GB2373619A - Measurement of traffic density - Google Patents

Measurement of traffic density Download PDF

Info

Publication number
GB2373619A
GB2373619A GB0107300A GB0107300A GB2373619A GB 2373619 A GB2373619 A GB 2373619A GB 0107300 A GB0107300 A GB 0107300A GB 0107300 A GB0107300 A GB 0107300A GB 2373619 A GB2373619 A GB 2373619A
Authority
GB
United Kingdom
Prior art keywords
lt
gt
sep
vehicles
satisfaction
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
GB0107300A
Other versions
GB0107300D0 (en
Inventor
Michael John Dalgleish
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Golden River Traffic Ltd
Original Assignee
Golden River Traffic Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Golden River Traffic Ltd filed Critical Golden River Traffic Ltd
Priority to GB0107300A priority Critical patent/GB2373619A/en
Publication of GB0107300D0 publication Critical patent/GB0107300D0/en
Publication of GB2373619A publication Critical patent/GB2373619A/en
Application status is Withdrawn legal-status Critical

Links

Classifications

    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions

Abstract

An apparatus and method of measuring the traffic density comprises automatic sensors providing input to a data processor in order to calculate a density value, the value being equated with a rating of driver satisfaction. In use a stream of vehicles are monitored and measurements taken of their speed, length, acceleration the time gap and distance between vehicles and the distribution of vehicles over lanes. Measurements are preferably made using an inductive wire loop. The data processing system is arranged to collate the values received for each vehicle passing the monitoring point and calculate a collective measurement of traffic density as a combination of the individual measurements. Preferably the calculation will be weighted according to the speed of a vehicle, the gap between the vehicle and relative speed. In one embodiment the value is used to predict the probability of an accident occurring and to position emergency vehicles accordingly. In a second embodiment the value is used to select a route taking into account values from locations within a road system.

Description

<img class="EMIRef" id="024181744-00010001" />

<tb>

MONITORING <SEP> A <SEP> VEHICULAR <SEP> STREAM <tb> <img class="EMIRef" id="024181744-00010002" />

<tb> The <SEP> present <SEP> invention <SEP> relates <SEP> to <SEP> an <SEP> apparatus <SEP> and <SEP> method <SEP> for <SEP> measuring <SEP> the <SEP> collective <tb> satisfaction of drivers of vehicles in a vehicular stream.

A highway operator, like the provider of any service, has a strong interest in understanding how satisfied his customers are with the service supplied. He wants to know to what level the drivers of a stream of vehicles entering, proceeding through, or leaving a section of road are collectively satisfied (or conversely frustrated).

Highway owners who wish to transfer maintenance obligations to contractors need to know that the end-users, the motorists, are getting a satisfactory service. At present this is estimated by monitoring lane availability. This is a manual process and has an unknown correlation to overall driver satisfaction.

At present there is no method known to automatically directly measure the satisfaction (or conversely frustration) of the drivers of vehicles in a vehicular stream.

Known approaches to this measurement problem include roadside interviews, where vehicles are stopped and the driver interviewed, and incident detection systems which record the occurrence of incidents.

Roadside interviews are costly and directly affect the parameter being measured, i. e. driver satisfaction. A traveller whose journey is interrupted will be less satisfied by the very fact of being stopped and questioned. In any case, such surveys can only be conducted from time to time and are laborious and manual. No continuous measure can be determined from such a method. It is therefore a fundamentally inaccurate method of measurement of satisfaction.

Incident detection systems only report incidents after they have happened. Such detection systems are thus inherently too late too reduce the risk of an incident occurring in the first place. Furthermore a parameter recording incidents is not directly <img class="EMIRef" id="024181744-00020001" />

<tb> related <SEP> to <SEP> satisfaction, <SEP> and <SEP> is"lumpy"in <SEP> its <SEP> output. <SEP> There <SEP> is <SEP> no <SEP> output <SEP> at <SEP> all <SEP> if <SEP> there <SEP> is <tb> no <SEP> incident. <tb>

Even if another method of manual assessment could be devised, such as looking at vehicles for certain behaviour, or looking for the rapid changing of lanes, such a method would be extremely impractical and costly, because of the labour required in difficult lighting and other conditions. It is therefore desirable to find some means of assessing driver satisfaction using parameters which can easily be automatically measured.

In accordance with a first aspect of the present invention, there is provided apparatus for measuring the collective satisfaction of vehicle drivers in a vehicular stream, comprising : automatic sensing means arranged in use to be installed on a road for monitoring the behaviour of vehicles on the road; and data processing means coupled to the automatic sensing means for receiving electrical sensing signals therefrom, and for processing the signals to provide a measurement of driver satisfaction.

It has been noticed that, when a driver is not satisfied, he shows his lack of satisfaction through his driving behaviour. Examples of such behaviour include drivers acting without taking account of the situation on the road, and needing to make late corrections to avoid a collision. Such behaviour can be identified from the positioning and speed of vehicles in a vehicle stream. This abnormal behaviour increases and decreases in frequency as frustration in the traffic stream waxes and wanes. Its measurement is thus a useful method for determining overall satisfaction, particularly when aggregated and smoothed.

