电动汽车充电桩安全管理研究

随着国家对环境保护和能源节约的重视以及新能源政策的实施,电动汽车行业迎来了快速发展。充电基础设施的建设,是加快电动汽车推广的重要需要。电动汽车充电基础设施建设和运营业务发展的机会也极大,随之而来的安全管理工作要求也越来越高。本文依据充电桩建设和运营的特点,剖析其中面临的安全风险,并提出加强安全管理工作的措施,为充电桩企业安全管理运营提供参考和借鉴。     

Assessing the representativeness of a smartphone-based household travel survey in Dar es Salaam,Tanzania

The household travel survey (HTS) finds itself in the midst of rapid technological change. Traditional methods are increasingly being sidelined by digital devices and computational power—for tracking movements, automatically detecting modes and activities, facilitating data collection, etc.. Smartphones have recently emerged as the latest technological enhancement. FMS is a smartphone-based prompted-recall HTS platform, consisting of an app for sensor data collection, a backend for data processing and inference, and a user interface for verification of inferences (e.g., modes, activities, times, etc.). FMS, has been deployed in several cities of the global north, including Singapore. This paper assesses the first use of FMS in a city of the global south, Dar es Salaam. FMS in Dar was implemented over a 1-month period, among 581 adults chosen from 300 randomly selected households. Individuals were provided phones with data plans and the FMS app preloaded. Verification of the collected data occurred every 3 days, via a phone interview. The experiment reveals various social and technical challenges. Models of individual likelihood to participate suggest little bias. Several socioeconomic and demographic characteristics apparently do influence, however, the number of days fully verified per individual. Similar apparent biases emerge when predicting the likelihood of a given day being verified. Some risk of non-random, non-response is, thus, evident.     

The identification and empirical characterization of vehicular (Lagrangian) fundamental diagrams in multilane traffic flow

Traditional macroscopic traffic flow modeling framework adopts the spatial–temporal coordinate system to analyze traffic flow dynamics. With such modeling and analysis paradigm, complications arise for traffic flow data collected from mobile sensors such as probe vehicles equipped with mobile phones, Bluetooth, and Global Positioning System devices. The vehicle‐based measurement technologies call for new modeling thoughts that address the unique features of moving measurements and explore their full potential. In this paper, we look into the concept of vehicular fundamental diagram (VFD) and discuss its engineering implications. VFD corresponds to a conventional fundamental diagram (FD) in the kinematic wave (KW) theory that adopts space–time coordinates. Similar to the regular FD in the KW theory, VFD encapsulates all traffic flow dynamics. In this paper, to demonstrate the full potential of VFD in interpreting multilane traffic flow dynamics, we generalize the classical Edie's formula and propose a direct approach of reconstructing VFD from traffic measurements in the vehicular coordinates. A smoothing algorithm is proposed to effectively reduce the nonphysical fluctuation of traffic states calculated from multilane vehicle trajectories. As an example, we apply the proposed methodology to explore the next‐generation simulation datasets and identify the existence and forms of shock waves in different coordinate systems. Our findings provide empirical justifications and further insight for the Lagrangian traffic flow theory and models when applied in practice. Copyright © 2015 John Wiley & Sons, Ltd.     

Biogeography-based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task-time management

Autonomous Underwater Vehicles (AUVs) are capable of spending long periods of time for carrying out various underwater missions and marine tasks. In this paper, a novel conflict-free motion planning framework is introduced to enhance underwater vehicle’s mission performance by completing maximum number of highest priority tasks in a limited time through a large scale waypoint cluttered operating field, and ensuring safe deployment during the mission. The proposed combinatorial route-path planner model takes the advantages of the Biogeography-Based Optimization (BBO) algorithm toward satisfying objectives of both higher-lower level motion planners and guarantees maximization of the mission productivity for a single vehicle operation. The performance of the model is investigated under different scenarios including the particular cost constraints in time-varying operating fields. To show the reliability of the proposed model, performance of each motion planner assessed separately and then statistical analysis is undertaken to evaluate the total performance of the entire model. The simulation results indicate the stability of the contributed model and its feasible application for real experiments.     

