Numerical Study of Hypersonic Boundary Layer Receptivity Characteristics Due to Freestream Pulse Waves

A finite difference method is used to do direct numerical simulation (DNS) of hypersonic unsteady flowfield under the action of freestream pulse wave. The response of the hypersonic flowfield to freestream pulse wave is studied, and the generation and evolution characteristics of the boundary layer disturbance waves are discussed. The effects of the pulse wave types on the disturbance mode in the boundary layer are investigated. Results show that the freestream disturbance waves significantly change the shock standoff distance, the distribution of flowfield parameters and the thermodynamic state of boundary layer. In the nose area, the main disturbance modes in the boundary layer are distributed near the fundamental mode. With the evolution of disturbance along with streamwise, the main disturbance modes are transformed from the dominant state of the fundamental mode to the collective leadership state of the second order and the third order harmonic frequency. The intensity of bow shock has significant effects on both the fundamental mode and the harmonic modes in each order. The strong shear structure of boundary layer under different types of freestream pulse waves reveals different stability characteristics. The effects of different types of freestream pulse waves are significant on the distribution and evolution of disturbance modes. The narrowing of frequency band and the decreasing of main disturbance mode clusters exist in the boundary layer both for fast acoustic wave, slow acoustic wave and entropy wave

Multi-stage deep learning approaches to predict boarding behaviour of bus passengers

Smart card data has emerged in recent years and provide a comprehensive, and cheap source of information for planning and managing public transport systems. This paper presents a multi-stage machine learning framework to predict passengers’ boarding stops using smart card data. The framework addresses the challenges arising from the imbalanced nature of the data (e.g. many non-travelling data) and the ‘many-class’ issues (e.g. many possible boarding stops) by decomposing the prediction of hourly ridership into three stages: whether to travel or not in that one-hour time slot, which bus line to use, and at which stop to board. A simple neural network architecture, fully connected networks (FCN), and two deep learning architectures, recurrent neural networks (RNN) and long short-term memory networks (LSTM) are implemented. The proposed approach is applied to a real-life bus network. We show that the data imbalance has a profound impact on the accuracy of prediction at individual level. At aggregated level, FCN is able to accurately predict the rideship at individual stops, it is poor at capturing the temporal distribution of ridership. RNN and LSTM are able to measure the temporal distribution but lack the ability to capture the spatial distribution through bus lines

On the Temporal-spatial Analysis of Estimating Urban Traffic Patterns Via GPS Trace Data of Car-hailing Vehicles

Car-hailing services have become a prominent data source for urban traffic studies. Extracting useful information from car-hailing trace data is essential for effective traffic management, while discrepancies between car-hailing vehicles and urban traffic should be considered. This paper proposes a generic framework for estimating and analyzing urban traffic patterns using car-hailing trace data. The framework consists of three layers: the data layer, the interactive software layer, and the processing method layer. By pre-processing car-hailing GPS trace data with operations such as data cutting, map matching, and trace correction, the framework generates tensor matrices that estimate traffic patterns for car-hailing vehicle flow and average road speed. An analysis block based on these matrices examines the relationships and differences between car-hailing vehicles and urban traffic patterns, which have been overlooked in previous research. Experimental results demonstrate the effectiveness of the proposed framework in examining temporal-spatial patterns of car-hailing vehicles and urban traffic. For temporal analysis, urban road traffic displays a bimodal characteristic while car-hailing flow exhibits a 'multi-peak' pattern, fluctuating significantly during holidays and thus generating a hierarchical structure. For spatial analysis, the heat maps generated from the matrices exhibit certain discrepancies, but the spatial distribution of hotspots and vehicle aggregation areas remains similar

Highly efficient and enantioselective hydrogenation of quinolines and pyridines with Ir-Difluorphos catalyst

