Micro-simulation models can model virtually any type of traffic management and control system including:
Producing optimal signal settings in a traffic model can be time consuming. One of the best ways of obtaining optimal signal plans is through the Transport Research Laboratory’s (TRL) TRANSYT program. Some micro-simulation models have interfaces to the TRANSYT signal optimisation program. Such interfaces allow optimised signal plans to be automatically produced with the model using TRANSYT. Once the model has been run, it is also possible to compare the outputs from the model with the equivalent predictions from TRANSYT such as the performance index, stops and delays.
SCOOT (Split, Cycle and Offset Optimisation Technique) is one of the world's leading adaptive urban traffic control systems. The only real way to predict the benefits of a SCOOT system is to link it up to a micro-simulation model of the proposed network.
Aimsun was first linked up to a SCOOT system back in 1994 as part of a project at the Institute for Transport Studies, University of Leeds. The European Commission funded CLAIRE/SAVE project assessed the benefits of using an artificial intelligence supervisor to aid the operation of a SCOOT UTC system. The project test site was Kingston-upon-Thames in London. An Aimsun model of the study area was built and was then linked to a real SCOOT system so that SCOOT could control the traffic signal settings in the model in response to simulated traffic going over simulated detectors in the model.
Siemens have recently developed an interface between their PC SCOOT system and the most commonly used micro-simulation models - Aimsun, VISSIM and S-Paramics. Fox Traffic Simulation has considerable experience in simulating the operation of SCOOT with micro-simulation.
The Urban Traffic Optimisation by Integrated Automation (UTOPIA) Urban Traffic Control system developed by MIZAR Automazione has been in operation in Turin, Italy since 1985. UTOPIA is a fully traffic responsive UTC system designed with the twofold objective to (i) optimise private traffic control at the area level and, simultaneously (ii) provide weighted and absolute priority for selected public transport vehicles. UTOPIA is based on a hierarchical and decentralised control concept. A key component of the UTOPIA system is the SPOT intelligent signal control processor. At the intersection level the SPOT traffic control units perform the decentralised control. Each intersection equipped with SPOT aims to minimise a set of cost functions over a rolling horizon of two minutes and co-operates with the neighbouring intersections by exchanging information on the traffic observed and the control decided locally. The optimisation and communication process is updated every three seconds. Stage change times are limited only by stage order and minimum/maximum stage durations. Priority PT vehicles are handled in terms of vehicle arrival time predictions and are represented as weighted platoons of private vehicles.
In DRIVE II project PRIMAVERA, bus priority in SPOT was adapted to allow it to use bus arrival time predictions based on local selective detection via loops, rather than a continuous vehicle monitoring system as used in Turin. This priority technique was tried on a site on the Dewsbury Road in Leeds and evaluated using simulation modelling and in field trials.
UTOPIA/SPOT is now used in several cities in Italy and also in the Netherlands, USA, Norway, Finland and Denmark.
An interface has been developed to allow Aimsun to interact with the UTOPIA control system. This makes it easy to evaluate the likely performance of the UTOPIA control system on any given network.
Vehicle Actuated Signals
One of the easiest ways of evaluating vehicle actuated control systems such as MOVA is with Aimsun Micro. Aimsun Micro has the standard NEMA logic for vehicle actuated signals built-in. This means that junctions using standard vehicle actuated logic can be easily set up without any need for the user to program in the logic themselves. All they need to do is put the appropriate detectors into their junction model, set the junction as be under VA control and define the associated parameters for calling each stage, such as gap out times.
Roundabouts are a common junction type in the UK. Roundabouts operate as a one-way circulatory system around a central island where entry is controlled by "Give Way" markings and priority must be given to traffic approaching from the right. Aimsun is able to assess the performance of most types of roundabout so that different alternatives can be tested before construction. Sometimes roundabouts are signalised and Aimsun has no problems modelling this control option.
Variable Message Signs
Variable message signs are becoming increasingly common alongside our roads. They are used to inform drivers of road conditions and give warnings and advice. Aimsun is able to model VMS operation and is also able to model driver reactions to the messages, such as to change route, choose a new destination, reduce speed etc.
Traffic Calming measures have been successfully used to reduce the adverse impact of motor vehicles on built-up areas. Traffic calming can reduce accident levels and also reduce the severity of accidents. Air pollution can also be reduced. Most micro-simulation models are able to model the effects of most traffic calming measures.
Although they haven't been specifically designed to model pedestrian movements, it is possible for micro-simulation models to produce a simplified representation of pedestrians so that their interactions with traffic schemes can be assessed.
To provide a more realistic representation of pedestrian movements TSS have linked up with Legion - producers of the Legion Studio pedestrian model so that plugins can be produced to allow Aimsun Micro to interact with Legion Studio. Legion Users now have a plugin that will let their pedestrian models interact with a free version of Aimsun and existing Aimsun users have a plug-in that will let their traffic models interact with a free version of Legion Studio.
Aimsun On-Line is a powerful new tool for use in traffic control centres to provide faster-than-real-time traffic forecasts for incident management.
Anticipation is becoming a key requirement in traffic management and control. Indeed, being able to forecast the evolution of traffic in a network is the basis on which many traffic management strategies and ITS applications rely. Real time prediction capabilities are therefore becoming a necessity for the management of both urban and motorway networks and other strategic services.
Aimsun Online is the solution offered by TSS to cover this growing need for real time forecasting: by effectively combining Aimsun Macro, Aimsun Meso and Aimsun Micro. Aimsun Online offers powerful and reliable short-term traffic predictions in just a few minutes of computation. Real time traffic management requires both real time detection data and an offline learning process that generates, maintains, improves and expands the knowledge of the system, increasing the number of tasks that can be carried out automatically, improving response times and leading to a more efficient real time application.
The offline learning process
Aimsun Online provides the tools and methodology for advanced filtering and data processing for detection pattern recognition and matrix adjustment. Using all the available historic detection data such a process will then generate a database with the different detection patterns and the OD matrices associated with each one of them. The set composed by those patterns and OD matrices will configure traffic and traffic demand at different times of the day, different weekdays and due to factors such as weather conditions.
The online simulation forecasting
The online part of the system consists mainly on a fully calibrated simulation model ready to be launched at any moment in response to an automatic or manual request. The duration of the forecasted period is obviously dependent on the objectives of the application, however a 30 minute rolling horizon is usually convenient for most of the prediction tasks.
Real time detection patterns are permanently compared to the ones stored in the database to determine to which historical traffic situation the current situation is closest to, so that when a simulation run is launched its initial state and traffic demand corresponds to the real one. Note that this pattern recognition procedure returns to the operator a confidence score which quantifies the reliability of the selected historical pattern. This score provides an additional function which is that of identifying specific, non recurrent and first time events in the learning process, such as incidents, exhibitions or sports matches. In these types of cases knowledge is increased and a new detection pattern (with its corresponding demand) is added to the database for future use.