Closed-form multiclass cell transmission model enhanced with overtaking,lane-changing,and first-in first-out properties

A novel multiclass macroscopic model is proposed in this article. In order to enhance first-in, first-out property (FIFO) and transmission function in the multiclass traffic modeling, a new multiclass cell transmission model with FIFO property (herein called FM-CTM) is extended from its prior multiclass cell transmission model (M-CTM). Also, to enhance its analytical compactness and resultant computational convenience, FM-CTM is formulated in this paper as a set of closed-form matrix equations. The objective is to improve the accuracy of traffic state estimation by enforcing FIFO property when a fast vehicle cannot overtake a slow vehicle due to a limitation of a single-lane road. Moreover, the proposed model takes into account a different priority for vehicles of each class to move forward through congested road conditions, and that makes the flow calculation independent from their free-flow speeds. Some hypothetical and real-world freeway networks with a constant or varying number of lanes are selected to verify FM-CTM by comparing with M-CTM and the conventional CTM. Observed densities of VISSIM and real-world dataset of I-80 are selected to compare with the simulated densities from the three CTMs. The numerical results show that FM-CTM outperforms the other two models by 15% of accuracy measures in most cases. Therefore, the proposed model is expected to be well applicable to the road network with a mixed traffic and varying number of lanes.     

The desulphurisation of shipping: Past,present and the future under a global cap

As from January 2020, the International Maritime Organization (IMO) is implementing a global 0.5% limit on the sulphur content of fuel, commonly known as the global sulphur cap. This limit is the latest policy in the efforts to reduce sulphur emissions from shipping, following the designation of emission control areas (ECAs) and other regional regulations. In this paper, a literature review is conducted of academic studies that have dealt with issues relating to the reduction of maritime sulphur emissions. Various recurring research themes are identified, spanning the areas of operations research, maritime economics and transport policy. The effects and implications of available compliance options are then analyzed from the perspectives of ship operators, shippers and consumers. Using lessons learned from the enforcement of ECA regulations, this is followed by an appraisal of various potential issues related to the enforcement of these new global regulations. It is found that a homogeneous enforcement regime is required to ensure a level playing field amongst ship operators and that the global sulphur cap may lead to serious market distortion, due to the potential short term rise of fuel prices. The paper concludes with a set of recommendations for future research on sulphur emissions from shipping in the aftermath of the global cap and, looking forward, to its relationship to the IMO strategy on the reduction of greenhouse gas (GHG) shipping emissions.     

Solving the gate assignment problem through the Fuzzy Bee Colony Optimization

In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of solving high-level combinatorial problems, like the Flight-Gate Assignment Problem (FGAP), with fast convergence performances. However, given that the FGAP can be often affected by uncertainty or approximation in data, in this paper we develop a new metaheuristic algorithm, based on the Fuzzy Bee Colony Optimization (FBCO), which integrates the concepts of BCO with a Fuzzy Inference System. The proposed method assigns, through the multicriteria analysis, airport gates to scheduled flights based on both passengers’ total walking distance and use of remote gates, to find an optimal flight-to-gate assignment for a given schedule. Comparison of the results with the schedules of real airports has allowed us to show the characteristics of the proposed concepts and, at the same time, it stressed the effectiveness of the proposed method.     

Locating emergency vehicles with an approximate queuing model and a meta-heuristic solution approach

In this paper, the location of emergency service (ES) vehicles is studied on fully connected networks. Queuing theory is utilized to obtain the performance metrics of the system. An approximate queuing model the (AQM) is proposed. For the AQM, different service rate formulations are constructed. These formulations are tested with a simulation study for different approximation levels. A mathematical model is proposed to minimize the mean response time of ES systems based on AQM. In the model, multiple vehicles are allowed at a single location. The objective function of the model has no closed form expression. A genetic algorithm is constructed to solve the model. With the help of the genetic algorithm, the effect of assigning multiple vehicles on the mean response time is reported.     

