EP1854086A2

EP1854086A2 - System and method for monitoring road traffic and for predicting the development of queues and slow-downs

Info

Publication number: EP1854086A2
Application number: EP05808844A
Authority: EP
Inventors: Giuseppe Faranda Cordella; Mario Gambera
Original assignee: Centro Ricerche Fiat SCpA
Current assignee: Anas SpA Con Socio Unico Soc
Priority date: 2005-03-04
Filing date: 2005-10-17
Publication date: 2007-11-14
Also published as: ITTO20050138A1; WO2006092659A2; WO2006092659A3; WO2006092659A8

Abstract

In a system (1) for monitoring road traffic and for predicting slows-downs, a plurality of detection devices (2) are positioned in succession along a carriageway of a road (3), each comprising a local processing unit (9) configured to generate traffic data relating to the transit of vehicles (6) along the road (3); and a central processing unit (4) is connected to the local processing units (9), and is configured to receive the traffic data, and to process in combination the traffic data received from the local processing units (9) to evaluate the fluidity of the traffic along the road (3), and to produce a prediction of the development of queues and slow-downs. Signalling device (13, 16) are connected to the central processing unit (4), and are activated by the central processing unit (4) in response to the prediction of development of queues and slow-downs.

Description

SYSTEM AND METHOD FOR MONITORING ROAD TRAFFIC AND FOR PREDICTING THE DEVELOPMENT OF QUEUES AND SLOW-DOWNS

TECHNICAL FIELD

The present invention relates to a system and a method for monitoring road traffic and for predicting the development of queues and slow-downs.

BACKGROUND ART

There are known systems for monitoring road traffic, comprising buried sensors embedded in the road surface, which provide information about the average speed and the flow of traffic, which is used to generate special indices for the purpose of identifying slow-downs and queues. The sensors are, for example, inductive coils operating on electromagnetic principles (in detail, the operating principle is based on the variation of the resonant frequency of an electrical circuit caused by the passage of the metallic body of a vehicle) , or piezoelectric sensors (whose operation is based on the conversion of a compressive force, applied to piezoelectric materials, to an electrical magnitude) . However, these systems have numerous disadvantages in terms of construction and expense, due in particular to the work required to excavate the road surface for installation and maintenance, and to the high risk of damage by wear and fracture, for example in the course of resurfacing work.

Other known traffic monitoring systems require the use of detection units positioned on the surface and transversely with respect to the axis of the road. These detection units are capable of detecting the presence of vehicles by evaluating the variations of a wave (electromagnetic or acoustic, for example) caused by the transit of vehicles along any given road section. The various detection units are of the "standalone" type, in the sense that each of them processes the detected data in an autonomous way to evaluate the traffic conditions in the given road section assigned to it. For example, each detection unit can detect the presence of a stationary vehicle, and consequently signal a hazardous situation to approaching vehicles, or to other detection units. However, these systems do not permit the complete monitoring of the road traffic conditions, or, in certain situations, the reliable and timely identification of slow-downs and queues.

DISCLOSURE OF INVENTION

The object of the present invention is therefore to provide a system and a method for monitoring road traffic which enable the known systems to be improved and enable the drawbacks associated with them to be overcome .

According to the present invention, therefore, a system and a method for monitoring road traffic and predicting slow-downs are provided, substantially as defined in Claims 1 and 23 respectively.

BRIEF DESCRIPTION OF THE DRAWINGS

To enable the present invention to be understood more fully, preferred embodiments thereof will now be described, purely by way of example and without restrictive intent, with reference to the attached drawings, in which:

Figure 1 shows a schematic representation of a traffic monitoring system according to one aspect of the present invention;

Figure 2 shows a schematic representation of a detection device belonging to the system of Figure 1;

Figure 3 shows time diagrams of electrical signals relating to the detection device of Figure 2;

- Figure 4 shows a flow diagram relating to operations carried out by local processing units belonging to the system of Figure 1;

Figure 5 shows a flow diagram relating to operations carried out by a central processing unit belonging to the system of Figure 1;

Figures 6 and 7 show schematically traffic situations corresponding to the detection of a minor slow-down and a serious slow-down, respectively, by the central processing unit of the system of Figure 1;

- Figures 8 and 9 show schematically traffic situations corresponding to the detection of a minor slow-down and a serious slow-down, respectively, in different embodiments of the present invention.