Typically this behaviour takes two modes. In the first mode, a continuous process is observed. An example of this may be seen on a two-lane carriageway. Due to a small proportion of the traffic stream travelling at a low speed, for example by choice or because of vehicle limitations, most of the traffic forms a high speed queue in the outside lane, leaving the inner lane almost unoccupied. The inter-vehicle gap in the outer lane is small in relation to speed of the vehicles. The capacity of the highway is <img class="EMIRef" id="024181744-00030001" />

<tb> dramatically <SEP> reduced <SEP> since <SEP> the <SEP> inner <SEP> lane <SEP> is <SEP> barely <SEP> used. <SEP> This <SEP> type <SEP> of <SEP> behaviour <SEP> reflects <tb> a <SEP> pent <SEP> up <SEP> desire <SEP> for <SEP> capacity, <SEP> which <SEP> incidentally <SEP> makes <SEP> the <SEP> problem <SEP> worse. <tb>

In the second mode, a discontinuous process is observed. An example of this may be seen in any road type. In the stream of traffic, a certain proportion of vehicles act as if the vehicle in front was not there. This is seen in two cases. Firstly, the vehicle travels very close behind the vehicle in front. Secondly, the gap between the vehicles closes rapidly before the driver, sensing a collision, brakes to a speed slower than the vehicle in front. This second process carries on continuously.

Therefore in preferred embodiments the automatic sensing means is arranged to monitor a plurality of properties of vehicle motion, and the data processing means is arranged to combine these to provide said measurement of driver satisfaction. The automatic sensing means may comprise one or more electronic sensing means.

The automatic sensing means is preferably arranged at a given location to monitor one or more of : the speed of vehicles; the time gap between successive vehicles; the distance between successive vehicles; the relative speed of successive vehicles; the acceleration of vehicles; the length of vehicles; the crossing of vehicles between lanes; and the distribution of the traffic stream in the lanes of a multi-lane road, the data processing means being arranged to determine the measurement of driver satisfaction based on the monitored parameters.

The or each automatic sensing means may comprise at least one wire loop located in each lane of the road.

The data processing means is preferably arranged to determine an individual satisfaction rating for each vehicle in the stream from the parameters recorded for that vehicle and a <img class="EMIRef" id="024181744-00040001" />

<tb> collective <SEP> measurement <SEP> of <SEP> driver <SEP> satisfaction <SEP> as <SEP> a <SEP> rolling <SEP> average <SEP> over <SEP> time <SEP> of <SEP> the <tb> individual <SEP> satisfaction <SEP> ratings. <tb>

The data processing means is preferably arranged to determine the individual satisfaction rating for a vehicle using a weighted sum of parameters corresponding to the speed of the vehicle, the gap between the vehicle and the preceding vehicle, and the relative speed of the vehicle compared to the preceding vehicle.

The data processing means may be arranged to report the collective measurement of driver satisfaction to an operator at predetermined intervals. Alternatively or in addition, the data processing means may be arranged to report the collective measurement of driver satisfaction to an operator whenever the collective measurement of driver satisfaction falls below a predetermined level.

If the automatic sensing means is arranged to monitor different classes of vehicle, the data processing means may be arranged to determine a collective measurement of satisfaction for each class of vehicle. The data processing means may also be arranged to determine a collective measurement of satisfaction for each lane of vehicles in a multi-lane road.

In accordance with a second aspect of the present invention there is provided a method for predicting the probability of a road accident occurring at or adjacent a predetermined location on a road system, comprising measuring the collective measurement of driver satisfaction at that location using the apparatus described above. This method may then be used to predict the required distribution of emergency vehicles.

The apparatus may also be used in a method for selecting routes.

In accordance with a third aspect of the present invention there is provided a method for measuring the collective satisfaction of drivers of vehicles in a vehicular stream, comprising : monitoring vehicles on a road using a set of automatic sensors; <img class="EMIRef" id="024181744-00050001" />

<tb> sending <SEP> electrical <SEP> sensing <SEP> signals <SEP> from <SEP> the <SEP> automatic <SEP> sensors <SEP> to <SEP> data <SEP> processing <tb> means <SEP> ; <SEP> and <tb> using the data processing means to process the electrical sensing signals to generate a collective measurement of driver satisfaction.

Thus at least in its preferred embodiments the invention captures various parameters of individual vehicle behaviour and relative behaviour of adjacent vehicles, and combines these parameters with predetermined weightings to provide a continuous integrated signal which is a function of the collective satisfaction of drivers of vehicles in the vehicular stream.

Some preferred embodiments of the invention will now be described by way of example only and with reference to the accompanying drawings, in which: Figure 1 shows the general layout of a section of road; Figure 2 shows a plan view of a measurement station; Figure 3 shows the organisation of a detector system; and Figure 4 shows a stream of vehicles and the various parameters to be measured.

Figure 1 shows the general layout of a section of road 1 with measuring stations 101, 102,103 and 104 installed over a section of the road 1 at 500 metre intervals. These measuring stations are able to record the number and speed of vehicles passing beneath them. Information recorded by each measuring station 101,102, 103,104 is passed back to a central location 111.

