The objectives of the project are (i) to establish environmental standards in consultation with all the relevant authorities throughout the EC and (ii) to develop a scheme for reducing environmental pollution in Central Business Districts through the use of traffic operation and control measures.
Within pollution reduction by information and control techniques (PREDICT), a multipath assignment model is used for assignment of traffic to the network. This is based on the stochastic assignment procedure developed by Dial. The algorithm assigns trips to all reasonable paths simultaneously in such a way that the resulting effect is identical to what would have been obtained had each path been assigned trips separately under certain choice probability assumptions. The assignment procedure can be used to select paths through the network which are environmentally optimal. The area under pollution control can cover the whole network or subnetwork. If the control area is a subnetwork, generally the model reduces significantly the congestion and the pollution of this area, trying at the same time not to increase the pollution at the periphery. The control area is defined by the link numbers of the network.
The accurate estimation of emissions from mobile sources needs a detailed representation of traffic flows. The functions of the traffic model within the PREDICT suite are fulfilled by TRANSYT. This has 2 main elements; a traffic model and a signal optimiser. The model represents traffic behaviour in a network of streets in which most junctions are controlled by traffic signals. The model predicts the value of a performance index for the network, which is a measure of the overall cost of traffic congestion, and is usually a weighted combination of the total amount of delay and the number of stops experienced by traffic.
The optimisation process adjusts the signal timings and checks, using the model, whether the adjustments reduce the performance index. By adopting only those adjustments which reduce the performance index, signal timings are successively improved until an optimum is reached. Output gives several measures related to overall traffic performance. These include total distance travelled, time spent on the network, and mean journey speeds over the network. Total delays and stops are also output, together with fuel consumption figures.
A prototype was made available on 25/08/92
The pollution reduction by information and control techniques (PREDICT) emissions model, known as PREMIT, uses the output from the traffic model to calculate the quantities of various pollutants emitted by traffic travelling on the road network. PREMIT is a model which takes account of the amount of time spent by vehicles in various driving modes. It can also model emissions of any pollutant and allows the urban vehicle fleet composition to be defined by the user. The model has been developed with maximum flexibility in mind, and it can be easily updated with data that reflect the emissions characteristics of new vehicles. The PREDICT emissions model takes into account the proportion of time spent in various driving modes (cruise, acceleration, deceleration, idle) and hence the quantity of pollutants emitted in each mode. The model works on the basis that these modes are a consequence of stopping and starting at the intersections between each link. The vehicles which are delayed along a link are assumed to step through each driving mode. Vehicles accelerate off from an intersection and then reach cruise speed. When they get within stopping distance of a stop line or the back of a queue, the vehicles will then decelerate to a halt and idle within the queue.
The model can also incorporate data for a range of vehicle types which are considered to have different emission characteristics. Any vehicle type can be represented so long as data are available for the vehicle's emission rates across all driving modes and the proportion it makes up of the whole fleet for the network being modelled. This capability allows the modelling of different vehicle types, with and without emission reduction devices. The emissions model calculates the emission rates of pollutants for each link within the road network. The emissions model can accept traffic performance data from any source or traffic model, provided it is correctly formatted. Having calculated pollutant emission rates for each link within the network, the model generates an output file which contains parameters that can be used to give a direct assessment of environmental performance, or can be input to a dispersion model to calculate resulting pollutant concentrations.
A prototype was made available on 25/08/92
The dispersion model currently used in the pollution reduction by information and control strategies (PREDICT) model suite is PREDCO. This is essentially a microscale model, in which local pollutant concentrations are determined as a result of emissions on particular links and prevailing atmospheric conditions. The input data required by this model are the definition of the road network, the location of the receptor where the pollutants are measured, traffic flows and speeds, the wind speed and direction. The road network must be reduced to a set of straight line sections which are specified by the coordinates of their endpoints.
The wind speed is given in metres per second and the wind direction in degrees from grid north (ie a wind blowing from the north has a direction of zero degrees, from the east 90 degrees). To run the pollution prediction routine of the model, an input file containing link and receptor location coordinates must be present. Traffic flow and speed data and the estimated prevailing emission rates are passed to the dispersion model internally from the assignment and the emission model, respectively. Meteorological data are generally assumed to be constant across the network.
A prototype was made available on 25/08/92
Strategy one, environmental optimisation of traffic signal settings, aimed to investigate a relatively low cost solution to the high pollution levels being encountered in some European cities. Overall, this strategy reduced pollutant emissions which arose when the manually optimised or nonoptimised network was optimised using TRANSYT. It also shows that changing the weightings between delays and stops would have had only a relatively small additional effect on overall pollutant emissions levels.