BEST MODE FOR CARRYING OUT THE INVENTION Figure 1 shows a traffic monitoring system 1, configured so as to evaluate the fluidity of road traffic and predict the development of queues and slowdowns. In detail, the traffic monitoring system 1 comprises a plurality of detection devices 2, and a central processing unit 4. The detection devices 2 are positioned, preferably at regular intervals (advantageously separated by a distance d of 30 metres), along a section of road 3, preferably free of intersections and junctions (in connection with this it should be emphasized that the term "road" here denotes any urban or suburban road or motorway) . The detection devices 2 are positioned in succession along one direction of travel of the road 3 (indicated by the arrow) so as to detect the same type of flow of vehicles 6, and are therefore indicated in Figure 1 by the progressive references Si, S₂, ... S_N (where N indicates the total number of detection devices 2 , which is preferably not less than three, for reasons which are made clear below) .

The central processing unit 4 is connected to the detection devices 2, in cable or wireless mode. In the latter case, the detection devices 2 and the said central processing unit 4 are provided with corresponding radio transceiver units 5.

In detail, each detection device 2 is configured to detect the passage of a vehicle 6 in a section of the road 3 for which it is responsible, and comprises a transmission unit 7, a corresponding reception unit 8, and a local processing unit 9, connected to the transmission unit 7, to the reception unit 8, and to the central processing unit 4. In particular (see also Figure 2) , the transmission unit 7 comprises a pair of transmitters 10 positioned side by side along the direction of travel, separated by an interval 1, and the reception unit 8 comprises a pair of receivers 11 positioned to mirror the arrangement of the transmitters 10; the transmission unit 7 and the reception unit 8 are conveniently integrated in corresponding road delineators 12, and are positioned on opposite sides of the road carriageway 3 and aligned in a transverse direction with respect to the said carriageway, so that each transmitter 10 is aligned transversely with respect to a corresponding receiver 11, and faces the latter.

Each transmitter 10 of the transmission unit 7 continuously generates a corresponding beam 14', 14'' of electromagnetic radiation (for example, infrared or laser radiation) which selectively hits a corresponding receiver 11 facing it. This receiver 11 consequently generates a logical detection signal having a first level, for example a high value, when it is hit by the electromagnetic radiation beam sent by the corresponding transmitter 10, and a second logic level, a low value in the example, in the opposite case. The detection signals (in other words the signals indicating the occurrence of a blockage of the beams 14 ', 14 ' ' ) are acquired by the corresponding local processing unit 9, to detect the transit of a vehicle 6 and the transit speed of the said vehicle 6.

In detail (see also Figure 3) , a vehicle 6 in transit initially intercepts the beam 14' generated by the first transmitter 10 of the transmission unit 7 of a given detection device 2, causing the switching of the detection signal generated by the latter (indicated by

T₁) , and the blocking of the beam 14' for a blocking time ti. The same vehicle 6 then intercepts the beam

14'' generated by the second transmitter 10, causing the switching of the corresponding detection signal

(indicated by r₂) , and the blocking of the beam 14'' for a blocking time t₂. The successive switching of the detection signals r_x and r₂ indicates to the local processing unit 9 that the transit of a vehicle 6 has taken place. The time interval Δt elapsing between the switching of the detection signals ri and r₂ indicates the speed of transit v of the vehicle 6, according to the formula:

1 v = — Δt

As described in detail below, the blocking times t_x and t₂ are also used by the local processing unit 9 to determine false detections, for example those due to signal "rebounds" or dazzle, and accordingly to reject the corresponding transit and transit speed data.

The traffic monitoring system 1 also comprises a plurality of signalling devices 13 (of the light or acoustic type) , positioned along the sides of the carriageway of the road 3, and connected to the central processing unit 4 (again in cable or wireless mode) . Conveniently, each of the signalling devices 13, which may for example consist of light sources and/or alphanumeric panels and/or acoustic indicators, is positioned at a corresponding detection device 2.

Additionally, an on-board unit 16 is provided in each vehicle 6, and is configured so as to communicate in wireless mode with the central processing unit 4, to generate signals (of the light or acoustic type) for the driver.

The general operation of the traffic monitoring system 1 is as follows (see also Figures 4 and 5) .

Each detection device 2, provided with a suitable software module, continuously acquires (box 20) data relating to the flow of vehicles 6 in the road section, for which it is responsible, and to the speed of these vehicles 6. In particular, each passage of a vehicle 6 causes an acquisition by the detection device 2 (indicated by a progressive index i) , which consists of the following data: date/time (timei) of detection, transit speed (vi) , expressed in km/hr, and blocking time (tθi) , expressed in seconds. In particular, the blocking time toi relates to the first receiver of the pair of receivers 11 (and therefore coincides with the blocking time t_x of Figure 3) .

