Management of traffic congestion in adaptive traffic signals using a novel classification-based approach

Traffic congestion is a critical problem which makes roads busy. Traffic congestion challenges traffic flow in urban areas. A growing urban area creates complex traffic problems in daily life. Congestion phenomena cannot be resolved only by applying physical constructs such as building bridges and motorways and increasing road capacity. It is necessary to build technological systems for transportation management to control the traffic phenomenon. In this article, a new idea is proposed to tackle traffic congestion with the aid of machine learning approaches. A new strategy based on a tree-like configuration (i.e. a decision-making model) is suggested to handle traffic congestion at intersections using adaptive traffic signals. Different traffic networks with different sizes, varying from nine to 400 intersections, are examined. Numerical results and discussion are presented to prove the efficiency and application of the proposed strategy to alleviate traffic congestion.     

Estimation of the Impact of Traveler Information Apps on Urban Air Quality Improvement

With the rapid growth of vehicle population and vehicle miles traveled, automobile emission has become a severe issue in the metropolitan cities of China. There are policies that concentrate on the management of emission sources. However, improving the operation of the transportation system through apps on mobile devices, especially navigation apps, may have a unique role in promoting urban air quality. Real-time traveler information can not only help travelers avoid traffic congestion, but also advise them to adjust their departure time, mode, or route, or even to cancel trips. Will such changes in personal travel patterns have a significant impact in decreasing emissions? If so, to what extent will they impact urban air quality? The aim of this study is to determine how urban traffic emission is affected by the use of navigation apps. With this work, we attempt to answer the question of whether the real-time traffic information provided by navigation apps can help to improve urban air quality. Some of these findings may provide references for the formulation of urban traffic and environmental policies.     

Traffic planning for non-homogeneous traffic

Traffic on Indian roads (both urban and inter-urban) consists of a variety of vehicles. These vehicles have widely different static and dynamic characteristics. The traffic is also very different from homogeneous traffic which primarily consists of motorized vehicles. Homogeneous traffic follows strict lane discipline as compared to non-homogeneous traffic. Western traffic planning methodologies mostly address the concerns of homogeneous traffic and therefore often prove inadequate in solving problems involving non-homogeneous traffic conditions as found in Indian cities. This paper presents studies conducted on non-homogeneous traffic. Section 1 presents a methodology to verify the continuity equation, the basic block of any traffic planning analysis. In § 2, the methodology developed is applied to modify the Highway Capacity Manual (HCM) 2000 density method to derive passengercar equivalencies (PCEs) or units (PCUs) for heavy vehicles and recreational vehicles. These PCUs appear as ‘ET’ and ‘ER’ in HCM tables. The density method assumes motorized, four-wheeler traffic, i.e., homogeneous traffic, and does not include motorized three-wheelers, motorized two-wheelers, and non-motorized traffic often present on Indian highways. By modifying the density method to represent non-homogeneous traffic, which includes significant percentages of motorized, three-wheelers, motorized two-wheelers, and non-motorized traffic entities, one can derive more accurate passenger car units for Indian conditions. Transport professionals can use these PCU values for accurate capacity, safety, and operational analysis of highways carrying non-homogeneous traffic.     

GACNet: A Generative Adversarial Capsule Network for Regional Epitaxial Traffic Flow Prediction

With continuous urbanization, cities are undergoing a sharp expansion within the regional space. Due to the high cost, the prediction of regional traffic flow is more difficult to extend to entire urban areas. To address this challenging problem, we present a new deep learning architecture for regional epitaxial traffic flow prediction called GACNet, which predicts traffic flow of surrounding areas based on inflow and outflow information in central area. The method is data-driven, and the spatial relationship of traffic flow is characterized by dynamically transforming traffic information into images through a two-dimensional matrix. We introduce adversarial training to improve performance of prediction and enhance the robustness. The generator mainly consists of two parts: abstract traffic feature extraction in the central region and traffic prediction in the extended region. In particular, the feature extraction part captures nonlinear spatial dependence using gated convolution, and replaces the maximum pooling operation with dynamic routing, finally aggregates multidimensional information in capsule form. The effectiveness of the method is evaluated using traffic flow datasets for two real traffic networks: Beijing and New York. Experiments on highly challenging datasets show that our method performs well for this task.     

