Providing personalized system optimum traveler information in a congested traffic network with mixed users

The advancement of information and communication technology allows the use of more sophisticated information provision strategies for real-time traffic management in a congested network. This article proposes a personalized system optimum traveler information (PSOI) system under ubiquitous communication, which allows traffic system operators to fully optimize and coordinate individuals' trip plans according to the personal attributes, such as real-time location, value of time, allowable budgets for congestion tolling, and willingness to take detours. We also developed an efficient queue-based evaluation and solution heuristic algorithm using mesoscopic simulation models to solve for near-optimal PSOI strategies—route suggestions for each individual traveler. The simulation optimization algorithm can account for different information users and provide predictive information that robustly accounts for potential decisions of other travelers in real time. Case studies were carried out on a test network and a real-world network, and the proposed heuristic algorithm is proven effective. Also, sensitivity analyses show that PSOI not only is an effective traffic management method in reducing average system travel time, but also potentially provides travelers with reasonable or even shorter travel times compared with other information users. Further, simulation results showed that even in mixed traffic, PSOI is able to shorten travel times for both users without information and users of other information types. Thus, PSOI is recommended by this article as an advantageous way for next-generation advanced information systems and dynamic traffic management.     

城市公交线网优化的线性模型

为了对现有公交线网进行优化,有效利用现有交通资源,解决城市交通问题,运用系统科学的思想,通过对城市公交线网优化的主要内容、优化原则、优化目标以及约束条件的分析,兼顾考虑乘客出行时间、公交线网密度和公交企业的利益以及公交线网的布局对整个城市的交通系统的影响,提出了优化目标的函数表达式及相应约束条件的数学表达式,建立了公交线网优化的线性模型,并给出了运用逐步筛选法对所提出的数学模型进行求解的方法。     

Multi-objective optimal predictive control of signals in urban traffic network

Traffic congestion in urban network has been a serious problem for decades. In this paper, a novel dynamic multi-objective optimization method for designing predictive controls of network signals is proposed. The popular cell transmission model (CTM) is used for traffic prediction. Two network models are considered, i.e., simple network which captures basic macroscopic traffic characteristics and advanced network that further considers vehicle turning and different traveling routes between origins and destinations. A network signal predictive control algorithm is developed for online multi-objective optimization. A variety of objectives are considered such as system throughput, vehicle delay, intersection crossing volume, and spillbacks. The genetic algorithm (GA) is applied to solve the optimization problem. Three example networks with different complexities are studied. It is observed that the optimal traffic performance can be achieved by the dynamic control in different situations. The influence of the objective selection on short-term and long-term network benefits is studied. With the help of parallel computing, the proposed method can be implemented in real time and is promising to improve the performance of real traffic network.     

部分控制交通网络的流量分配问题及模型

为了研究由于交通控制设备资源有限,只有部分节点受到控制时交通流在路网上的分配,讨论了交通分配的一般准则和方法,并通过对出行者在控制节点和未控制节点的路径选择行为的假设,建立了混合交通配流模型。模型的最优状态为,出行者在未控制节点处都是选择从该节点到达终点路段综合费用最小的路段,而在控制节点处完全服从控制策略的引导选择路径。研究表明,当所有节点都被控制时,模型等价于SO模型;当所有节点都不受控制时,模型等价于UE模型。用一个简单的路网进行算例分析,说明了模型的可行性和合理性。     

Improved calibration of simulation models in railway dynamics: application of a parameter identification process to the multi-body model of a TGV train

This paper aims at estimating the vehicle suspension parameters of a TGV (Train à Grande Vitesse) train from measurement data. A better knowledge of these parameters is required for virtual certification or condition monitoring applications. The estimation of the parameter values is performed by minimising a misfit function describing the distance between the measured and the simulated vehicle response. Due to the unsteady excitation from the real track irregularities and nonlinear effects in the vehicle behaviour, the misfit function is defined in the time domain using a least squares estimation. Then an optimisation algorithm is applied in order to find the best parameter values within the defined constraints. The complexity of the solution surface with many local minima requires the use of global optimisation methods. The results show that the model can be improved by this approach providing a response of the simulation model closer to the measurements.     

A cellular automata traffic flow model combined with a BP neural network based microscopic lane changing decision model

The purpose of this study is to propose a cellular automata (CA) traffic flow model with high accuracy for lane change decision and name it LCCAM. A driving simulator experiment was conducted to find factors affecting lane changing decisions. A back-propagate (BP) neural network was used to obtain the lane changing rules for the microscopic lane changing decision model (LCDM), and the collected accurate vehicular trajectory data were used to train the BP neural networks for the prediction of lane changes. After comparing different input variable combinations, the most accurate input setting was determined, including the locations and velocities of neighboring vehicles, inner/outer lane indicator, and the speed limits of the corresponding lane. Then, the determined BP neural network was adopted in the LCCAM as the LCDM. Simulation results showed that the LCCAM can capture important characteristics such as the mean velocities and the number of lane changes well, by comparing with observed traffic flow. Meanwhile, the LCCAM illustrates a better performance in replicating the number of lane changes than the other reference CA models. The research results show that the LCCAM proposed in this study will have potential and value for autonomous driving and active safety analysis in the future.     

A new parameter identification methodology for a modal analysis test of a rail vehicle

The validation of vehicle mathematical models is a key part of the virtual acceptance process since it is essential to ensure a precise representation of the reality. The model validation procedure should include validation of stationary but also dynamic tests. However, parameter identification from on-track tests is a challenging task due to the non-controlled excitation and the great variability of the test results. Thus, an alternative solution by means of a vehicle modal analysis is proposed, developing a parameter identification methodology for dynamic vehicle model parameters. This methodology calculates estimated values of the vehicle model parameters that have an influence on the excited vehicle vibration modes. Moreover, a new criterion for taking into account the effect of the measurement uncertainties on the selection process of the vehicle parameters is developed. Finally, experimental results show that not only estimations of the suspension stiffness parameters can be obtained, but damping values and structural frequencies from the vehicle bodies can also be estimated.     