The local processing unit 9 of each detection device therefore carries out (box 22) a local filtering of the data relating to each acquisition, in order to reject false readings. In detail, all acquisitions for which any of the following relations are true are eliminated:

V₁ = 0; vi ≥ MAX (where MAX is a calibration threshold depending on the characteristics of the transmission and reception units 7, 8, being set to 255 for example) ; or

- —si i- < 2.5 (in order to eliminate acquisitions

3.6 corresponding to objects with a length of less than 2.5 m, since they cannot indicate the passage of a vehicle) .

The local processing unit 9 therefore groups the acquisitions which it has found to be reliable into processing blocks, each having a predetermined duration, for example fifteen seconds, and within these. processing blocks (box 24) it carries out suitable aggregation operations to calculate synthesis indicators (an operation known as first-level aggregation) . In detail, the following synthesis indicators are calculated:

n, the number of acquisitions judged to be valid within each processing block;

- F, the traffic flow, expressed in vehicles/hour and determined according to the relation F = n -240;

Tomed, the mean blocking time, expressed in seconds, and determined according to the relation:

Tomed - ; n Vmed, the mean speed of the vehicles, expressed in km/hr, and determined according to the relation:

n

Vmax, the maximum speed of the vehicles, expressed in km/hr, equal to max(vi); and

Vmin, the minimum speed of the vehicles, expressed in km/hr, equal to min(vi) .

Each processing unit 9 therefore periodically sends the previously calculated synthesis indicators to the central processing unit 4 (box 26) by means of data packets relating to each processing block. The central processing unit 4 can therefore access the following data:

R_{jf s} = (n_j#s, F_j/S, Tomed_jfS, Vmed_jiS, Vmax_j#s, Vmin_jfS) , where j is an index relating to the chronological ordering of the data (and therefore indicating a given processing block of 15 seconds) , s is an index relating to the detection device 2 from which the data are sent and to its position along the carriageway, and R_j,_s indicates the set of synthesis indicators supplied by the given detection device s in the given processing block j .

The central processing unit 4, also provided with a suitable software module, carries out suitable calculations (described in detail below) on the basis of the data received from the various detection devices 2, to predict the occurrence of slow-downs and queues and signal them timely. As a function of the result of these calculations, the central processing unit 4 causes the signalling devices 13 to be switched on or off, according to the_. traffic conditions and the consequent predicted level of risk. For example, the signalling devices 13 are controlled by the central processing unit 4 so that they emit a light signal which has different colours and different pulsation frequencies according to the seriousness of the risk situation which has been determined. For example, the following coding can be provided:

presence of a slow-down (steady yellow light) ;

queue developing (flashing yellow light) ; and

- stationary traffic (red light) .

As well as generating these signals, the central processing unit 4 communicates in wireless mode

(particularly by means of the radio transceiver unit

5) , to the on-board unit 16 in each vehicle 6, information relating to the traffic fluidity, and in particular relating to predicted travel times, advisory speed and alarms (for example alarms indicating a queue or a slow-down in the road section which the vehicle 6 is about to enter) . The on-board unit 16 conveniently comprises a display (not shown) on which the received information is shown, and/or audio devices (not shown) which can communicate this information acoustically to the driver . Additionally, in order to identify any malfunctions and in order to enable a calibration procedure to be carried out on the detection devices 2, the central processing unit 4 periodically implements a procedure for controlling the detection devices 2.

In detail, this control procedure can be applied to the set of synthesis indicators R_j,_s received in a given monitoring interval (advantageously an interval of two hours in a period of the day statistically characterized by low traffic levels, for example the interval from midnight to two a.m.) The acquired data are then centrally filtered (as described below) in order to delete the non-significant data packets, after which the mean value VMED₃ of the mean speeds Vmedj_fS of the vehicles in the various processing blocks j is calculated for each detection device s:

∑ vmed_js

VMEDs = _j

Num

where Num indicates the number of processing blocks j within the monitoring interval .

A malfunction in a detection device s is then signalled if one of the following relations is true:

VMEDg not defined (in other words, all the data packets have been deleted in the aforesaid central filtering step) ; or

- VMEDg > 200. A further mean VM of all the mean values VMED_S previously calculated, relating to the various detection devices s, is then calculated:

∑VMED_S VM = -

N

where N indicates the total number of detection devices 2 within the traffic monitoring system 1.

This further mean VM, together with the corresponding mean value VMED₃, will then be used to calibrate each detection device s. In detail, the mean speed data Vmed_j,_s, calculated by each detection device 2 in all the processing blocks up to the next control procedure, are modified as follows:

Vmedj,g = Vmed_j/S + VM - VMEDs.

In other words, the value VM - VMEDs is added to each individual speed measurement, in order to relate the mean of the speeds detected by each detection device s to the global mean of the speeds detected by all the detection devices .

