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• A meso-level hotspot identification approach is proposed for suburban arterials. • Intersection-segment analysis units are merged considering crash distribution. • Arterial influence area is determined according to surrounding land use. • Meso-level analysis is superior to the micro-level approach in the field application. Accurate identification of crash hotspots forms the foundation of roadway safety improvement. The Highway Safety Manual micro-level approach uses individual intersections and road segments as analysis units, and correspondingly identifies some isolated road entities as hotspots. However, because traffic police and administrative agencies routinely conduct safety improvement based on multiple continuous segments and intersections, the identification of hotspots at the micro-level is inefficient for field application. To better meet this need, this study proposes a new meso-level approach to identify hotspots, specifically on suburban arterials. Meso-level analysis units of three different configurations (201, 150, and 100 units) were obtained by combining a set number of intersections and their adjacent segments according to crash distribution and homogeneity. Their influence areas were determined according to the proportion of urbanized land in areas perpendicularly adjacent to the arterials. Three Bayesian Poisson-lognormal conditional autoregressive models (PLN-CAR) considering spatial correlations were developed for each unit configuration, using the full Bayesian (FB) method to ameliorate random fluctuation in crash counts. Potential for safety improvement (PSI) values were calculated based on the modeling results and were used to identify hotspots. Two measures, i.e., the concentrated degree of hotspots (CDH) and the hotspot identification accuracy (HIA), were proposed to make a quantitative and comparative evaluation. Results showed that 1) arterials with more parallel roads suffer lower crash risk, and 2) considering both the hotspot distribution and the identification accuracy, the 150 meso-level unit configuration was the best. The proposed meso-level hotspot identification method promises to be adaptive to safety improvement practices on suburban arterials. [ABSTRACT FROM AUTHOR]

Developing new visible light-triggered NIR photochromic sensors for the accurate detection of volatile amine vapors in the solid state is highly desirable. Herein, three novel dithienylethene-bridged difluoroboron β-diketonate dyes Di-BF 2 bdk-DTE1-Di-BF 2 bdk-DTE3 , in which BF 2 bdk group not only functions as electron acceptor but also responds to amine vapors, have been developed. Upon alternating irradiation with blue light (460–470 nm) and near-infrared light (760–770 nm), they displayed excellent NIR photochromism and fluorescent switching behaviors in toluene and in the PMMA film. Moreover, they showed an unprecedented triple sensing performance for volatile n -propylamine vapor in the PMMA film state (fluorescent-colorimetric-colorimetric). Therefore, they would be utilized as triple sensing membranes for selective detection towards volatile primary or secondary amine vapors in environmental and biological systems. Image 1 • Three novel dithienylethene-bridged difluoroboron β-diketonate dyes were developed. • They displayed excellent NIR optical switching behaviors in toluene and PMMA film upon irradiation with blue/NIR light. • They showed an unprecedented triple sensing performance for volatile n -BuNH 2 vapor in the PMMA film. [ABSTRACT FROM AUTHOR]

• A regional safety analytical approach was proposed considering traffic crashes and violations. • Significant variables for traffic crashes and violations were determined. • The spatial distributions for crash-prone and violation-prone areas were analyzed separately in Suzhou. • The influence mechanisms of the key contributing factors in hazardous areas were examined. During rapid urbanization and increase in motorization, it becomes particularly important to understand the relationships between traffic safety and risk factors in order to provide targeted improvements and policy recommendations. Violations and police enforcement are key variables, but the endogenous relationship between crashes and violations has made these variables unreliable and has limited their use. To manage this problem, this study developed a systematic approach for the joint modeling of crashes and violations to identify crash and violation hotspots and examine the mechanisms underlying macro-level contributing factors. Socio-economic, road network, public facility, traffic enforcement, and land use intensity data from 115 towns in Suzhou, China, were collected as independent variables. A bivariate negative binomial spatial conditional autoregressive model (BNB-CAR) and the potential for safety improvement (PSI) method were adopted to identify crash-prone and violation-prone areas, and an interpretable machine learning framework was applied to explore the factors' effects by area. Results showed that the proposed framework was able to accurately identify problem areas and quantify the impact of key factors, which, in Suzhou, were the number of traffic police and their daily patrol time. Considering such enforcement-related information provided important insights into reducing crash and violation frequency; for example, keeping the number of traffic police and daily patrol time under certain thresholds (number of police lower than 11 and patrol time lower than 2.3 h in this sample) was as effective as increasing these numbers for reducing the probability of high-crash and high-violation areas. The proposed approach can help traffic administrators identify the key contributing factors, especially enforcement factors, in crash-prone and violation-prone areas and provide guidelines for improvement. [ABSTRACT FROM AUTHOR]