A method for estimation of vehicle inertial parameters

In this paper, a new method is presented for estimating the current sprung mass inertial parameters of a vehicle, such as the mass, pitch and roll mass moments of inertia, and lateral and longitudinal centre of gravity locations. The method measures the sprung mass response when the vehicle is driven over an unknown and unmeasured random road profile. From these measurements, the equivalent free-decay responses are extracted and modal analysis techniques used to estimate the sprung mass natural frequencies, damping ratios and mode shapes. This information is combined with a simplified vehicle estimation model, least squares analysis and known equivalent stiffness parameters to estimate the vehicles’ inertial parameters. The results obtained from several simulation examples show that estimates of the inertial parameters generally have small relative errors.     

A self-regulation traffic-condition-based route guidance strategy with realistic considerations: Overlapping routes,stochastic traffic,and signalized intersections

Variable message signs that provide various types of route guidance information have been widely deployed in large cities. To release proper information only using easily collected data, a simple traffic-condition-based (TCB) route guidance strategy was recently proposed. The strategy works based on the estimation of free-flow and congested traffic conditions and is capable of approximating user optimal equilibrium stably. Due to little consideration of the complexity of urban road networks, the TCB strategy is still away from field applications in urban areas. To further push the strategy toward field tests, this article improves the TCB strategy in the following aspects: supplementing the strategy with a self-regulation ability by considering existing traffic conditions; decomposing link capacity to solve the problem of overlapping routes by comparing link capacity on alternative routes; coping with stochastic traffic; and the impact of signalized intersections by utilizing aggregated data. A scenario for an urban road network in Beijing, China, is simulated to test the improved strategy, and the simulation results clearly indicate the effectiveness of the proposed improvements. The improvements extend the TCB strategy on moderately complicated urban road networks, and still have the advantages of simple diversion rules, easily obtained input data, and stable and effective diversion processes.     

Reliability-based network flow estimation with day-to-day variation: A model validation on real large-scale urban networks

Day-to-day variation in the travel times of congested urban transportation networks is a frustrating phenomenon to the users of these networks. These users look pessimistically at the path travel times, and learn to spend additional time to safeguard against serious penalties that await late arrivals at the destinations. These additional expenses are charges similar to the tolls in system equilibrium flow problem, but may not be collected. With this conjecture, the user equilibrium (UE) formulation of congested network flow problem would lack some necessary factors in addressing appropriate path choices. This study, following a previous work proposing pessimistic UE (PUE) flow, aims to show how to measure this additional travel cost for a link, and investigates how different is PUE from UE, and when such differences are pronounced. Data are collected from the peak-hour travel times for the links of paths in the city of Tehran, to estimate the variance of travel times for typical links. Deterministic functions are obtained by calibrating the standard deviation of the daily variations of link travel times, and probabilistic functions by the technique of copula. UE and PUE traffic assignment models are built and applied to three large cities of Mashhad, Shiraz, and Tehran in Iran. The results show that the estimated flows by PUE model replicate the observed flows in screen lines much better than the UE model, particularly for longer trips. Since PUE is computationally equivalent to UE, this improvement is attained virtually at no cost.     

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme.     

与路外停车场协调的城市路内停车设施选址优化模型

为了使路内外停车设施合理分布,减小停车成本,满足城市停车需求,保证城市动、静态交通的整体效率,从路内外停车设施的关系出发,比较路内外停车设施的车辆停放特性及泊车者的停车行为特性,得出路内停车行为发生的特点;并且通过分析路内停车设施对动态交通的影响,建立了离散和连续的延误评价分析模型。假设停车需求与路外停车场已知,在综合考虑路内停车设施对动态交通的影响、绕行距离、步行距离及泊位供应量的基础上,提出一种多层次、多目标优化模型。用遗传算法求解,算例计算结果表明,与路外停车场协调设置路内停车带,不仅能满足停车需求,而且能使平均绕行距离和平均步行距离分别减少80％和65．5％。     

双开口式逆流左转车道几何设计及信号配时优化

为解决单开口式（即仅有1个预信号开口）逆流左转车道（即通过预信号控制动态借用的出口车道）的长度与左转交通需求匹配效果不佳的问题,通过对单开口式逆流左转车道的设计进行分析,提出1种双开口式（即设置2个预信号开口）逆流左转车道的设计及控制方法。结合逆流左转车道的车辆运行规则,分析单开口式、双开口式逆流左转车道上车辆排队行为特征差异,构建逆流左转车道通行能力计算模型和延误计算模型。考虑主预信号协调控制、饱和度、交通波传递等约束条件,以车均延误最小为优化目标,采用0-1变量表示各个预信号开口是否启用,将常规设计、单开口式、双开口式信号配时整合到1个统一的混合整数非线性规划优化模型中,并给出逆流左转车道长度的设计依据。通过案例分析发现：①在逆流左转车道长度为80 m时,交叉口通行能力提升幅度最大;②当通行能力满足需求时,逆流左转车道长度越短,交叉口延误降低越明显;③若为保证通行能力而采用较长的逆流左转车道时,双开口式逆流左转车道通行效率优于单开口式;④综合考虑延误、通行能力等因素,单开口式逆流左转车道长度宜设置为40~60 m,而双开口式宜设置为80 m左右;⑤双开口式逆流左转车道可根据需要选择是否启用每个预信号开口,应用较为灵活,适用于各种流量场景。