Performance analysis of downlink shared channels in a UMTS network

In light of the expected growth in wireless data communications and the commonly anticipated up/downlink asymmetry, we present a performance analysis of downlink data transfer over \textsc{d}ownlink \textsc{s}hared \textsc{ch}annels (\textsc{dsch}s), arguably the most efficient \textsc{umts} transport channel for medium-to-large data transfers. It is our objective to provide qualitative insight in the different aspects that influence the data \textsc{q}uality \textsc{o}f \textsc{s}ervice (\textsc{qos}). As a most principal factor, the data traffic load affects the data \textsc{qos} in two distinct manners: {\em (i)} a heavier data traffic load implies a greater competition for \textsc{dsch} resources and thus longer transfer delays; and {\em (ii)} since each data call served on a \textsc{dsch} must maintain an \textsc{a}ssociated \textsc{d}edicated \textsc{ch}annel (\textsc{a}-\textsc{dch}) for signalling purposes, a heavier data traffic load implies a higher interference level, a higher frame error rate and thus a lower effective aggregate \textsc{dsch} throughput: {\em the greater the demand for service, the smaller the aggregate service capacity.} The latter effect is further amplified in a multicellular scenario, where a \textsc{dsch} experiences additional interference from the \textsc{dsch}s and \textsc{a}-\textsc{dch}s in surrounding cells, causing a further degradation of its effective throughput. Following an insightful two-stage performance evaluation approach, which segregates the interference aspects from the traffic dynamics, a set of numerical experiments is executed in order to demonstrate these effects and obtain qualitative insight in the impact of various system aspects on the data \textsc{qos}

Video vehicle detection at signalised junctions: a simulation-based study

Many existing advanced methods of traffic signal control depend on information about approaching traffic provided by inductive loop detectors at particular points in the road. But analysis of images from CCTV cameras can in principle provide more comprehensive information about traffic approaching and passing through junctions, and cameras may be easier to install and maintain than loop detectors, and some systems based on video detection have already been in use for some time. Against this background, computer simulation has been used to explore the potential of existing and immediately foreseeable capability in automatic on-line image analysis to extract information relevant to signal control from images provided by cameras mounted in acceptable positions at signal-controlled junctions. Some consequences of extracting relevant information in different ways were investigated in the context of an existing detailed simulation model of vehicular traffic moving through junctions under traffic-responsive signal control, and the development of one basic and one advanced algorithm for traffic-responsive control. The work was confined as a first step to operation of one very simple signalcontrolled junction. Two techniques for extraction of information from images were modelled - a more ambitious technique based on distinguishing most of the individual vehicles visible to the camera, and a more modest technique requiring only that the presence of vehicles in any part of the image be distinguished from the background scene. In the latter case, statistical modelling was used to estimate the number of vehicles corresponding to any single area of the image that represents vehicles rather than background. At the simple modelled junction, each technique of extraction enabled each of the algorithms for traffic-responsive control of the signals to achieve average delays per vehicle appreciably lower than those given by System D control, and possibly competitive with those that MOVA would give, but comparison with MOVA was beyond the scope of the initial study. These results of simulation indicate that image analysis of CCTV pictures should be able to provide sufficient information in practice for traffic-responsive control that is competitive with existing techniques. Ways in which the work could be taken further were discussed with practitioners, but have not yet been progressed

How TRAF-NETSIM Works.

This paper describes how TRAF-NETSIM works in detail. It is a review of the TRAF-NETSIM micro-simulation model, for use in the research topic "The Development of Queueing Simulation Procedures for Traffic in Bangkok". TRAF-NETSIM is a computer program for modelling of traffic in urban networks. It is written in the FORTRAN 77 computer language. It uses bit-manipulation mechanisms for "packing" and "unpacking" data and a program overlay structure to reduce the computer memory requirements of the program. The model is based on a fixed time, and discrete event simulation approach. The periodic scan method is used in the model with a time interval of one second. In the model, up to 16 different vehicle types with 4 different vehicle categories (car, carpool, bus and truck) can be identified. Also, the driver's behaviour (passive, normal, aggressive), pedestrians' movement, parking and blocking (eg a broken-down car) can be simulated. Moreover, it has the capability to simulate the effects of traffic control ranging from a simple stop sign controlled junction to a dynamic/real time control system. The effects of spillbacks can be simulated in detail. The estimation of fuel consumption and vehicle emissions are optional simulations. Car following and lane changing models are incorporated into TRAF-NETSIM. The outputs can be shown in US standard units, Metric units, or both

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challenges for reinforcement learning in terms of adapting to dynamic traffic environment and coordinating thousands of agents including vehicles and pedestrians. A key factor in the success of modern reinforcement learning relies on a good simulator to generate a large number of data samples for learning. The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios. This motivates us to create a new traffic simulator CityFlow with fundamentally optimized data structures and efficient algorithms. CityFlow can support flexible definitions for road network and traffic flow based on synthetic and real-world data. It also provides user-friendly interface for reinforcement learning. Most importantly, CityFlow is more than twenty times faster than SUMO and is capable of supporting city-wide traffic simulation with an interactive render for monitoring. Besides traffic signal control, CityFlow could serve as the base for other transportation studies and can create new possibilities to test machine learning methods in the intelligent transportation domain.Comment: WWW 2019 Demo Pape