Comparative analysis & modelling for riders’ conflict avoidance behavior of E-bikes and bicycles at un-signalized intersections

With the increasing popularity of electric-assist bikes (E-bikes) in China, U.S. and Europe, the corresponding safety issues at intersections have attracted the attention of researchers. Understanding the microscopic behavior of E-bike riders during conflicts with other road users is fundamental for safety improvement and simulation modeling of E-bikes at intersections. This study compared the conflict avoidance behaviors of E-bike and conventional bicycle riders using field data extracted from video recordings of different intersections. The impact of conflicting road user type and gender on E-bikes and bicycles were analyzed. Compared with bicycles, E-bikes appeared to enable more flexibility in conflict avoidance behavior. For example, E-bikes would behave like bicycles when conflicting with motor vehicles/Ebikes, and behave more like motor vehicles when conflicting with bicycles/pedestrians. Based on this, we built an Extended Cyclist Conflict Avoidance Movement (ECCAM) model, which can represent the conflict avoidance behavior of E-bikes/bicycles at mixed traffic flow un-signalized intersections. Field data were applied to validate the proposed model, and the results are promising

Thermodynamic and kinetic study of CO2 adsorption/desorptionon amine-functionalized sorbents

473-482The thermodynamic and kinetic characteristics of CO2 adsorption of SBA-16 loaded with pentaethylenehexamine (PEHA) have been investigated using adsorption column system. The Langmuir isotherm model fitts the CO2 adsorption isotherms well, and the average isosteric heat of adsorption is 59.6 kJ/mol, indicating that the CO2 adsorption on PEHA-loaded SBA-16 is chemisorption. The Avrami fractional dynamics model is very suitable for illustrating the adsorption behaviour of CO2 adsorption, and the results of kinetic analysis show that increasing the partial pressure of CO2 or the working temperature is beneficial to the adsorption of CO2. Three desorption methods were evaluatedto achieve the optimal desorption method. The results show that VTSA and steam stripping method are effective methods for industrial CO2 desorption. Steam stripping may be more suitable for plants that already have low-cost steam. The activation energy Ea of CO2 adsorption/desorption is calculated using Arrhenius equation. The activation energy Ea of CO2 adsorption/desorption was calculated using the Arrhenius equation. The results show that the absolute value of Ea (adsorption) decreases with the increase of CO2 partial pressure. In addition, the Ea value of vacuum rotary regeneration method and steam stripping method is smaller than the Ea value of temperature swing regeneration

Robust elbow angle prediction with aging soft sensors via output-level domain adaptation

Wearable devices equipped with soft sensors provide a promising solution for body movement monitoring. Specifically, body movements like elbow flexion can be captured by monitoring the stretched soft sensors’ resistance changes. However, in addition to stretching, the resistance of a soft sensor is also influenced by its aging, which makes the resistance a less stable indicator of the elbow angle. In this paper, we leverage the recent progress in Deep Learning and address the aforementioned issue by formulating the aging-invariant prediction of elbow angles as a domain adaption problem. Specifically, we define the soft sensor data (i.e., resistance values) collected at different aging levels as different domains and adapt a regression neural network among them to learn domain-invariant features. However, unlike the popular pairwise domain adaptation problem that only involves one source and one target domain, ours is more challenging as it has “infinite” target domains due to the non-stop aging. To address this challenge, we novelly propose an output-level domain adaptation approach which builds on the fact that the elbow angles are in a fixed range regardless of aging. Experimental results show that our method enables robust and accurate prediction of elbow angles with aging soft sensors, which significantly outperforms supervised learning ones that fail to generalize to aged sensor data

Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework. The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamic

A Novel Facile and Green Synthesis Protocol to Prepare High Strength Regenerated Silk Fibroin/SiO 2 Composite Fiber

Abstract(#br)In this work, regenerated silk fibroin (RSF) and silicon dioxide (SiO 2 ) composite fiber was successfully extruded by wet spinning method. The effect of SiO 2 addition on structure of the composite fiber at microscopic level is studied, which subsequently correlated to the mechanical performance. The best concentration ratio for composite fiber is identified by screening SiO 2 concentration from 0.025 w/w% to 0.5 w/w%. The experimental results revealed that the SiO 2 at a low concentration of 0.1 w/w% was well distributed. The breaking stress, breaking strain and Young’s modulus at 0.1 w/w% SiO 2 addition of the RSF fibers increased considerably compared to the neat RSF fibers from 243±3 to 458±21 MPa, 51±4 % to 54±7 % and 6.34±0.55 to 11.69±1.12 GPa, respectively. To the..

Timetable coordination of first trains in urban railway network: A case study of Beijing

A model of timetable coordination of first trains in urban railway networks, based on the importance of lines and transfer stations, is proposed in this paper. A sub-network connection method is developed, and a mathematical programming solver is utilized to solve the suggested model. A simple test network and a real network of Beijing urban railway network are modeled to verify the effectiveness of our suggested model. Results demonstrate that the proposed model is effective in improving the transfer performance in that they reduce the connection time significantly

Designing robust schedule coordination scheme for transit networks with safety control margins

We propose a robust schedule coordination scheme which combines timetable planning with a semi-flexible departure delayed control strategy in case of disruptions. The flexibility is provided by allowing holding for the late incoming bus within a safety control margin (SCM). In this way, the stochastic travel time is addressed by the integration of real-time control and slacks at the planning phase. The schedule coordination problem then jointly optimises the planning headways and slack times in the timetable subject to SCM. Analytical formulations of cost functions are derived for three types of operating modes: uncoordinated operation, departure punctual control and departure delayed control. The problem is formulated as a stochastic mixed integer programming model and solved by a branch-and-bound algorithm. Numerical results provide an insight into the interaction between SCM and slack times, and demonstrate that the proposed model leads to cost saving and higher efficiency when SCM is considered. Compared to the conventional operating modes, the proposed method also presents advantages in transfer reliability and robustness to delay and demand variation

An efficient disposable and flexible electrochemical sensor based on a novel and stable metal carbon composite derived from cocoon silk

Abstract(#br)The present work reports cocoon silk fibroin (SF)as a unique precursor for the in-situ fabrication of well-engineered, stable and leach free gold nanoparticle doped carbonaceous materials (AuNPs@NSC). In principle, at the molecular level, SF has a singular structure that can be converted to a N-doped aromatic carbon structure by heat treatment. The electrochemical properties of the prepared nanocomposite were examined by cyclic voltammetry and differential pulse voltammetry. A flexible three electrode sensor system with AuNPs@NSC-modified working electrodes has been developed, to achieve easy operation and quick and accurate responses. The electrochemical results showed that the sensor made by the AuNPs@NSC-modified working electrode demonstrated high sensitivity for the detection of rutin, which is attributed to the good distribution of the AuNPs on the carbon matrix. Using differential pulse voltammetry (DPV), the AuNPs@NSC electrode was found to have a linear response in the range of 0.11–250 μM and a comparably low limit of detection of 0.02 μM (S/N = 3). To ensure the accuracy and applicability of the sensors, the concentration of rutin in the commodity (rutin capsule, 10 mg/capsule) was examined, and the sensor provided high precision with a minimum relative error (RE) of 3.3%. These findings suggest that AuNPs@NSC can be considered to be a potential electrode material for the development of electrochemical devices and has great potential in extending their application to the flexible sensor field