Figure 2 is a plan view of an individual measurement station 101. The measurement station 101 comprises sets of sensors 122,123 connected to a detector system 121. The sensors 122,123 are arranged in pairs so that there are two sensors 122,123 over each traffic lane 2,3, 4 in the road. In a preferred embodiment the sensors 122,123 consist of wire loops, each about two metres square and including three turns, although it will <img class="EMIRef" id="024181744-00060001" />

<tb> be <SEP> understood <SEP> that <SEP> any <SEP> suitable <SEP> sensor <SEP> may <SEP> be <SEP> used. <SEP> The <SEP> sensors <SEP> 122, <SEP> 123 <SEP> are <SEP> attached <tb> to"loop <SEP> detectors"which <SEP> provide <SEP> either <SEP> on/off <SEP> detection <SEP> or <SEP> an <SEP> analogue <SEP> representation <tb> for each vehicle. Loop sensors and other sensors and/or detectors have a number of distinct detection or sensing points whilst the vehicle is in a sensing area or at a sensing point.

Figure 3 is a schematic diagram showing the organisation of the detector system The output from the sensors 122,123 is recorded at the measurement station 101 by the detector system 121. The detector subsystem passes the output from the sensors to a roadside processing unit (RPU) 124.

The RPU 124 is programmed to calculate the following data for each or a sample of vehicles: <img class="EMIRef" id="024181744-00060002" />

<tb> 'Time <SEP> of <SEP> arrival <SEP> and <SEP> departure <SEP> over <SEP> the <SEP> sensors <SEP> 122, <SEP> 123. <tb>

. <SEP> Direction <SEP> of <SEP> travel <SEP> of <SEP> each <SEP> vehicle. <tb>

'Speed <SEP> and/or <SEP> acceleration/deceleration <SEP> whilst <SEP> in <SEP> the <SEP> sensing <SEP> area. <tb>

'Vehicle <SEP> length <SEP> and/or <SEP> type. <tb>

'Lane <SEP> number <SEP> and <SEP> if <SEP> straddling <SEP> over <SEP> a <SEP> lane <SEP> boundary. <tb>

'Vehicle <SEP> gaps <SEP> to <SEP> adjacent <SEP> vehicles. <tb>

'Other <SEP> data <SEP> which <SEP> will <SEP> depend <SEP> on <SEP> the <SEP> actual <SEP> sensor <SEP> and/or <SEP> detector <SEP> type.. <tb>

In the case of an array of sensors 122,123 of two loops per lane as shown in Figure 2, the RPU 124 calculates the speed of each vehicle by measuring the time difference between the arrival time of the vehicle under the first loop 122 and the arrival time under the second loop 123. Having determined the speed of the vehicle, and having determined the speed of the preceding vehicle by the same method, the RPU then calculates the headway (a length measurement) from the preceding vehicle, the gap (a time measurement) from the preceding vehicle, and the acceleration of the vehicle whilst it passes through the sensing area. The acceleration of the vehicle is detected by calculating the instantaneous speed based on the arrival time of the front of the vehicle at each sensor's leading edge, say VI, and then calculating the instantaneous speed of the rear of the vehicle as it leaves each sensor's trailing edge, say V2, and dividing the <img class="EMIRef" id="024181744-00070001" />

<tb> difference <SEP> in <SEP> these <SEP> two <SEP> speeds <SEP> by <SEP> the <SEP> time <SEP> difference <SEP> to <SEP> derive <SEP> the <SEP> acceleration. <SEP> The <tb> RPU <SEP> 124 <SEP> then <SEP> further <SEP> determines <SEP> each <SEP> vehicle's <SEP> length, <SEP> type <SEP> and <SEP> whether <SEP> the <SEP> vehicle <SEP> is <tb> straddling a lane.

The RPU 124 is connected to a communications link to allow its output to be available at an online"Section Processing Computer" (SRC) at the central location 111 shown in Figure 1. The connection is made by a point-to-point link or by a networked cable.

Alternatively wireless communications are used, for example GSM cellular links or a GPRS packet switched system.

In order to put the invention into effect these vehicle records are listed in a tabular database and sequential records are compared according to a suitable algorithm.

A suitable algorithm will now be described. The individual satisfaction index (Slvehtcle) for each vehicle is derived from three data items: Slvehicle = Fi + F2 + F3 where Fi is a function of speed of the vehicle just detected, F2 is a function of the gap between this vehicle and the vehicle proceeding, and F3 is a function of the speed of this vehicle minus the speed of the previous vehicle.

In order to determine an overall view of driver satisfaction a collective satisfaction index (SI) is calculated as a an average or integral of all of the individual satisfaction indices Vehicle, i. e.

SI = Integral of all or a sample of vehicles over time + F2 + F3 In a preferred embodiment, for each vehicle: Fi = (MIN (Vs, 70) ) * 0.0143 F2 = MIN (Gs, 2000) * 0. 0005 F3 = MIN (Gs- (100 * Vs-Vp)), 100) * 0.02 <img class="EMIRef" id="024181744-00080001" />

<tb> where <SEP> : <tb> MIN <SEP> (x, <SEP> y) <SEP> is <SEP> an <SEP> operator <SEP> which <SEP> selects <SEP> the <SEP> lower <SEP> of <SEP> x <SEP> and <SEP> y. <tb>

Vs is the speed of the subject vehicle expressed in kilometres per hour Vp is the speed of the previous vehicle expressed in kilometres per hour Gs is the gap between the subject vehicle and the preceding vehicle in this lane expressed in milliseconds.

In certain circumstances it may be more appropriate and useful to take a sample of the vehicular stream in order to determine the SI for different classes of drivers or vehicles.