The operations carried out by the central processing unit 4 will now be described in detail, with reference to Figure 5.

First, the central processing unit 4 receives (box 30) a set of synthesis indicators Rj_/S from each detection device s and for each processing block j . It then carries out a central filtering of the received data (box 32) , and in particular it deletes the mean speed values Vmed_j,s for the data packets for which one of the following conditions is met:

- n-_j,_a < 2; or

Tomed_j,_s = 0,

and also carries out the previously described calibration, modifying the value of the mean speeds Vmed_j,_g so as to take any errors in the calibration of the detection devices 2 into account.

The central processing unit 4 then carries out suitable monitoring to identify the occurrence of slow-downs which are minor or serious (queues) . For this purpose, the central processing unit 4 processes in combination the data received from groups of detection devices 2 arranged in adjacent positions along the direction of travel, conveniently from groups of three detection devices 2. In particular, in order to identify a slowdown at the position of a detection device s, the data supplied by the detection device s itself and by the immediately preceding and following detection devices s-1 and s+1 are processed in combination.

In detail (box 34) , for each detection device s and for each processing device j the central processing unit 4 monitors the occurrence of the following first relation, to identify a minor slow-down at the detection device s: (Vmed_j,g<S₁ AND Vmed_j+1,s<S₁ AND Vmed_j+2,_a<Si AND NOT Vmedj. i_fβ+i<Si AND NOT Vmed_j-_2/S+i<S₁ AND (Vmed_j+i,_s+1<Si OR Vmedj₊2,_s+i<Si) ) ,

where S₁ is a first speed threshold, set at 55 km/hr for example .

Figure 6 shows schematically a traffic condition corresponding to the occurrence of a minor slow-down. As can be seen, the occurrence of the aforesaid first relation causes the identification of vehicles 6 having a mean speed below the first threshold Si by the detection device s in a given processing block j and in the next two processing blocks j+1 and j+2; the identification of vehicles 6 having a mean speed' greater than the first threshold S₁ by the immediately successive detection device s+1 in the preceding two processing blocks j-1, j-2; and the identification of vehicles 6 having a mean speed below the first threshold Si by the immediately successive detection device s+1 in at least one of the two successive processing blocks j+1, j+2.

The central processing unit 4 also monitors the occurrence of the following second relation (box 36) , to identify a serious slow-down (or queue) at the position of the detection device s:

(Vmedj,_g<S₂ AND Vmed_j+i,_a<S₂ AND NOT Vmed_j-i,_s-i<S₂ AND NOT Vmedj-₂,g-i<S₂ AND (Vmedj₊i_/S_i<S₂ OR Vmed_j+2/S-i<S₂) )

where S₂ is a second speed threshold, below the first speed threshold Si, for example 30 km/hr. Figure 7 shows schematically a traffic condition corresponding to the occurrence of a serious slow-down. As can be seen, the occurrence of the aforesaid second relation causes the identification of vehicles 6 having a mean speed below the second threshold S₂ by the detection device s in the given processing block j and in the next processing block j+1; the identification of vehicles 6 having a mean speed greater than the second threshold S₂ by the immediately preceding detection device s-1 in the preceding two processing blocks j-1, j-2; and the identification of vehicles 6 having a mean speed below the second threshold S₂ by the immediately preceding detection device s-1 in at least one of the two successive processing blocks j+1, j+2.

As a function of the result of the aforesaid monitoring, the central processing unit 4 activates (box 38) the corresponding hazard signals by means of the signalling devices 13 and/or by means of the onboard units 16.

Since the arrangement of the detection devices 2 described above allows the discrete localized identification of the occurrences of slow-downs and queues, the signalling of queues or slow-downs is maintained for a relatively short time, conveniently equal to 10 minutes, unless new alarms appear.

Before receiving a new set of synthesis indicators R_j, S relating to a new detection block j , the central processing unit 4 checks (box 40) whether a predetermined time interval has elapsed since the last control of the detection devices 2, and, if the answer is affirmative (box 42) , starts the procedure for controlling the detection devices 2 as described above.

An examination of the characteristics of the traffic monitoring system according to the present invention will clearly reveal the advantages which it offers.

In particular, the traffic monitoring system 1 provides reliable and timely predictive information on the variation of traffic, by suitably combining the data relating to the flow and the speed of the vehicles in transit detected by a plurality of detection devices 2 positioned along the carriageway.

In the described solution, the local filtering operations carried out by the local processing units 9 can overcome the errors caused by the fact that the viewpoint of the detection devices 2, which are positioned transversely with respect to the carriageway, is not optimal. For example, there are inevitable occurrences of blocking on multi-lane roads, caused by vehicles in the second and third lanes being superimposed on vehicles in the first lane, and these errors would cause distortions in the mean speed estimates. The reliability of the system is further enhanced by the fact that the operation of the detection devices 2 is periodically monitored and that the devices are periodically calibrated.