The combination of the readily available chiral bisphosphine ligand Difluorphos with [Ir(COD)Cl] 2 in THF resulted in a highly efficient catalyst system for asymmetric hydrogenation of quinolines at quite low catalyst loadings (0.05-0.002 mol%), affording the corresponding products with high enantioselectivities (up to 96%), excellent catalytic activities (TOF up to 3510 h -1 ) and productivities (TON up to 43000). The same catalyst was also successfully applied to the asymmetric hydrogenation of trisubstituted pyridines with nearly quantitative yields and up to 98% ee. In these two reactions, the addition of I 2 additive is indispensable; but the amount of I 2 has a different effect on catalytic performance

The Sihailongwan Maar Lake, northeastern China as a candidate Global Boundary Stratotype Section and Point for the Anthropocene Series

Sihailongwan Maar Lake, located in Northeast China, is a candidate Global boundary Stratotype Section and Point (GSSP) for demarcation of the Anthropocene. The lake’s varved sediments are formed by alternating allogenic atmospheric inputs and authigenic lake processes and store a record of environmental and human impacts at a continental-global scale. Varve counting and radiometric dating provided a precise annual-resolution sediment chronology for the site. Time series records of radioactive (239,240Pu, 129I and soot 14C), chemical (spheroidal carbonaceous particles, polycyclic aromatic hydrocarbons, soot, heavy metals, δ13C, etc), physical (magnetic susceptibility and grayscale) and biological (environmental DNA) indicators all show rapid changes in the mid-20th century, coincident with clear lithological changes of the sediments. Statistical analyses of these proxies show a tipping point in 1954 CE. 239,240Pu activities follow a typical unimodal globally-distributed profile, and are proposed as the primary marker for the Anthropocene. A rapid increase in 239,240Pu activities at 88 mm depth in core SHLW21-Fr-13 (1953 CE) is synchronous with rapid changes of other anthropogenic proxies and the Great Acceleration, marking the onset of the Anthropocene. The results indicate that Sihailongwan Maar Lake is an ideal site for the Anthropocene GSSP

Predicting boarding and alighting behaviour of bus passengers with smart card data using machine learning techniques

Developing an efficient public transport is an important initiative to ease traffic congestion and to reduce energy usage and air pollution. In addition to a well-planned network, an advanced public transport system should offer a comfortable, safe and reliable service for passengers, which requires an appropriate strategy of management and operation. The development of smart infrastructure in public transport ticketing systems has not only improved the operation efficiency and enhanced passenger travel experience, but the development has also made available millions of passengers’ daily travel records. This valuable data source can be used to analyse passengers’ travel behaviour and travel demand, which in turn can help bus companies offer better public transport services for passengers. This study mainly aims to understand and predict the boarding and alighting behaviour of bus passengers, from smart card records, using machine learning (ML) approaches. Firstly, a gradient boosting decision tree (GBDT) ML model is trained with features of passengers’ boarding records, their travel history, as well as weather conditions and travel history. The model is then applied to estimate the alighting stop for each smart card trips. Secondly, a multi-stage deep learning-based ML framework is developed which utilises the fully connected network, recurrent neural network (RNN) and long short-term memory (LSTM) network, to predict the hourly boarding behaviour (on whether to travel and which bus stop to use) for every smart card user. Thirdly, a deep generative adversarial network (Deep-GAN) is proposed to counter the issue with imbalanced data, where positive instances (in our case a boarding instance in any given hour) is much less than negative instances, and to improve the prediction on the hourly boarding demand from the smart card data. The studies indicate that: i) ML techniques are an effective predictive tool to deal with multiple variable and non-linear relations; ii) including weather conditions and travel history can significantly improve the performance of predictive models; iii) the problem of innumerable classes of data fields and imbalanced data records significantly reduce the accuracy of predictive models; iv) In addition to providing good prediction power, GBDT-based ML models provide the ability to rank the relative importance of features; v) RNN and LSTM are capable of capturing the temporal characteristics (i.e. the peak hours) of passengers’ boarding behaviour; vi) Deep-GAN models can be effectively used for reducing the problem of data imbalance and enhancing the performance of the predictive models