For example the SI applicable to just passenger cars, excluding other vehicles such as trucks or buses may be more useful since private car drivers are much more likely to select alternative routes should they be dissatisfied with the current route, or conversely accept higher charges on the current route if very satisfied, whereas a bus or truck is more likely to have less flexibility with regard to route. Busses generally use a route defined by passenger requirements, and trucks are more likely to have a high running . cost per mile, and their drivers are therefore less interested in driver satisfaction.

Although the functions in this embodiment above are linear it will be understood that the function may be non-linear, for example in a square law function, to more precisely measure the effects of the various components in contributing to the satisfaction index.

Fi reflects the fact that vehicles travelling at speed in general reflect a measure of satisfaction. It has a low weighting in the SI since it only reflects the general desire to minimise journey time, irrespective of traffic and road conditions.

F2 is incorporated because vehicles travel close to the vehicle in front in an attempt to force the vehicle ahead to drive faster, almost pretending that the vehicle in front is not there. At a two second gap or more, this is assumed to be at a negligible level. At smaller gaps, this is more and more significant.

F3 reflects the aggressive behaviour of approaching a vehicle at such speed that the application of brakes will be inevitable. <img class="EMIRef" id="024181744-00090001" />

<tb> A <SEP> smoothed <SEP> value <SEP> the <SEP> individual <SEP> vehicle <SEP> readings <SEP> is <SEP> calculated <SEP> by <SEP> rolling <SEP> averaging <tb> over <SEP> a <SEP> period <SEP> of <SEP> between <SEP> one <SEP> minute <SEP> to <SEP> fifteen <SEP> minutes. <tb>

Provision is made for the SI data to be stored minute-by-minute and week-by-week. For data stored on a minute-by-minute basis, the mean SI for a particular day of week is compared with the previous SIs and the percentage difference smoothed before being compared with a threshold. If it exceeds a pre-set value then an alarm output is triggered.

In the preferred embodiment of the present invention, the RPU 124 is a Marksman 660TM traffic sensor manufactured by Golden River Traffic Ltd, Churchill Road, Bicester, Oxon. UK (www. goldenriver. com). This system is configured to give a vehicle-by-vehicle output (individual data records). Each output record corresponds to one vehicle. This data is fed to the online"Section Processing Computer" (SRC) at the central location 111 through a hard-wired RS232 connection. The SRC is programmed to compare the interval vehicle gap, headway and speed according to the algorithm detailed above.

The SI readings are analysed over a time period, programmable between 1 to 5 minutes. The data are summarised into mean, average and 85% percentile. These values are then compared with individual alarm thresholds and rate of change threshold. If the SI value is low, but the rate of change is high and positive, the system will forecast or predict future levels exceeding the alarm, and give an early warning of a deteriorating situation.

The detection of lane changing is more difficult than taking samples of gap and headway and more sensors are required. There are a number of methods of measuring lane changes per metre of lane per unit of time. One method is to use video image processing. A pole can have mounted thereon two video cameras pointing in opposite directions, each camera covering up to 250 metres of road. Such a method will therefore require a pole every 500 metres. An alternative method is to use sensors formed by long narrow loops along the lane boundaries. Such a method is useful if a large amount of lane changing ("straddling") is expected. <img class="EMIRef" id="024181744-00100001" />

<tb>

It <SEP> will <SEP> be <SEP> understood <SEP> that <SEP> the <SEP> invention <SEP> is <SEP> not <SEP> restricted <SEP> to <SEP> the <SEP> algorithm <SEP> described <tb> above. <SEP> Indeed, <SEP> gap, <SEP> headway <SEP> or <SEP> speed <SEP> alone <SEP> may <SEP> be <SEP> used <SEP> as <SEP> a <SEP> linear <SEP> measurement <SEP> of <tb> satisfaction. However, as headway increases beyond a certain level (say equivalent to a 5 second gap), increasing headways do not relate to more satisfaction. To accommodate this a non-linear function should be introduced to improve the relevance of the output.

Relative acceleration of a vehicle is often an indicator of the following driver "provoking"the driver in front into driving closer to the vehicle in front of him. As such, the acceleration of the vehicle behind is used as a modifier of the preceding intervehicle gap.

Figure 4 shows a stream of vehicles and the various additional parameters to be measured in a refinement to the method. Another indicator of satisfaction of the vehicle in the centre of the diagram (shown as Lv) is the subsequent movement of that vehicle into a gap (Lb + Lv + Lf) which causes the SI of the two adjacent lane vehicles to be adversely affected. Such movement of vehicle into small gap may occur for example on the approach to an exit from a dual carriageway or motorway. This behaviour will generally require to be monitored with a sensor (e. g. a video camera) which covers a broad are within which the manoeuvre occurs.

One of the disadvantages of the fixed spot measurement of SI is that this event will only be detected if it occurs directly at the measurement station. In a further refinement, the sensor system is a video, microwave, infrared or similar area sensing device, which monitors such a movement during its occurrence over an extended distance limited by the technology of the detector, but typically between 20 and 3000 meters.

It will be appreciated that remote sensing, microwave and video image processing may also be used to measure the parameters used in the invention. Indeed, if a large area is covered by a single sensor, more sophisticated parameters for each vehicle can be determined.

