An ALNS-based approach for the traffic-police-routine-patrol-vehicle assignment problem in resource allocation analysis of traffic crashes.

OBJECTIVES: Imbalances between limited police resource allocations and the timely handling of road traffic crashes are prevalent. To optimize resource allocations and route choices for traffic police routine patrol vehicle (RPV) assignments, a dynamic crash handling response model was developed. METHODS: This approach was characterized by two objective functions: the minimum waiting time and the minimum number of RPVs. In particular, an adaptive large neighborhood search (ALNS) was designed to solve the model. Then, the proposed ALNS-based approach was examined using comprehensive traffic and crash data from Ningbo, China. RESULTS: Finally, a sensitivity analysis was conducted to evaluate the bi-objective of the proposed model and simultaneously demonstrate the efficiency of the obtained solutions. Two resolution methods, the global static resolution mode, and real-time dynamic resolution mode, were applied to explore the optimal solution. CONCLUSIONS: The results show that the optimal allocation scheme for traffic police is 13 RPVs based on the global static resolution mode. Specifically, the average waiting time for traffic crash handling can be reduced to 5.5 min, with 53.8% less than 5.0 min and 90.0% less than 10.0 min.

Investigating built environment and traffic flow impact on crash frequency in urban road networks.

While numerous studies have examined the factors that influence crash occurrence, there remains a gap in understanding the intricate relationship between built environment, traffic flow, and crash occurrences across different spatial units. This study explores how built environment attributes, and dynamic traffic flow characteristics affect crash frequency by focusing on proposed traffic density-based zones (TDZs). Utilizing a comprehensive dataset from Greater Melbourne, Australia, this research emphasizes on the dynamic traffic flow variables and insights from the Macroscopic Fundamental Diagram model, considering parameters such as shockwave velocity and congestion index. The association between the potential influencing factors and crash frequency is examined using a random parameter negative binomial regression model. Results indicate that the data segmentation based on TDZs is instrumental in establishing a more refined crash model compared to traditional planning-based zones, as demonstrated by improved goodness-of-fit measures. Factors including density (e.g., employment density), network design (e.g., road density and highway density), land use diversity (e.g., job-housing balance and land use mixture), and public transit accessibility (e.g., bus route density) are significantly associated with crash occurrence. Furthermore, the unobserved heterogeneity effects of the shockwave velocity and congestion index on crashes are revealed. The study highlights the significance of incorporating dynamic traffic flow variables in understanding crash frequency variations across different spatial units. These findings can inform optimal real-time traffic monitoring, environmental design, and road safety management strategies to mitigate crash risks.

What can we learn from the AV crashes? - An association rule analysis for identifying the contributing risky factors.

The objective of this study is to explore the contributing risky factors to Autonomous Vehicle (AV) crashes and their interdependencies. AV crash data between 2015 and 2023 were collected from the autonomous vehicle collision report published by California Department of Motor Vehicles (DMV). AV crashes were categorized into four types based on vehicle damage. AV crashes features including crash location and time, driving mode, vehicle movements, crash type and vehicle damage, traffic conditions, and among others were used as potential risk factors. Association Rule Mining methods (ARM) were utilized to identify sets of contributing risky factors that often occur together in AV crashes. Several association rules suggest that AV crashes result from complex interactions between road factors, vehicle factors, and environmental conditions. No damage and minor crashes are more likely affected by the road features and traffic conditions. In contrast, the movements of vehicles are more sensitive to severe AV crashes. Improper vehicle operations could increase the probability of severe AV crashes. In addition, results suggest that adverse weather conditions could increase the damage of AV crashes. AV interactions with roadside infrastructure or vulnerable road users on wet road surfaces during the night could potentially lead to significant loss of life and property. Furthermore, the safety effects of vehicle mode on the different AV crash damage are revealed. In some contexts, the autonomous driving mode can mitigate the risk of crash damages compared with conventional driving mode. The findings of this study should be indicative of policy measures and engineering countermeasures that improve the safety and efficiency of AV on the road, ultimately improving road transportation's overall safety and reliability.

An integrated data- and theory-driven crash severity model.