Duncan–Chang <i>E</i>-<i>υ</i> Model Considering the Thixotropy of Clay in the Zhanjiang Formation

The clays of the Zhanjiang Formation in the coastal area of Beibu Gulf of China are thixotropic, and the existing constitutive relationship models relevant for clay are incapable of accurately simulating their stress–strain relationships. It is vital to study the changes of mechanical properties of Zhanjiang Formation clay that occur during thixotropy, and to establish a constitutive model considering thixotropy. The varying measures of its shear strength, cohesion, internal friction angle, and initial tangential modulus during thixotropy were investigated by means of triaxial consolidation and drainage tests. Furthermore, the quantitative relationships between the clay’s cohesion, internal friction angle, and initial tangential modulus of the clay and time were examined. This relationship was introduced into the Duncan–Chang model, and a Duncan–Chang model considering the thixotropy of clay was developed. The established model was used to make predictions to assume the validation of the experimental data, and numerical simulations were then carried out. All of the results from the model’s prediction, numerical simulation and experimental measurements were compared against each other in order to verify the reasonableness of the model we had utilized. The results positively demonstrated that: (1) the shear strength, cohesion, angle of internal friction, and initial tangent modulus of the clay gradually increases with longer curing times, and eventually it will stabilize; and (2) compared with the Duncan–Chang model not considering thixotropy, the established thixotropic model is better able to reflect the influence of clay thixotropy on the clay stress–strain relationship, as its mean relative error is smaller. The results of this study provide references for calculating strength and deformation of the clay thixotropy. Further, it also provides references for bearing load calculations of pile foundations in thixotropic clay strata when subjected to long-term loading conditions

Predicting Hourly Boarding Demand of Bus Passengers Using Imbalanced Records From Smart-Cards: A Deep Learning Approach

The tap-on smart-card data provides a valuable source to learn passengers’ boarding behaviour and predict future travel demand. However, when examining the smart-card records (or instances) by the time of day and by boarding stops, the positive instances (i.e. boarding at a specific bus stop at a specific time) are rare compared to negative instances (not boarding at that bus stop at that time). Imbalanced data has been demonstrated to significantly reduce the accuracy of machine-learning models deployed for predicting hourly boarding numbers from a particular location. This paper addresses this data imbalance issue in the smart-card data before applying it to predict bus boarding demand. We propose the deep generative adversarial nets (Deep-GAN) to generate dummy travelling instances to add to a synthetic training dataset with more balanced travelling and non-travelling instances. The synthetic dataset is then used to train a deep neural network (DNN) for predicting the travelling and non-travelling instances from a particular stop in a given time window. The results show that addressing the data imbalance issue can significantly improve the predictive model’s performance and better fit ridership’s actual profile. Comparing the performance of the Deep-GAN with other traditional resampling methods shows that the proposed method can produce a synthetic training dataset with a higher similarity and diversity and, thus, a stronger prediction power. The paper highlights the significance and provides practical guidance in improving the data quality and model performance on travel behaviour prediction and individual travel behaviour analysis

Bus OD matrix reconstruction based on clustering Wi-Fi probe data

The estimation of citywide passenger demand plays a vital role in system planning, operation, and management of the urban transit system. The Wi-Fi probe data, one of the emerging crowdsourcing data, is utilized to collect traces of smartphone users in this study. We establish a framework for OD matrix reconstruction, including extracting features for transit patronage and distinguishing them from non-transit users based on K-means clustering. Such a framework makes partial OD matrix more reliable. A probabilistic estimation method of bus OD matrix reconstruction is then proposed based on the partial OD matrix and the number of boarding and alighting passengers. A field study was carried out on bus line 5 in Suzhou, China. Compared to the measured ground truth, the difference in OD-level is 0.5–1.5 passengers per stop, showing that the proposed method for OD matrix reconstruction is reliable