Experimental study and modeling of car-following behavior under high speed situation

To investigate the car-following behavior under high speed driving conditions, we performed a set of 11-car-platoon experiments on Hefei airport highway. The formation and growth of oscillations have been analyzed and compared with that in low speed situations. It was found that there is considerable heterogeneity for the same driver over different runs of the experiment. This intra-driver heterogeneity was quantitatively depicted by a new index and incorporated in an enhanced two-dimensional intelligent driver model. Using both the new high-speed and the previous low-speed experimental data, the new and three existing models were calibrated. Simulation results show that the enhanced model outperforms the three existing car-following models that do not take into account this intra-driver heterogeneity in reproducing the essential features of the traffic in the experiments.     

Mental models of driving and speed: biases,choices and reality

This paper provides a review of research performed by Svenson with colleagues and others work on mental models and their practical implications. Mental models describe how people perceive and think about the world including covariances and relationships between different variables, such as driving speed and time. Research on mental models has detected the time-saving bias [Svenson, O. (1970). A functional measurement approach to intuitive estimation as exemplified by estimated time savings. Journal of Experimental Psychology, 86, 204–210]. It means that drivers relatively overestimate the time that can be saved by increasing speed from an already high speed, for example, 90–130?km/h, and underestimate the time that can be saved by increasing speed from a low speed, for example, 30–45?km/h. In congruence with this finding, mean speed judgments and perceptions of mean speeds are also biased and higher speeds given too much weight and low speeds too little weight in comparison with objective reality. Replacing or adding a new speedometer in the car showing min per km eliminated or weakened the time-saving bias. Information about braking distances at different speeds did not improve overoptimistic judgments of braking capacity, but information about collision speed with an object suddenly appearing on the road did improve judgments of braking capacity. This is relevant to drivers, politicians and traffic regulators.     

A copula-based approach for estimating the travel time reliability of urban arterial

Estimating the travel time reliability (TTR) of urban arterial is critical for real-time and reliable route guidance and provides theoretical bases and technical support for sophisticated traffic management and control. The state-of-art procedures for arterial TTR estimation usually assume that path travel time follows a certain distribution, with less consideration about segment correlations. However, the conventional approach is usually unrealistic because an important feature of urban arterial is the dependent structure of travel times on continuous segments. In this study, a copula-based approach that incorporates the stochastic characteristics of segments travel time is proposed to model arterial travel time distribution (TTD), which serves as a basis for TTR quantification. First, segments correlation is empirically analyzed and different types of copula models are examined. Then, fitting marginal distributions for segment TTD is conducted by parametric and non-parametric regression analysis, respectively. Based on the estimated parameters of the models, the best-fitting copula is determined in terms of the goodness-of-fit tests. Last, the model is examined at two study sites with AVI data and NGSIM trajectory data, respectively. The results of path TTD estimation demonstrate the advantage of the proposed copula-based approach, compared with the convolution model without capturing segments correlation and the empirical distribution fitting methods. Furthermore, when considering the segments correlation effect, it was found that the estimated path TTR is more accurate than that by the convolution model.     

A hybrid deep learning based traffic flow prediction method and its understanding

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. While existing DNN models can provide better performance than shallow models, it is still an open issue of making full use of spatial-temporal characteristics of the traffic flow to improve their performance. In addition, our understanding of them on traffic data remains limited. This paper proposes a DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy. The DNN-BTF model makes full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow. Inspired by recent work in machine learning, an attention based model was introduced that automatically learns to determine the importance of past traffic flow. The convolutional neural network was also used to mine the spatial features and the recurrent neural network to mine the temporal features of traffic flow. We also showed through visualization how DNN-BTF model understands traffic flow data and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model. Data from open-access database PeMS was used to validate the proposed DNN-BTF model on a long-term horizon prediction task. Experimental results demonstrated that our method outperforms the state-of-the-art approaches.     

轨道交通车辆的工艺流程标准化

从生产线建立、工艺流程编制、工艺布局设计和生产能力等方面进行分析,提出了轨道交通车辆工艺流程标准化设计方法和优化方法。基于精益管理理念和工序能力测算法,可以合理编制标准化工艺流程,从而降低劳动强度,提高生产效率。     

技术站间货物列车协同配流方案研究

技术站间货物列车协同作业组织模式，可实现各站获益，整体加强，对提升铁路运输生产效率具有重要意义.本文建立技术站间货物列车协同配流模型.模型以最大化两技术站的正点出发列车数作为目标函数，采用启发式遗传算法进行寻优，得到货物列车解编顺序和配流方案.最后，通过对算例进行实验分析，验证协同配流模型的实用性.结果表明，技术站间协同配流作业明显压缩车辆在站总停留时间，增加了阶段计划内正点出发列车数，进而提高了技术站内线路使用能力.     