It will also be appreciated that the invention has a considerable number of applications. Certain drivers express their lack of satisfaction by acting more aggressively and <img class="EMIRef" id="024181744-00110001" />

<tb> indulging <SEP> in <SEP> more <SEP> risky <SEP> driving. <SEP> In <SEP> some <SEP> individual <SEP> cases, <SEP> this <SEP> can <SEP> result <SEP> in <SEP> the <tb> phenomenon <SEP> known <SEP> as"road <SEP> rage". <SEP> Since <SEP> a <SEP> system <SEP> according <SEP> to <SEP> the <SEP> invention <SEP> can <tb> detect such behaviour, action can be taken to calm the traffic flow by a number of methods, long-term or short term. For example, messages can be displayed on roadside signs to give motorists information, which would tend to calm them. A longer-term approach would be to monitor this measurement of frustration and use any consistent increase of frustration (the inverse of satisfaction in this context) as a method of triggering the consideration of capacity or geometry improvements. The net effect of these measures would be to increase satisfaction, decrease frustration, and thereby reduce accidents.

A further example of how such a measurement could be useful is in the case of a highway operator. His success in delivering a satisfactory service can be assessed by measuring the collective satisfaction of the vehicular stream over the area of his operations with the invention. If an operator had too many road works in progress, then the dissatisfaction would be registered and the operator could be penalised. If the road was kept free of roadworks then the motorist's satisfaction would increase. The quantifiable measurement of SI can be an indication to an operator of the success of his policies. This is especially useful if the operator is paid partially or wholly in relation to the measured satisfaction level.

In a further application, the data about satisfaction may be combined with journey time or distance information to calculate a"preferred route", which is optimised to give the driver the greatest overall satisfaction in travelling. In other words the route supplied in a routing algorithm reflects the enjoyment of drivers rather than shortest distance or fastest time. In this way tourists can plot the most enjoyable route, even varying this by the time of day or by the day of week.

In another refinement of the device, the ratio of SI between different vehicle samples may be used to extract more detailed information about traveller satisfaction. If the SI for a sample of vehicles from an inner lane is low at certain times compared with the SI from an outer lane, or varies at ant time during the day, this may be a trigger for investigation. In a similar way, various other samples and consequent ratios may be <img class="EMIRef" id="024181744-00120001" />

<tb> calculated <SEP> (involving <SEP> for <SEP> example <SEP> the <SEP> vehicle <SEP> class, <SEP> length, <SEP> height, <SEP> speed, <SEP> and <SEP> gap <SEP> to <tb> preceding <SEP> vehicle) <SEP> to <SEP> usefully <SEP> further <SEP> dissect <SEP> the <SEP> SI <SEP> for <SEP> the <SEP> entire <SEP> stream <SEP> to <SEP> either <tb> monitor more closely various classes of vehicle and or driver combinations, or as a further method of extracting useful information and/or instigating an alarm or further investigation.

The invention is not restricted to use on public roads. Operators of car parks, airport terminals, sea ports, and drive-through facilities such as wild life parks and zoos where customers remain in their cars would also be able to refine their services using the system of the present invention.

Claims (19)

