Flow-based accessibility measurement: The Place Rank approach

In this paper, we empirically test the viability of a flow-based approach as an alternative to transport accessibility measurement. To track where commuters travel from and to (but not commute times), we use transactional smartcard data from residents in Singapore to construct the (daily) spatial network of trips generated. We use the Place Rank method to demonstrate the viability of the flow-based approach to study accessibility. We compute the Place Rank of each of 44 planning areas in Singapore. Interestingly, even though the spatial network is constructed using only origin–destination information, we find that the travel time of the trips out of each planning area generally decreases as the area’s Place Rank increases. The same is also the case for in-vehicle time, number of transfers in the network and transfer time. This shows that a flow-based approach can be used to measure the notion of accessibility, which is traditionally assessed using travel time information in the system. We also compare Place Rank with other indicators, namely, bus stop density, eigenvector centrality, clustering coefficient and typographical coefficient to evaluate an area’s accessibility. The results show that these indicators are not as effective as the Place Rank method.     

Estimating traffic volumes for signalized intersections using connected vehicle data

Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.     

Japanese perspective of driver information systems

A perspective view of of Japanese R&D activities of Driver Information Systems is given from the standpoint of developing joint projects by public and private sectors. First, a brief history of the R&D activities is illustrated from above mentioned standpoint. Then, two major projects, AMTICS and RACS, and the social backgrounds of these projects are explained. In order to give a clear idea of both projects some technical details are treated.Based on the history and the present status of the developments, the basic design conceptions of Japanese Driver Information Systems are explained and several factors or reasons which have influenced to the design conception are discussed. Then, the promoting systems of the R&D activities are generally explained and present status of the projects and future problems in developing more advanced systems are also handled. A brief discussion on the establishment of international standard is also mentioned as a final comment.     

基于小波分析的大跨径桥梁健康监测数据预处理研究

针对小波分析方法在桥梁健康监测系统中面临的小波基函数合理选择问题,采用一周期性仿真信号作为算例,考察了在其他研究领域内广泛用来进行信号去噪的Haar、Db、Sym、Coif、Bior、Rbio、Dmey等7种小波基函数,选取出去噪效果最显著的Bb8小波基函数.依托已建立的江阴大桥健康监测系统,对具有明显周期性的塔顶GPS的竖向数据进行去噪计算,并将计算结果与采用低通滤波进行去噪的结果进行比较分析.结果表明:由于小波方法采用了比低通滤波方法更为细致的处理方法,其去噪效果比低通滤波方法更有效.     

桩一土一桥梁结构地震台试验数据处理方法

地震模拟振动台试验测点多,加载工况多,采集到的数据量大,且受噪声等因素干扰,测试数据与真实信号间有一定的差别。只有将测试数据经过科学的处理、修正和分析,去伪存真,才能得出正确的试验结果和结论。简要介绍桩一土一桥梁结构相互作用地震模拟振动台试验项目的概况,详细论述该试验数据的处理思路与方法,为以后相关试验研究提供参考。     

CAN总线技术在中通客车上的应用

介绍CAN总线技术在中通客车上的应用状况,探讨中通传统客车及新能源客车的总线数据采集和分析方法。     

1种基于浮动车数据的多车车速融合算法

在浮动车处理技术中,多浮动车样本车速的融合是整个计算的最后1个环节,算法的好坏直接影响到动态交通信息的准确性。从多权重系数和多种路况状态的角度构建了1种新的基于浮动车数据的多车车速融合算法,该算法从浮动车行驶特征等角度,综合考量在表征实时路况时浮动车多车样本间的共性与个性差异去融合多车车速,提高了实时路况的准确性,并且可根据实际交通环境快速调整相关参数。最后通过实证分析对其准确性进行了评估验证,结果表明能有效提高动态交通信息的准确性,具有良好的实用性。     

基于FCD和DAB交通信息采集与服务系统研究

城市交通问题日趋严重,交通信息化服务成为了智能交通领域又一热点。基于浮动车（floating car data FCD）和DAB（digital audio broadcasting）的交通信息采集与服务系统,对交通信息数据源、数据处理及信息解码分发至终端等方面进行了研究,阐述了系统总体结构、关键技术、核心算法。该系统通过采集处理、多源数据信息融合、信息发布和信息终端软件开发,采用TPEG（transportprotocol experts group）信息编码技术,编码成二进制码流文件,并传输给DAB数字服务器进行信息分发,进而将交通信息实时传输到移动终端,为出行者提供可靠的、科学的交通信息服务。     

基于CAN总线的柴油机数据采集系统设计

设计了基于CAN总线的柴油机运行参数采集系统,以C8051F040单片机为核心,采用传感器、信号调理模块和AT45DB161存储芯片实现数据的采集与存储,阐述了系统的硬件结构和程序设计。试验表明,设计的系统可快速准确地采集柴油机各种运行参数,实现实时采集、历史数据存储,扩展方便,经济实用。     

大数据时代天津智慧城市智能交通建设与道路交通发展展望

通过对天津市交通强国建设、京津冀交通一体化及第三届世界智能大会的介绍,从交通运输管理系统、出行服务系统、电子付费系统和交通监控指挥系统智能化升级等方面阐述了天津市逐步进入智能交通应用阶段的情况。在此基础上,论述了5G时代,在“创新、协调、绿色、开放、共享”五大发展理念的引领下,城市道路在道路横断面布置、自行车道设置、共享单车停车点布设、步行交通系统多样化等方面创新设计的思路,展望了在未来万物互联的智能世界中,低碳生态城市和以公共交通为主导的城市综合交通系统的发展前景。