In the field of freeway traffic safety research, there is an increasing focus in studies on how to reduce the frequency and severity of traffic crashes. Although many studies divide factors into “human-vehicle-road-environment” and other dimensions to construct models whichshowthe characteristic patterns of each factor's influence on crash severity, there is still a lack of research on the interaction effect of road and environment characteristics on the severity of a freeway traffic crash. This research aims to explore the influence of road and environmental factors on the severity of a freeway traffic crash and establish a prediction model towards freeway traffic crash severity. Firstly, the obtained historical traffic crash data variables were screened, and 11 influencing factors were summarized from the perspective of road and environment, and the related variables were discretized. Furthermore, the XGBoost (eXtreme Gradient Boosting) model was established, and the SHAP (SHapley Additive exPlanation) value was introduced to interpret the XGBoost model; the importance ranking of the influence degree of each feature towards the target variables and the visualization of the global influence of each feature towards the target variables were both obtained. Then, the Bayesian network-based freeway traffic crash severity prediction model was constructed via the selected variables and their values, and the learning and prediction accuracy of the model were verified. Finally, based on the data of the case study, the prediction model was applied to predict the crash severity considering the interaction effect of various factors in road and environment dimensions. The results show that the characteristic variables of road side protection facility type (RSP), road section type (LAN), central isolation facility (CIF), lighting condition (LIG), and crash occurrence time (TIM) have significant effects on the traffic crash prediction model; the prediction performance of the model considering the interaction of road and environment is better than that of the model considering the influence of single condition; the prediction accuracy of XGBoost-Bayesian Network Model proposed in this research can reach 89.05%. The identification and prediction of traffic crash risk is a prerequisite for safety improvement, and the model proposed and results obtained in this research can provide a theoretical basis for related departments in freeway safety management.
The last mile problem of E-grocery Distribution comprises one of the most costly and highest polluting components of the supply chain in which companies deliver goods to end customers. To reduce transport cost and fuel emissions, a new element of ground-based delivery services, autonomous delivery vehicles (ADVs), is included in the E-grocery distribution system for improving delivery efficiency. Thus, the objective of this study is to optimize a two-echelon distribution network for efficient E-grocery delivery, where conventional vans serve the delivery in the first echelon and ADVs serve delivery in the second echelon. The problem is formulated as a two-echelon vehicle routing problem with mixed vehicles (2E-VRP-MV) with a nonlinear objective function, in which the total transport and emission costs are optimized. This optimization is based on the flow assignment at each echelon and to realize routing choice for both the van and ADV. A two-step clustering-based hybrid Genetic Algorithm and Particle Swarm Optimization (C-GA-PSO) algorithm is proposed to solve the problem. First, the end customers are clustered to the intermediate depots, named satellites, based on the minimized distance and maximized demand. To enhance the efficiency of resolving the 2E-VRP-MV-model, a hybrid GA-PSO algorithm is adopted to solve the vehicle routing problem. Computational results of up to 21, 32, 50, and 100 customers show the effectiveness of the methods developed here. At last, the impacts of the layout of the depot-customer and customer density on the total cost are analyzed. This study sheds light on the tactical planning of the multi-echelon sustainable E-grocery delivery network.
Background: Cancer has been responsible for a large number of human deaths in the 21st century. Establishing a controllable, biomimetic, and large-scale analytical platform to investigate the tumor-associated pathophysiological and preclinical events, such as oncogenesis and chemotherapy, is necessary. Methods and Results:This study presents antitumor investigation in a parallel, largescale, and tissue-mimicking manner based on well-constructed chemical gradients and heterotypic three-dimensional (3D) tumor cocultures using a multifunctionintegrated device. The integrated microfluidic device was engineered to produce a controllable and steady chemical gradient by manipulative optimization. Array-like and size-homogeneous production of heterotypic 3D tumor cocultures with in vivolike features, including similar tumor-stromal composition and functional phenotypic gradients of metabolic activity and viability, was successfully established. Furthermore, temporal, parallel, and high-throughput analyses of tumor behaviors in different antitumor stimulations were performed in a device based on the integrated operations involving gradient generation and coculture. Conclusion:This achievement holds great potential for applications in the establishment of multifunctional tumor platforms to perform tissue-biomimetic neoplastic research and therapy assessment in the fields of oncology, bioengineering, and drug discovery.
Variational mode decomposition (VMD), a recently developed adaptive mode decomposition technique, has attracted much attention in various fields. However, due to the assumption that the obtained intrinsic mode functions should be band-limited and separable in the Fourier domain, VMD has experienced many obstacles when processing wideband nonstationary signals. In this paper, a new method named fractional iterative variational mode decomposition (FrIVMD) is proposed for the decomposition of a multicomponent linear frequency modulation signal. By accurately estimating the chirp rate of the linear frequency modulation (LFM) component, the original signal is mapped to the fractional Fourier domain by the fractional Fourier transform (FRFT), where the corresponding LFM component is narrowly banded. Then, the conventional VMD is applied to separate the components. Finally, the signal mode in the time domain is obtained by the inverse FRFT. Numerical and real-world vibration signals are employed to validate the effectiveness of the FrIVMD technique. The results prove that the proposed method performs well for noisy signals and even signals containing weak components.
Decentralized freight decision has been proved to be one of the inhibitors to achieve a sustainable transport network. One important method also a key challenge is to determine how to coordinate and consolidate the transportation flow to get the best logistics performance. This study presents an intermodal transportation network considering freight consolidation through freight forwarders’ cooperation. We formulate the problem as a minimum intermodal transport cost model, which is a nonlinear, nonconvex and discontinuous function that involves volume economies of scale, distance economies of scale and vehicle size economies of scale. A hybrid genetic algorithm (GA) and particle swarm optimization (PSO) algorithm in combination with a batch strategy are used to solve the problem. Five different transport demand scenarios are tested on a real case on “China Railway Express” (Crexpress). The choices of reasonably corridor and fleet size combination are provided.
The public housing PPP projects have encountered a cold reception from the government, which constrained solving the urban housing problem. This paper builds a dynamic game model under incomplete contract conditions, analyzes the key factors affecting the signing of PPP contracts by dynamic evolutionary game analysis, and verifies these factors by simulation. The results show that fiscal spending smoothing, risk transfer, and government performance can promote government to adopt cooperation strategy. Expected project benefits and government performance incentives can promote private capital to adopt cooperation strategy. Changes in transaction cost have a significant impact on the decision of cooperation strategy.
Although the National Transportation Safety Board has strongly recommended the application of collision avoidance warning systems and automated emergency braking (CAWS/AEB), the transit industry has lagged behind autos and trucks. CAWS/AEB for buses is more complex than for cars and trucks and represents a niche market. If bus manufacturers and suppliers are to invest in developing this technology, they will need to be convinced there is a market for it. Transit agencies also will need to be convinced that the technology works and is affordable. For both vendors and purchasers, a business case could be made that some or all of the expense of procuring and deploying the technology can be recovered through savings from reduced insurance premiums and claims payouts by preventing or mitigating collisions. This paper describes two methods for estimating the business case for equipping transit buses with CAWS/AEB. First, industrywide data on the numbers of collisions and casualty and liability expenses incurred by transit agencies were examined, and statistical relationships were modeled to determine whether reducing the numbers of collisions would reduce casualty and liability expenses. Second, individual collision claims records for specific transit agencies were examined to determine whether the collisions could have been prevented by CAWS/AEB, and the potential savings.
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