线性交通空间动态视觉公共艺术设计探究

目的探讨线性交通空间引入动态视觉公共艺术的现实意义,并提出相应的设计建议。方法分析线性交通空间的特性及公共艺术的发展情况,结合国内外相关设计案例,归纳、总结动态视觉公共艺术介入线性交通空间的设计思路和方向。结论公共艺术是城市文化传播的重要载体和窗口,在大数据和信息化的时代,线性交通应更注重乘客在旅途中的立体式感官体验;应用新媒体技术、数控技术,结合多种创新艺术表现形式的动态视觉公共艺术,符合城市公共艺术发展,改善了线性交通环境,促进了城市及地域文化交流的需求。     

交通声景的概念与特点解析

交通噪声是城市主要噪声源之一,在解读了原始声景研究范畴与研究内容后,通过对道路交通特点的分析,提出了交通声景的概念,并从人、声音、环境三个角度阐述了交通声景的研究内容。通过对交通声景与建筑声景研究对象、调节手段进行比较后,对交通声景的特点进行了解析。交通声景的设计在人们的日常生活中占有重要的地位,建立一个恰当的交通声景评价体系需要大量的分析与积累。     

基于统计的网络流量模型及异常流量发现

基于从网络流量的大小和流量曲线的形状两个方面的研究,建立了一种基于统计的网络流量模型,提出了计算正常情况网络流量曲线的算法.通过对比正常网络流量曲线和异常网络流量曲线之间的差距,实现了对异常数据流的自动检测.实验表明,该模型不仅可以模拟与网络实测数据相似的网络流量,而且具有一定的异常流量发现能力.     

Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors

Freeway crash occurrences are highly influenced by geometric characteristics, traffic status, weather conditions and drivers’ behavior. For a mountainous freeway which suffers from adverse weather conditions, it is critical to incorporate real-time weather information and traffic data in the crash frequency study. In this paper, a Bayesian inference method was employed to model one year's crash data on I-70 in the state of Colorado. Real-time weather and traffic variables, along with geometric characteristics variables were evaluated in the models. Two scenarios were considered in this study, one seasonal and one crash type based case. For the methodology part, the Poisson model and two random effect models with a Bayesian inference method were employed and compared in this study. Deviance Information Criterion (DIC) was utilized as a comparison factor. The correlated random effect models outperformed the others. The results indicate that the weather condition variables, especially precipitation, play a key role in the crash occurrence models. The conclusions imply that different active traffic management strategies should be designed based on seasons, and single-vehicle crashes have different crash mechanism compared to multi-vehicle crashes.     

基于车流密度的道路噪声源动态辐射模型

相较于车流相对稳定的高速公路而言,城市交通流因上下班高峰、红绿灯、交叉路口等影响,具有明显的波动性。现有噪声预测模式中,声源辐射模型采用小时流量及设计速度作为独立变量,车速与车流量没有关联,仅适用于测量常年平均声级,不能正确反映城市道路噪声的动态变化特征。文章对交通流模型进行了调查,以行车速度与车流密度制约关系为基础,结合道路车辆的物理模型及声源辐射模型,建立以车流密度为变量的噪声源动态声源辐射计算模型,描述城市道路交通噪声辐射声级随车流密度的动态变化特征,与已有模型进行对比并通过实测,验证了新模型能更好地反映城市道路车辆声源辐射声级的变化情况。     

