Adaptive Kernel Density Estimation for Traffic Accidents Based on Improved Bandwidth Research on Black Spot Identification Model

At present, the total length of accident blackspot accounts for 0.25% of the total length of the road network, while the total number of accidents that occurred at accident black spots accounts for 25% of the total number of accidents on the road network. This paper describes a traffic accident black spot recognition model based on the adaptive kernel density estimation method combined with the road risk index. Using the traffic accident data of national and provincial trunk lines in Shanghai and ArcGIS software, the recognition results of black spots were compared with the recognition results of the accident frequency method and the kernel density estimation method, and the clustering degree of recognition results of adaptive kernel density estimation method were analyzed. The results show that: the accident prediction accuracy index values of the accident frequency method, kernel density estimation method, and traffic accident black spot recognition model were 14.39, 16.36, and 18.25, respectively, and the lengths of the traffic accident black spot sections were 184.68, 162.45, and 145.57, respectively, which means that the accident black spot section determined by the accident black spot recognition model was the shortest and the number of traffic accidents identified was the largest. Considering the safety improvement budget of 20% of the road length, the adaptive kernel density estimation method could identify about 69% of the traffic accidents, which was 1.13 times and 1.27 times that of the kernel density estimation method and the accident frequency method, respectively

Literature review of driving risk identification research based on bibliometric analysis

In order to understand the current research status and development direction of driving risk identification at home and abroad, relevant literatures in the field of driving risk identification from the China National Knowledge Infra-structure (CNKI) and Web of Science (WOS) in recent 12 years (2011–2022) were selected as research samples, and literature metrology tools VOSviewer and Citespace were used for visual analysis. The situation was analyzed from the aspects of chronological distribution, national cooperation network, distribution of domestic institutions, journal performance and keywords overview, literature coupling clustering and research hotspots. The results show that the number of published papers fluctuates year by year, and China, the United States and Germany have the largest number of published papers. The United States is at the center of international cooperation. The CNKI shows that universities in China such as Chang'an University and Chongqing Jiaotong University have published a large number of documents. According to the statistics of WOS, Accident Analysis & Prevention is the most widely published journal in the world. The average level of the journal is high and the quality of articles is better. Combining the research contents of CNKI and WOS, the main research directions can be clustered into five cluster themes by using the coupling function in VOSviewer, including driving risk assessment considering driver factors, the influence of driving environment on driving risk, driving risk assessment considering multi-source characteristic data, multi-aspect research on driving risk and risk identification of non-traditional vehicles in specific scenarios. Human-machine co-driving, artificial intelligence, intelligent driving, risk identification and natural driving are the current research hotspots and the future research trends

Adaptive Kernel Density Estimation for Traffic Accidents Based on Improved Bandwidth Research on Black Spot Identification Model

At present, the total length of accident blackspot accounts for 0.25% of the total length of the road network, while the total number of accidents that occurred at accident black spots accounts for 25% of the total number of accidents on the road network. This paper describes a traffic accident black spot recognition model based on the adaptive kernel density estimation method combined with the road risk index. Using the traffic accident data of national and provincial trunk lines in Shanghai and ArcGIS software, the recognition results of black spots were compared with the recognition results of the accident frequency method and the kernel density estimation method, and the clustering degree of recognition results of adaptive kernel density estimation method were analyzed. The results show that: the accident prediction accuracy index values of the accident frequency method, kernel density estimation method, and traffic accident black spot recognition model were 14.39, 16.36, and 18.25, respectively, and the lengths of the traffic accident black spot sections were 184.68, 162.45, and 145.57, respectively, which means that the accident black spot section determined by the accident black spot recognition model was the shortest and the number of traffic accidents identified was the largest. Considering the safety improvement budget of 20% of the road length, the adaptive kernel density estimation method could identify about 69% of the traffic accidents, which was 1.13 times and 1.27 times that of the kernel density estimation method and the accident frequency method, respectively

Pressing Induced Caking: A General Strategy to Scale-Span Molecular Self-Assembled Materials

We report that under mechanical pressure, caking of the precipitated molecular self-assemblies may lead to bulk supramolecular films. Massive fabrication of supramolecular films becomes possible using a simple household noodle machine. The film can be endowed diversified functions by depositing various functional ingredients via co-precipitation

Sensitive Room-Temperature H₂S Gas Sensors Employing SnO₂ Quantum Wire/Reduced Graphene Oxide Nanocomposites

Metal oxide/graphene nanocomposites are emerging as one of the promising candidate materials for developing high-performance gas sensors. Here, we demonstrate sensitive room-temperature H₂S gas sensors based on SnO₂ quantum wires that are anchored on reduced graphene oxide (rGO) nanosheets. Using a one-step colloidal synthesis strategy, the morphology-related quantum confinement of SnO₂ can be well-controlled by tuning the reaction time, because of the steric hindrance effect of rGO. The as-synthesized SnO₂ quantum wire/rGO nanocomposites are spin-coated onto ceramics substrates without further sintering to construct chemiresistive gas sensors. The optimal sensor response toward 50 ppm of H₂S is 33 in 2 s, and it is fully reversible upon H₂S release at 22 °C. In addition to the excellent gas adsorption of ultrathin SnO₂ quantum wires, the superior sensing performance of SnO₂ quantum wire/rGO nanocomposites can be attributed to the enhanced electron transport resulting from the favorable charge transfer of SnO₂/rGO interfaces and the superb transport capability of rGO. The easy fabrication and room-temperature operation make our sensors highly attractive for ultrasensitive H₂S gas detection with less power consumption