For crash severity modeling, researchers typically view theory-driven models and data-driven models as different or even conflicting approaches. The reason is that the machine-learning models offer good predictability but weak interpretability, while the latter has robust interpretability but moderate predictability. In order to alleviate the tension between them, this study proposes an integrated data- and theory-driven crash-severity model, known as Embedded Fusion model based on Text Vector Representations (TVR-EF), by leveraging the complementary strengths of both. The model specification consists of two parts. (i) the data-driven component not only mitigate the deficiencies of traditional econometric models, where one-hot encoding is frequently used and makes it impossible to observe semantic relatedness between variable categories, but also enhances the interpretability for the relationship between crash severity and potential influencing factors using the learned embedding weight matrix. (ii) In the theory-driven component, the multinomial logit model is implemented as a 2D-Convolutional Neural Network (2D-CNN) to increase flexibility and decrease dependency on prior knowledge for different crash-severity outcomes. A crash dataset from Guangdong Province, China, is utilized to estimate the TVR-EF model, which is then benchmarked against two traditional econometric models and three widely used machine-learning models. Results indicate that TVR-EF model does not only improve the predictive performance but also makes it easier to interpret.

Mechanically Robust, Recyclable, and Self-Healing Polyimine Networks.

To achieve energy saving and emission reduction goals, recyclable and healable thermoset materials are highly attractive. Polymer copolymerization has been proven to be a critical strategy for preparing high-performance polymeric materials. However, it remains a huge challenge to develop high-performance recyclable and healable thermoset materials. Here, polyimine dynamic networks based on two monomers with bulky pendant groups, which not only displayed mechanical properties higher than the strong and tough polymers, e.g., polycarbonate, but also excellent self-repairing capability and recyclability as thermosets are developed. Owing to the stability of conjugation effect by aromatic benzene rings, the final polyimine networks are far more stable than the reported counterparts, exhibiting excellent hydrolysis resistance under both alkaline condition and most organic solvents. These polyimine materials with conjugation structure can be completely depolymerized into monomers recovery in an acidic aqueous solution at ambient temperature. Resulting from the bulky pendant units, this method allows the exchange reactions of conjugation polyimine vitrimer easily within minutes for self-healing function. Moreover, the introduction of trifluoromethyl diphenoxybenzene backbones significantly increases tensile properties of polyimine materials. This work provides an effective strategy for fabricating high-performance polymer materials with multiple functions.

GRIK5 stimulates colon cancer growth and metastasis through cAMP/PKA/CADM3 signaling.

Glutamate receptor, ionotropic, kainate 5 (GRIK5) is a member of glutamate receptors participating, and the kainate receptor family has been proved to regulate cell proliferation and transformation. Our study aimed at exploring the role of GRIK5 in colon tumor progression. Three hundred and ninety eight colon cancer patients in The Cancer Genome Atlas Program (TCGA) data set and 26 clinical colon cancer patients were included for GRIK5 expression and prognosis analysis. GRIK5 overexpressed HCT116 and CT26 cell lines were established for cell proliferation and Transwell assay. Western blot analysis and immunostaining assay was conducted to evaluate the activation of activation of cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA)/cell adhesion molecular 3 (CADM3) signaling in cell lines and tumor tissues. Subcutaneous CT26-bearing mice model was established to examine GRIK5-induced tumor growth and metastasis in vivo. Our study identified GRIK5 to be upregulated in patients with colon cancer, and higher GRIK5 levels correlated with the poor overall survival in patients. In vitro, GRIK5 overexpression markedly enhanced the proliferative properties and aggressive behaviors of colon cancer cells. Mechanistically, GRIK5 induced the activation of cAMP/PKA signaling, proceeded with augmented CADM3 expression, eventually resulting in sustained tumor growth. GRIK5 was a crucial scaffold in enabling colon cancer growth and metastasis, which offered a promising target for therapeutic manipulation of colon cancer.

A resampling approach to disaggregate analysis of bus-involved crashes using panel data with excessive zeros.

Public bus constitutes more than 70% of the overall road-based public transport patronage in Hong Kong, and its crash involvement rate has been the highest among all public transport modes. Though previous studies had identified explanatory factors that affect the crash risk of buses, use of considerably imbalanced crash data with excessive zero observations could lead to inaccurate parameter estimation. This study aims to resolve the excess zero problem of disaggregate analysis of bus-involved crashes based on synthetic data using a Synthetic Minority Over-Sampling Technique for panel data (SMOTE-P). Dataset comprising crash, traffic, and road inventory data of 88 road segments in Hong Kong during the period from 2014 to 2017 is used. To assess the data balancing performance, other common data generation approaches such as Random Under-sampling of the Majority Class (RUMC) technique, Cluster-Based Under-Sampling (CBUS), and mixed resampling, are also considered. Random effect Poisson (REP) models based on synthetic data and random effect zero-inflated Poisson (REZIP) model based on original data are estimated. Results indicate that REP model based on synthetic data using SMOTE-P outperforms REZIP model based on original data and REP models based on synthetic data using RUMC, CBUS and mixed approaches, in terms of statistical fit, prediction error, and explanatory factors identified. Results of model estimation based on SMOTE-P suggest that factors including morning peak, evening peak, hourly traffic flow, average lane width, road length, bus stop density, percentage of bus in the traffic stream, and presence of bus priority lane all affect the bus-involved crash frequency. More importantly, this study provides a feasible solution for disaggregate crash analysis with imbalanced panel data.

