交通公路工程数据采集中计算机的作用分析

计算机交通公路工程数据采集技术是一项基础性技术手段。文章介绍了计算机交通公路工程数据采集系统的组成与特征,从计算机交通公路工程数据采集和通信控制软件、计算机交通预测和事故检测软件与计算机可视化信息和集成软件三个方面,分析了计算机在交通公路工程数据采集中的具体应用。     

数据通信在城市交通控制系统中的应用

一、城市交通控制系统在改善道路通行状况、减轻交通堵塞所造成的损失等方面，城市交通控制系统发挥着不可替代的作用。城市交通控制系统是由计算机、信号控制机、检测系统、通信系统组成的。它依据路网上的实际交通流数据和各个路口机的运行参数，来对分散在各路口的信号控制机进行集中式的协调和控制，从整体上统一控制各个路口交通流和路口机运行参数，控制交通秩序。交通信息系统是城市交通控制系统的重要组成部分之一，它通过数据采集、传输和管理技术，使得交通管理人员能够准确获得路网各个路口的交通流量和路口机的运行状态，从而为…     

一种基于AI算法的雷视融合事件检测系统

目前公路对于事件检测精度要求较高,仅仅使用单一的视频事件检测或雷达技术不足以适应高精度的安全驾驶需要.利用AI视频交通事件检测系统与雷达深度融合实现功能取长补短,是一种可靠交通事件检测方式.本文将此种信息采集方式应用于重点路段、互通枢纽及隧道区相关问题的探讨,资以实现更好的交通控制与管理,为车辆在公路上提供更安全高效的...     

交通工程检测技术与质量控制思路

为保证交通工程质量,从道路工程质量检测、交通安全设施检测、交通环境检测等方面提出交通工程检测要点,介绍相关检测技术。在此基础上,探究交通工程质量控制思路,包括完善质量管理体系、加强检测设备管理、加大检测人员培训和管理力度等,以期为相关人员提供有益参考,进一步提高城市交通的安全性和效率。     

视频事件检测和图像筛选技术在永武高速公路视频监控系统的运用

事件检测是智能交通系统中的重要组成部分和关键技术。文中结合当前我省高速公路监控系统已有监控设施,对基于视频图像的交通事件检测进行了研究,并完成了系统的开发和运用。     

视频事件检测技术在隧道应急处置中的应用

文章通过分析视频事件检测技术在隧道内的应用环境与广西高速公路隧道的特点,从智慧化、一体化的角度出发,提出一种依托视频事件检测技术的隧道应急处置应用方案。该方案以视频事件检测技术作为隧道内异常交通事件的主动检测手段,借助隧道综合监控系统与应急救援处置系统提供后续应急处置功能,结合广西高速公路隧道管理需求与隧道事件应急处置机制,形成高效简捷的隧道应急处置流程。部署试用后的效果表明,该方案可大幅缩减隧道内平均应急救援处置时间,对于提高高速公路隧道内的交通事件检测发现能力、优化应急处置流程和效率提供了借鉴与参考。     

桥梁桩基检测低应变检测技术应用

低应变检测技术是一种非破坏性评估方法,能够识别桥梁桩基结构问题,为更好地验证该技术的检测效果,结合某桥梁工程实例,对该技术的操作要点与检测精准度进行分析。经对比验证,发现该技术在实际应用中存在一定的局限性,在实际应用中联合使用其他检测技术能更有效地保证检测结果的准确性与可靠性。     

城市道路交通拥堵状况自动检测算法研究

道路拥堵状况相关信息能够为交管部门制定决策、采取措施提供便利,对缓解城市道路所承受的压力、使道路通行能力得到提升具有重大意义。以自动检测算法为落脚点,针对检测道路拥堵程度展开研究。考虑到常规检测算法并不能够精确、快速地判断道路状况,提出对道路各个方向进行分别检测的方法,将交通灯可能带来的影响纳入考虑范围,基于道路车流量、承载量,对路面拥堵状况加以检测,在此基础上,对量化道路拥堵状况、划分拥堵等级的要点进行说明。希望给相关人员以启发,为今后估计道路拥堵状况等工作提供有价值的参考。     

基于移动电话的交通事件检测方法研究

对使用移动电话进行交通事件检测的原理、国外研究现状进行了介绍,并结合我国国情,提出了改善交通事件检测效果的对策,最后对综合应用移动电话和常规交通检测器的交通事件检测方法进行了展望.     

