Evaluation of safety impact of access management in urban areas

The access provided by streets and highways to adjacent lands are managed by controlling the spacings between the access points including signals, driveways, and media openings on mid-block segments, and setting the limit on the corner clearances around intersections. There have been studies on evaluating the impact of access management techniques on safety and mobility in urban areas. Samples of mid-block segments and intersections can be collected from selected arterials. Because the mid-block segments or intersections in the same arterials share the same missing information, safety and mobility on them show unique features that should be taken into account when modeling. In this study panel data models were proposed for safety analysis on mid-block segments and intersections. A virtual mid-block segment was assumed to exist for each arterial. The observations of the mid-block segments on this arterial were viewed as repeated observations for the virtual mid-block segment. This perspective of the mid-block segments or intersections over space made it feasible for the panel data model to evaluate the impact of access management techniques on safety. In addition, this study also recognized that interdependency existed between safety and mobility for a mid-block segment or an intersection. Therefore, for mid-block segments, simultaneous equation models were adopted by integrating with the panel data modeling structure. For intersections, the interdependence between safety and mobility wasn\u27t considered due to the lack of data, and only count data models combining with the panel data structure was developed. Data were collected from different sources for the urban areas of Southern Nevada. The results from the models for mid-block segments indicate that there is a strong interdependency between safety and mobility. The length of mid-block segments, driveway density, and median opening density are very significant factors that influence crash rate on mid-block segments. From the results of the models for intersections, it was found that corner clearance significantly influenced the number of crashes occurred at intersections. Other factors also influence the occurrence of crashes at intersections that include land use, traffic flow, number of lanes, and posted speed limit

Short-term Traffic Flow Prediction Based on Genetic Artificial Neural Network and Exponential Smoothing

In order to improve the accuracy of short-term traffic flow prediction, a combined model composed of artificial neural network optimized by using Genetic Algorithm (GA) and Exponential Smoothing (ES) has been proposed. By using the metaheuristic optimal search ability of GA, the connection weight and threshold of the feedforward neural network trained by a backpropagation algorithm are optimized to avoid the feedforward neural network falling into local optimum, and the prediction model of Genetic Artificial Neural Network (GANN) is established. An ES prediction model is presented then. In order to take the advantages of the two models, the combined model is composed of a weighted average, while the weight of the combined model is determined according to the prediction mean square error of the single model. The road traffic flow data of Xuancheng, Anhui Province with an observation interval of 5 min are used for experimental verification. Additionally, the feedforward neural network model, GANN model, ES model and combined model are compared and analysed, respectively. The results show that the prediction accuracy of the optimized feedforward neural network is much higher than that before the optimization. The prediction accuracy of the combined model is higher than that of the two single models, which verifies the feasibility and effectiveness of the combined model

Simulation and Analysis of the Buffer Function of Freeway Greening on Out-of-Control Vehicles

Freeway greening and traffic safety has aroused more and more attention. The purpose of this study is to investigate the role of flexible green planting in buffering out-of-control vehicles. The stopping distances of vehicle rushing into greening were tested at 8 group of initial speeds range from 5km/h to 40km/h in green belt, and a simplified mathematical model was built up to simulate the buffering process. The results indicated that the greening plants have certain buffering effect on vehicles and alleviate the crash damage within 30m of stopping distance for the vehicles under 40km/h, while the simplified mathematical model could reflect the buffering process of the plants actually by simulating the speed attenuation process, and it was found that the two types of resistance produced by the plants, i.e. counterforce of plants trunks and friction of branches and leaves, are the major factors during the vehicle deceleration.</p

Simulation and Analysis of the Buffer Function of Freeway Greening on Out-of-Control Vehicles

Freeway greening and traffic safety has aroused more and more attention. The purpose of this study is to investigate the role of flexible green planting in buffering out-of-control vehicles. The stopping distances of vehicle rushing into greening were tested at 8 group of initial speeds range from 5km/h to 40km/h in green belt, and a simplified mathematical model was built up to simulate the buffering process. The results indicated that the greening plants have certain buffering effect on vehicles and alleviate the crash damage within 30m of stopping distance for the vehicles under 40km/h, while the simplified mathematical model could reflect the buffering process of the plants actually by simulating the speed attenuation process, and it was found that the two types of resistance produced by the plants, i.e. counterforce of plants trunks and friction of branches and leaves, are the major factors during the vehicle deceleration.</p