Safety effects of law enforcement cameras at non-signalized crosswalks: A case study in China.

Pedestrians are vulnerable when crossing the street, especially at non-signalized crosswalks. In China, in spite of the priority that laws entitle the pedestrians, the yielding rates at non-signalized crosswalks are relatively low. In light of this situation, law enforcement cameras have been used to increase the percentage of drivers yielding to pedestrians. This study investigates the effectiveness of law enforcement cameras on drivers yielding behavior and vehicle-pedestrian conflicts at non-signalized crosswalks. Using Unmanned Aerial Vehicle (UAV) and roadside video recording, information including pedestrian characteristics, vehicular characteristics and environmental factors are collected. The conflict indicators used include Post-Encroachment Time (PET), Time to Collision (TTC), and Deceleration to Safety Time (DST). In this study, a conflict classification framework based on PET, TTC and DST using Support Vector Machine algorithm is employed. A multinomial logit regression model is used to identify the factors contributing to the conflicts. Then, binary logit regression models are constructed to analyze the effects of law enforcement cameras on drivers yielding behavior. Conflict study reveals that the implementation of law enforcement cameras would increase the probability of slight conflict but decrease the probability of serious conflict. Yielding behavior analysis shows that the illegitimate yielding behavior percentages are over 10 %, indicating the necessity of improving the awareness of yielding rules, and the implementation of law enforcement cameras would increase the yielding and legitimate yielding probability. Moreover, factors including the adjacent vehicle yielding behavior, number of lanes between pedestrian and vehicle, pedestrian speed change, pedestrian waiting time, pedestrian accepted gap time, vehicle upstream speed and vehicle speed change are significantly associated with conflict severity and drivers yielding behavior. We recommend that supplementary facilities and measures should be used to improve the safety performance of law enforcement cameras.

Role of exposure in bicycle safety analysis: Effect of cycle path choice.

Despite the recognized environmental and health benefits of cycling, bicyclists are vulnerable to severe injuries and mortalities in the road crashes. Therefore, it is of paramount importance to identify the possible factors that may affect the bicycle crash risk. However, reliable estimates of bicycle exposure are often not available for the safety risk evaluation of different entities. The objective of this study is to advance the estimation of exposure in the bicycle safety analysis, using the detailed origin-destination data of each trip of the London public bicycle rental system. Two approaches including shortest path method (SPM) and weighted shortest path method (WSPM) are proposed to model the bicycle path choice and to estimate the bicycle distance traveled (BDT). Then, the bicycle crash frequency models that adopt BDTs as the exposure estimated using SPM and three WSPMs are developed. Three exposure measures including bicycle trips, bicycle time traveled (BTT), and BDT are assessed. Results indicate that the bicycle crash frequency models that incorporate the BDTs using WSPM have superior model fit. Moreover, the bicycle crash frequency model that incorporate the BDTs as the exposure outperforms those that incorporate the bicycle trips and BTT as the exposures. Findings of current study are indicative to the development of bicycle crash frequency model. Moreover, it should enhance the understanding on the roles of environmental, traffic and bicyclist factors in bicycle crash risk, based on appropriate estimates of bicycle exposures. Therefore, it should be useful to the transport planners and engineers for the development of bicycle infrastructures that can improve the overall bicycle safety in the long run.

Prevalence and Spectrum of Predisposition Genes With Germline Mutations Among Chinese Patients With Bowel Cancer.