线阵CCD油管螺纹测量系统程序开发

基于线阵CCD的油管外螺纹参数检测系统是一种先进的非接触式工业检测系统,包括硬件和软件两部分。针对该系统的重要组成部分———控制、计算程序的设计和开发进行了详细的阐述。该程序的开发基于Windows 2000,开发工具为C Builder 6.0,包括数据采集与存储、滤波、边界点选取、参数计算等多个模块。程序功能完整,界面友好,控制精确,计算结果能够满足测量系统的精度要求。     

“互联网+”交通信号辅助优化机制探索

为优化城市道路交通信号控制方法,本文结合交通信号控制系统建设发展现状,分析当前各大城市交通信号控制系统普遍存在的问题,立足于互联网环境下的浮动车数据,提出基于互联网平台大数据的交通信号控制辅助优化机制。研究发现可利用互联网路口拥堵报警数据及时有效发现问题路口,利用路段拥堵指数及路口交通流参数变化趋势辅助评估配时方案的优化效果,并通过成都市应用实例证明该机制适用于当前交通控制场景需求,可有效辅助交通信号优化工作,是传统交通模式向真正智能交通模式过渡的阶梯。     

基于灰色关联度的交通安全道路影响因素辨析

道路系统中诸因素共同作用决定了系统的交通安全发展态势。如何在众多因素中寻找主要致因,是进行道路安全改善时所必须面对的问题。考虑到数据获取贫信息的特点,采用灰色关联分析,不但可以提高现有数据的利用率,而且弥补常用数理统计方法作系统分析所导致的缺憾。实例分析表明:采用此方法所得到的主要道路影响因素和交通安全之间的关联度大小与实地踏查结果相吻合,结果说明灰色关联度可以简单而高效地进行系统因素辨析。     

Study on road section environmental traffic capacity model and algorithm under double constraints

The coordinated development of city traffic and environment is a key research content in traffic field in twenty-first Century. Among them, road section environmental traffic capacity analysis is one of the important research issues. It can provide solid theoretical basis and reliable data support for road network traffic optimization control, road traffic pollution control and city traffic structure optimization. This paper analyzed main factors which impacted environmental traffic capacity from two aspects, including road capacity constraint conditions and road traffic pollution control constraint conditions. Then, road section environmental traffic capacity optimization model was established, and method of improved augmented Lagrange function was used to solve the model. Case study showed that, (1) The environmental traffic capacity optimal model and methodology were effective; (2) In order to ensure road section environmental traffic capacity greater than (or equal to) road capacity, some measures could be taken including adjusting motor vehicle type proportion as well as improving emission characteristics of motor vehicles exhausting pollutants.     

Effective arterial road incident detection: A Bayesian network based algorithm

Timely and accurate incident detection is an essential part of any successful advanced traffic management system. The complex nature of arterial road traffic makes automated incident detection a real challenge. Stable performance and strong transferability remain major issues concerning the existing incident detection algorithms. A new arterial road incident detection algorithm TSC_ar is presented in this paper. In this algorithm, Bayesian networks are used to quantitatively model the causal dependencies between traffic events (e.g. incident) and traffic parameters. Using real time traffic data as evidence, the Bayesian networks update the incident probability at each detection interval through two-way inference. An incident alarm is issued when the estimated incident probability exceeds the predefined decision threshold. The Bayesian networks allow us to subjectively build existing traffic knowledge into their conditional probability tables, which makes the knowledge base for incident detection robust and dynamic. Meanwhile, we incorporate intersection traffic signals into traffic data processing. A total of 40 different types of arterial road incidents are simulated to test the performance of the algorithm. The high detection rate of 88% is obtained while the false alarm rate of the algorithm is maintained as low as 0.62%. Most importantly, it is found that both the detection rate and false alarm rate are not sensitive to the incident decision thresholds. This is the unique feature of the TSC_ar algorithm, which suggests that the Bayesian network approach is advanced in enabling effective arterial road incident detection.     

Special issue on intelligent transportation systems,big data and intelligent technology

ABSTRACT

Based on the increasing demands of transportation development, the concept of an Intelligent Transportation System (ITS) has received increasing attention in both academic and industry arenas. It integrates information, communications, computers and other technologies, and applies them in the field of transportation to build an integrated system of people, roads and vehicles by utilizing advanced data communication technologies. It can establish a large, fully functioning, real-time, accurate and efficient transportation management system. Intelligent transportation systems shift the focus from road managers to road users. In order to achieve this purpose, intelligent transportation systems use advanced technology to provide drivers with convenient information to help reduce traffic congestion and to increase available road capacity. This special issue is dedicated to exploring the most recent advances in intelligent transportation systems and big data based on intelligent technology.     

