Gps in Time Domain

8/16/2019 Gps in Time Domain

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Pergamon

Transp. Res.-C. Vol. 3, No. 4, pp. 193-209, 1995

Copyright (3 1995 Elsevier Science Ltd

Printed in Great Britain. AI1

rights reserved

0968-090X/95 9.50+ 0.00

0968-09OX 95)OOOOG2

GLOBAL POSITIONING SYSTEMS IN THE TIME DOMAIN:

HOW USEFUL A TOOL FOR INTELLIGENT

VEHICLE-HIGHWAY SYSTEMS?

R. ZITO, G. D’ESTE and M. A. P. TAYLOR

Transport Systems Centre, University of South Australia. The Levels, South Australia 5095,

Australia

Recei ved 31 Januar y 1995)

Abstract --

Much of the research and development work in intelligent vehicle-highway systems

(IVHS) relies on the availability of methods for locating and monitoring vehicles (e.g. “probe

vehicles”) in real time across a road network. This paper considers the use of the global positioning

system (GPS) as one method for obtaining information on the position, speed and direction of

travel of vehicles. It reports the results of a series of field studies, in which real-time GPS data were

compared to data collected by an instrumented vehicle, under a range of physical and traffic

conditions. The field studies and consequent data analysis provide a picture of the reliability and

usefulness of GPS data for traffic monitoring purposes, and hence the possibilities for the use of

GPS in IVHS projects. The use of GPS receivers tailored for mobile applications, and able to

provide direct observations of vehicle speed and travel direction, coupled with database manage-

ment using geographic information systems (GIS) software, was found to provide a reliable and

efficient system for vehicle monitoring. Field data collection under “ideal” GPS conditions indi-

cated that accurate speed and position data were readily obtained from the GPS. Under less

favourabie conditions (e.g. in downtown networks), data accuracy decreased but useful infor-

mation could still be obtained. In addition, the conditions and situations under which GPS data

errors could be expected were noted. The finding that it is possible to relate standard GPS signal

quality indicators to increased errors in speed and position provides an enhanced degree of

confidence in the use of the GPS system for real-time traffic observations.

INTRODUCTION

Much of the research and development work in IVHS relies on the availability of methods

for locating and monitoring vehicles (e.g. “probe vehicles”) in real time across a road

network. This paper considers the use of the global positioning system (GPS) as one

method for obtaining information on the position, speed and direction of travel of

vehicles. It reports the results of a series of field studies, in which real-time GPS data were

compared to data collected by an instrumented vehicle, under a range of physical and

traffic conditions. The field studies and consequent data analysis provide a picture of the

reliability and usefulness of GPS data for traffic monitoring purposes, and hence the

possibilities for the use of GPS in IVHS projects.

The GPS consists of some 24 satellites encircling the earth at inclined orbits of 60”.

There are six terrestrial control stations that update the satellites with new information as

it comes to hand (see Fig. 1 for a schematic representation of the GPS system). GPS is

owned and maintained by the U.S. Department of Defense, and is available worldwide to

any user who has a GPS receiver. The basic output from a receiver is the x, y and z

coordinates for a moving or stationary object, at possible update rates of the order of

once/s. Figure 1 shows the three segments that make up the GPS system, with the user

segment being the final segment where the GPS data can be used in many different appli-

cations, such as transport planning, management, control and scheduling, and hence can

play a potentially important role in IVHS.

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al.

The PS ystem

Space Segment

Fig.

I.

Schematic representation of the GPS system and its components.

HOW DOES GPS WORK?

GPS satellites are essentially radio stations that are constantly emitting data, at a

transmission frequency of approx 1500 MHz. Data are received and processed by GPS

receivers to obtain the latitude, longitude and height coordinates of the receiver’s antenna.

The advantage of using satellites is that as long as there is a clear view to the sky the

coordinates of the receiver can be calculated anywhere in the world: it is also an all

weather system that will give 24 h coverage once fully implemented. Any other terrestrial

system would have problems with signal blockage due to the topography of the earth’s

surface. A satellite system therefore provides the best means of worldwide coverage.

