Automatic Incident Detection on Freeways
The purpose of automatic incident detection algorithms is the identification of traffic disturbances. Urban and freeway networks require different types of methods due to the diversity of traffic flows.
The research topic at the department is observing the usability of freeway incident detection algorithms. Traditional logics check certain parameters of the traffic flow (usually in every 30 seconds) and compare them to fixed threshold values. If a value is beyond the threshold, the algorithm sends an alarm signal. Therefore, calibrating parameters of algorithms is a key issue. Too loose threshold values result in a high false alarm rate, whereas too strict thresholds lead to increasing number of undetected incidents.
An example of exceeding the threshold value (occupancy)
Former pieces of research focused on calibrating parameters of single algorithms. The research done at the department has two unique aspects:
- the threshold values of algorithms are not fixed values, they are functions of traffic volume, that has proven to be the most important factor of incident detection
- and at a specific section not only one but also more types of algorithms have been applied simultaneously. The value of traffic volume continously determines the algorithm type that should be used on a section.
Therefore the main goal of the research was determining appropriate types of algorithms and their threshold values among different traffic volume conditions. Three requirements have to be met: if an incident has occurred, it has to be detected but when no incident happens no alarm signal is accepted and certainly the time to detect has to be as low as possible.
The relationship among the measures of performance.
If one becomes better, another one becomes worse. This makes calibration difficult. The test network has been constructed in Vissim microscopic traffic simulator. The algorithms have been coded in MATLAB using the Vissim COM interface. This way the two pieces of software are connected. MATLAB logics observe the Vissim traffic state real-time and give alarm signals if needed.
Vissim-MATLAB real-time communication
Emission Modeling and Control
The greatest challenge of modern engineering is the design for sustainable development. In case of road traffic engineering a guiding principle of this aim is the reduction of emission and fuel consumption. Our research aims at creating traffic and emission models and designing control to abate environmental pollution.
Based on the microscopic emission models (Copert, HBEFA) of different pollutants (such as CO, CO2, NOX, HC) and the macroscopic description of traffic flow a complex traffic-emission model was created. A controller for traffic emission optimization will be designed based on the complex model after its validation. The final goal is to establish a multi-criteria control that also optimizes fuel consumption and emission of traffic in addition to the classic objective function: travel times.
A complex traffic-emission model was elaborated based on the macroscopic description of traffic flow
- Modeling of air pollutant concentration in residential areas.
- Control design for the stabilization of shock waves and emission reduction on freeway.
- The figures depict an example for CO pollutant concentration without (left) and with control measures. (right)
Related Publication:
Csikós A, Tettamanti T and Varga I (2014), "Modeling of emission in urban traffic networks" (Research Report no. SCL-001/2014)
Freeway Traffic Modeling and Control
The control of motorway networks provides great opportunities to prevent traffic jams and to reduce traffic emissions and fuel consumption.
The control design is based on macroscopic models which consider traffic as a compressible fluid and the flow is described using loop detector measurements of the traffic variables (i.e. traffic flow [veh/h], traffic density [veh/km], traffic mean speed [km/h]). These continuum models rely on the conservation law of vehicles and offer a basis for control design to avoid possible traffic jams, traffic shockwaves and the reducing of traffic emissions and consumption.
The most important control measures:
- dynamic speed limits: restricting flow speed, local capacity drop can be handled and traffic breakdown can be prevented.
- ramp metering: control of ramp flow merging the main lane provides opportunity for traffic flow stabilization.
With the joint use of these tools a multicriteria control can be designed in order to optimize travel times, fuel consumption and emissions.
Pattern Recognition for Urban Traffic Speed Prediction
Traffic speed or simply congestion prediction algorithm for urban road traffic networks can be achived by pattern recognition. The motivation of the prediction is to provide short time forecast in order to support ITS functionalities, such as traveler information systems, route guidance (navigation) systems, as well as adaptive traffic control systems.
A potential and efficient solution to this problem is the application of a soft computing method. Namely, an artificial neural network (ANN) is used for the forecast by involving the measured speed patterns.
The ANN is trained by using data produced by realistic microscopic road traffic simulator (Vissim). The proposed algorithm is developed and analyzed on a real-word test network (part of downtown in Budapest).
The test network for the case study
The input of the model is calculated from mean speed data on links of the road network during 5 minutes sampling intervals. The output of the model is defined as 4 different speed categories.
The applied MuliLayer Perceptron model for ANN training
For the analysis a train data set with 2500 records and a test data set with 1000 records were applied.
Slightly decreasing recognition rate over the 5, 15 and 30 minutes forecasts
Related Publication:
T. Tettamanti, A. Csikós, Zs. Viharos, K.B. Kis, I. Varga: Traffic speed prediction method for urban networks – an ANN approach, 4th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2015), Paper 11, IEEE Xplore ISBN: 978-963-313-142-8.
T. Tettamanti, A. Csikós, K. B. Kis, Zs. J. Viharos, I. Varga: Pattern recognition based speed forecasting methodology for urban traffic network, Transport, ACCEPTED
