Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.     

融合D-S理论的锅炉人因贝叶斯网络安全性定量分析

针对船用锅炉人因安全性分析中存在的知识不确定性,采用D-S证据理论对多专家信息进行融合并建立考虑人因的贝叶斯网络,得到节点条件概率的区间表示形式.经加权平均后代入贝叶斯网络计算,与面向对象贝叶斯网络和FTA等方法的对比显示,该方法能够更加有效地融合不同专家信息,也更为符合工程实际.     

城市道路交叉口借道右转车道设置方法

为了弥补交叉口空间布局中对借道右转车道研究的不足,分析了借道右转车辆在两相邻交叉口间的运行过程,将两交叉口间距分为四部分。采用波动理论、考虑驾驶员的操作顺适性以及采用概率论方法,建立了借道右转的右转车道适宜开口位置模型、借道右转的绿化带开口长度模型和路段右转车辆的合流段长度模型,在此基础上得到了实施借道右转的两交叉口最小间距模型。实例分析表明,所得模型符合两相邻交叉口间的交通流运行实际,对于判断两相邻交叉口是否适合于设置借道右转,以及设置借道右转时如何对各构成部分进行分析具有指导意义。     

装配式梁桥横向协同工作性能度量指标的研究

为了表征运营期间装配式梁桥的横向协同工作性能,提出了一种间接度量指标——相邻预制梁间的应变相关系数;同时建立了装配式梁桥的简化力学模型,通过推导建立了该指标的解析表达式。理论分析和数值仿真研究表明,在保证足够的样本长度条件下,交通流荷载趋向统计稳定,此时结构特性成为该度量指标的唯一影响因素,证明了该指标具备表征装配式梁桥横向协同工作性能的条件。     

驳船的影响概率破舱稳性单因素研究

概率破舱稳性是干货船破舱稳性计算与校核的一项强制性要求,它不同于传统的破舱稳性计算,计算中涉及到大量的破损舱室的组合,目前仍然是在分舱之后才能进行分析计算,若不满足则需要返回重新分舱。在初步设计阶段,如何有效分舱以使船舶分舱设计能满足概率破舱稳性,以减少分舱的盲目性与重复性,从而找到基于概率破舱稳性的分舱指导原则,这将具有重要的理论意义和工程应用价值。以某实际工程驳船为研究对象,设计了大量的分舱方案,并分别对其进行概率破舱稳性计算。根据计算结果对影响分舱指数A的因素进行了定性分析,提出了改进分舱指数A的设计准则。     

内河航道工程财务效益探讨

以杭嘉湖航道网改造工程为实例,采用敏感性分析和概率分析的方法,分别按改扩建项目和新建项目,探讨不同收费方式下内河航道工程的财务效益。研究结果表明,运量大的长湖申线有一定的财务效益,项目自身具有生存能力,但运量小的六平申线,财务效益差,投资决策应注重其社会效益。     

Bayesian estimation of multinomial probit models of work trip choice

In this paper, we estimate a multinomial probit model of work trip mode choice in Seoul, Korea, using the Bayesian approach with Gibbs sampling. This method constructs a Markov chain Gibbs sampler that can be used to draw directly from the exact posterior distribution and perform finite sample likelihood inference. We estimate direct and cross-elasticities with respect to travel cost and the value of time. Our results show that travel demands are more sensitive to travel time than travel cost. The cross-elasticity results show that the bus has a greater substitute relation to the subway than the auto (and vice versa) and that an increase in the cost of an auto will increase the demand for bus transport more so than that of the subway.     

A new channel allocation scheme for hierarchical wireless networks

A channel allocation scheme for hierarchical wireless networks was proposed in terms of the connection-level quality of service. The channel allocation scheme was analyzed using both horizontal channel borrowing and vertical traffic overflowing. Pre-emptive priority strategies are used to classify real-time services and non-real-time services, real-time service is given higher priority for it is allowed to pre-empt channels used by non-real-time service. Some channel borrowing thresholds and acceptance ratios are used to avoid channel locking or dynamic power control, which can also be dynamically adjusted according to network load. Simula-tion results show that the proposed schemes can improve the system performance.