However GPS does suffer from signal blockage: for example GPS signals will not pene-

trate into tunnels or go through bridges, or may have difficulties penetrating through tree-

lined streets. This can cause some concerns if tracking a vehicle with GPS as these features

are quite common in urban areas. However, at present GPS gives better global coverage

than any other system currently available. One objective of this paper is to test the ability

of GPS to provide good locational data in various parts of an urban area. A second

objective is to examine the practical ability of GPS to provide information about the

speed of a moving vehicle.

The basis of the GPS system is triangulation. The distances from a set of satellites to the

GPS receiver are measured using the speed of light and the time taken for the GPS signal

to travel from each satellite to the GPS receiver. These times are found by examining the

phase shift between the GPS signal and the receiver. Therefore timing is a crucial part of

GPS. GPS receivers on the ground need consistent clocks that are accurate to the nano-

second for good positioning results, while the satellites use atomic clocks for their timing

functions. Once the distance is found, the position in space of the satellite must be

obtained. This position is transmitted from the control segment (see Fig. I) and relayed to

the GPS receiver through the satellite, as part of the signal received from the satellite.


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Global positioning systems

195

Finally, corrections must be made for atmospheric conditions. The GPS signal (which

is, of course, electromagnetic radiation) will travel at different speeds through mediums of

different density. Since the timing is so critical these types of errors must be corrected for,

since as the GPS signal travels from space to the earth it passes through the ionosphere,

troposphere and the atmosphere. Corrections must be made depending on the conditions of

these media. The control segment provides data for the changing atmospheric conditions. As

an example of how necessary a good timing system is, an induced error of 1 ps could pro-

duce a range error of 300 m (the range is the distance from a satellite to the GPS receiver).

The nature of the triangulation calculation is such that there are four unknowns, which

may be seen as the X, V, z coordinates of the receiver and the clock drift of the receiver.

The clock drift is the difference in synchronization between the satellite’s clock and the

receiver’s clock. This must be determined for an accurate range measurement to be

calculated. Three satellites in view can be pictured as the intersection of three spheres for

which the radius of each sphere is equal to the range value of each satellite. The inter-

section of three spheres yields two points in space, one of which will be on the surface of

the earth (the calculated location point of the receiver) while the other will be in outer

space and can be ignored. However, this still leaves the clock drift as an unknown. In the

situation where only three satellites are in view, the procedure adopted in GPS location is

to calculate the x and J’ coordinates of the receiver and the clock drift variable for it, and

to leave the z coordinate undetermined. When at least four satellites are in view then the

four known range values can be used to solve for all four unknowns, so the x, y, z and

clock drift variable can be computed.

When the GPS system was first introduced there were large portions of a day where less

than three operational satellites were in view in any given area of the globe. The GPS

system could only be used for a limited time each day. By 1994, however, with 24 satellites

encircling the earth, coverage of at least three satellites in view for 24 h a day was just

about achievable at any point on the earth’s surface. Figure 2 provides a typical indication

of satellite visibility over a 24 h period.

GPS transmits data on two wavelengths: one is Coarse Acquisition Code (C/A Code)

which has a wavelength of 24 cm and the other is Precise Acquisition Code (P Code) that

has a wavelength of 19 cm. The smaller the wavelength, the more accurate the position

calculation will be. However, the cost of the receivers needed to obtain the P Code data is

Numberof Visible Satellites us Tine

Latitude :34 48’48”s

Longitude :I33 37’84”E

Hmber of Satellites

15 ~

1 J._._..______..._.__._._..__._..__._._.__.__._._.___..__.___._._.___“_I

13J_.__.___._.._____._____._.._.._._..- - ---.-.- .- _.-...._..._._..._._____..1

12 ]_..______.__.-_-.-._.-._-__.---.--..-.---..__._ ..__...._----.J

I I J.-__._--_- ._._.-I..--- --..- -.--.-.--...- ----. .___.__.....____.__.1

10 _1....-..__._.__.___.._.._.___..___.___.._____.-.__-.^_..____

9

J...-..-._...__-._.._.-..-._.--.-- _..___..-.- __.._._.-.-_-- ..-...--..-

_.._ .-._..J

1. _,.___..__,____.,._._._

..-.-...- ___..J

a

--.-. -- ----.-_. _.-.

.-...

-_. _.______._...______..._...._.._._..___..I

0: 00

4:00

a

00 12:00

16:&l 20:00 24:

00

The

Increment of 60.8 minutes

Fig. 2. A sample plot of number of GPS satellites in view over hours of the day,


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usually more expensive than the C/A Code receivers. For transportation applications the

accuracies obtained by the C/A Code receivers have proven satisfactory for most appli-

cations. In contrast the P Code receivers are more suited to surveying applications.

