Heterogeneous traffic flow modelling for an arterial using grid based approach

A grid based modelling approach akin to cellular automata (CA) is adopted for heterogeneous traffic flow simulation. The road space is divided into a grid of equally sized cells. Moreover, each vehicle type occupies one or more cell as per its size unlike CA traffic flow model where each vehicle is represented by a single cell. Model needs inputs such as vehicle size, its maximum speed, acceleration, deceleration, probability constants, and arrival pattern. The position and speed of the vehicles are assumed to be discrete. The speed of each vehicle changes according to its interactions with other vehicles, following some stochastic rules depending on the circumstances. The model is calibrated and validated using real data and VISSIM. The results indicate that grid based model can reasonably well simulate complex heterogeneous traffic as well as offers higher computational efficiency needed for real time application.     

钦州湾波浪条件数值模拟研究

通过三娘湾海洋站风浪资料得出外海波要素,应用风区推算、抛物型缓坡方程、Boussinesq方程建立数学模型,对钦州湾港区波浪条件进行模拟计算,得出钦州湾波浪的波浪场状况。     

Sequential data assimilation in an upwelling influenced estuary

We have evaluated the impact of assimilating chlorophyll, nitrate, phosphate, silicate and ammonium into a coupled 1D hydrodynamic ecosystem model (GOTM-ERSEM) in an upwelling influenced estuary. The assimilation method chosen is the Ensemble Kalman Filter (EnKF), which has been demonstrated to improve field estimates of key variables (chlorophyll, nutrients) for bulk algal bloom prediction. The 1D model has been set up for a central station inside the Ría de Vigo (Spain). Data from bi-weekly surveys are used to constrain the model. Temperature and salinity profiles are used to ensure the correct representation of the water structure through a relaxation scheme. Chlorophyll extracts and nutrients at three depths are assimilated sequentially during 1 year simulation (1991). The assimilation period includes episodes of active upwelling and downwelling. All five assimilated variables are successfully constrained and represent a large improvement on the reference simulation (without assimilation). Small divergences can be related to poorly resolved physical processes in the model. The assimilation was further evaluated by comparing observed biomass partitioning with model results. Diatoms accounted for the largest biomass update and the largest improvement in terms of percentage of variance explained (R²). This is particularly significant as they represent the 46% of the yearly integrated observed biomass of the planktonic autotrophs. Nonetheless the R² value was low for all phytoplankton groups. Bacteria and nanoflagellates showed an improvement with respect to their yearly Root Mean Square (RMS), while the other functional groups worsen or remained unaffected. Chlorophyll assimilation was responsible for most of the impact on the phytoplankton biomass with small contributions from the silicate. It had minor impact on the updates of nutrients which in turn corrected the state variables related to the detrital pool. In this current setting, combined assimilation of chlorophyll and nutrients is not sufficient to produce a skillful simulation of the phytoplankton succession.     

Development and validation of a Kalman filter-based model for vehicle slip angle estimation

It is well known that vehicle slip angle is one of the most difficult parameters to measure on a vehicle during testing or racing activities. Moreover, the appropriate sensor is very expensive and it is often difficult to fit to a car, especially on race cars. We propose here a strategy to eliminate the need for this sensor by using a mathematical tool which gives a good estimation of the vehicle slip angle. A single-track car model, coupled with an extended Kalman filter, was used in order to achieve the result. Moreover, a tuning procedure is proposed that takes into consideration both nonlinear and saturation characteristics typical of vehicle lateral dynamics. The effectiveness of the proposed algorithm has been proven by both simulation results and real-world data.     

