An efficient freight transportation system is a core part of the modern urban logistics. The transportation system depends on an efficient urban road network. In urban road networks, the failure of ...some components leads to others failing in succession, triggering cascading failures. This may cause the collapse of the freight transportation system. A cascading failure model is proposed for analyzing the propagation of failures on urban road networks, which can provide guidance for the planning of freight transportation routes in case of emergency. When analyzing the cascading failure process, failed components are commonly deleted permanently. Travelers will avoid failed components, resulting in no increase in traffic flow on the failed components. At the same time, vehicles on these failed components will evacuate slowly to alleviate congestion. Considering this point, this paper proposes a dynamic cascading failure model. This model is established based on a hybrid routing strategy in which traffic flow is distributed according to global and local information. Specifically, the new input traffic demand is assigned along efficient paths in an urban road network (using global information). Efficient paths are determined according to the weights of all edges in urban road networks. Meanwhile, failed edges transfer some traffic flow to their normally working neighbors by self-organization (using local information). The proportion of transferred traffic flow is determined by the residual capacity of the failed edges' normally working neighbors. The feasibility of the proposed model is verified in a partial road network in Changchun, China.
Ionic liquids (ILs) have been explored as a surface modification strategy to promote the oxygen reduction reaction (ORR) on Pt/C and their chemical structures were identified to have strong influence ...on the ORR activities. To better understand the roles of anion and cation of ILs on the catalytic reaction, two cations (MTBD+ and bmim+) were paired with three anions (TFSI−, beti−, and C4F9SO3−) to form various IL structures. By systematically varying the IL combinations and studying their effects on the electrochemical behaviors, such as electrochemical surface area and specific ORR activities, it was found that cation structure had a higher influence than anion, and the impact of the MTBD+ series was stronger than the bmim+ series. In addition to the investigation in the half-cell, studies were also extended to the membrane electrode assembly (MEA). Considerable performance enhancements were demonstrated in both the kinetic region and high current density region with the aid of IL. This work suggests that IL modification can provide a complementary approach to improve the performance of proton exchange membrane fuel cells.
This article proposes a machine learning–based travel mode detection method using urban residents’ travel routes as the data source, collected via smartphone global positioning system modules. A ...data-driven machine learning strategy was chosen in the model construction. This study performed data cleaning and mining on over 4400 pieces of urban resident travel records containing several millions of global positioning system tracking points. Series of characteristic values of speed, travel distance, and direction are calculated, which reflect the travel mode of smartphone holders. In travel mode identification, first, the transition regions of travel segments of different travel modes are effectively distinguished; then, continuous tracking points for single-mode travel are connected into single-mode travel segments. The travel mode of the surveyed subjects is identified based on the calculated features of average speed, average acceleration, and average change of direction within each single-mode segment. The random forest method is chosen as the basis model to classify travel mode. Three-quarters of the travel records were used to construct the random forest classifier, and the detection accuracy of the established model for the remaining ¼ of the travel record reached 94.4%. The proposed method uses massive smartphone global positioning system tracking points as the basis; the detection results are consistent with manually collected prompted recall survey records.
The vehicle trajectory data is a feasible way for us to understand and reveal urban traffic conditions and human mobility. Therefore, it is extremely valuable to have a fine-grained picture of ...large-scale vehicle trajectory data, particularly in two different modes, taxis and buses, over the same period at an urban scale. This paper integrates the trajectory data of approximately 7,000 taxis and 1,500 buses in Changchun City, China and accesses the temporal geographically-explicit network of public transport via sequential snapshots of vehicle trajectory data every 30 seconds of the first week in March 2018. In order to reveal urban traffic conditions and human mobility, we construct two-layer urban traffic network (UTN) between these two different transport modes, take crossings as nodes and roads as edges weighted by the volume or average speed of vehicles in each hour. We released this temporal geographically-explicit network of public transport and the dynamics, weighted and directed UTN in simple formats for easy access.
Walking habits can affect the self-organizing movement in pedestrian flow. In China, pedestrians prefer to walk along the right-hand side in the collision-avoidance process, and the same is true for ...the left-hand preference that is followed in several countries. Through experiments with pedestrian flow, we find that the relative position between pedestrians can affect their moving preferences. We propose a kind of collision-avoidance force based on the social force model, which considers the predictions of potential conflict and the relative position between pedestrians. In the simulation, we use the improved model to explore the effect of moving preference on the collision-avoidance process and self-organizing pedestrian movement. We conclude that the improved model can bring the simulation closer to reality and that moving preference is conducive to the self-adjustment of counterflow.
This paper presents a dilemma-zone (DZ) avoidance-guiding system for vehicles approaching an intersection. The purpose of the system is to assist drivers in determining the driving behavior and to ...prevent vehicles from being caught in a DZ at the onset of a yellow phase. The optimal driving behavior is determined through warning information or a detailed guiding strategy. To calculate the guiding strategies, a DZ-guiding algorithm is proposed with a special focus on the vehicle DZ state and the interaction between vehicles. A simulation-based study proved the function of the proposed system and the effectiveness of the algorithm. It is found that, based on the conditions of driver's comfort and car-following safety, the guiding system can provide proper guidance for vehicles and can determine the optimal driving behavior in advance.
The accurate diagnosis of Alzheimer’s disease (AD) in the early stages, such as significant memory concern (SMC) and mild cognitive impairment (MCI), is essential in order to slow its progression ...through timely treatment. Recent achievements have shown that fusing multimodal neuroimaging data effectively facilitates AD diagnosis. However, most proposed fusion methods simply add or concatenate multimodal features and do not make full use of nonlinear features and texture features across the range of modalities. This paper proposes a diagnostic model that effectively diagnoses AD in different stages by fusing functional magnetic resonance imaging (fMRI) and structural MRI (sMRI) information. First, fMRI and sMRI scans are preprocessed, and mean regional homogeneity (mReHo) transformation is performed for the preprocessed fMRI scans. Then, 3DMR-PCANet extracts features of mReHo images. The basic ResNet module is stacked to build a 3DResNet-10 model for feature extraction of sMRI scans. Next, two image features are fused by kernel canonical correlation analysis. Finally, a support vector machine (SVM) is utilized for the classification of fused features. Experimental results on the Alzheimer's Disease Neuroimaging dataset demonstrate the effectiveness of the proposed method. Specifically, this method improves on the accuracy, specificity, sensitivity, F1 value and area under the curve (AUC) of existing methods in comparisons of the normal control (NC) versus SMC, NC versus MCI, NC versus AD, SMC versus MCI, SMC versus AD, and MCI versus AD groups, which confirms that the proposed method can mine information on the correlation between fMRI and sMRI data of the same subject and can effectively classify AD patients in different stages.
A transportation (automotive service) facility location problem is important in urban infrastructure planning and construction. To handle it, researchers have proposed a number of stochastic/random ...models for locating an automotive service enterprise. However, most of them fail to describe all kinds of uncertainty, e.g., data imprecision. By considering regional constraints, this work proposes a new random fuzzy cost-profit equilibrium model by using uncertainty data and management methods. It presents a hybrid algorithm integrating stochastic fuzzy simulation and particle swarm optimization to solve the location problem of an automobile service enterprise. In addition, since risk factors can impact a decision, this work conducts a risk performance analysis when locating an automotive service enterprise. A practical example is given to illustrate the proposed model and algorithm.
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