As well as range data being transmitted by GPS, once an hour ephemeris and almanac

data are also sent. These are data packets that contain atmospheric and clock correction

parameters. Almanac data contains corrections to satellite clocks and atmospheric delay

parameters. It also contains approximate orbital constants for each satellite, allowing the

GPS receiver to obtain a quick fix on the satellites that are in view. The ephemeris data

contains accurate orbital constants that are used in the position calculations. These data

are sent to the satellites from the control segment as shown in Fig. 1. It is crucial to

the GPS system and must be accurate and reliable for the GPS system to provide good

positioning solutions. As well as the GPS system supplying locational data it also supplies

parameters that can be used to assess the reliability of the GPS data. One of these

parameters is the position dilution of precision (PDOP). Geometrically PDOP can be

thought of as the inverse of the volume of the pyramid formed by the satellites in view and

the GPS antenna. Since triangulation is the basis of GPS, the geometry of the satellite

constellation will affect the quality of the position calculation. For example if the satellites

used are close to the horizon the quality of the positional calculation will be in doubt, and

the value of the PDOP will be high, i.e. greater than five. An ideal situation exists when

the satellites are directly above the antenna at an even spacing. Good quality results will

emerge and a low PDOP value, i.e. less than three, will be found. Thus the PDOP value

can be used as a reliability indicator. This is useful, for it means that although GPS

is not a 100% reliable system there are readily available indicators that can provide

information on the quality and reliability of the observed GPS data.

GPS errors

In a system such as GPS there are bound to be a number of inherent errors. These

include satellite orbit errors, satellite clock errors, receiver noise errors, tropospheric and

ionospheric errors, coordinate transformations and selective availability. Satellite orbit

errors occur due to the inherent limitations in modelling the exact orbits of the satellites.

Satellite clock errors occur due to the inconsistencies in the clocks used by the GPS

satellites, and the applied corrections not being exact. Multi-path errors occur when a

reflected signal (e.g. from a tall building or an escarpment) reaches a GPS receiver and

interferes with the direct signal, so inducing an error. Receiver noise error is due to the

time taken for the GPS signal to actually get into the hardware of the GPS receiver. This

error is usually receiver specific.

Other errors, previously mentioned, were the

tropospheric and ionospheric errors that are caused by the GPS signal being delayed due

to atmospheric effects. A secondary source of error, resulting from the processing of the

data received from the GPS, could be the transformation of GPS global coordinates into a

local system. This can occur surprisingly often but is easily eliminated merely by deter-

mining the correct transformation to use to properly locate the GPS coordinates on the

available map base However, the biggest source of error from GPS is due to

sel ecti ve

avai lab i l i ty : the error deliberately inserted into the GPS signals by the U.S. Department of

Defense. The system was, after all, primarily introduced for military purposes. The sole

purpose of selective availability is to degrade the accuracies of GPS for real-time non

U.S.-military users. Errors are introduced into the almanac and ephemeris data via the

control segment. Selective availability is constantly on, and apparently the only period for

which it has been disabled was during the Gulf War crisis in the Middle East, when the

U.S. Armed Forces were obliged for logistical reasons to use some civilian GPS receivers

to augment their supply of military units.

GPS accuracy

With all the sources of error described above, just what are the actual positional

accuracies obtainable with GPS? In most transportation applications it is necessary to talk

about real-time accuracies. Real-time can be defined as the instantaneous position output


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4000SST 19 Feb Pillar 15

197

Fig. 3. The variability of GPS position information as shown by the apparent movement of a GPS receiver

located at a known point over an I h period.

of the GPS receiver available at any point in time. It can be thought of as the coordinates

given by the GPS receiver without any post-processing being undertaken. Heuristically it

can be stated that in absolute positioning mode GPS can have accuracies of about f 50 m:

absolute position refers to uncorrected output from a GPS receiver. The error specifi-

cation for GPS provided by the U.S. Department of Defense is that location errors will be

better than f 50 m for 95% of the time. If the coordinates are corrected by the use of a

differential correction either in real-time or after post-processing then the accuracy is

increased to f 5 m. Figure 3 provides an indication of how the GPS readings drift, in

absolute positioning mode about a point of known location. In this example the variation

is well within the f 50 m criterion mentioned, but this is probably due to a high quality

receiver being used in this case, the Trimble 4000SST GPS unit, and the presence of at

least four satellites in view for the entire duration of the test.

