基于元胞自动机的高速铁路列车群追踪运行仿真模型

为分析高速铁路列车在追踪间隔缩小时的运行状态,根据准移动闭塞系统原理,设置列车区间运行和车站运行的演化规则,建立准移动闭塞条件下基于元胞自动机的列车群追踪运行仿真模型。利用元胞自动机对京沪高速铁路线路"上海虹桥—南京南"运行图中追踪运行的10列车进行建模仿真,分析仿真结果验证到达间隔时间是制约追踪间隔的瓶颈,并得出后行列车满足追踪间隔4 min或3 min的情况下,其运行速度不受前行列车的干扰。     

An improved cellular automaton with axis information for microscopic traffic simulation

Cellular Automaton (CA), an efficient dynamic modeling method that is widely used in traffic engineering, is newly introduced for traffic load modeling. This modeling method significantly addresses the modest traffic loads for long-span bridges. It does, however, require improvement to calculate precise load effects. This paper proposed an improved cellular automaton with axis information, defined as the Multi-axle Single-cell Cellular Automaton (MSCA), for the precise micro-simulation of random traffic loads on bridges. Four main ingredients of lattice, cells’ states, neighborhoods and transition rules are redefined in MSCA to generate microscopic vehicle sequences with detailed vehicle axle positions, user-defined cell sizes and time steps. The simulation methodology of MSCA is then proposed. Finally, MSCA is carefully calibrated and validated using site-specific WIM data. The results indicate: (1) the relative errors (REs) for the traffic parameters, such as volumes, speeds, weights, and headways, from MSCA are basically no more than ±10% of those of WIM data; (2) the load effects of three typical influence lines (ILs) with varied lengths of 50, 200 and 1000 m are also confidently comparable, both of which validate the rationality and precision of MSCA. Furthermore, the accurate vehicle parameters and gaps generated from MSCA can be applied not only for precise traffic loading on infrastructures but also for the accurate estimation of vehicle dynamics and safety. Hence, wide application of MSCA can potentially be expected.     

Influence of priority taking and abstaining at single-lane roundabouts using cellular automata

Existing roundabout simulation models fail to consider all types of driver behavior which compromises their accuracy and ability to accurately evaluate roundabout performance. Further, these non-compliant driver behaviors, including priority taking and priority abstaining, are inconsistent with existing traffic flow theories. In this paper, a new cellular automata model, C.A.Rsim, is developed and calibrated with field data from five single-lane roundabouts in four northeastern states. Model results indicate that approximately 20% of the individuals in the driver population are inclined to priority taking and approximately 20% are inclined to priority abstaining behavior, though the observed levels of these types of behavior are naturally lower and vary with traffic volume. The model results also corroborate other research indicating that current models can overestimate capacity at higher circulating volumes, possibly a result of the jamming effect produced by priority taking behavior. The reduction in priority abstaining behavior, which is observed at older roundabouts, significantly reduces delay and queue length in certain traffic volumes. C.A.Rsim is also more parsimonious than many existing microsimulation models. These results provide insight on how variations in conflicting flow (i.e., traffic volume and turning movement balance) impact the amount of observed non-compliant behavior.     

Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: A case study from Israel

The purpose of this paper is to examine the performance of a new operational system for measuring traffic speeds and travel times which is based on information from a cellular phone service provider. Cellular measurements are compared with those obtained by dual magnetic loop detectors. The comparison uses data for a busy 14 km freeway with 10 interchanges, in both directions, during January–March of 2005. The dataset contains 1 284 587 valid loop detector speed measurements and 440 331 valid measurements from the cellular system, each measurement referring to a 5 min interval. During one week in this period, 25 floating car measurements were conducted as additional comparison observations. The analyses include visual, graphical, and statistical techniques; focusing in particular on comparisons of speed patterns in the time–space domain. The main finding is that there is a good match between the two measurement methods, indicating that the cellular phone-based system can be useful for various practical applications such as advanced traveler information systems and evaluating system performance for modeling and planning.     

Cellpath: Fusion of cellular and traffic sensor data for route flow estimation via convex optimization

A new convex optimization framework is developed for the route flow estimation problem from the fusion of vehicle count and cellular network data. The issue of highly underdetermined link flow based methods in transportation networks is investigated, then solved using the proposed concept of cellpaths for cellular network data. With this data-driven approach, our proposed approach is versatile: it is compatible with other data sources, and it is model agnostic and thus compatible with user equilibrium, system-optimum, Stackelberg concepts, and other models. Using a dimensionality reduction scheme, we design a projected gradient algorithm suitable for the proposed route flow estimation problem. The algorithm solves a block isotonic regression problem in the projection step in linear time. The accuracy, computational efficiency, and versatility of the proposed approach are validated on the I-210 corridor near Los Angeles, where we achieve 90% route flow accuracy with 1033 traffic sensors and 1000 cellular towers covering a large network of highways and arterials with more than 20,000 links. In contrast to long-term land use planning applications, we demonstrate the first system to our knowledge that can produce route-level flow estimates suitable for short time horizon prediction and control applications in traffic management. Our system is open source and available for validation and extension.     

