This study investigates the personal and societal impacts of motorcycle ban policy on the home-to-work morning commute of motorcyclists using self-reported travel behavioral data and stated travel mode shift response under such policy collected in Foshan City, China. The policy aims to force all motorcyclists in the city to shift to walk, bike, bus, or car mode by 2020. The complex impacts of the policy on travel mode shifts are studied across population sub-groups defined by gender and residential status (migrants and residents). For model estimation, random parameters multinomial logit models with heterogeneity in parameter means and variances were estimated to better track unobserved heterogeneity compared to their counterpart models with fixed means and variances. Model estimation results show that contributing factors of travel mode shift response include individual and household sociodemographic characteristics, travel-related behaviors, and residential location factors. In addition, out-of-pocket cost, opportunity cost of travel time, emissions, and energy consumption before and after motorcyclists would make their travel mode shifts and safety-related impacts were quantified and compared across the population sub-groups. The results show that motorcyclists, on average, would experience significant increases in their out-of-pocket cost and opportunity cost of travel time, particularly for male migrant motorcyclists. These results suggest that policy and infrastructural support for using public transit, walk, and bike modes, household mobility, and plan to purchase a car were likely to affect the personal and societal impacts of the motorcycle ban policy on travel mode shifts.

Personal and societal impacts of motorcycle ban policy on motorcyclists’ home-to-work morning commute in China

In order to achieve the goal of mutual coordination between the urban construction and sustainable development of geological and ecological environment, the evaluation method and planning strategy for urban geological and ecological environment quality are proposed. In this study, taking the urban planning of Wansheng Economic Development Zone in Chongqing as an example, the status of geological and ecological environment quality from 2007 to 2019 is analyzed. Twenty evaluation indicators from the five aspects of land, hydrologic, resource, ecologic, and geological environment are selected to construct an evaluation system of urban geological and ecological environment quality. The evaluation indicator system of urban geological and ecological environment quality is established, the weights are determined with the analytic hierarchy process (AHP), and the grading standard of evaluation indicator is given. The urban geological and ecological environment quality is assessed in the way of fuzzy comprehensive evaluation, and the countermeasures for urban and rural planning are proposed. The results show that this system can improve the quality of geological and ecological environment in Wansheng Economic Development Zone in Chongqing; the geological and ecological environment quality of the eco-industrial functional zone 2 (F6) and the eco-industrial functional zone 3 (F7) has been maintained well, while the environment quality of other functional zones has shown a significant downward trend. Finally, the planning suggestions are adapting to the urban geological and ecological environment from three aspects of geological, ecological, and artificial environment. The research in this study provides a good guideline for the overall planning development of the city and rapid assessment of the ecological environment quality.

Evaluation and planning of urban geological and ecological environment quality

The purpose of this paper is to evaluate remaining useful life (RUL) of machine tools, which is a key indicator for equipment maintenance and health management. The proposed method takes the factors of reliability, maintainability and economy into account and constructs the evaluation index system based on analytical hierarchy process (AHP) method. Euclid approach degree is then calculated through the fuzzy matter-element model in order to assess the health condition and determine RUL. An application example of a heavy-duty machine tool is provided to demonstrate the feasibility in engineering practice, results that are compared with fuzzy comprehensive evaluation and grey relational analysis illustrate the reliability and accuracy of the method.

Remaining useful life assessment of machine tools based on AHP method and Euclid approach degree

The localization of continuous objects and the scheduling of resources are challenging issues in wireless sensor networks (WSNs). Due to the irregular shape of the continuous target area and the sensor deployment in WSNs, the sensor data are always discrete and sparse, and most network resources are also limited by the node energy. To achieve faster detection and tracking of continuous objects, we propose a convolution-based continuous object localization algorithm (named CCOL). Moreover, we implement the idea of greedy and dynamic programming to design an energy-saving and efficient strategy model (named MSSM) to respond to emergencies caused by multiple continuous targets in most specific WSNs. The simulation experiments demonstrate that CCOL is superior to other localization algorithms in terms of time complexity and execution performance. Furthermore, the feasibility of the multinode scheduling strategy is verified by setting different mobile nodes to respond to the target area in certain green WSNs.