A differential correction involves the use of another GPS receiver located at a known

reference point. Therefore the errors in GPS positioning it undergoes can be determined

and correction vectors calculated. These vectors can then be applied to another GPS

receiver to achieve greater accuracies, even with selective availability in place. Without

selective availability absolute positioning accuracies would drop to f 5 m and differen-

tially corrected locations would be accurate to the sub-metre level. Drane (1992) describes

the theoretical background to, and analysis of, GPS errors.

GPS APPLICATIONS IN TRAFFIC STUDIES

GPS time, position and speed data can be usefully employed in traffic studies and in

vehicle tracking. Speed and acceleration are important indicators for assessing vehicle and

traffic systems performance, especially in congested conditions (Taylor, 1992). The follow-

ing sections discuss the availability and accuracy of speed and acceleration data derived

from GPS observations taken in moving vehicles.

GPS and speed

There are two ways in which speed can be calculated using GPS. The first and more

obvious is to derive it from the position calculations since all GPS position calculations

are time tagged. It is a simple matter of dividing the distance travelled between GPS

readings by the difference in time between these readings to obtain speed. Combining this

with the fact that present-day GPS receivers can output position readings at rates of up to

10 p/s, a large set of speed data observations can be obtained. However, as discussed in

the last section, uncorrected GPS coordinates can have errors of about f 50 m, so that on


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198 R. Zito et al.

a second by second basis the error in speed calculated from distance travelled over a short

time interval can be up to 180 km/h. This result would make the speed value meaningless.

However, during extensive practical testing this magnitude of speed error was never

experienced.

Many GPS receivers available at the lower end of the market do not directly output

speed so the method of calculating speed from distance travelled over time is the only way

available to obtain a speed value. To make use of this method it is necessary to apply

correction techniques, including Kalman filtering, to the data to improve the accuracy of

the derived speed (Zito and Taylor, 1994).

The second method of obtaining speed data is to obtain a GPS receiver that outputs

speed directly: the one used for the tests described in this paper is the Trimble SV6 GPS

receiver. This unit employs a method independent of the position calculations to obtain

both speed and direction of travel (bearing). The method, described by May (1992) uses

the Doppler effect to measure the rate of change of the GPS signal and hence derives a

speed directly, in principle the same method as that used in conventional speed surveys

using a radar gun. The series of tests performed as part of this study indicated that this

method gave far superior speed accuracies to those based on speeds calculated from

changes in position over time.

Speed data were collected by the use of the University of South Australia Transport

Systems Centre (TSC)‘s instrumented Toyota Camry Sedan. The GPS antenna was

mounted on the roof of the vehicle and data were logged on a laptop computer in the car.

The car was permanently equipped with the Australian Road Research Board (ARRB)‘s

Travel Time Data Acquisition System (TTDAS). This system includes an optical trans-

ducer connected to the speedometer cable of the vehicle, so that speed readings could be

made directly. See Young and Taylor (1993) for a description of the TSC instrumented

vehicle and its capabilities. The data from TTDAS consists of time tagged distance and

speed readings, taken at a rate of 1 reading/s, and were logged on a second laptop com-

puter. TTDAS gives a reliable speed reading to within f 1 km/h. Matching the TTDAS

speeds to the speed readings given by GPS allowed speed errors to be calculated and

analyzed.

Tests comparing speed values from TTDAS and the Trimble SV6 GPS receiver were

conducted at Mallala, a small rural town north of Adelaide, South Australia. This site was

chosen because of its topography and the small volumes of traffic in the area. A long flat

stretch of road with an all-round clear view to the sky was used just outside this small

township, the speed limit on that stretch of road was 110 km/h so high-speed tests could

be performed. The method involved driving the instrumented vehicle at acceleration rates

ranging from 1 to 8 km/h/s along the test section whilst recording data using GPS and

TTDAS simultaneously. Figure 4 shows a typical result for the TTDAS and GPS speed

profiles. At the scale used for this plot the two speeds appear to match extremely well.