交通流诱导系统实时方法研究

交通流的实时诱导系统多为建立在单位时间的信息重构,一旦在周期时间内事故发生,则造成行驶车辆正常诱导无法进行。通过对实际交通状况的分析,提出一个新的并且简单可行的解决方案。方案在原有系统中增加了对实时事件突发诱导系统的修正,减少系统应变时间。此方案是建立在现有的诱导基础之上的改进技术,可以在交通流诱导系统中大范围推广应用。     

Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses

Travel time information is a vital component of many intelligent transportation systems (ITS) applications. In recent years, the number of vehicles in India has increased tremendously, leading to severe traffic congestion and pollution in urban areas, particularly during peak periods. A desirable strategy to deal with such issues is to shift more people from personal vehicles to public transport by providing better service (comfort, convenience and so on). In this context, advanced public transportation systems (APTS) are one of the most important ITS applications, which can significantly improve the traffic situation in India. One such application will be to provide accurate information about bus arrivals to passengers, leading to reduced waiting times at bus stops. This needs a real-time data collection technique, a quick and reliable prediction technique to calculate the expected travel time based on real-time data and informing the passengers regarding the same. The scope of this study is to use global positioning system data collected from public transportation buses plying on urban roadways in the city of Chennai, India, to predict travel times under heterogeneous traffic conditions using an algorithm based on the Kalman filtering technique. The performance of the proposed algorithm is found to be promising and expected to be valuable in the development of APTS in India. The work presented here is one of the first attempts at real-time short-term prediction of travel time for ITS applications in Indian traffic conditions.     

基于流量统计实时识别QQ语音通信的方法

针对腾讯QQ语音应用的识别问题,建立了客户源端模型,并对流量进行了离线分析。在此基础上,提出了基于流量统计的两种识别方法:Bayesian识别法和心跳识别法。一方面,利用QQ语音应用的源端编码特点,得出语音数据流量的本质特征,并使用Bayesian理论进行检测。另一方面,针对QQ语音应用中一些非语音数据包的周期性特点,利用卡方检验,提出了心跳识别的方法。实验结果表明,这两种方法综合使用,可以很有效地实时检测QQ语音流量。     

轨道交通的声屏障技术研究

在现场测量、实验的基础上，研究了轻轨交通噪声的频率特性，分析总结了目前常见声屏障的优缺点。开发研制了两种阻抗复合型声屏障，由于其独特的结构设计，声屏障有能够防止雨尘侵害的显著优点。混响室测试表明，在轻轨交通噪声的主要频率范围100-4000Hz内，该声屏障吸声性能十分出色。性能优良的新声屏障是治理城市轻轨交通噪声的一种很好手段。     

Green Innovation for Urban Traffic

With the development of economy, the issue of urban traffic is becoming highlighted, the pollution of the urban traffic has become one of the main factors to influence environment. It faces the broader problem of achieving sustainable development of urban transportation at present. The current situation of urban traffic is analyzed through studying the existing articles at home and abroad in this paper; the problems such as green innovation of urban traffic are elaborated. In addition, this paper puts forward the relevant measures to promote the development of green innovation of the urban traffic.     

Freeway safety as a function of traffic flow

In this paper, we present evidence of strong relationships between traffic flow conditions and the likelihood of traffic accidents (crashes), by type of crash. Traffic flow variables are measured using standard monitoring devices such as single inductive loop detectors. The key traffic flow elements that affect safety are found to be mean volume and median speed, and temporal variations in volume and speed, where variations need to be distinguished by freeway lane. We demonstrate how these relationships can form the basis for a tool that monitors the real-time safety level of traffic flow on an urban freeway. Such a safety performance monitoring tool can also be used in cost-benefit evaluations of projects aimed at mitigating congestion, by comparing the levels of safety of traffic flows patterns before and after project implementation.     

