Cellular Automata Model for Traffic Flow with Optimised Stochastic Noise Parameter

Based on the existing safe distance cellular automata model, an improved cellular automata model based on realistic human reactions is proposed in this paper, which aims to reproduce the characteristics of congested traffic flow. In the proposed model, the stochastic noise param-eter is optimised by considering driving behavioural dif-ference. The relative speed, gap and acceleration of the front vehicle are introduced into the optimised stochastic noise parameter oriented to describing the asymmetric acceleration behaviour of drivers in congestion. The sim-ulation results show that an uneven distribution of accel-eration trajectories of vehicles experiencing congestion exhibited on the spatial-temporal diagram of the pro-posed model is reproduced. Based on the analysis of the NGSIM, compared with the model with traditional sto-chastic noise parameter, the vehicles that move accord-ing to the proposed model can follow more easily and more realistically. Then the actual gap of vehicles can be better reflected by the proposed model and the change of vehicle speed is more stable. Additionally, the traffic efficiency from two aspects of flow and speed shows that the proposed model can significantly improve the traffic efficiency in the medium high density region

Analysis of Traffic Operation Performances at Roundabouts

AbstractThe traffic operation performances at roundabouts are complicated under the influence of confluence operations. On the basis of the Mengxi square in Zhenjiang city roundabouts survey using video cameras, some parameter performances of different vehicle types on weaving sections and circulating lanes are analyzed, which included the velocity distribution, the gap distribution, as well as the distance distribution of lane changing. Based on the analysis, some of the conclusions are as follows: 1) The vehicle velocity of inner circulating lane is larger than the outer circulating lane 2) The entry vehicle velocity is smallest 3) The distribution of entry gaps is different from circulating gaps 4) the characteristics of vehicle teams are mostly same in different lanes 5) When the type is bigger, the lane changing distance is smaller 6) For the same type, the distance of lane changing shows ladder-like distribution.The research result will be a base for improving the capacity of roundabouts

Sustained Negative BOLD Response in Human fMRI Finger Tapping Task

In this work, we investigated the sustained negative blood oxygen level-dependent (BOLD) response (sNBR) using functional magnetic resonance imaging during a finger tapping task. We observed that the sNBR for this task was more extensive than has previously been reported. The cortical regions involved in sNBR are divided into the following three groups: frontal, somatosensory and occipital. By investigating the spatial structure, area, amplitude, and dynamics of the sNBR in comparison with those of its positive BOLD response (PBR) counterpart, we made the following observations. First, among the three groups, the somatosensory group contained the greatest number of activated voxels and the fewest deactivated voxels. In addition, the amplitude of the sNBR in this group was the smallest among the three groups. Second, the onset and peak time of the sNBR are both larger than those of the PBR, whereas the falling edge time of the sNBR is less than that of the PBR. Third, the long distance between most sNBR foci and their corresponding PBR foci makes it unlikely that they share the same blood supply artery. Fourth, the couplings between the sNBR and its PBR counterpart are distinct among different regions and thus should be investigated separately. These findings imply that the origin of most sNBR foci in the finger-tapping task is much more likely to be neuronal activity suppression rather than “blood steal.

Nonlinear FEM Analysis on Composite Beams with Web Opening Under Negative Bending Moment

In order to investigate the shear behaviour and main factors of steel-concrete composite beam with web opening under negative moment, simply supported composite beam under concentrated load was analysis through finite element method. The finite element software ANSYS is used to calculate and analyse nonlinearly ten specimens. The main changing parameters are thickness, reinforcement ratio of slab and size of opening. The analysis indicates that stiffness and ultimate capacity will reduce greatly after web opening under negative bending moment. Through increasing the thickness of concrete slab, its bearing capacity can be enhanced markedly, and increasing the reinforcement ratio of concrete slab can improve its deformability effectively. Concrete slab makes a great contribution to shear capacity of web opening composite beam under negative moment. With the increase of the height or width of opening, the shear force that concrete slab undertakes will increase relatively

Numerical analysis on global serviceability behaviours of tall Glulam frame buildings to the Eurocodes and UK National Annexes