Background: Bowel cancer is the third-most common cancer and the second leading cause of cancer-related death worldwide. Bowel cancer has a substantial hereditary component; however, additional hereditary risk factors involved in bowel cancer pathogenesis have not been systematically defined. Materials and Methods: A total of 573 patients with bowel cancer were enrolled in the present study, of whom 93.72% had colorectal cancer (CRC). Germline mutations were integrated with somatic mutation information via utilizing target next-generation sequencing. Results: Pathogenic/Likely Pathogenic (P/LP) germline alterations were identified in 47 (8.2%) patients with bowel cancer and the ratio of the number of these patients with family history was significantly higher in the P/LP group than that noted in the non-pathogenic (Non-P) group. Certain rare germline alterations were noted, such as those noted in the following genes: FANCD2, CDH1, and FLCN. A total of 32 patients (68.1%) had germline alterations in the DNA-damage repair (DDR) genes and homologous recombination (HR) accounted for the highest proportion of this subgroup. By comparing 573 patients with bowel cancer with reference controls (China_MAPs database), significant associations (p < 0.01) were observed between the incidence of bowel cancer and the presence of mutations in APC, ATM, MLH1, FANCD2, MSH3, MSH6, PMS1, and RAD51D. Somatic gene differential analysis revealed a marked difference in 18 genes and a significant difference was also noted in tumor mutation burden (TMB) between germline mutation carriers and non-germline mutation subjects (p < 0.001). In addition, TMB in DDR mutation groups indicated a dramatic difference compared with the non-DDR mutation group (p < 0.01). However, no statistically significant differences in TMB were noted among detailed DDR pathways for patients with bowel cancer, irrespective of the presence of germline mutations. Moreover, a significantly higher level (p < 0.0001) of mutation count was observed in the DDR group from The Cancer Genome Atlas (TCGA) database and the DDR and non-DDR alteration groups displayed various immune profiles. Conclusion: Chinese patients with bowel cancer exhibited a distinct spectrum of germline variants, with distinct molecular characteristics such as TMB and DDR. Furthermore, the information on somatic mutations obtained from TCGA database indicated that a deeper understanding of the interactions among DDR and immune cells would be useful to further investigate the role of DDR in bowel cancer.

Roles of infrastructure and land use in bicycle crash exposure and frequency: A case study using Greater London bike sharing data.

Cycling is increasingly promoted as a sustainable transport mode. However, bicyclists are more vulnerable to fatality and severe injury in road crashes, compared to vehicle occupants. It is necessary to identify the contributory factors to crashes and injuries involving bicyclists. For the prediction of motor vehicle crashes, comprehensive traffic count data, i.e. AADT and vehicle kilometer traveled (VKT), are commonly available to proxy the exposure. However, extensive bicycle count data are usually not available. In this study, revealed bicycle trip data of a public bicycle rental system in the Greater London is used to proxy the bicycle crash exposure. Random parameter negative binomial models are developed to measure the relationship between possible risk factors and bicycle crash frequency at the zonal level, based on the crash data in the Greater London in 2012-2013. Results indicate that model taking the bicycle use time as the exposure measure is superior to the other counterparts with the lowest AIC (Akaike information criterion) and BIC (Bayesian information criterion). Bicycle crash frequency is positively correlated to road density, commercial area, proportion of elderly, male and white race, and median household income. Additionally, separate bicycle crash prediction models are developed for different seasons. Effects of the presence of Cycle Superhighway and proportion of green area on bicycle crash frequency can vary across seasons. Findings of this study are indicative to the development of bicycle infrastructures, traffic management and control, and education and enforcement strategies that can enhance the safety awareness of bicyclists and reduce their crash risk in the long run.

Comparison of empirical Bayes and propensity score methods for road safety evaluation: A simulation study.

Statistical evaluation of road safety interventions can be undertaken using a variety of different approaches, typically requiring different assumptions to obtain causal identification. In this paper, we conduct a simulation study to compare the performance of empirical Bayes (EB) and propensity score (PS) based methods, which have featured prominently in the recent literature, in settings with and without violation of key assumptions. The estimators considered include EB, inverse probability weighting (IPW), and Doubly Robust (DR) estimation. We find that while the EB approach has good finite sample properties when model assumptions are met, the consistency of this estimator is substantially diminished when the reference and treated sites follow different functions. The IPW estimator performs well in large samples, but requires a correctly specified PS model with sufficient overlap in covariate distributions between treated and control units. The DR estimator allows for violation of assumptions in either the regression or PS model, but not both. We find that this added level of robustness affords overall better performance than attained via EB or IPW estimation.

Construction of flame retardant nanocoating on ramie fabric via layer-by-layer assembly of carbon nanotube and ammonium polyphosphate.

A new flame retardant nanocoating has been constructed by the alternate adsorption of polyelectrolyte amino-functionalized multiwall carbon nanotube (MWNT-NH2) and ammonium polyphosphate (APP) onto flexible and porous ramie fabric. Scanning electron microscopy indicates that the adsorbed carbon nanotube coating is a randomly oriented and overlapped network structure, which is a promising candidate for flame retardancy applications. Attenuated total reflection Fourier transform infrared spectroscopy and energy-dispersive X-ray analysis confirm that the APP is successfully incorporated into the multilayers sequentially. Assessment of the thermal and flammability properties for the pristine and nanocoated ramie fabrics shows that the thermal stability, flame retardancy and residual char are enhanced as the concentration of MWNT-NH2 suspension and number of deposition cycles increases. The enhancements are mostly attributed to the barrier effect of intumescent network structure, which is composed of MWNT-NH2 and the absorbed APP.