台州市信号灯交叉口碳排放测算及优化策略

近年来随着小汽车保有量的不断增多,机动车尾气成为碳排放的主要组成部分。文中以信号灯控制交叉口运行的小汽车为研究对象,通过研究小汽车在交叉口减速、怠速、加速三种工况下车辆的排放特性,将信号灯交叉口小汽车温室气体排放量量化。本文以台州市市府大道-中心大道交叉口为例,通过实地调查该交叉口当前平峰期和高峰期信号灯配时特性、交通运行特性等实际参数,对二氧化碳排放量进行测算,最终得出该交叉口在平峰期和高峰期的二氧化碳小时排放量,为后续该交叉口采取优化策略后在碳排放指标方面的量化效果提供参考依据,同时为台州市信号灯交叉口碳排放量测算提供参考方法。     

基于环形线圈检测器采集信息的交通状态分类方法应用研究

在智能交通系统中,交通状态判别算法通常被用来进行道路环境中实时交通状态的判断。这些算法将外场设备采集到的实时交通流数据与既有的交通状态分类标准特征作比较,来识别交通系统运行的状态。应用聚类分析方法,结合数据预备技术和交通工程技术,对环形线圈监测系统采集的交通流基础特征数据进行挖掘,实现了一种交通状态分类方法,并对交通管理控制系统中实时交通状态的判断识别提供可靠的参照标准。     

Two-class freeway traffic regulation to reduce congestion and emissions via nonlinear optimal control

The objective of this paper is the regulation of freeway traffic by means of optimal control techniques. A first innovative aspect of the proposed approach is the adopted objective function in which, besides the reduction of traffic congestion (which is typically considered in traffic control schemes), the minimization of traffic emissions is also included. Moreover, a multi-class framework is defined in which two classes of vehicles (cars and trucks) are explicitly modelled, and specific control actions for each vehicle class are sought. This results in the formulation of a multi-objective optimal control problem which is described in the paper and for which a specific solution algorithm is developed and used. The algorithm exploits a specific version of the feasible direction algorithm whose effectiveness is demonstrated in the paper by means of simulation results.     

A problem of limited-access special lanes. Part II: Exploring remedies via simulation

Spatiotemporal analyses of freeway sites in Part I have shown that special-lane access points are prone to become bottlenecks. These can degrade traffic flows, sometimes in all lanes. Part II explores select impacts of re-designing the means of entering and exiting a special lane, and of altering the policy governing its use. Parametric tests were conducted using a computer simulation model that was calibrated to one of the sites studied in Part I; one with a buffer-separated carpool lane. Though less reliable than what might have been observed via experiments in real settings, the simulated findings seem to offer useful insights nonetheless.The findings indicate that traffic conditions would improve at the site by elongating the carpool lane’s buffer opening beyond its present length of 400 m. Yet, only modest improvements were predicted, even when the opening was elongated to 1000 m or more. Greater benefits were predicted from disentangling the movements made into and out of the carpool lane. This was achieved by placing first a buffer opening to serve only ingress, followed by another immediately downstream to serve egress. The benefits of this treatment were again limited, even when each tandem opening was elongated to a length of 700 m. Fully removing the buffer that physically separates the carpool lane from the regular ones was predicted to bring the greatest improvements to traffic. Also examined was pending legislation that would leave the carpool-lane buffer in place, while limiting the times of day when the lane is reserved for special use. Simulations predict that this legislation would degrade travel conditions below those that presently occur at the site. The extent of this predicted degradation varied, depending upon the time of day when the lane-use restriction went into effect.     