Spatial-temporal traffic flow pattern identification and anomaly detection with dictionary-based compression theory in a large-scale urban network

Traffic flow pattern identification, as well as anomaly detection, is an important component for traffic operations and control. To reveal the characteristics of regional traffic flow patterns in large road networks, this paper employs dictionary-based compression theory to identify the features of both spatial and temporal patterns by analyzing the multi-dimensional traffic-related data. An anomaly index is derived to quantify the network traffic in both spatial and temporal perspectives. Both pattern identifications are conducted in three different geographic levels: detector, intersection, and sub-region. From different geographic levels, this study finds several important features of traffic flow patterns, including the geographic distribution of traffic flow patterns, pattern shifts at different times-of-day, pattern fluctuations over different days, etc. Both spatial and temporal traffic flow patterns defined in this study can jointly characterize pattern changes and provide a good performance measure of traffic operations and management. The proposed method is further implemented in a case study for the impact of a newly constructed subway line. The before-and-after study identifies the major changes of surrounding road traffic near the subway stations. It is found that new metro stations attract more commute traffic in weekdays as well as entertaining traffic during weekends.     

Minimizing CO2e emissions by setting a road toll

The main purpose of this paper is to develop a bi-level pricing model to minimize the CO₂e emissions and the total travel time in a small road network. In the lower level of the model, it is assumed that users of the road network find a dynamic user equilibrium which minimizes the total costs of those in the system. For the higher level of the model, different road toll strategies are applied in order to minimize the CO₂e emissions. The model has been applied to an illustrative example. It shows the effects on traffic flows, revenues, total time and CO₂e emissions for different numbers of servers collecting tolls and different pricing strategies over a morning peak traffic period. The results show that the CO₂e emissions produced can be significantly affected by the number of servers and the type of toll strategy employed. The model is also used to find the best toll strategy when there is a constraint on the revenue that is required to be raised from the toll and how this affects the emissions produced. Further runs compare strategies to minimize the CO₂e emissions with those that minimize total travel time in the road system. In the illustrative example, the results for minimizing CO₂e emissions are shown to be similar to the results obtained from minimizing the total travel time.     

临时关闭道路以疏导城市道路交通流的可行性分析

文章基于Braess悖论,以单位车辆的平均出行时间为最优目标,以网络道路系统为约束条件,从逆向思维角度建立了通过临时关闭道路引导交通流以缓解城市道路交通拥挤的Narsh均衡非线性规划模型,并以北京二环道路网络为研究对象进行了实例分析。结果表明,在城市道路网中,关闭部分路段并不能明显缓解交通拥堵现状,但可以在不影响路网整体通行时间的条件下改善路网局部拥堵状况。建议城市道路交通临时疏导方案应该根据路网交通流的分布特点和道路通过能力等因素进行制订。     

Adaptive cruise control design for active congestion avoidance

We present an adaptive cruise control (ACC) strategy where the acceleration characteristics, that is, the driving style automatically adapts to different traffic situations. The three components of the concept are the ACC itself, implemented in the form of a car-following model, an algorithm for the automatic real-time detection of the traffic situation based on local information, and a strategy matrix to adapt the driving characteristics (that is, the parameters of the ACC controller) to the traffic conditions. Optionally, inter-vehicle and infrastructure-to-car communication can be used to improve the accuracy of determining the traffic states. Within a microscopic simulation framework, we have simulated the complete concept on a road section with an on-ramp bottleneck, using empirical loop-detector data for an afternoon rush-hour as input for the upstream boundary. We found that the ACC vehicles improve the traffic stability and the dynamic road capacity. While traffic congestion in the reference scenario was completely eliminated when simulating a proportion of 25% ACC vehicles, travel times were already significantly reduced for much lower penetration rates. The efficiency of the proposed driving strategy even for low market penetrations is a promising result for a successful application in future driver assistance systems.     

Urban road traffic noise spatiotemporal distribution mapping using multisource data

Many residents are disturbed by road traffic noise which needs to be controlled and managed. The noise map is a helpful and important tool for noise management and acoustical planning in urban areas. However, the static noise map is not sufficient for evaluating noise annoyance at different temporal periods. It is necessary to develop the dynamic noise map or the noise spatiotemporal distribution. In this study, a method about urban road traffic noise spatiotemporal distribution mapping is proposed to obtain the representative road traffic noise maps of different periods. This method relies on the proposed noise spatiotemporal distribution model with two time-dependent variables - traffic density and traffic speed, and the spatiotemporal characteristics derived from multisource data. There are three steps in the method. First, the urban road traffic noise spatiotemporal distribution model is derived from the law of sound propagation. Then, the temporal characteristics are extracted from traffic flow detecting data and E-map road segment speed data by the outlier detection analysis. Finally, the noise distributions corresponding to different periods are calculated by an efficient algorithm which can save 90% above of the computing time. Moreover, a validation experiment was conducted to evaluate the accuracy of the proposed method. There is only 2.26-dB[A] mean absolute error that is within an acceptable range, which shows that the method is effective.