Real-Time Transportation Mode Detection Using Smartphones and Artificial Neural Networks: Performance Comparisons Between Smartphones and Conventional Global Positioning System Sensors

Traditionally, traffic monitoring requires data from traffic cameras, loop detectors, or probe vehicles that are usually operated by dedicated employees. In efforts to reduce the capital and operational costs associated with traffic monitoring, departments of transportation have explored the feasibility of using global positioning system (GPS) data loggers on their probe vehicles that are postprocessed for analyzing the traffic patterns on desired routes. Furthermore, most cell phones are equipped with embedded assisted-GPS (AGPS) chips, and if the mode of transportation the phone is in can be anonymously identified, the phones can be treated as if they are probe vehicles that are voluntarily hovering throughout the city, at minimal additional costs. Emerging cell phones known as “smartphones” are equipped with additional sensors including an accelerometer and magnetometer. The accelerometer can directly measure the acceleration values, as opposed to having acceleration values derived from speed values in conventional GPS sensors. The magnetometer can measure mode-specific electromagnetic levels. Smartphones are subscribed with roadside Internet data plans that can provide an essential platform for real-time traffic monitoring. In this article, neural network-based artificial intelligence is used to identify the mode of transportation by detecting the patterns of distinct physical profile of each mode that consists of speed, acceleration, number of satellites in view, and electromagnetic levels. Results show that newly available values in smartphones improve the mode detection rates when compared with using conventional GPS data loggers. When smartphones are in known orientations, they can provide three-dimensional (3-D) acceleration values that can further improve mode detection accuracies.     

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地铁走行轨杂散电流的存在,会对埋在地下的金属产生电化学腐蚀作用。在实时监测系统中,通过改变电路的结构与电源正极在电路中的接入点,模拟普通走行轨、交叉点、分叉点和列车运行过程中的杂散电流,进而分析在不同地质条件下杂散电流对金属的腐蚀情况。通过对实时监测模拟装置中的工作电流的变化,研究杂散电流的大小和分布规律,为在城市轨道交通的设计、施工和旧线改造过程中削弱或减小杂散电流,提供相关的实验数据。     

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北京地铁5号线通信系统除传输、无线通信系统外的公务电话、专用电话、闭路电视监视、广播、时钟、通信电源及接地、通信设备集中监测告警等共7个子系统介绍。     

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该系统利用微机检测船用主机缸套冷却水进出机温度等参数,及时输出控制量给变频器,调节海水泵转速,使冷却水温度保持恒定。同时利用软硬件相结合的方法成功解决了船舶机舱中的干扰,特别是变频器的干扰。     

浅谈辽宁省高速公路联网监控系统改造规划

对辽宁省高速公路联网监控系统改道规划进行了详细论述。     

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盾构井在盾构吊装期间,侧向压力主要由临时环梁承担,开孔周边受力情况较为复杂。本文通过对地铁车站盾构井处临时环梁进行三维建模计算,并与传统二维计算结果进行对比,得出如下结论： 1）二维计算存在局部弯矩过大及配筋过于保守等缺点,有一定的优化余地; 2）相对于二维计算,三维整体建模计算结果更加合理,得出的结构尺寸及配筋结果对于类似工程具有一定的参考意义。     

防灾安全监控系统CAD软件设计与应用

首先介绍了防灾安全监控系统CAD软件的编制目的,然后详细介绍了软件主要模块的设计方法和应用情况,最后对软件下一步的发展进行了展望.     

深港隧道下穿运营地铁和商业街地层沉降控制技术

介绍广深港客运专线深港隧道下穿深圳地铁1号线、地下商业街工程,综合应用拱部高压水平旋喷桩、长管棚、超前深孔预注浆作为隧道超前支护,采用洞桩法施做钻孔灌注桩和桩间咬合旋喷桩作为隧道两侧的围护结构,CRD法开挖和支护,以及施工过程实时监控量测,及时反馈、指导施工,通过小导洞对隧道上方地层实施加固补偿注浆,分层分部位加固土体,精确控制沉降,确保隧道上方建筑物的结构安全和地铁运行安全。     

基于物联网技术的轨道电路轨旁设备强电危害监测系统研究

轨道电路轨旁设备是铁路信号系统的重要组成部分,其安装位置的电磁环境较为复杂,且容易遭受雷电、工频、电力故障等引起的强电危害.本文研究了一种基于物联网技术的轨道电路轨旁设备强电危害监测系统.该系统主要由轨旁监测设备、云端设备和监测终端组成,具有良好的功耗控制和扩展性,实现了对现场强电危害水平的量化分析,并提供了较为直观的...     

制动误踩油门电子制动控制系统

介绍一种制动误踩油门电子制动控制系统的构成、工作原理,并在实车上模拟运行验证其可行性。