Identifying tyre models directly from vehicle test data using an extended Kalman filter

Individual tyre models are traditionally derived from component tests, with their parameters matched to force and slip measurements. They are imported into vehicle models which should, but do not always properly provide suspension geometry interaction. Recent advances in Global Positioning System (GPS)/inertia vehicle instrumentation now make full state measurement viable in test vehicles, so tyre slip behaviour is directly measurable. This paper uses an extended Kalman filter for system identification, to derive individual load-dependent tyre models directly from these test vehicle state measurements. The resulting model therefore implicitly compensates for suspension geometry and compliance. The paper looks at two variants of the tyre model, and also considers real-time adaptation of the model to road surface friction variations. Test vehicle results are used exclusively, and the results show successful tyre model identification, improved vehicle model state prediction – particularly in lateral velocity reproduction – and an effective real-time solution for road friction estimation.     

Investigating the influence of rail grinding on stability,vibration, and ride comfort of high-speed EMUs using multi-body dynamics modelling

ABSTRACT

High-speed electric multiple units (EMUs) have been popularised rapidly all around the world and have become a major transportation method. Increases in running velocity and wheel-rail deterioration lead to excessive vibration and reduced ride comfort, which are common issues encountered in the operation of high-speed EMUs. While built-in sensors on a car body are able to detect abnormal vibrations in the car body itself, they cannot effectively reflect the ride comfort of passengers. Wheel-rail profile matching can improve the wheel-rail interaction, and rail grinding has thus been introduced as a practical solution to alleviating the aforementioned problems. Nonetheless, the working mechanism of rail grinding has not been investigated theoretically. This study develops flexible car body and human body models based on the rigid-flexible coupled method to systematically study the effects of wheel-rail wear and rail grinding on passenger ride comfort. Case studies show that the proposed models can predict the ride comfort of passengers accurately. It is also demonstrated that rail grinding can significantly alleviate excessive vibration and improve passenger ride comfort in the long term. A long-term investigation reveals that rail grinding can improve the smoothness of the rail surface and reduce the damage to the rail.     

Modelling of wear and fatigue defect formation in wheel–rail contact

The model for analysing wear and fatigue defect formation is developed based on the approaches of contact and fracture mechanics. The model includes the solution of the contact problem for the wheel and rail to find the shape, size and position of the contact zones and the contact stresses and calculation of the surface wear and the function of damage accumulation in the rail and wheel. The wear rate and the worn-profile evolution of the wheel surface are calculated using both statistic and deterministic approaches to modelling of vehicle dynamics (tribo-dynamic modelling). The influence of the evolution of the wheel–rail profiles due to wear on the damage accumulation process is analysed. It is shown that for some values of the wear rate coefficient, the wear process can prevent the crack initiation under the wheel surface.     

Driver models for personalised driving assistance

We propose a learning-based driver modelling approach which can identify manoeuvres performed by drivers on the highway and predict the future driver inputs. We show how this approach can be applied to provide personalised driving assistance. In a first example, the driver model is used to predict unintentional lane departures and a model predictive controller is used to keep the car in the lane. In a second example, the driver model estimates the preferred acceleration of the driver during lane keeping, and a model predictive controller is implemented to provide a personalised adaptive cruise control. For both applications, we use a combination of real data and simulation to evaluate the proposed approaches.     

Factors affecting the operational success of strategic airline alliances

With increased liberalisation in major air transport markets, the intensity of competition has increased amongst air carriers. Airlines have responded to the competitive pressures in many ways, one of which has been the formation of alliances. These alliances are linkages between the firms at various operational levels. They go beyond the common interlining agreement to encompass certain marketing and cost-reducing features. However, the question is how the success of these alliances can be ensured? While companies' culture compatibility is important and much has been written in that area, this paper focuses on factors that affect operational success of airline alliances. The operational success is measured by the change in the level of partners' inter-hub traffic due to formation of the alliance. This research has developed a methodology which could be used as a management tool to measure alliances' operational success before embarking on such agreements. The analysis of recent major alliances covering 52 inter-hub routes suggests that the main factors ensuring the alliances' operational success are: the partners' network size and their compatibility, the frequency of service between the hubs of the partners, the flight connection time at the hub and the level of competition on their network.