基于AR模型的并靠两船相对运动预报研究

根据两船相对运动的特点,利用两船模相对运动的测量数据,运用基于自回归模型的时间序列分析法,建立了两船相对运动的数学模型,并给出了运动姿态的预报值.通过本方法的研究,可以得到满意的相对运动预报精度,为两船补给波浪、补偿装置的开发打下了理论基础.     

大学计算机基础课程教学方式的改革与实践

随着信息技术和计算机技术迅速发展和普及，传统的计算机基础教学方式面临新的挑战。本文探讨了当前形势下大学计算机基础课程教学模式的改革方向，根据多年的教学实践，探讨了高校计算机基础教学改革模式。     

A novel approach for the modelling of air quality dynamics in underground railway stations

Indoor air quality in subterranean train stations is a concern in many places around the globe. However, due to the specificity of each case, numerous parameters of the problem remain unknown, such as the braking discs particle emission rate, the ventilation rate of the station or the complete particle size distribution of the emitted particles. In this study the problem of modelling PM₁₀ concentration evolution in relation with train traffic is hence addressed with a particle-mass conservation model which parameters are fitted using a genetic algorithm. The parameters of the model allow to reproduce the dynamics and amplitude of four field data sets from the French and Swedish underground contexts and comply with realistic bounds in terms of emissions, deposition and ventilation rate.     

Model based urban traffic control,part II: Coordinated model predictive controllers

The present paper describes how to use coordination between neighbouring intersections in order to improve the performance of urban traffic controllers. Both the local MPC (LMPC) introduced in the companion paper (Hao et al., 2018) and the coordinated MPC (CMPC) introduced in this paper use the urban cell transmission model (UCTM) (Hao et al., 2018) in order to predict the average delay of vehicles in the upstream links of each intersection, for different scenarios of switching times of the traffic lights at that intersection. The feedback controller selects the next switching times of the traffic light corresponding to the shortest predicted average delay. While the local MPC (Hao et al., 2018) only uses local measurements of traffic in the links connected to the intersection in comparing the performance of different scenarios, the CMPC approach improves the accuracy of the performance predictions by allowing a control agent to exchange information about planned switching times with control agents at all neighbouring intersections. Compared to local MPC the offline information on average flow rates from neighbouring intersections is replaced in coordinated MPC by additional online information on when the neighbouring intersections plan to send vehicles to the intersection under control. To achieve good coordination planned switching times should not change too often, hence a cost for changing planned schedules from one decision time to the next decision time is added to the cost function. In order to improve the stability properties of CMPC a prediction of the sum of squared queue sizes is used whenever some downstream queues of an intersection become too long. Only scenarios that decrease this sum of squares of local queues are considered for possible implementation. This stabilization criterion is shown experimentally to further improve the performance of our controller. In particular it leads to a significant reduction of the queues that build up at the edges of the traffic region under control. We compare via simulation the average delay of vehicles travelling on a simple 4 by 4 Manhattan grid, for traffic lights with pre-timed control, traffic lights using the local MPC controller (Hao et al., 2018), and coordinated MPC (with and without the stabilizing condition). These simulations show that the proposed CMPC achieves a significant reduction in delay for different traffic conditions in comparison to these other strategies.     

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.     

基于基准有限元模型的箱梁TMD振动控制研究

针对高架桥梁结构引起的振动噪声问题,研究TMD控制箱梁结构振动的特性。为了获得精准的箱梁有限元模型,首先以铁路32 m简支箱梁桥为原型,按10:1的几何相似比设计制作简支箱梁缩尺试验模型,应用ANSYS软件建立初始动力有限元模型;对有限元模型模态分析与试验模型模态测试得到的自由模态信息进行误差分析,并采用基于灵敏度分析的模型修正技术对初始动力有限元模型弹性模量和密度进行修正,得到基准有限元模型,误差确认结果显示修正后的有限元模型更精准地反应箱梁的振动特性;进一步利用基准有限元模型,开展TMD控制简支箱梁桥振动的研究,研究结果表明TMD对于抑制桥梁竖向共振有很好的效果。