Background

CRONOS was developed in the nineties. Simulation tests on different networks were carried out and have proved the efficiency of CRONOS to optimize a simple or complex intersection or a zone of several intersections .

CRONOS has been integrated into the Intelligent Intersection system (see GRETIA- The Intelligent Intersection project: the management of signalized intersections ) whose objectives are to manage any urban intersection with traffic signals. CRONOS receives from this system the output of an Automatic Incident Detection function. It can react to any incident in the intersection environment.

More recently, CRONOS has been assessed on a single intersection in terms of fluidity, fuel consumption and pollutants.

Principles

CRONOS is based on several different principles:

•  It tries to minimize the traffic criterion of total delay on the intersection over a time horizon of around one minute.

•  It controls zones of several intersections in the same optimization process: the advantage is to be (in principle) more coherent and to need traffic flow measurements only at the limit of the zone.

•  It is a-cyclical and no stage is defined in advance. The intersection is defined as a set of safety constraints . Any solution that verifies these safety constraints is a possible solution for the algorithm.

•  It uses in priority video-based measurements in order to have richer information than with magnetic loops. The queue lengths are measured directly and what is going on in the intersection is known.

 

Functions

CRONOS has various modules:

•  the forecasting module

•  the simulation module

•  the optimization module

The time is discretized by increments of 1 to a few seconds. The traffic measurements are given to two modules: the forecasting module and the simulation module. The first one forecasts the future arrivals at the entry points of the network. The second one is used to compute the traffic criterion for each tested configuration of traffic signals. An optimization method looks for the traffic signal states which minimize a given criterion on a time horizon of around one minute. After minimization, the algorithm provides the traffic signal states to be applied at the intersection for the next increment. Then, the horizon is shifted one increment and a new optimization begins one increment later.

Forecasting module

arrivals of vehicles at the entry points of the zone over the whole horizon. This forecast is used by the simulation module in order to know what the future demand arriving inside the zone will be.

Simulation module

This module propagates the vehicles inside the zone according to predefined traffic signal colors at each intersection. It uses the traffic measurements elaborated each second. The traffic flow is modeled along the links but also in the stocking zones inside each intersection.

optimization module

From a given traffic signal state at the intersection (the present traffic signal colors just before optimization), the goal of the optimization method is to look for the next optimal switchovers of the intersection traffic signals that minimize a given criterion. This optimization is based on a modified version of the Box algorithm . Using possible traffic signal state solutions, this method is based on successive trials where the solution giving the highest criterion value is modified until convergence. Its advantage is to have a polynomial computational time according to the number of controlled intersections (and not exponential as other methods). The consequence is to be able to control simultaneously, in the same optimization process, a zone of several intersections or a complex intersection with a great number of traffic signals.

Results

An experiment was run over an 8-month period from July 1998 to February 1999 on a single complex intersection in Val de Marne near INRETS.

CRONOS was assessed with respect to a traffic signal plan with micro-control functions (AUTONOME) built by the Val de Marne traffic engineers for this intersection.

The average benefits of CRONOS compared to this strategy are 20% of the total delay, 11% of the total number of stops, 8% of the percentage of stopping vehicles whatever the traffic situation (from very light to dense traffic).

Concerning the environmental aspects, the average benefits of CRONOS are 4% of the total cost in terms of the fuel consumption and CO2 emission . This result corresponds to a 15% benefit on the part due to delays and stops .

Implementation

A prototype of CRONOS runs on any PC Windows98, NT or Unix platform. The French company CITILOG has received a licence to use the related intellectual property rights .

References

[1] Boillot F., Blosseville J.M., Lesort J.B., Motyka V., Papageorgiou M., Sellam S., "Optimal signal control of urban traffic networks", Sixth International Conference on Road Traffic Monitoring and Control, IEE London, n° 355, 75-79, April 1992.

[2] Boillot F., Midenet S., Pierrelée J.C., "Real-life CRONOS evaluation", Tenth International Conference on Road Traffic Information and Control, IEE London, n° 472, 182-186, April 2000.