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A Convolution Location Method for Multi-Node Scheduling in Wireless Sensor Networks

Efficient Virtual Machine (VM) placement and task scheduling is considered a major challenge in cloud computing, given that the scheduling results directly affect user satisfaction and vendor benefits. This paper investigates the VM and task joint scheduling (VTJS) problem, and establishes a multi-objective mathematical model with the aim to minimize makespan, cost, and total tardiness. To solve this problem, a problem-specific three-layer encoding approach is designed, and a decomposition-based multi-objective evolutionary algorithm with pre-selection and dynamic resource allocation (MOEA/D-PD) is proposed, in which a customized two-stage guided local search method is also embedded. In MOEA/D-PD, a classifier model is built to filter the offspring solutions in decision space so that only promising solutions are evaluated, and computational resources are dynamically allocated to the subproblems on the bases of their contributions. The proposed algorithm is validated on a series of instances of different scales and compared with six state-of-the-art MOEAs. Experimental results show that the proposed algorithm outperforms the most known approaches from the literature. 

Decomposition-based multi-objective evolutionary algorithm for virtual machine and task joint scheduling of cloud computing in data space

Cloud computing seems to be the result of advancements in distributed computing, parallel computing, and network computing. The management and allocation of cloud resources have emerged as a central research direction. An intelligent resource allocation system can significantly minimize the costs and wasting of resources. In this paper, we present a task scheduling technique based on the advanced Phasmatodea Population Evolution (APPE) algorithm in a heterogeneous cloud environment. The algorithm accelerates up the time taken for finding solutions by improving the convergent evolution of the nearest optimal solutions. It then adds a restart strategy to prevent the algorithm from entering local optimization and balance its exploration and development capabilities. Furthermore, the evaluation function is meant to find the best solutions by considering the makespan, resource cost, and load balancing degree. The results of the APPE algorithm being tested on 30 benchmark functions show that it outperforms similar algorithms. Simultaneously, the algorithm solves the task scheduling problem in the cloud computing environment. This method has a faster convergence time and greater resource usage when compared to other algorithms.

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Task Scheduling in Cloud Computing Environment Using Advanced Phasmatodea Population Evolution Algorithms

An adaptive stochastic central force optimisation algorithm for node localisation in wireless sensor networks

Launching the evaluation research of green transportation can grasp the green transportation implementation accurately and diagnose the existing problems. From the perspective of green transportation, this research chose Dongguan “motorcycle ban” policy as the research object, and constructed secondary evaluation index system in transportation and environmental harmony, transportation and future harmony, transportation and society harmony, transportation and resource harmony, four dimensions. Then it used fuzzy comprehensive evaluation model to do the evaluation and found that the policy’s positive effect on the Dongguan green transportation development was average. Finally, based on the evaluation results, measures and advices were proposed.

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Fuzzy Comprehensive Evaluation Research of Goal Attainment of Green Transportation Development Policy —Take Dongguan “Motorcycle Ban” Policy as an Example

A Hybrid Parallel Algorithm With Multiple Improved Strategies

Heuristic optimization algorithms have been proved to be powerful in solving nonlinear and complex optimization problems; therefore, many effective optimization algorithms have been applied to solve optimization problems in real-world scenarios. This paper presents a modification of the recently proposed Gaining–Sharing Knowledge (GSK)-based algorithm and applies it to optimize resource scheduling in the Internet of Vehicles (IoV). The GSK algorithm simulates different phases of human life in gaining and sharing knowledge, which is mainly divided into the senior phase and the junior phase. The individual is initially in the junior phase in all dimensions and gradually moves into the senior phase as the individual interacts with the surrounding environment. The main idea used to improve the GSK algorithm is to divide the initial population into different groups, each searching independently and communicating according to two main strategies. Opposite-based learning is introduced to correct the direction of convergence and improve the speed of convergence. This paper proposes an improved algorithm, named parallel opposition-based Gaining–Sharing Knowledge-based algorithm (POGSK). The improved algorithm is tested with the original algorithm and several classical algorithms under the CEC2017 test suite. The results show that the improved algorithm significantly improves the performance of the original algorithm. When POGSK was applied to optimize resource scheduling in IoV, the results also showed that POGSK is more competitive.

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Peilin Song

Papers

Task Scheduling in Cloud Computing Environment Using Advanced Phasmatodea Population Evolution Algorithms

An adaptive stochastic central force optimisation algorithm for node localisation in wireless sensor networks

Fuzzy Comprehensive Evaluation Research of Goal Attainment of Green Transportation Development Policy —Take Dongguan “Motorcycle Ban” Policy as an Example

A Hybrid Parallel Algorithm With Multiple Improved Strategies

A New Gaining-Sharing Knowledge Based Algorithm with Parallel Opposition-Based Learning for Internet of Vehicles