  1. <img class="EMIRef" id="024181745-00130001" />
    <tb>
    CLAIMS <SEP> : <tb> 1. <SEP> Apparatus <SEP> for <SEP> measuring <SEP> the <SEP> collective <SEP> satisfaction <SEP> of <SEP> vehicle <SEP> drivers <SEP> in <SEP> a <tb> vehicular stream, comprising: automatic sensing means arranged in use to be installed on or near a road for monitoring the behaviour of vehicles on the road; and data processing means coupled to the automatic sensing means for receiving electrical sensing signals therefrom, and for processing the signals to provide a measurement of driver satisfaction.
    2. Apparatus as claimed in claim 1, wherein the automatic sensing means is arranged to monitor a plurality of properties of vehicle motion, and the data processing means is arranged to combine these to provide said measurement of driver satisfaction.
    3. Apparatus as claimed in claim I or 2, wherein the automatic sensing means is arranged at a given location to monitor one or more of : the speed of vehicles; the time gap between successive vehicles; the distance between successive vehicles; the relative speed of successive vehicles; the acceleration of vehicles; the length of vehicles; the crossing of vehicles between lanes; and the distribution of the traffic stream in the lanes of a multi-lane road, and wherein the data processing means is arranged to determine the measurement of driver satisfaction based on the monitored parameter (s).
    4. Apparatus as claimed in claim 1, 2 or 3, wherein the or each automatic sensing means comprises at least one wire loop located in each lane of the road.
    5. Apparatus as claimed in any preceding claim, wherein the data processing means is arranged to determine an individual satisfaction rating for each vehicle in the stream <img class="EMIRef" id="024181745-00140001" />
    <tb> from <SEP> the <SEP> parameters <SEP> recorded <SEP> for <SEP> that <SEP> vehicle <SEP> and <SEP> a <SEP> collective <SEP> measurement <SEP> of <SEP> driver <tb> satisfaction <SEP> as <SEP> a <SEP> combination <SEP> of <SEP> the <SEP> individual <SEP> satisfis <SEP> n <SEP> ratings. <tb>
    6. Apparatus as claimed in claim 5, wherein the data processing means is arranged to determine the individual satisfaction rating for a vehicle using a weighted sum of parameters corresponding to the speed of the vehicle, the gap between the vehicle and the preceding vehicle, and the relative speed of the vehicle compared to the preceding vehicle.
    7. Apparatus as claimed in any preceding claim, wherein the automatic sensing means is arranged to monitor different classes of vehicle and the data processing means is arranged to determine a collective measurement of satisfaction for each class of vehicle.
    8. Apparatus as claimed in any preceding claim, wherein the data processing means is arranged to determine a collective measurement of satisfaction for each lane of vehicles in a multi-lane road.
    9. Apparatus as claimed in any preceding claim, wherein the data processing means is arranged to report the collective measurement of driver satisfaction to an operator at predetermined intervals.
    10. Apparatus as claimed in any preceding claim, wherein the data processing means is arranged to report the collective measurement of driver satisfaction to an operator whenever the collective measurement of driver satisfaction falls below a predetermined level.
    11. A method for predicting the probability of a road accident occurring at or adjacent a predetermined location on a road system, comprising measuring the collective measurement of driver satisfaction at that location using the apparatus of any preceding claim. <img class="EMIRef" id="024181745-00150001" />
    <tb>
    12. <SEP> A <SEP> method <SEP> for <SEP> positioning <SEP> emergency <SEP> vehicles <SEP> in <SEP> a <SEP> road <SEP> system, <SEP> comprising <tb> predicting <SEP> the <SEP> probability <SEP> of <SEP> an <SEP> accident <SEP> occurring <SEP> at <SEP> or <SEP> adjacent <SEP> each <SEP> of <SEP> a <SEP> plurality <SEP> of <tb> locations using the method of claim 9, and determining the required distribution of emergency vehicles accordingly.
    13. A method for selecting a route within a road system, comprising taking into account previous measurements of collective satisfaction ratings made using the apparatus of any of claims 1 to 8 at a plurality of locations on the road system.
    14. A method for measuring the collective satisfaction of drivers of vehicles in a vehicular stream, comprising: monitoring vehicles on a road using a set of automatic sensors; sending electrical sensing signals from the automatic sensors to data processing means; and using the data processing means to process the electrical sensing signals to generate a collective measurement of driver satisfaction.
    15. Apparatus for measuring the collective satisfaction of drivers of vehicles in a vehicular stream substantially as hereinbefore described with reference to the accompanying drawings.
    16. A method for measuring the collective satisfaction of drivers of vehicles in a vehicular stream substantially as hereinbefore described with reference to the accompanying drawings. <img class="EMIRef" id="024181745-00160001" />
    <tb>
    Amendments <SEP> to <SEP> the <SEP> claims <SEP> have <SEP> been <SEP> filed <SEP> as <SEP> follows <tb> 1. <SEP> Apparatus <SEP> for <SEP> measuring <SEP> the <SEP> collective <SEP> satisfaction <SEP> of <SEP> vehicle <SEP> drivers <SEP> in <SEP> a <tb> vehicular stream, comprising: automatic sensing means arranged in use to be installed on or near a road for monitoring the speed of vehicles on the road; and data processing means coupled to the automatic sensing means for receiving electrical sensing signals therefrom, and for processing the signals to provide a measurement of driver satisfaction, the measurement of driver satisfaction being a function of the relative speed of successive vehicles.
  2. 2. Apparatus for measuring the collective satisfaction of vehicle drivers in a vehicular stream, comprising: automatic sensing means arranged in use to be installed on or near a road for monitoring the speed of vehicles on the road and the distance between successive vehicles; and data processing means coupled to the automatic sensing means for receiving electrical sensing signals therefrom, and for processing the signals to provide a measurement of driver satisfaction, the arrangement being such that the measurement of driver satisfaction is high if the distance between successive vehicles is high and the speed of vehicles is low, and the measurement of driver satisfaction is low if the distance between successive vehicles is low and the speed of vehicles is high.
  3. 3. Apparatus as claimed in claim 2, wherein the data processing means is arranged so that the relative speed of successive vehicles is taken into account in the determination of the measurement of driver satisfaction.
  4. 4. Apparatus as claimed in claim 1,2 or 3, wherein the automatic sensing means is arranged to monitor a plurality of properties of vehicle motion, and the data processing means is arranged to combine these to provide said measurement of driver satisfaction.
  5. 5. Apparatus as claimed in any preceding claim, wherein the automatic sensing means is further arranged at a given location to monitor: <img class="EMIRef" id="024181745-00170001" />
    <tb> the <SEP> acceleration <SEP> of <SEP> vehicles <SEP> ; <tb> the <SEP> length <SEP> of <SEP> vehicles <SEP> ; <tb> the crossing of vehicles between lanes; and the distribution of the traffic stream in the lanes of a multi-lane road, and wherein the data processing means is arranged to include the additionally monitored parameter (s) in the determination of the measurement of driver satisfaction.
  6. 6. Apparatus as claimed in any preceding claim, wherein the or each automatic sensing means comprises at least one wire loop located in each lane of the road.
  7. 7. Apparatus as claimed in any preceding claim, wherein the data processing means is arranged to determine an individual satisfaction rating for each vehicle in the stream from the parameters recorded for that vehicle and a collective measurement of driver satisfaction as a combination of the individual satisfaction ratings.
  8. 8. Apparatus as claimed in claim 7, wherein the data processing means is arranged to determine the individual satisfaction rating for a vehicle using a weighted sum of parameters corresponding to the speed of the vehicle, the gap between the vehicle and the preceding vehicle, and the relative speed of the vehicle compared to the preceding vehicle.
  9. 9. Apparatus as claimed in any preceding claim, wherein the automatic sensing means is arranged to monitor different classes of vehicle and the data processing means is arranged to determine a collective measurement of satisfaction for each class of vehicle.
  10. 10. Apparatus as claimed in any preceding claim, wherein the data processing means is arranged to determine a collective measurement of satisfaction for each lane of vehicles in a multi-lane road.
  11. 11. Apparatus as claimed in any preceding claim, wherein the data processing means is arranged to report the collective measurement of driver satisfaction to an operator at predetermined intervals. <img class="EMIRef" id="024181745-00180001" />
    <tb>
  12. 12. <SEP> Apparatus <SEP> as <SEP> claimed <SEP> in <SEP> any <SEP> preceding <SEP> claim, <SEP> wherein <SEP> the <SEP> data <SEP> processing <SEP> means <tb> is <SEP> arranged <SEP> to <SEP> report <SEP> the <SEP> collective <SEP> measurement <SEP> of <SEP> driver <SEP> satisfaction <SEP> to <SEP> an <SEP> operator <tb> whenever the collective measurement of driver satisfaction falls below a predetermined level.
  13. 13. A method for predicting the probability of a road accident occurring at or adjacent a predetermined location on a road system, comprising measuring the collective measurement of driver satisfaction at that location using the apparatus of any preceding claim.
  14. 14. A method for positioning emergency vehicles in a road system, comprising predicting the probability of an accident occurring at or adjacent each of a plurality of locations using the method of claim 13, and determining the required distribution of emergency vehicles accordingly.
  15. 15. A method for selecting a route within a road system, comprising taking into account previous measurements of collective satisfaction ratings made using the apparatus of any of claims 1 to 10 at a plurality of locations on the road system.
  16. 16. A method for measuring the collective satisfaction of drivers of vehicles in a vehicular stream, comprising: monitoring the speed of vehicles on a road and the distance between successive vehicles using a set of automatic sensors; sending electrical sensing signals from the automatic sensors to data processing means; and using the data processing means to process the electrical sensing signals to generate a collective measurement of driver satisfaction, the measurement of driver satisfaction being determined to be low if the speed of vehicles is low and the gap between successive vehicles is large and high if the speed of vehicles is high and the gap between successive vehicles is small. <img class="EMIRef" id="024181745-00190001" />
    <tb>
  17. 17. <SEP> A <SEP> method <SEP> for <SEP> measuring <SEP> the <SEP> collective <SEP> satisfaction <SEP> of <SEP> drivers <SEP> of <SEP> vehicles <SEP> in <SEP> a <tb> vehicular <SEP> stream, <SEP> comprising <SEP> : <tb> monitoring the speed of vehicles on a road using a set of automatic sensors; sending electrical sensing signals from the automatic sensors to data processing means; and using the data processing means to process the electrical sensing signals to generate a collective measurement of driver satisfaction as a function of the relative speed of successive vehicles.
  18. 18. Apparatus for measuring the collective satisfaction of drivers of vehicles in a vehicular stream substantially as hereinbefore described with reference to the accompanying drawings.
  19. 19. A method for measuring the collective satisfaction of drivers of vehicles in a vehicular stream substantially as hereinbefore described with reference to the accompanying drawings.
GB0107300A 2001-03-23 2001-03-23 Measurement of traffic density Withdrawn GB2373619A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
GB0107300A GB2373619A (en) 2001-03-23 2001-03-23 Measurement of traffic density