Even when the scales of the graphs are enlarged the lines still overlap. This is quantified by

Fig. 5, where it can be seen that most of the speed errors lie in the range of f 2 km/h with

only a few outliers. This is a pleasing result, since it also shows that the speed error is not

related to the actual speed of travel, but more so on the GPS conditions. The mean speed

error for this set of data is 0.21 km/h with a standard deviation of 1.35 km/h. Remember

that the TTDAS system is only capable of accuracies to within f 1 km/h.

An interesting observation to come from this test and many others performed is that

the mean of the speed error has always been skewed positively. This is probably best

explained by the fact th_tt when the vehicle is stationary i.e. its speed is zero km/h, the

GPS receiver may still be recording a speed of up to 2.0 km/h. This is due to the inherent

errors that are involved in GPS but can mainly be attributed to selective availability.

Another interesting observation is that when the speed errors are collected and plotted

on a histogram, the distribution of the errors has a similar form to a normal distribution

(see Fig. 6). In fact, the distribution is not normal, with the differences essentially being

that there are outliers present, the shape of the curve is “spikier” than that of the normal

distribution (observed kurtosis coefficient of 4.600 compared to an expected value of three


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Comparison of llDAS and SV6 Speed

0 500

1000

1500 2000

2500

Time (sets)

Fig. 4. Comparison of SV6 and TTDAS speeds (Mallala data).

Speed Error v Speed

All Data Sets From Mallalo

6

Speed (km/h)

Fig. 5. GPS speed error vs vehicle speed (Mallala data).

Speed Error Bins

Speed

Enor Bh’ts

km/h) (0.5 Intervals)

Fig. 6. Speed error histogram (Mallala data)


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R. Zito et al.

Comparison of SV6 Direct and Calculated Speed

20

40 60

80

IQ) 120

SV6 Dlred Speed km/h)

Fig. 7.

Comparison of calculated and direct GPS speeds with actual travel speed obtained from TTDAS

(Mallala data).

for the normal distribution), and the mean is skewed slightly negative (skewness coeffi-

cient -0.009). As a comparison 72.5% of the speed errors lie within 1 SD of the mean,

hence the spikiness of the curve. For the normal distribution, 67.5% of the data points

would lie within 1 SD of the mean. However, 94.5% of the speed errors in Fig. 6 lie within

+2 SD of the mean, whereas for the normal distribution 96.6% of the data points lie

within that range. To confirm this visual discrepancy between the observed distribution of

errors and the normal distribution, a x2 test of goodness-of-fit was conducted by fitting a

normal distribution with the observed mean and standard deviation of the errors to the

data in Fig. 6. This yielded a x2 statistic of 70.85 (14 df), which is highly significant (the

0.01 significance level is 29.14). The discrepancy is probably best explained by the fact that

there are always likely to be a number of outliers occurring when the GPS receiver loses

lock on a satellite, or there is a change in the visible constellation of satellites.

All the above results have dealt with speed values obtained directly from the GPS

receiver. Figure 7 shows a comparison of calculated speed (as the speed calculated from

the change in distance over time) as well as the direct speed observations. The relation-

ship between actual direct (GPS) speed and actual (TTDAS) speed is a line at 45”,

which is consistent with the earlier findings. A linear regression performed on the data

yielded a line of best fit with

r =

0.999 and a slope of 0.999 for a data set of some 1593

Acceleration Error v Acceleration

All Cuta Sets From Mallala

.

I

Accelemtlon

km/h/s)

Fig. 8. GPS acceleration error vs acceleration given by TTDAS (Mallala data).


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Acceleration Error Bins

201

Accdomtlon Error

Fig. 9. Acceleration error histogram for the Mallala data.

observations. Although there are a number of outliers visible in this plot, this could be

expected given the large data set-the interesting question is under what conditions

can these outliers occur and can these conditions be anticipated given knowledge of GPS

signal quality indicators such as PDOP? This question is pursued in subsequent sections

of this paper. Interestingly enough, calculated speed showed an almost random relation-

ship with the TTDAS speed, which is reflected in an

r2

value of 0.001 in comparisons of

those two data variables. This further emphasises the finding that, whenever possible,

direct GPS speed must be obtained, since calculated speed gives such poor results. Receivers

that enable direct speed to be obtained are now more common and are continuing to

reduce in price, in this way enabling the mass market availability of GPS.