Another particularly advantageous aspect is the fact that the detection devices 2 are provided with corresponding local processing units 9 which can carry out preliminary aggregation operations on the detected transit and speed data. In this way, the data sent to the central processing unit 4 are mediated and have been checked as to their reliability, thus simplifying the processing and reducing the response time of the central processing unit 4.

Tests conducted by the applicant have demonstrated a high reliability of the slow-down and queue signalling. In particular, it has been found out that the probability of false signals over a period of one day is less than 10% for the occurrence of minor slowdowns, and is negligible (in other words less than 1%) for the occurrence of serious slow-downs. Additionally, the delay in the signalling of the occurrence of a minor slow-down has been found to be approximately 45 s, and the delay in the signalling of the occurrence of a serious slow-down has been found to vary from a minimum of 30 s to a maximum of 64 s; in all cases, these times are extremely brief, and allow timely signalling and a considerable reduction of the risks of accident for approaching vehicles.

In this connection, the possibility of communicating hazardous situations directly to drivers in vehicles, by means of the on-board units 16, is advantageous.

Finally, the solution described and illustrated herein can clearly be modified and varied without departure from the scope of the present invention, as defined by the attached claims .

In particular, different forms of monitoring can be provided for identifying the occurrence of minor and serious slow-downs .

For example, if the detection devices 2 are positioned at greater intervals d, of 150 metres for example, the occurrence of a minor slow-down is identified, if the following relation is true:

(Vmed_j,_g<Si AND Vmed_j+i,_s<Si AND (Vmed_j+lfS+1<Si OR Vmed_j+2,s₊i<Si) )

corresponding to the situation schematically shown in Figure 8. As can be seen, the occurrence of the aforesaid relation causes the identification of vehicles 6 having a mean speed below the first threshold S₁ by the detection device s in the given processing block j and in the immediately successive processing block j+1, and the identification of vehicles 6 having a mean speed below the first threshold Si by the immediately successive detection device s+1 in at least one of the two successive detection blocks j +1, j+2. In this case, the tests conducted by the applicant revealed that the probability of false signals over a period of one day was less than 15% for the occurrence of minor slowdowns, and that the delay in the signalling of this occurrence of slow-downs was approximately 30 s.

Alternatively, serious slow-downs (or queues) can be identified at the position of the detection device s whenever the following relation appears:

(Vmedj,_s<S₂ AND Vmedj,_a+1<S₂ AND Vmed_j/a+2<S₂ AND F_j(S+3=0 AND F_{j ι S+4}=0 AND Fj , _S+5=O)

corresponding to the situation shown schematically in Figure 9. As can be seen, the occurrence of the aforesaid relation causes the identification of vehicles 6 having mean speeds below the second threshold S₂ by the detection devices s, s+1 and s+2 in the given processing block j , and the identification of a zero flow of vehicles 6 by the detection devices s+3, s+4 and s+5 in the same given processing block j . It should be noted that, in this case, the traffic data received from six consecutive detection devices 2 along the direction of travel are processed in combination.

Additionally, the synthesis indicators could be calculated in a different way with respect to what has been described; for example, a harmonic mean operation could be carried out on the acquired data.

If the central processing unit 4 is not provided with a wireless transmission unit, the information on the variation of the traffic can be distributed to the various vehicles by means of radio communication devices (integrated into the signalling devices 13 for example) located along the carriageway and connected in cabled mode to the central processing unit 4.

If the road has two directions of travel, detection devices 2 can be provided along each direction of travel. In this case, the transmission units 7 can be positioned on both sides of the carriageway, and the corresponding reception units 8 can be positioned along the axis of the road, for example in a traffic divider. Additionally, the transmission unit 7 and the reception unit 8 of the detection devices 2 can be positioned on the same side of the carriageway (being conveniently integrated into a single delineator) . In this case, suitable means can be provided on the opposite side of the carriageway, in a facing position, to reflect the beam 14', 14'' of electromagnetic radiation generated by the transmitters 10 of the transmission unit 7.

The first and second transmitters 10 of each transmission unit 7 can transmit electromagnetic radiation at different frequencies (for example, 5 kHz for the first and 15 kHz for the second) .

The transmitters 10 and the receivers 11 could also operate with sound waves (ultrasound, for example) or with other types of mechanical or electrical signal: in all cases, the operation of the detection devices 2 will still be based on the alteration of a transmitted signal caused by the transit of vehicles.

