Analysing busway station capacity under mixed stopping and non-stopping operation

Busway stations are the interface between passengers and services. The station is crucial to line operation as it is typically the only location where buses can pass each other. Congestion may occur here when buses manoeuvring into and out of the platform lane interfere with bus flow, or when a queue of buses forms upstream of the platform lane blocking the passing lane. Further, some systems include operation where express buses do not observe the station, resulting in a proportion of non-stopping buses. It is important to understand the operation of the station under this type of operation and its effect on busway capacity. This study uses microscopic simulation to treat the busway station operation and to analyse the relationship between station potential capacity where all buses stop, and Mixed Potential Capacity where there is a mixture of stopping and non-stopping buses. First, the micro simulation technique is used to analyze the All Stopping Buses (ASB) scenario and then statistical model is tuned and calibrated for a specified range of controlled scenarios of dwell time characteristics Subsequently, a mathematical model is developed for Mixed Stopping Buses (MSB) Potential Capacity by introducing different proportions of express (or non-stopping) buses. The proposed models for a busway station bus capacity provide a better understanding of operation and are useful to transit agencies in busway planning, design and operation.

Traffic safety at road-rail level crossings using a driving simulator and traffic simulation

A number of Intelligent Transportation Systems (ITS) were used with an advanced driving simulator to assess its influence on driving behavior. Three types of ITS interventions namely, Video in-vehicle (ITS1), Audio in-vehicle (ITS2), and On-road flashing marker (ITS3) were tested. Then, the results from the driving simulator were used as inputs for a developed model using a traffic micro-simulation (Vissim 5.4) in order to assess the safety interventions. Using a driving simulator, 58 participants were required to drive through a number of active and passive crossings with and without an ITS device and in the presence or absence of an approaching train. The effect of driver behavior changing in terms of speed and compliance rate was greater at passive crossings than at active crossings. The difference in speed of drivers approaching ITS devices was very small which indicates that ITS helps drivers encounter the crossings in a safer way. Since the current traffic simulation was not able to replicate a dynamic speed change or a probability of stopping that varies based on different ITS safety devices, some modifications of the current traffic simulation were conducted. The results showed that exposure to ITS devices at active crossings did not influence the drivers’ behavior significantly according to the traffic performance indicators used, such as delay time, number of stops, speed, and stopped delay. On the other hand, the results of traffic simulation for passive crossings, where low traffic volumes and low train headway normally occur, showed that ITS devices improved overall traffic performance.

Models of protein linear molecular motors for dynamic nanodevices

Protein molecular motors are natural nano-machines that convert the chemical energy from the hydrolysis of adenosine triphosphate into mechanical work. These efficient machines are central to many biological processes, including cellular motion, muscle contraction and cell division. The remarkable energetic efficiency of the protein molecular motors coupled with their nano-scale has prompted an increasing number of studies focusing on their integration in hybrid micro- and nanodevices, in particular using linear molecular motors. The translation of these tentative devices into technologically and economically feasible ones requires an engineering, design-orientated approach based on a structured formalism, preferably mathematical. This contribution reviews the present state of the art in the modelling of protein linear molecular motors, as relevant to the future design-orientated development of hybrid dynamic nanodevices. © 2009 The Royal Society of Chemistry.

Using Bayesian Mixture Models That Combine Expert Knowledge and GIS Data to Define Ecoregions

Conservation planning and management programs typically assume relatively homogeneous ecological landscapes. Such “ecoregions” serve multiple purposes: they support assessments of competing environmental values, reveal priorities for allocating scarce resources, and guide effective on-ground actions such as the acquisition of a protected area and habitat restoration. Ecoregions have evolved from a history of organism–environment interactions, and are delineated at the scale or level of detail required to support planning. Depending on the delineation method, scale, or purpose, they have been described as provinces, zones, systems, land units, classes, facets, domains, subregions, and ecological, biological, biogeographical, or environmental regions. In each case, they are essential to the development of conservation strategies and are embedded in government policies at multiple scales.