Some insights in novel risk modeling of liquefied natural gas carrier maintenance operations

This study discusses the analysis of various modeling approaches and maintenance techniques applicable to the Liquefied Natural Gas (LNG) carrier operations in the maritime environment. Various novel modeling techniques are discussed; including genetic algorithms, fuzzy logic and evidential reasoning. We also identify the usefulness of these algorithms in the LNG carrier industry in the areas of risk assessment and maintenance modeling.     

Enabling a viable technique for the optimization of LNG carrier cargo operations

In this study, we optimize the loading and discharging operations of the Liquefied Natural Gas (LNG) carrier. First, we identify the required precautions for LNG carrier cargo operations. Next, we prioritize these precautions using the analytic hierarchy process (AHP) and experts’ judgments, in order to optimize the operational loading and discharging exercises of the LNG carrier, prevent system failure and human error, and reduce the risk of marine accidents. Thus, the objective of our study is to increase the level of safety during cargo operations.     

Characteristics analysis for travel behavior of transportation hub passengers using mobile phone data

The travel behavior of passengers from the transportation hub within the city area is critical for travel demand analysis, security monitoring, and supporting traffic facilities designing. However, the traditional methods used to study the travel behavior of the passengers inside the city are time and labor consuming. The records of the cellular communication provide a potential huge data source for this study to follow the movement of passengers. This study focuses on the passengers’ travel behavior of the Hongqiao transportation hub in Shanghai, China, utilizing the mobile phone data. First, a systematic and novel method is presented to extract the trip information from the mobile phone data. Several key travel characteristics of passengers, including passengers traveling inside the city and between cities, are analyzed and compared. The results show that the proposed method is effective to obtain the travel trajectories of mobile phone users. Besides, the travel behavior of incity passengers and external passengers are quite different. Then, the correlation analysis of the passengers’ travel trajectories is provided to research the availability of the comprehensive area. Moreover, the results of the correlation analysis further indicate that the comprehensive area of the Hongqiao hub plays a relatively important role in passengers’ daily travel.

     

Visualisation of uncertainty in probabilistic traffic models for policy and operations

This study investigates different methods to visualise uncertainty in static representations of probabilistic traffic models predictions on road-networks. Although various graphical cues may be used to represent uncertainty it is not a priori clear which of them are most suited for this purpose, since their legibility, intelligibility and the degree to which they interfere with other graphical elements in a representation differ widely. Several graphical uncertainty representations were therefore developed and analysed in expert sessions. A selection of the initial set of uncertainty visualisations was further evaluated in a cognitive alternative task-switching experiment. The results show that graphical representations are able to convey uncertainty information relatively accurately, while some uncertainty visualisations outperform others. It depends on the model and scenario which representation is most suited for a given application. This paper presents an overview of possible graphic uncertainty representations and the considerations involved when applying them to uncertainty in traffic model visualisations.     

Key technologies of ship remote control system in inland waterways under ship-shore cooperation conditions北大核心CSCD

［Objective ］ To meet the requirements of remotely controlling ship in curved, narrow and crowded inland waterways, this paper proposes an approach that consists of CNN-based algorithms and knowledge based models under ship-shore cooperation conditions. ［Method］On the basis of analyzing the characteristics of ship-shore cooperation, the proposed approach realizes autonomous perception of the environment with visual simulation at the core and navigation decision-making control based on deep reinforcement learning, and finally constructs an artificial intelligence system composed of image deep learning processing, navigation situation cognition, route steady-state control and other functions. Remote control and short-time autonomous navigation of operating ships are realized under inland waterway conditions, and remote control of container ships and ferries is carried out. ［Results］The proposed approach is capable of replacing manual work by remote orders or independent decision-making, as well as realizing independent obstacle avoidance, with a consistent deviation of less than 20 meters. ［Conclusions］The developed prototype system carries out the remote control operation demonstration of the above ship types in such waterways as the Changhu Canal Shenzhou line and the Yangtze River, proving that a complete set of algorithms with a CNN and reinforcement learning at the core can independently extract key navigation information, construct obstacle avoidance and control awareness, and lay the foundation for inland river intelligent navigation systems. © 2022 Journal of Clinical Hepatology. All rights reserved.