In modelling strategy two, pollution sensitive traffic rerouting, various percentages of compliance to the hypothesised transmission of information to vehicles were considered. For evaluation purposes, the particular scenario was examined in which 30 percent of vehicles complied with the guidance advice given. Emissions levels on the ring network within the case study area were then examined using the pollution reduction by information and control techniques (PREDICT) model suite. This strategy produced reductions in the levels of carbon monoxide, nitrogen oxides and hydrocarbon emissions over the base case.
The modelling of the third strategy, introduction of clean vehicles, was again achieved using the PREDICT model suite. This was run initially with 10 percent of petrol cars having a catalytic converter. Subsequent model runs represented an increasing proportion of catalytic converters. The results obtained clearly indicated that the introduction of catalytic converters on all petrol cars looks to be a very effective way of reducing pollutant emissions within an urban area. If the emissions of other vehicle types in the fleet (in the case study area, diesel taxis and buses) were reduced then network emissions could be reduced even further. This strategy therefore promises a much higher and healthier standard of air quality within European cities in the future.
Strategy four, environmental area licensing, would involve restricting access to a defined central area of a c ity to vehicles fitted with a catalytic converter. Again, this strategy appeared to be potentially effective at reducing the pollutant emissions within the controlled area. The percentage of vehicles fitted with catalytic converters outside the controlled area had a small effect on the total emissions for the whole case study network.
A prototype was made available on 08/25/92
Road traffic is currently a major contributor to overall air pollution levels experienced in urban areas. As traffic levels rise towards the end of this century, urban congestion is set to become an even greater problem in terms of its environmental effects. Many countries are therefore looking for ways to reduce and minimise these effects.
Potential solutions to traffic related pollution include use of new vehicle technologies and improved traffic control techniques. Within this framework, the pollution reduction by information and control techniques (PREDICT) project was undertaken to assess the potential of new road transport informatics (RTI) technologies for pollution reduction. PREDICT focused primarily on developing alternative RTI-base control strategies and evaluating their effects on air pollution levels.
A number of RTI-based control strategies and schemes were developed within PREDICT to reduce environmental pollution in urban areas. These encompassed environmental optimisation of traffic signal timings, pollution sensitive traffic rerouting, introduction of clean vehicles and environmental area licensing. Each of the PREDICT control strategies was developed with particular environmental policy objectives in mind. These included reduction in pollution levels over a wide area, reduction in pollution levels within a defined city centre area and reduction in pollution levels at particular hot spots on the road network. Each of these was seen as a legitimate policy objective in the light of increasing concern over urban air pollution.
A major aspect of PREDICT was the development of a comprehensive model suite. This forms a powerful tool which can be used to predict and analyse the environmental effects of different traffic management measures and control strategies. The emissions model included in the suite represents a significant advance on previous models, with representation of traffic activity at the microlevel. The model suite was applied within th e PREDICT project to evaluate the effects and impacts of the 4 PREDICT control strategies. This showed that there is significant potential for use of road transport informatics (RTI) technologies in reducing air pollution. Effective control strategies can make use of RTI in conjunction with pollution monitoring and prediction systems, and can also incorporate use of vehicle emissions control technologies.
The results suggested that the most effective control strategies for reducing air pollution will combine traffic restrictions or demand management techniques with emissions reducing engine technologies. However, the cost and/or institutional barriers associated with such strategies are probably the greatest. Other strategies may be more appropriate in some cities, depending on the exact nature of the pollution problem and on local political acceptability considerations.
A prototype was made available on 08/25/92
This will largely be based on modelling which will predict the pollution effects of various RTI strategies. Pollution monitoring systems will also be studied to develop and define monitoring plans and a `pollution advisory' tool which will give traffic controllers an overall picture of both environmental and traffic conditions on their road network.
The consortium has already developed a model which calculates air pollution levels on the basis of traffic signal timing and route guidance systems amongst other things. The model will be refined and enhanced to include a capability within the vehicle emission sub-model for the separate representation of vehicles with and without emission reducing devices. After a review of the model specifications in the boarder context of the project, the model will be validated on the basis of historical data from a European city.
The potential for the reduction of pollution levels will be assessed against all relevant RTI based traffic operations and control measures. The benefits of implementing a given control measure will be assessed in a European city, comparing its effects (including those on human health) with a base scenario representing the existing situation. New, more stringent, pollution standards will be recommended which should be feasible as a result of the introduction of RTI.
At a later date the project will consider using real time data from the monitoring tool in a model describing the complexities of gas dynamics, air mass transport, urban topography and traffic flow. This tool would be used to generate detailed short term pollution forecasts.
Software; assessment of benefits from pollution reduction.
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- natural sciencesearth and related environmental sciencesenvironmental sciencespollution
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- engineering and technologyenvironmental engineeringenergy and fuels