Stochastic eco-routing in a signalized traffic network

In this paper, an eco-routing algorithm is developed for vehicles in a signalized traffic network. The proposed method incorporates a microscopic vehicle emission model into a Markov decision process (MDP). Instead of using GPS-based vehicle trajectory data, which are used by many existing eco-routing algorithm, high resolution traffic data including vehicle arrival and signal status information are used as primary inputs. The proposed method can work with any microscopic vehicle model that uses vehicle trajectories as inputs and gives related emission rates as outputs. Furthermore, a constrained eco-routing problem is proposed to deal with the situation where multiple costs present. This is done by transferring the original MDP based formulation to a linear programming formulation. Besides the primary cost, additional costs are considered as constraints. Two numerical examples are given using the field data obtained from City of Pasadena, California, USA. The eco-routing algorithm for single objective is compared against the traditional shortest path algorithm, Dijkstra’s algorithm. Average reductions of CO emission around 20% are observed.     

Long short-term memory neural network for traffic speed prediction using remote microwave sensor data

Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamic in an effective manner. The LSTM NN can overcome the issue of back-propagated error decay through memory blocks, and thus exhibits the superior capability for time series prediction with long temporal dependency. In addition, the LSTM NN can automatically determine the optimal time lags. To validate the effectiveness of LSTM NN, travel speed data from traffic microwave detectors in Beijing are used for model training and testing. A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability.     

A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data

Traffic crashes occurring on freeways/expressways are considered to relate closely to previous traffic conditions, which are time-varying. Meanwhile, most studies use volume/occupancy/speed parameters to predict the likelihood of crashes, which are invalid for roads where the traffic conditions are estimated using speed data extracted from sampled floating cars or smart phones. Therefore, a dynamic Bayesian network (DBN) model of time sequence traffic data has been proposed to investigate the relationship between crash occurrence and dynamic speed condition data. Moreover, the traffic conditions near the crash site were identified as several state combinations according to the level of congestion and included in the DBN model. Based on 551 crashes and corresponding speed information collected on expressways in Shanghai, China, DBN models were built with time series speed condition data and different state combinations. A comparative analysis of the DBN model using flow detector data and a static Bayesian network model was also conducted. The results show that, with only speed condition data and nine traffic state combinations, the DBN model can achieve a crash prediction accuracy of 76.4% with a false alarm rate of 23.7%. In addition, the results of transferability testing imply that the DBN models are applicable to other similar expressways with 67.0% crash prediction accuracy.     

新冠疫情期间人员流动恢复指数研究

2019年12月份爆发的新型冠状病毒肺炎疫情蔓延全国。疫情期间,全国范围交通管控导致交通状态明显有别于正常状态。为此,本文从公路、铁路、水路、民航、城市交通等多种交通方式出发选取客运指标,利用合成指数方法构建人员流动指数。在省级层面构建省际人员流动指数和省内人员流动指数,分析疫情期间省际和省内人员流动指数特征,以及公路、铁路、水路、民航、城市交通等各项子指标的变化特征,旨在准确监控疫情期间客运交通状态,准确掌握各种交通方式受疫情影响程度,为特殊时期管理部门对旅客运输采取针对性管控提供依据。     

学校周边道路交通管理设施设置研究

文章分析了学校周边道路交通特性和学生过街交通行为特性,并结合学校周边交通管理设施设计原则,提出了一套人性化、完善的设施设置方法,为提高学生上下学过街安全性,引导过往车辆顺畅通行,完善交通管理设施设置方法提供参考。     

考虑右转车流的交叉口交通岛比选设计

交通岛设计的关键是确定右转车流在进口道分流区及出口道合流区的交通组织,在此基础上进行方案设计和通行能力评价。目前的设计缺乏对在不同右转流量条件下方案优化和比选时的定量化分析。本文提出右转车流在不同交通组织条件下的四种交通岛设计方案,并采用通行能力模型对进口道分流区及出口道合流区通行能力进行评价,提供不同右转车流需求下的交通岛最佳设计方案。算例结果表明:随着右转车流量的变化,本文提出的四种交通设计方案与右转车流的四个流量区间一一对应,能够满足复杂条件下的城市交通精细化设计需求,解决了目前设计单一化、非定量化的弊端。