基于元胞自动机的城市道路仿真系统

为解决实时交通仿真系统对计算效率的要求，本文提出一种针对交通信号控制下城市道路路段的元胞自动机模型。在模型中采取了适合城市交通特点的元胞空间划分和时间步长方案以及元胞更新规则。本文还给出基于此模型的一个小型仿真系统的软件体系结构。另外，本模型还能反应出驾驶员的进取性（aggressiveness）的状况，并对驾驶员存不同进取性下对变通的影响做了研究。通过现场采集的交通数据和CORSIM仿真平行的对比试验表明本文提出的模型在路段平均速度意义下与实际变通数据符合较好，并具有较高的运行效率。     

基于双倍视距的交通信号控制的元胞自动机模型

Nagel和Schreckenberg于1992年提出了著名的Nagel-Schrechenberg（NaSch）元胞自动机模型，该模型虽然形式十分简单，但却可以描述一些实际交通现象。同时也存在很多不足之处，如NaSch模型的最大流量和临界密度远低于实际交通中的数值，它也不能重现交通流的一些重要特性。本文提出了一种考虑当前车辆前方两个车辆影响的具有交通信号控制的单车道元胞自动机模型，根据双倍视距模型的思想修改了NaSch模型的加速规则，计算机模拟结果表明，考虑了次近邻影响的模型能够呈现真实交通中观察到的一些复杂交通流现象，且最大流量和临界密度比未考虑次近邻影响的模型都有了显著提高；此外，模拟结果也表明了交通信号周期对最大流量和临界密度的影响，最大流量随交通信号周期的增大而减小，临界密度随交通信号周期的增大而增大；最后，文中还讨论了近邻车辆的敏感系数和次近邻车辆的敏感系数的影响。模拟结果表明，固定任何一个参数的值，最大流量都会随另一个参数的增大而增加，但是对流量的影响显著，说明近邻车辆的影响大于次近邻车辆的影响。     

Brake light cellular automaton model with advanced randomization for traffic breakdown

Traffic breakdown is one of the most important empirical phenomena in traffic flow theory. Unfortunately, it cannot be simulated by many traffic flow models. In order to clarify its mechanism, the new brake light cellular automaton model has been proposed. Comparing with previous brake light models, three different aspects have been considered: (i) drivers tend to take large decelerations if the time gap is smaller than the safe time gap and the leading vehicle’s brake light is on; (ii) the brake light rule is set according to the reality; (iii) the randomization rule is put forward before the acceleration rule to weaken the impact of brake light on driving behaviors. Analyses show that the new model can explain the mechanism of traffic breakdown and the failures of other brake light models. Simulations confirm that all empirical features of traffic breakdown are successfully reproduced. At last, brake light models are calibrated and validated by the I-80 empirical data provided by NGSIM. Results show that the performance of the new model is the best and models in the three-phase theory are not necessarily better than models in the fundamental diagram approach and vice versa, at least for the brake light models.     

Soft Radial Basis Cellular Neural Network (SRB-CNN) based robust low-cost truck detection using a single presence detection sensor

This paper does present a comprehensive concept for a robust and reliable truck detection involving solely one single presence sensor (e.g. an inductive loop, but also any other presence sensor) at a signalized traffic junction. Hereby, two operations modes are distinguished: (a) during green traffic light phases, and (b) a much challenging case, during red traffic light phases. First, it is shown how difficult the underlying classification task is, this mainly due to strongly overlapped classes, which cannot be easily separated by simple hyper-planes. Then, a novel soft radial basis cellular neural/nonlinear network (SRB-CNN) based concept is developed, validated and extensively benchmarked with a selection of the best representatives of the current related state-of-the-art classification concepts (namely the following: support vector machines with radial basis function, artificial neural network, naive Bayes, and decision trees). For benchmarking purposes, all selected competing classifiers do use the same features and the superiority of the novel CNN based classifier is thereby underscored, as it strongly outperforms the other ones. This novel SRB-CNN based concept does satisfactorily fulfill the hard industrial requirements regarding robustness, low-cost, high processing speed, low memory consumption, and the capability to be deployed in low cost embedded systems.     

元胞自动机交通流模型的随机规则

元胞自动机模型是近年刚发展起来的交通流模型,由于其建模简单、易于在电脑上实现及可模拟复杂的非线性交通现象而得到了迅速的发展.本文研究了基于NaSch模型的一维交通流元胞自动机模型,并对模型中随机规则在模拟复杂交通现象中所起的作用进行了分析.通过对NaSch模型和VDR模型的基本图和时空分布图进行分析,得出模型中的随机规则是模拟实际交通流中的很多非线性现象的主要规则.