Finally, when the occurrence of a slow-down has been identified at a detection device s, the central processing unit 4 not only can activate the signalling device 13 in the corresponding position, but also can activate further signalling devices 13 located upstream with respect to the direction of travel, so as to provide timely signalling to approaching vehicles 6.

Claims

1. A system (1) for monitoring road traffic and for predicting slow-downs, comprising, in combination:

a plurality of detection devices (2) arranged along a carriageway of a road (3) and each comprising a local processing unit (9) configured to generate traffic data relating to the transit of vehicles (6) along the said road (3) ; and

a central processing unit (4) connected to said local processing units (9) , and configured to receive and process in combination the traffic data generated by said local processing units (9) to evaluate the traffic fluidity along said road (3) and to provide a prediction of the development of queues and slow-downs.

2. The system according to claim 1 , wherein said detection devices (2) are arranged in succession along said carriageway, and are configured in such a manner that each detection device (2) generates traffic data relating to the transit of vehicles (6) along a corresponding section of said road (3) ; and wherein each of said detection devices (2) also comprises a sensor element (7, 8) connected to said local processing unit (9) and configured to detect data relating to the transit and the speed of transit of said vehicles (6) along said section.

3. The system according to claim 2, wherein said sensor element (7, 8) comprises a transmitter element (7) and a receiver element (8) ; said transmitter element (7) being configured to generate a signal and said receiver element (8) being configured to receive said signal, said data relating to transit and transit speed being a function of an alteration of said signal as a result of the transit of said vehicles (6) .

4. The system according to claim 3 , wherein said transmitter element (7) and said receiver element (8) are arranged on the surface transversely with respect to said carriageway, in facing positions on opposite sides of said carriageway.

5. The system according to claim 3 , wherein said transmitter element (7) and said receiver element (8) are positioned on the surface in adjacent positions on a first side of said carriageway, a reflection element being provided on a second side of said carriageway, facing said first side.

6. The system according to any one of claims 2-5, wherein said traffic data are an aggregation within predetermined time intervals of said transit data and speed of transit data detected by said sensor element (7, 8) .

7. The system according to claim 6, wherein said traffic data comprise at least a number of passages (n) and a mean speed (Vmed) of said vehicles (6) , calculated within said time intervals.

8. The system according to claim 6 or 7, wherein said time intervals are equal to 15 s.

9. The system according to any one of claims 6-8, wherein said local processing units (9) are configured to send said traffic data to said central processing unit (4) in packets, each relating to a corresponding one of said time intervals.

10. The system according to any one of claims 2-9, wherein said sensor element (7, 8) is configured to additionally detect transit time data of said vehicles

(6) through said section, and wherein said local processing units (9) are configured to identify and reject incorrect readings, for which said speed of transit data and transit time data have specified relations with predetermined thresholds .

11. The system according to any one of the preceding claims, wherein said central processing unit (4) is configured to process in combination the traffic data received from groups of said detection devices (2) .

12. The system according to claim 11, wherein said groups comprise at least a given detection device (s) , an immediately preceding detection device (s-1) and an immediately successive detection device (s+1) along a direction of travel of said road (3) .

13. The System according to claim 12 , wherein said traffic data comprise at least a number of passages (n) and a mean speed (Vmed) of said vehicles (6) calculated within predetermined time intervals; and wherein said central processing unit (4) is configured to determine a slow-down at the position of said given detection device (s) in the case in which the mean speed (Vmed) detected by said given detection device (s) is below a speed threshold in at least a first and a second consecutive time interval (j, j+1) , and the mean speed (Vmed) detected by one out of said immediately preceding detection device (s-1) and said immediately successive detection device (s+1) is below said speed threshold in at least one out of said second time interval (j +1) and a third time interval (j+2) following said second time interval (j+1) .

14. The system according to claim 13, wherein said central processing unit (4) is configured to determine a first degree of slow-down at the position of said given detection device (s) in the case in which the mean speed detected by said given detection device (s) is below a serious slow-down speed threshold (S₂) in said first and in said second time interval (j, j+1); the mean speed detected by said immediately preceding detection device (s-1) is below said serious slow-down speed threshold (S₂) in at least one out of said second time interval (j+1) and said third time interval (j+2) ; and the mean speed detected by said immediately preceding detection device (s-1) is below said serious slow-down speed threshold (S₂) in a fourth and a fifth time interval (j-1, j-2) preceding said first time interval (j) .