Freeway Incident Frequency Analysis Based on CART Method

Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.</p

Analysis on Influencing Factors Identification of Crash Rates Using Tobit Model with Endogenous Variable

The objective of this study is to identify the influencing factors of crash rates from the perspective of access management techniques in urban areas. The target areas are located in the Las Vegas Metropolitan area, and 19 arterials are selected. In order to address the interdependency between crash rates and travel speeds, and left-censored issue, a tobit model with endogenous variable is presented. The structure of the tobit model addresses the left-censored issue for the segments meanwhile the endogeneity issue between crash rates and travel speeds is explained. The results indicate that there is a strong interdependency between crash rates and travel speeds. The segment length, driveway density, median opening density, posted speed limit and AADT per lane are statistically significant factors that influence crash rates on segments, moreover, crash rates are significantly influenced by two-directional median opening density. </p

Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis

Purpose – This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs. Design/methodology/approach – The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously. Findings – The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability. Originality/value – The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability

Comparison of sequencing-based and array-based genotyping platforms for genomic prediction of maize hybrid performance

Genomic selection (GS) is a powerful tool for improving genetic gain in maize breeding. However, its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms. Although sequencing-based and array-based genotyping platforms have been used for GS, few studies have compared prediction performance among platforms. In this study, we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing (GBS), a 40K SNP array, and target sequence capture (TSC) using eight GS models. The GBS marker dataset yielded the highest predictabilities for all traits, followed by TSC and SNP array datasets. We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs, and BayesB, GBLUP, and RKHS performed well, while XGBoost performed poorly in most cases. We also selected significant SNP subsets using genome-wide association study (GWAS) analyses in three panels to predict hybrid performance. GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost, but depended heavily on the GWAS panel. We conclude that there is still room for optimization of the existing SNP array, and using genotyping by target sequencing (GBTS) techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays

Transcriptomic Insights Into Root Development and Overwintering Transcriptional Memory of Brassica rapa L. Grown in the Field

As the only overwintering oil crop in the north area of China, living through winter is the primary feature of winter rapeseed. Roots are the only survival organ during prolonged cold exposure during winter to guarantee flowering in spring. However, little is known about its root development and overwintering memory mechanism. In this study, root collar tissues (including the shoot apical meristem) of three winter rapeseed varieties with different cold resistance, i.e., Longyou-7 (strong cold tolerance), Tianyou-4 (middle cold tolerance), and Lenox (cold-sensitive), were sampled in the pre-winter period (S1), overwintering periods (S2–S5), and re-greening stage (S6), and were used to identify the root development and overwintering memory mechanisms and seek candidate overwintering memory genes by measuring root collar diameter and RNA sequencing. The results showed that the S1–S2 stages were the significant developmental stages of the roots as root collar diameter increased slowly in the S3–S5 stages, and the roots developed fast in the strong cold resistance variety than in the weak cold resistance variety. Subsequently, the RNA-seq analysis revealed that a total of 37,905, 45,102, and 39,276 differentially expressed genes (DEGs), compared to the S1 stage, were identified in Longyou-7, Tianyou-4, and Lenox, respectively. The function enrichment analysis showed that most of the DEGs are significantly involved in phenylpropanoid biosynthesis, plant hormone signal transduction, MAPK signaling pathway, starch and sucrose metabolism, photosynthesis, amino sugar and nucleotide sugar metabolism, and spliceosome, ribosome, proteasome, and protein processing in endoplasmic reticulum pathways. Furthermore, the phenylpropanoid biosynthesis and plant hormone signal transduction pathways were related to the difference in root development of the three varieties, DEGs involved in photosynthesis and carbohydrate metabolism processes may participate in overwintering memory of Longyou-7 and Tianyou-4, and the spliceosome pathway may contribute to the super winter resistance of Longyou-7. The transcription factor enrichment analysis showed that the WRKY family made up the majority in different stages and may play an important regulatory role in root development and overwintering memory. These results provide a comprehensive insight into winter rapeseed's complex overwintering memory mechanisms. The identified candidate overwintering memory genes may also serve as important genetic resources for breeding to further improve the cold resistance of winter rapeseed