With the advent of the era of big data, data has become bigger than ever. Recently, as a fundamental task of pattern recognition, predict and data mining, feature selection has aroused wide public concern. However, extant methods on feature selection have an $O(\left|C\right|^x\left|U\right|^y)$ time complexity, which is the bottleneck preventing people from exploring knowledge in large-scale or high-dimensional datasets. Based on bijective soft sets, we propose a new rationale for feature selection, which can help break that bottleneck. Subsequently, this paper proposes an $O(\left|U\right|)$ feature-selection method whose computational time increases linearly only with the number of instances. To validate the proposed method, we conduct extensive experiments on the University of California Irvine (UCI) datasets in which large-scale and high-dimensional datasets containing four million instances and over three million features are included. The results reveal that the proposed method is an efficient, effective, and outperforms traditional methods in runtime, which can save massive computing resources. Moreover, the proposed method can be applied to feature selection for large-scale and gigantic-dimensional datasets, which are difficult to process with traditional methods.

Identifying and quantifying the influential factors on incident clearance time can benefit incident management for accident causal analysis and prediction, and consequently mitigate the impact of non-recurrent congestion. Traditional incident clearance time studies rely on either statistical models with rigorous assumptions or artificial intelligence (AI) approaches with poor interpretability. This paper proposes a novel method, gradient boosting decision trees (GBDTs), to predict the nonlinear and imbalanced incident clearance time based on different types of explanatory variables. The GBDT inherits both the advantages of statistical models and AI approaches, and can identify the complex and nonlinear relationship while computing the relative importance among variables. One-year crash data from Washington state, USA, incident tracking system are used to demonstrate the effectiveness of GBDT method. Based on the distribution of incident clearance time, two groups are categorized for prediction with a 15-min threshold. A comparative study confirms that the GBDT method is significantly superior to other algorithms for incidents with both short and long clearance times. In addition, incident response time is found to be the greatest contributor to short clearance time with more than 41% relative importance, while traffic volume generates the second greatest impact on incident clearance time with relative importance of 27.34% and 19.56%, respectively.

The vehicle routing problem (VRP) is a well-known combinatorial optimization issue in transportation and logistics network systems. There exist several limitations associated with the traditional VRP. Releasing the restricted conditions of traditional VRP has become a research focus in the past few decades. The vehicle routing problem with split deliveries and pickups (VRPSPDP) is particularly proposed to release the constraints on the visiting times per customer and vehicle capacity, that is, to allow the deliveries and pickups for each customer to be simultaneously split more than once. Few studies have focused on the VRPSPDP problem. In this paper we propose a two-stage heuristic method integrating the initial heuristic algorithm and hybrid heuristic algorithm to study the VRPSPDP problem. To validate the proposed algorithm, Solomon benchmark datasets and extended Solomon benchmark datasets were modified to compare with three other popular algorithms. A total of 18 datasets were used to evaluate the effectiveness of the proposed method. The computational results indicated that the proposed algorithm is superior to these three algorithms for VRPSPDP in terms of total travel cost and average loading rate.