Modeling free-flow speed according to different water depths—From the viewpoint of dynamic hydraulic pressure

In this paper, we propose a method of modeling free flow speed from the viewpoint of hydroplaning. First, the lift forces for different water depths were estimated using Bernoulli’s equation. Compared with the result of the experimental test performed by the Japan Automobile Research Institute, the hydrodynamic pressure coefficient was determined to be 0.03 (tf s²/m⁴). The validation of the predicted lift force is found in another published paper. A very good match is found between the computed values by the proposed numerical model and the data in other published papers. Then, the loss of contact force is considered to evaluate the hydroplaning performance of a tire. To simulate the hydroplaning speed, a tire-sliding model was utilized to obtain the traction and friction forces between the road surface and the tire. The observation data obtained in Japan in 2009 is compared with the physically computed hydroplaning speed, yielding the conclusion that the traction force at the measured desired speed is, on average, 23.4% of the traction force at hydroplaning speed. The analytical model offers a useful tool to quantitatively show that the free flow speed changes as the water depth increase.     

基于客流需求的城市轨道交通动态时刻表优化模型

为使城市轨道交通列车运行时刻表更贴合客流需求,依据不断变化的客流需求确定每列车的发车时刻和停站时间,采用多目标优化方法构建以乘客出行时间费用和列车运行时间费用最小为目标、列车发车时刻和停站时间为决策变量的城市轨道交通动态时刻表优化模型,并采用粒子群算法求解。以广州地铁13号线为例进行验证,结果表明优化后的时刻表更满足客流需求,能有效地提高乘客出行效率,具有更好的动态适应性。     

共享停车泊位数量可变下交通流逐日演化规律

为了揭示在共享停车泊位数量可变条件下的网络交通流逐日演化规律,首先构建了共享泊位交易系统,并考虑了交易市场中的共享泊位提供者可以选择2种异质性的价格预期方式,即理性预期方式和幼稚预期方式;而后对共享泊位的均衡价格、2种提供者的占比差、高峰时段公交和小汽车需求的演化规律进行了分析;其次,以2条平行路径的路网为例,对网络交通流量分配的最终演化结果进行了分析;最后,在对上述2个系统的最终演化状态给出定量判据后,以北京市实际路网为例进行了数值试验。理论分析和数值试验结果表明:①对于共享泊位交易系统,若供给曲线斜率小于需求曲线斜率,则共享泊位交易系统的唯一均衡解可实现无条件渐进稳定;否则若理性提供者的交易与预测成本之和大于幼稚提供者,则存在临界提供者选择强度,使得共享泊位交易系统在大于此临界值条件下出现分岔或混沌现象;②对于网络交通流系统,若出行成本对路径流量敏感度小,路径选择概率对出行成本敏感度小,小汽车需求量不大,则系统唯一的均衡解可能是渐进稳定的,否则系统会出现分岔或混沌状态;③当共享泊位交易系统处于渐进稳定状态时,若提供者对共享泊位的价格变动不敏感,用户对其价格变动敏感,潜在共享泊位需求量不大,理性提供者的交易与预测成本之和并非远大于幼稚提供者,提供者选择强度不大,则由于受到共享泊位交易总量的限制,高峰时段的均衡小汽车需求不大,导致网络交通流系统的最终演化状态容易趋向于渐进稳定;④当共享泊位交易系统处于混沌状态时,网络交通流系统会产生更加严重的分岔与混沌现象。     

The identification and empirical characterization of vehicular (Lagrangian) fundamental diagrams in multilane traffic flow

Traditional macroscopic traffic flow modeling framework adopts the spatial–temporal coordinate system to analyze traffic flow dynamics. With such modeling and analysis paradigm, complications arise for traffic flow data collected from mobile sensors such as probe vehicles equipped with mobile phones, Bluetooth, and Global Positioning System devices. The vehicle‐based measurement technologies call for new modeling thoughts that address the unique features of moving measurements and explore their full potential. In this paper, we look into the concept of vehicular fundamental diagram (VFD) and discuss its engineering implications. VFD corresponds to a conventional fundamental diagram (FD) in the kinematic wave (KW) theory that adopts space–time coordinates. Similar to the regular FD in the KW theory, VFD encapsulates all traffic flow dynamics. In this paper, to demonstrate the full potential of VFD in interpreting multilane traffic flow dynamics, we generalize the classical Edie's formula and propose a direct approach of reconstructing VFD from traffic measurements in the vehicular coordinates. A smoothing algorithm is proposed to effectively reduce the nonphysical fluctuation of traffic states calculated from multilane vehicle trajectories. As an example, we apply the proposed methodology to explore the next‐generation simulation datasets and identify the existence and forms of shock waves in different coordinate systems. Our findings provide empirical justifications and further insight for the Lagrangian traffic flow theory and models when applied in practice. Copyright © 2015 John Wiley & Sons, Ltd.