GPS and acceleration

Acceleration is a derived quantity, and neither GPS nor TTDAS provide it directly.

Acceleration must be calculated using the change in speed divided by the change in time.

The value of acceleration can then be seen to be relatively reliable since the change in time

is small at approx 1 s. Figure 8 shows acceleration error, defined as the difference between

the GPS-derived acceleration and the TTDAS acceleration, plotted against the TTDAS

acceleration. It shows similar properties to Fig. 5 in that even at the higher and lower

acceleration rates the acceleration error does not seem to increase at all but seems to stay

within the range of *2 km/h/s.

When the acceleration errors are grouped and displayed on a histogram (Fig. 9) it can

be seen that the distribution is very much gathered around a mean value that is close to

zero. In fact the mean of the acceleration errors is 0.003 km/h/s, with a standard deviation

of 0.66 km/h/s. It was also found that 84.6% of the errors lie within ?C1 SD of the mean

and 94.5% lie within *2 SD of the mean. The distribution has a positive skew, with a

skewness coefficient of 1.029. In similar but more pronounced fashion to the results for

the speed errors, the observed error distribution is “spikier” than the normal distribution

(observed kurtosis coefficient of 14.17). Since the acceleration values are derived from the

speed values perhaps these results are not really surprising. However the results are

encouraging and give a user some confidence of accuracy and reliability in the results

obtained by the GPS system under good GPS conditions, as found at the Mallala test site.

USE OF THE GPS SYSTEM IN A DOWNTOWN AREA

Although the tests performed at Mallala show that the accuracies obtained by GPS can

be quite satisfactory for traffic monitoring, the conditions these tests were performed in do

not represent typical urban driving conditions. One of the major disadvantages of GPS is


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al.

Fig. 10. Map trace of a GPS downtown run in the CBD of Adelaide, Australia.

that it can suffer from signal blockage, and nowhere is that more apparent than in the

downtown area, or central business district (CBD) of a city, where high rise buildings

form concrete canyons that make it difficult for the GPS signals to reach down to the

antenna on a vehicle. Figure 10 shows a run performed by the TSC instrumented car

whilst driven in the CBD of Adelaide, South Australia. Adelaide is the capital city of that

state and has a population of 1 million people.

The track of the vehicle in Fig. 10 has been represented by the use of three different

shapes: squares, triangles and circles. The squares represent points where only three

satellites were in view, for which only a two-dimensional fix was able to be calculated. The

triangles represent the points where PDOP exceeds three and/or the number of satellites

(NSAT) is only equal to three. The circles represent the points where signals from four

satellites were available to obtain a fix and PDOP was less than three. Generally it can be

seen that the triangles and squares lie further from the centre lines of the roads than the

circles. This is a useful result since it means that a GPS user can gain some idea on the

integrity and quality of the results from the GPS system, by observing NSAT as well as

the PDOP. This information is readily available as a standard part of the GPS output

data. For example, if the GPS data shows that there are only three satellites in view and

that the PDOP is greater than three then the user must treat the corresponding GPS

positional data with care, as it is likely to be unreliable. In addition, the GPS data stream

is a sequence of second-by-second observations in which there may be some missed

observations due to signal blockage, but these may not be important in providing an

overall picture of the progression of the vehicle. By way of comparison, during the

Mallala test there were always at least four satellites in view and the PDOP was always

less than three.

If the positional accuracies may be degraded in the CBD, then what happens to the

accuracies of the GPS speed data? The speeds logged by the Trimble SV6 GPS receiver

used in the CBD run were analyzed in similar fashion to the Mallala data set. It was found

that the speed accuracies were also degraded in the CBD. Figure 11 shows that there is

a wider spread of the speed errors over the entire speed range when compared to the

Mallala results (Fig. 5).


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Speed Error v Speed

All

Cata From

City

Run

10

.

a-

.

203

-a

Speed (km/h)

Fig.

I I.

GPS speed error vs vehicle speed (Adelaide CBD data).

GPS Run in the CBD

7-

6 -.