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
GB0107300A GB2373619A (en) 2001-03-23 2001-03-23 Measurement of traffic density

Publications (2)

Publication Number Publication Date
GB0107300D0 GB0107300D0 (en) 2001-05-16
GB2373619A true GB2373619A (en) 2002-09-25

Family

ID=9911400

Family Applications (1)

Application Number Title Priority Date Filing Date
GB0107300A Withdrawn GB2373619A (en) 2001-03-23 2001-03-23 Measurement of traffic density

Country Status (1)

Country Link
GB (1) GB2373619A (en)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
ES2350551A1 (en) * 2010-08-06 2011-01-25 Universidad De Málaga Traffic management system and procedure.
WO2015147766A2 (en) 2014-03-26 2015-10-01 Ford Otomotiv Sanayi Anonim Sirketi A safe monitoring system
CN105679024A (en) * 2016-02-19 2016-06-15 上海果路交通科技有限公司 Road intersection queuing length calculation method
GB2536028A (en) * 2015-03-05 2016-09-07 Red Fox Id Ltd Vehicle detection apparatus
EP3082119A1 (en) 2015-04-15 2016-10-19 VITRONIC Dr.-Ing. Stein Bildverarbeitungssysteme GmbH Distance measurement of vehicles
US9821812B2 (en) 2015-04-23 2017-11-21 Ford Global Technologies, Llc Traffic complexity estimation
CN107481529A (en) * 2017-08-25 2017-12-15 安徽实运信息科技有限责任公司 A kind of traffic guidance delivery system based on magneto-dependent sensor
CN107507426A (en) * 2017-08-25 2017-12-22 安徽实运信息科技有限责任公司 A kind of urban track traffic for passenger flow guidance information publishing system