15. The system according to claim 14, wherein said central processing unit (4) is configured to determine a second degree of slow-down, smaller than said first degree, at the position of said given detection device (s) in the case in which the mean speed detected by said detection device (s) is below a minor slow-down speed threshold (Si) , greater than said serious slowdown speed threshold (S₂), in said first, second and third time intervals (j, j+1, j+2) ; the mean speed detected by said immediately successive detection device (s+1) is below said minor slow-down speed threshold (Si) in at least one out of said second and said third time interval (j+1, j+2) ; and the mean speed detected by said immediately successive detection device (s+1) is above said minor slow-down speed threshold (S_x) in said fourth and fifth time intervals (j-1, j-2) .

16. The system according to claim 14, wherein said central processing unit (4) is configured to determine a second degree of slow-down, smaller than said first degree, at the position of said given detection device (s) in the case in which the mean speed detected by said detection device (s) is below a minor slow-down speed threshold (Si) , greater than said serious slow- down speed threshold (S₂) , in said first and second time intervals (j , j+1), and the mean speed detected by said immediately successive detection device (s+1) is below said minor slow-down speed threshold (Si) in at least one out of said second time interval (j+1) and said third time interval (j+2) .

17. The system according to claim 11, wherein said groups comprise at least one given detection device (s) ; a first and a second detection device (s-1, s-2) preceding said given detection device (s) with respect to one direction of travel of said road (3) ; and a third, fourth and fifth detection device (s+1, s+2, s+3) following said given detection device (s) with respect to said direction of travel.

18. The system according to claim 17, wherein said traffic data comprise at least a number of passages (n) and a mean speed (Vmed) of said vehicles (6) calculated within predetermined time intervals; and wherein said central processing unit (4) is configured to determine a slow-down at the position of said given detection device (s) in the case in which the mean speeds (Vmed) detected by said given detection device (s) and by said first and second detection devices (s-1, s-2) are each below a speed threshold (S₂) in a given time interval

(j); and the numbers of passages (n) detected by said third, fourth and fifth detection devices (s+1, s+2, s+3) are each zero in said given time interval (j).

19. The system according to any one of the preceding claims, wherein said traffic data comprise at least a number of passages (n) and a mean speed (Vmed) of said vehicles (6) calculated within predetermined time intervals; and wherein said central processing unit (4) is configured to periodically monitor the operation of said detection devices (2) , and to signal a malfunction in a given detection device (s) if the mean value (VMEDs) of the mean speeds (Vmed) detected by said given detection device (s) within a monitoring period has a predetermined relation with a malfunction threshold.

20. The system according to claim 19, wherein said central processing unit (4) is also configured to calibrate said given detection device (s) , modifying the values of said mean speeds (Vmed) as a function of said mean value (VMEDs) .

21. The system according to any one of the preceding claims, additionally comprising signalling devices (13, 16) connected to said central processing unit (4) , and activated by said central processing unit (4) in response to said prediction of development of queues and slow-downs.

22. The system according to claim 21, wherein said signalling devices (13, 16) comprise first indicators (13) positioned along the said carriageway, and second indicators (16) positioned on board said vehicles (6) , said second indicators (16) being connected in wireless mode to said central processing unit (4) .

23. A method for monitoring road traffic and predicting slow-downs, characterized by comprising, in combination,

- providing a plurality of detection devices (2) along a carriageway of a road (3) ;

generating traffic data relating to the transit of vehicles (6) along said road (3) at each of said detection devices (2) ; and

- processing in combination said traffic data to evaluate the fluidity of traffic along said road (3) and to produce a prediction of the development of queues and slow-downs .

24. The method according to claim 23, wherein providing detection devices comprise positioning said detection devices (2) on the surface and in succession along said carriageway, each in a corresponding section of said road (3) , each of the said detection devices (2) generating transit data and transit speed data of said vehicles (6) through said corresponding section; and wherein generating traffic data comprises locally processing the transit data and speed of transit data generated by each detection device (2) .

25. The method according to claim 24, wherein locally processing comprises aggregating said transit and transit speed data within predetermined time intervals.

26. The method according to claim 25, wherein aggregating comprises calculating at least a number of passages (n) and a mean speed (Vmed) of said vehicles (6) within each of said time intervals.

27. The method according to claim 25 or 26, wherein said time intervals are equal to 15 s.

28. The method according to any one of claims 24-27, wherein said detection devices (2) also generate transit time data of said vehicles (6) , and wherein locally processing comprises identifying and rejecting incorrect detections for which said transit speed data and transit time data have specific relations with predetermined thresholds .

29. The method according to any one of claims 23-28, wherein processing in combination comprises processing the traffic data relating to groups of said detection devices (2) .

30. The method according to claim 29, wherein said groups comprise at least a given detection device (s) , an immediately preceding detection device (s-1) and an immediately successive detection device (s+1) along a direction of travel of said road (3) .