,---

No

Sots

PDOP

Fig. 12. GPS speed error. number of satellites in view and PDOP vs time. for the Adelaide CBD data

Analyzing the results of Fig. 11 revealed that the average speed error for the CBD data

was 0.60 km/h with a standard deviation of 4.2 km/h. Both the mean error and the

standard deviation of the errors are about three times those found for the Mallala results,

but they are still good enough for the data to be useful. Speed error, the number of

satellites (NSAT) and PDOP were then plotted against time (Fig. 12). The general trend

of these plots shows that the speed errors seem to increase whenever the PDOP values are

high (i.e. greater than three) and/or NSAT drops to three.

Thus the experiments have shown that, even though in some circumstances the GPS

system can appear to provide unreliable results, the user can always obtain some indi-

cation on the quality of the GPS signals by keeping track of standard GPS variables such

as the number of satellites present and the PDOP. However, another important concern is

when shadowing from the surrounding buildings is encountered, and NSAT drops below

three, so a GPS fix cannot be made. Occurrences of this phenomenon are shown in Fig. 10

by the large gaps between the dots in some of the tracks. What can also be seen in this

figure is the self-adjusting nature of the GPS system. When observations were missed the

GPS receiver was able to regain lock on at least three satellites within a short period of

time (of the order of a few seconds). Thus although GPS by itself may not be a 100%

reliable system, it is capable of providing an ongoing stream of position and speed data at

a minimum frequency of observation likely to be useful for most traffic monitoring tasks.


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The value of the GPS position and speed data can be assessed by reference to GPS system

parameters provided with that data.

GPS AND RELIABILITY

As indicated above, GPS alone may not provide a completely reliable vehicle tracking

system in some environments. Whether this is a problem will depend upon the application

being considered. For example, if GPS were being used by a courier company to monitor

the location of their fleet, then a temporary loss of lock on a vehicle would not incur any

major consequences. However if a vehicle tracking system was being used by emergency

service for routing and dispatching then the consequences of having that type of a system

working for only 90-95% of the time might be less acceptable.

There are a few techniques that could be applied to GPS to make it more reliable, see

for instance Sweeney and Loughmiller (1993). The first of these is to incorporate a dead

reckoning system together with GPS. The dead reckoning system could implement the

use of inertial systems to determine the position of the vehicle from a starting location.

Inertial systems do suffer from long term drift so GPS could be used to apply periodic

corrections to bring the system back on track. In this way the advantage of both GPS and

inertial systems are utilized to give a vehicle tracking system that is as close to 100%

reliable as one could hope to get at present. However this option is resource hungry, for

not only does the GPS hardware have to be obtained but also the gyroscopes for the

inertial system must be purchased. Then the appropriate software must be developed or

acquired to link the two systems together. If this option were to be employed, the large

resource implications required to implement the system, especially in fleet applications,

might make it prohibitive.

A simpler option than the inertial system could be to make use of a magnetic compass

and a transducer connected to the speedometer cable of the car. In this way if GPS were

to lose lock on the satellites then the compass would give the direction of travel and the

transducer could give the distance travelled so a position could be calculated from this

information until GPS regained lock. This option would give good reliability but would

still need advanced software and hardware to make it operational. Connection to terres-

trial positioning and communications systems such as the ANTTS (Automatic Network

Travel Time System) in Sydney, Australia (Longfoot and Quail, 1990) or the Houston

Automatic Vehicle Identification (AVI) system (Levine and McCasland, 1994) would be

another option.

The simplest option would be to store the last direction of travel and speed and

periodically extend the track of the vehicle using these parameters. This would not be as

reliable a solution as the first two options but it would be easier to implement, more cost

effective, and for certain applications, such as off-line monitoring of a vehicle traversing a

pre-selected route (e.g. a bus), would be quite suitable.

When accuracy is a particular concern then there are options to apply corrections to

the GPS coordinates either in real-time or in post-processing, The correction is called a

differential correction and takes the calculated apparent errors of a GPS receiver fixed on

a known point and applies them to the roving GPS receiver. Dailey et al (1993) discussed

an example of a pilot system using differential GPS as part of a traffic information and

management system in Seattle. A differential GPS system requires a reliable com-

munications link to pass the corrections from the base station to the rover for real-time

applications. For the post-processing option differential processing software must be

purchased or developed, and the GPS receivers that are able to perform this correction are

usually more expensive. However, if the roving receiver does not have at least three

satellites in view it still cannot calculate a position so the correction cannot be made.

