Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.     

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.     

基于GPS和GSM的清管器智能跟踪系统

为了在清管施工中实时跟踪清管器,设计一种基于GPS系统和GSM通信系统的智能清管器跟踪系统,系统包括发射机、中继器和监控系统。发射机搭载在清管器上持续发射电磁信号和静磁信号,中继器预先埋设在设定的跟踪点接收和转发通过信息;清管器通过跟踪点时,中继器接收发射机发出的信号,精确识别出清管器并将通过时间和跟踪点GPS位置信息通过GSM网络发送至监控系统;监控系统接收到信息后,在监控界面地图上实时显示通过位置和通过时间。清管器卡堵时,系统可及时在地图上指示卡堵区间和到达卡堵现场路径。现场应用表明:该系统实现了清管器智能跟踪,通过时间和通过位置记录精确,降低清管施工中安全风险,节约成本。     

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.     

电动汽车大数据处理活动中的伦理问题研究

为了解车辆零部件运行状态,分析整车故障原因,技术人员通过车载终端把电动汽车的整车控制系统各节点发送至大数据平台进行查阅、分析和下载使用.大数据的使用引发了信息工程领域的伦理问题.本文重点阐述了电动汽车产业运用大数据时可能产生的伦理问题及其产生原因,探索强化责任、加强职业道德教育等解决办法,并结合伦理学理论提出了建议.     

缸内直喷的丰田卡罗拉1.2T怠速不稳解析

一辆2020款丰田卡罗拉1.2T汽车,行驶里程为1000KM,出现怠速不稳甚至熄火,经过专用诊断仪测试,汽车的高压燃油压力最高为16MPA,小于正常的20PMA,而且高压压力值随着怠速时间延长逐渐下降,甚至达到0.2MPA,启动时不容易着车,反复启动着车正常,高压压力无规律波动,更换高压油泵故障仍出现,高压油泵属于日本...     

重型卡车驾驶室造型趋势研究

本文从整体造型风格、造型轿车化以及信息化、智能化对造型的影响等方面探讨了当代重型卡车车身造型设计的发展趋势.     

基于力学分析的皮卡车牵引能力研究

本文介绍了一种基于力学分析的皮卡车(也称多用途货车)牵引能力的理论计算和试验方法,得出了决定皮卡车牵引能力的主要因素有:皮卡车动力传动系统能力、驱动形式、结构强度,挂车类型以及道路情况等。经验证,理论计算得到的牵引能力与试验结果一致性较好:通过皮卡车牵引能力的计算和验证,能够有效指导企业进行设计开发,充分发挥皮卡车较强的牵引能力特性,推动皮卡车市场发展。     

重型汽车低附着路面制动稳定性的仿真研究

文章利用trucksim重型汽车动力学仿真软件,对六轮双轴重型汽车在低附着路面左右车轮附着系数不一致情况下进行紧急制动的行驶工况进行了仿真研究.研究结果表明在低附着路面进行紧急制动时,对制动轮进行制动压力控制,有ABS控制的重型汽车比没有ABS控制的重型汽车具有更好地行驶稳定性.但在低附着路面上,有ABS控制的重型汽车...