGF-BB in the range of 1.0 × 10(-14) M~3.16 × 10(-12) M, and the detection limit is 2.9 fM. The proposed portable biosensor had been successfully applied to assay the PDGF-BB in saliva samples.

Fluorescence sensor for Cu(II) in the serum sample based on click chemistry.

Cu(II) can be reduced to Cu(I) by sodium ascorbate (SA) in situ, which in turn induces CuAAC reaction between the weak fluorescent compound (3-azido-7-hydroxycoumarin) and propargyl alcohol to form a strong fluorescent compound. Based on such principle, a simple and sensitive fluorescence sensor for Cu(II) can be developed, which combines the character of high selectivity of click chemistry and high sensitivity of fluorescence detection. The value of fluorescence increase factor shows a good linear relationship with the concentration of Cu(II) in the range of 0.25 µM-2.5 µM with a detection limit of 0.08 µM. In addition, the developed sensor shows high selectivity towards Cu(II) assay even in the presence of other common metal ions and it has been successfully applied to detect Cu(II) in human serum with satisfactory results.

A portable chemical sensor for histidine based on the strategy of click chemistry.

A novel portable chemical sensor is developed in combination of the personal glucose meters (PGM) with click chemistry for sensitive and selective determination of histidine. Invertase-labeled alkynyl-DNA can be modified onto the surfaces of Streptavidin Magnespheres Paramagnetic Particles (PMPs) through copper(I) catalyzed azide-alkyne cycloaddition (CuAAC) reaction and formed invertase-functionalized PMPs, which can be separated easily. The presence of invertase can convert sucrose to glucose and can be monitored by the PGM easily. The presence of histidine can inhibit the CuAAC, so the read-out signal of PGM decreased. The difference in signals from the PGM before and after addition of histidine has a good linear correlation with the logarithm of the histidine concentrations in the range of 0.01~100 µM with a detection limit of 3.4 nM, which is lower than those of many other chemical sensors. Moreover, the assay of histidine in milk samples is demonstrated with satisfactory results.

Fourier transform infrared investigation on the state of water in reverse micelles of quaternary ammonium gemini surfactants C12-s-C(12).2Br in n-heptane.

The state of water in the reverse micelles of C12-s-C(12).2Br homologues has been investigated by Fourier transform infrared spectroscopy. The results showed that the solubilized water had four states: the quaternary ammonium head-group-bound, the Br--bound, the bulklike, and the free water. With increasing W0, the number of bulklike water per surfactant (nb) rapidly increased, which indicated swelling of the reverse micelle. The number of the head-bound water per surfactant (nN+) gradually increased. This was attributed to a reduction of the interfacial curvature, which permitted more water molecules to associate with the ionic heads of surfactants and also led to a part of n-hexanol being expelled from the interface and thus water filled up. Owing to the existence of n-hexanol in the interface, the head-bound water of the present system was smaller than that of AOT system at the same W0. The number of counterion-bound water per surfactant (nBr-) remained unchanged with W0. This was due to much smaller dissociation of the head of C12-2-C(12).2Br than that of AOT. With increasing s, unchanged nN+ is attributed to the comprehensive effects of enlarged head, which promotes the hydration, increased ionization degree, and reduced size of the water pool. Owing to increased ionization degree, nBr- increases with s.