Glued-laminated timber (Glulam) is an innovative engineered timber product and has been widely used for constructing spatial grand timber structures and tall timber buildings due to its exceptional natural attraction, easy processing, decent fire resistance and outstanding structural performance. However, global serviceability performances of tall timber buildings constructed from Glulam products for beams, columns and bracings and CLT products for lift core and floors under wind load are not well known yet though they are crucial in structural design and global analysis. In this study, finite element software SAP2000 is used to numerically simulate the global static and dynamic serviceability behaviours of a 105 m high 30-storey tall Glulam building with CLT lift core and floors assumed in Glasgow, Scotland, UK. The maximum horizontal storey displacement due to wind is 58.5% of the design limit and the maximum global horizontal displacement is 49.7% of the limit set to the Eurocodes. The first three lowest vibrational frequencies, modes and shapes of the building are obtained, with the fundamental frequency being 33.3% smaller than the code recommended value due to its low mass and stiffness. The peak acceleration of the building due to wind is determined to the Eurocodes and ISO 10137. The results show that the global serviceability behaviours of the building satisfy the requirements of the Eurocodes and other design standards. Parametric studies on the peak accelerations of the tall Glulam building are also conducted by varying timber material properties and building masses. Increasing the timber grade for CLT members, the generalised building mass and the generalised building stiffness can all be adopted to lower the peak accelerations at the top level of the building so as to reduce the human perceptions to the wind induced vibrations with respect to the peak acceleration

Few-shot Class-incremental Learning: A Survey

Few-shot Class-Incremental Learning (FSCIL) presents a unique challenge in machine learning, as it necessitates the continuous learning of new classes from sparse labeled training samples without forgetting previous knowledge. While this field has seen recent progress, it remains an active area of exploration. This paper aims to provide a comprehensive and systematic review of FSCIL. In our in-depth examination, we delve into various facets of FSCIL, encompassing the problem definition, the discussion of primary challenges of unreliable empirical risk minimization and the stability-plasticity dilemma, general schemes, and relevant problems of incremental learning and few-shot learning. Besides, we offer an overview of benchmark datasets and evaluation metrics. Furthermore, we introduce the classification methods in FSCIL from data-based, structure-based, and optimization-based approaches and the object detection methods in FSCIL from anchor-free and anchor-based approaches. Beyond these, we illuminate several promising research directions within FSCIL that merit further investigation

Modelling the Selection of Waiting Areas on Subway Platforms Based on the Bacterial Chemotaxis Algorithm

Based on the bacterial chemotaxis algorithm, a new waiting-area selection model (WASM) is proposed that predicts well the pedestrian distribution in subway waiting areas. WASM regards passengers waiting on a subway platform as two-dimensional points and adopts an essential rejection factor to determine the target waiting area. Based on WASM, three experiments were carried out to explore how passenger volume, waiting-area capacity, and staircase position affect the number and distribution of waiting passengers. The experimental results show the following. 1) Regardless of the passenger flow, passengers prefer waiting areas that are between the stairs. 2) Setting proper capacity limits on waiting areas can help to improve subway transportation efficiency when passenger flow is relatively high. 3) The experimental results show that the closer the staircases, the more passengers are left stranded on the platform

Estimation of Confidence in the Dialogue based on Eye Gaze and Head Movement Information

In human-robot interaction, human mental states in dialogue have attracted attention to human-friendly robots that support educational use. Although estimating mental states using speech and visual information has been conducted, it is still challenging to estimate mental states more precisely in the educational scene. In this paper, we proposed a method to estimate human mental state based on participants’ eye gaze and head movement information. Estimated participants’ confidence levels in their answers to the miscellaneous knowledge question as a human mental state. The participants’ non-verbal information, such as eye gaze and head movements during dialog with a robot, were collected in our experiment using an eye-tracking device. Then we collect participants’ confidence levels and analyze the relationship between human mental state and non-verbal information. Furthermore, we also applied a machine learning technique to estimate participants’ confidence levels from extracted features of gaze and head movement information. As a result, the performance of a machine learning technique using gaze and head movements information achieved over 80 % accuracy in estimating confidence levels. Our research provides insight into developing a human-friendly robot considering human mental states in the dialogue