Multi-level trajectory learning for traffic behavior detection and analysis

Motion patterns can be learnt automatically based on object trajectories data extracted by means of video tracking, which is an effective approach for modeling and analyzing traffic behavior. In this paper, a multi-level motion pattern learning approach for traffic behavior analysis is presented, which takes into account the spatial characteristics, direction characteristics, and type characteristics of trajectories. At the spatial level, improved Hausdorff distance measurement is applied to construct a spatial similarity matrix of the trajectories collected, and spectral clustering is used to achieve spatial pattern learning. At the directional level, the start and end points of trajectories are fitted using a Gaussian mixed model to extract the distribution of entry and exit zones. Then, the direction pattern is obtained from the regional centers of the pairwise distribution zones. At the type level, the type pattern is acquired through a K-means clustering algorithm that considers multiple classification features of trajectories. Based on the learned multi-level motion patterns, abnormal behavior detection algorithms are further developed by means of pattern matching. Finally, our approach is tested with several video sequences from real-world traffic scenarios. Some typical traffic behaviors in the test scenarios are successfully recognized and analyzed and examples of abnormal traffic behaviors are also reliably detected.     

人车混行城市隧道交通噪声对行人影响的实验研究

为研究人车混行城市隧道内机动车噪声对行人的影响，文章在洞山隧道内部、口部、外部等间距布置8个测点，利用RTA840双通道实时分析仪测试了20min内的交通噪声等效声压级、频谱和车辆从行人旁边经过时3s内的交通噪声瞬时声压级、频谱。测得：(1) 隧道内部的交通噪声等效声压级为82dB(A)，比隧道外等效声压级高10dB(A)。(2) 隧道外部、口部、内部的交通噪声频谱曲线特征相同，在20~2 000Hz范围内声压级较高且呈“M”形。(3) 当车辆在隧道内部从行人旁通过时，交通噪声瞬时声压级达到86dB(A)；瞬时噪声频谱相比于背景噪声频谱在20~8 000Hz频段范围内声压级均有明显增大。最后结合测试结果和洞山隧道实际从吸声、隔声、管理三方面总结隧道内声环境改善措施。     

Comprehension and acceptability of on-board traffic information: Beliefs and driving behaviour

Co-Drive on-board traffic information system is a complementary tool providing a dynamic management of transportation infrastructure and traffic as well as the diffusion of accurate real-time information about the road environment and motorists’ driving behaviour. The aim of this study was to examine drivers’ acceptability of Co-Drive by investigating the impact of traffic information provided via on-board display devices on motorists’ beliefs and behaviour.     

香港交通噪声问题及其控制政策综述

不同于世界其他大城市,香港因其特有的噪声问题,有时会被冠以世界最嘈杂的城市的称号。对此,香港政府为改善噪声环境付出了极大的努力,并且取得了一定的成效。然而,主要的噪声问题仍然存在,部分缘于其拥挤的城市结构以及八十年代之前城市规划时对噪声污染问题的忽视,这些都不是一朝一夕之间能解决的问题。香港的噪声问题中,又以交通噪声最为严重,80%的香港人在不同程度上受到道路交通噪声的影响。该文介绍了香港交通噪声问题及其现行的交通噪声政策,并讨论了现行政策所遇到的问题,最后介绍了由香港声学学会提议的进一步的交通噪声控制策略。     

有砟轨道动力性能分析的频域方法

建立有砟轨道的三层连续弹性支承模型,推导了钢轨、轨枕和道床的运动方程,并在频域内求得简谐激励下的稳态响应。再引入轮轨相互作用频域模型,并借助动柔度法计算轮轨力。在此基础上,采用MATLAB平台自编程序,实现轨道不平顺激励下的频域分析。在案例分析中,综合考虑轮轨力、力传递率、轨道各部件的振动等指标,对弹性扣件、枕下弹性垫板、复合轨枕、道砟垫以及它们的组合等多种减振措施进行对比。结果表明：减振措施使得系统的固有频率发生改变,进而导致各指标在不同频率范围出现复杂的变化规律;相比于弹性扣件,枕下弹性垫板和道砟垫表现出更好的综合效果,而复合轨枕的减振效果有限。