31. The method according to claim 30, wherein generating traffic data comprises calculating a number of passages (n) and a mean speed (Vmed) of said vehicles (6) within predetermined time intervals; and wherein processing traffic data relating to groups of said detection devices (2) comprises determining a slow-down at the position of said given detection device (s) in the case in which the mean speed (Vmed) detected by said given detection device (s) is below a speed threshold in at least a first and a second consecutive time interval (j, j+1) , and the mean speed

(Vmed) detected by one out of said immediately preceding detection device (s-1) and said immediately successive detection device (s+1) is below said speed threshold in at least one out of said second time interval (j +1) and a third time interval (j+2) following said second time interval (j+1) .

32. The method according to claim 31, wherein processing traffic data relating to groups of said detection devices (2) comprises determining a first degree of slow-down at the position of said given detection device (s) in the case in which the mean speed detected by said given detection device (s) is below a serious slow-down speed threshold (S₂) in said first and said second time interval (j, j+1) ; the mean speed detected by said immediately preceding detection device (s-1) is below said serious slow-down speed threshold (S₂) in at least one out of said second time interval (j+1) and said third time interval (j+2) ; and the mean speed detected by said immediately preceding detection device (s-1) is greater than said serious slow-down speed threshold (S₂) in a fourth and a fifth time interval (j-1, j-2) preceding said first time interval (j) .

33. The method according to claim 32, wherein processing of traffic data relating to groups of said detection devices (2) comprises determining a second degree of slow-down, smaller than said first degree, at the position of said given detection device (s) in the case in which the mean speed detected by said given detection device (s) is below a minor slow-down speed threshold (S_x) , greater than said serious slow-down speed threshold (S₂) , in said first, second and third time intervals (j, j+1, j+2); the mean speed detected by said immediately successive detection device (s+1) is below said minor slow-down speed threshold (Si) in at least one out of said second and said third time interval (j+1, j+2) ; and the mean speed detected by said immediately successive detection device (s+1) is greater than said minor slow-down speed threshold (Si) in said fourth and fifth time intervals (j-1, j-2) .

34. The method according to claim 32, wherein processing of the traffic data relating to groups of said detection devices (2) comprises determining a second degree of slow-down, smaller than said first degree, at the position of said given detection device (s) in the case in which the mean speed detected by said given detection device (s) is below a minor slowdown speed threshold (S₁) , greater than said serious slow-down speed threshold (S₂) , in said first and second time intervals (j, j+1) ; and the mean speed detected by said immediately successive detection device (s+1) is below said minor slow-down speed threshold (Si) in at least one out of said second time interval (j+1) and said third time interval (j+2) .

35. The method according to claim 29, wherein said groups comprise at least one given detection device

(s) ; a first and a second detection device (s-1, s-2) preceding said given detection device (s) with respect to one direction of travel of said road (3) ; and a third, fourth and fifth detection device (s+1, s+2, s+3) following said given detection device (s) with respect to said direction of travel.

36. The method according to claim 35, wherein generating traffic data comprises calculating a number of passages (n) and a mean speed (Vmed) of said vehicles (6) within predetermined time intervals; and wherein processing of traffic data relating to groups of said detection devices (2) comprises the determination of a slow-down at the position of said given detection device (s) in the case in which the mean speeds (Vmed) detected by said given detection device (s) and by said first and second detection devices (s-1, s-2) are each below a speed threshold

(S₂) in a given time interval (j); and the numbers of passages (n) detected by said third, fourth and fifth detection devices (s+1, s+2, s+3) are each zero in said given time interval (j) .

37. The method according to any one of claims 23-36, wherein generating traffic data comprises calculating a number of passages (n) and a mean speed (Vmed) of said vehicles (6) within predetermined time intervals; and also comprises periodically monitoring the operation of said detection devices (2) and signalling of a malfunction in a given detection device (s) if the mean value (VMEDs) of the mean speeds (Vmed) detected by said given detection device (s) within a monitoring period has a predetermined relation with a malfunction threshold.

38. The method according to claim 37, additionally comprising calibrating the said given detection device

(s) by modifying the values of said mean speeds (Vmed) as a function of said mean value (VMEDs) .

39. The method according to any one of claims 23-38, additionally comprising signalling of said slow-downs and queues to said vehicles (6) in response to said prediction of the development of queues and slow-downs.

40. The method according to claim 39, wherein signalling comprises activating first indicators (13) positioned along said carriageway, and second indicators (16) positioned on board said vehicles (6) .

41. A software module which, when loaded into local processing units (9) and into a central processing unit (4) belonging to a system (1) for monitoring road traffic and for predicting slow-downs according to any^¬ one of claims 1-22, implement a method for monitoring road traffic and for predicting slow-downs according to any one of claims 23-40.