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3539900A (en) * 1969-06-04 1970-11-10 Gen Electric Rectifier lockout circuit
EP0293724A1 (en) * 1987-05-27 1988-12-07 Siemens Aktiengesellschaft Method using measuring techniques for determining road traffic intensity
WO1994011839A1 (en) * 1992-11-19 1994-05-26 Kjell Olsson Prediction method of traffic parameters
DE4300650A1 (en) * 1993-01-08 1994-07-14 Refit Ev Determination of vehicle-classified traffic flow data
US5861820A (en) * 1996-11-14 1999-01-19 Daimler Benz Ag Method for the automatic monitoring of traffic including the analysis of back-up dynamics
US6177885B1 (en) * 1998-11-03 2001-01-23 Esco Electronics, Inc. System and method for detecting traffic anomalies

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3539900A (en) * 1969-06-04 1970-11-10 Gen Electric Rectifier lockout circuit
EP0293724A1 (en) * 1987-05-27 1988-12-07 Siemens Aktiengesellschaft Method using measuring techniques for determining road traffic intensity
WO1994011839A1 (en) * 1992-11-19 1994-05-26 Kjell Olsson Prediction method of traffic parameters
DE4300650A1 (en) * 1993-01-08 1994-07-14 Refit Ev Determination of vehicle-classified traffic flow data
US5861820A (en) * 1996-11-14 1999-01-19 Daimler Benz Ag Method for the automatic monitoring of traffic including the analysis of back-up dynamics
US6177885B1 (en) * 1998-11-03 2001-01-23 Esco Electronics, Inc. System and method for detecting traffic anomalies

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Golden River - M661 Marksman Traffic Counter May 2001 *
Golden River - M-Target Software Press release 09.06.00 *
searchpdf.adobe.com/proxies/0/19/23/2.html - 19.06.1998 - Details of Golden River Marksman 660 *

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
ES2350551A1 (en) * 2010-08-06 2011-01-25 Universidad De Málaga Traffic management system and procedure.
WO2012017104A1 (en) * 2010-08-06 2012-02-09 Universidad De Malaga System and method for managing traffic
WO2015147766A2 (en) 2014-03-26 2015-10-01 Ford Otomotiv Sanayi Anonim Sirketi A safe monitoring system
GB2536028A (en) * 2015-03-05 2016-09-07 Red Fox Id Ltd Vehicle detection apparatus
WO2016139456A1 (en) * 2015-03-05 2016-09-09 Red Fox I.D. Ltd Vehicle detection apparatus
GB2536028B (en) * 2015-03-05 2018-05-09 Red Fox Id Ltd Vehicle detection apparatus with inductive loops
US10109187B2 (en) 2015-03-05 2018-10-23 Red Fox I.D. Ltd Vehicle detection apparatus
EP3082119A1 (en) 2015-04-15 2016-10-19 VITRONIC Dr.-Ing. Stein Bildverarbeitungssysteme GmbH Distance measurement of vehicles
US9821812B2 (en) 2015-04-23 2017-11-21 Ford Global Technologies, Llc Traffic complexity estimation
CN105679024A (en) * 2016-02-19 2016-06-15 上海果路交通科技有限公司 Road intersection queuing length calculation method
CN107481529A (en) * 2017-08-25 2017-12-15 安徽实运信息科技有限责任公司 A kind of traffic guidance delivery system based on magneto-dependent sensor
CN107507426A (en) * 2017-08-25 2017-12-22 安徽实运信息科技有限责任公司 A kind of urban track traffic for passenger flow guidance information publishing system

Also Published As

Publication number Publication date
GB0107300D0 (en) 2001-05-16

Similar Documents

Publication Publication Date Title
Kyte et al. Effect of weather on free-flow speed
DE19643454C2 (en) Method and device for transmitting data for traffic situation assessment
US7672764B2 (en) System and method for recording physical response
US8700299B2 (en) Navigation device, recommended speed arithmetic device, and recommended speed presentation device
US7317406B2 (en) Infrastructure-based collision warning using artificial intelligence
DE60201075T2 (en) Traffic control system with road use charges depending on the streaming stage of the roads
US10384678B1 (en) Autonomous vehicle action communications
Yannis et al. Pedestrian gap acceptance for mid-block street crossing
DE60107938T2 (en) Automatic accident
EP1176569B1 (en) Method for monitoring the condition of traffic for a traffic network comprising effective narrow points
US7804423B2 (en) Real time traffic aide
Lee et al. Analysis of crash precursors on instrumented freeways
Liang et al. Effect of environmental factors on driver speed: a case study
US7990286B2 (en) Vehicle positioning system using location codes in passive tags
EP0680648B1 (en) Traffic monitoring system with reduced communications requirements
US10242513B1 (en) Shared vehicle usage, monitoring and feedback
Jung et al. Rainfall effect on single-vehicle crash severities using polychotomous response models
US7783420B2 (en) Route guidance systems, methods, and programs
US20160171521A1 (en) Road segment safety rating system
US20120274481A1 (en) Driver Safety Enhancement Using Intelligent Traffic Signals and GPS
EP1652128B1 (en) Traffic information system
US9932033B2 (en) Route risk mitigation
US7343242B2 (en) Traffic status detection with a threshold method
DE102015100812A1 (en) A method of using road level images to enhance a mode of automated driving for a vehicle
US20130162449A1 (en) Traffic Routing Using Intelligent Traffic Signals, GPS and Mobile Data Devices

Legal Events

Date Code Title Description
WAP Application withdrawn, taken to be withdrawn or refused ** after publication under section 16(1)