The easiest solution of all but probably the least practical is to only use GPS when there

are at least five satellites in view. This will increase the reliability of GPS data and give

better quality solutions. It will not, however, guarantee that lock will not be lost in the

concrete canyons of the CBD, although it will reduce the chances. Software can be


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purchased from GPS receiver distributors that will allow planning of a GPS run. Statistics

are given on how many satellites will be in view for how long and what position in the sky

they will be. Although this solution is easy to implement it is not very practical for most

applications, as it limits the use of GPS to time periods favoured by the GPS system

rather than for time periods of interest to the traffic engineer or the transport services

operator.

USES OF GPS IN TRAVEL TIME SURVEYS

Travel time and delay data are important in assessing the performance of a road traffic

system, especially in urban areas (Taylor et al., 1995). Travel time and delay also provide

necessary information for use in route guidance and congestion monitoring systems

(Taylor, 1992). Since GPS has the ability to log time-tagged position and speed data it is

an ideal tool for travel time surveys. Linking GPS to a geographical information system

(GIS) creates a powerful data collection, display and analysis technique. Such a technique

has been developed at TSC, by linking the GPS data and the MapInfo for Windows GIS

software. Figure 13 shows an example of the interactive data display and analysis method,

in this case applied to data from a travel time survey performed on the Eastern Freeway

in Melbourne, Australia. The dialogue box labelled “Info Tool” displays a number of

attributes which have been associated with each point in the travel time survey. These

attributes include an individual identification number, the longitude and latitude of the

point, the actual time of observation, the elapsed time from the beginning of the journey,

the total distance travelled in the journey so far, the distance between the last reading and

the present one, the speed of the vehicle at that point, the percentage stopped time so far

in the journey and the direction of travel (as a compass bearing). The advantages of dis-

playing the results in a GIS are apparent, for example the display of the street network

.

.:’

Info

Tool

ID: 354

k

Total_Distnce. 9.880

Mta_Dtstance. 0.120

Speed:/61

1

Stop_Tlme_ 33.0

I----+

earlng~ N

I I

able: EASTIPM x

Fig. 13. GIS interactive map display of a GPS travel time survey on the Eastern Freeway in Melbourne,

Australia.


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et al

Eastern Freeway 4 May PM

Peak

steppe

lTDAS

d Time

Eastern Freewa y 4 Ma y PM Peak

Stopped

‘/TRACK

Time

21

aa

oving

Moving

fime

Time

79

80

Fig. 14. Comparison of stopped time recorded by TTDAS and GPS (Eastern Freeway, Melbourne)

together with the travel time observation points provides a clear picture of the survey

route. In addition, the GIS also provides a powerful database analysis tool, so that the

attributes associated with each data observation can be queried. Figure 13 also displays an

example of a simple query: what data points in the journey have a speed value less than or

equal to 5 km/h? The corresponding points meeting this condition have been displayed as

squares along the path trace in the figure. The ability to provide interactive query and

answer information of this type enables the analyst to determine the congested sections

along the surveyed route. Figure 13 shows that the main areas of congestion occur before

entry onto the freeway and at a couple of the exit points along the freeway. This kind of

data can be used to determine where heavy congestion occurs. Zito and Taylor (1994)

provided a full description of this system.

Stopped time

The amount of stopped time in a journey is an important performance indicator when

assessing the efficiency of a road system and the level and extent of congestion. As

described above, GPS has the ability to calculate and store stopped time data. When the

stopped times given by TTDAS were first compared to GPS stopped times, the GPS

values significantly underestimated the TTDAS figures, which were known to be accurate.

This initial comparison used a GPS speed of zero as the indicator that the vehicle was

stopped. As indicated earlier in this paper, the GPS receiver may still be recording speeds

of up to 2 km/h when it is actually stationary. Therefore there is an obvious error when

trying to calculate the amount of time that the probe vehicle is stationary (actual speed

zero). Choice of a criterion that all GPS speeds of less than 1 km/h indicate that the

vehicle is stationary was found to produce reliable estimates of stopped time. Figure 14

indicates that the difference in the percentage of observed stopped times between TTDAS

and GPS was only 1%. This result has been consistently repeated in other GPS travel time

runs. It suggests that the GPS technique has significant potential for use in travel time,

delay and congestion studies, given its low cost and versatility.

DATA FORMAT PROTOCOLS

Present day GPS can provide a user with large amounts of data, at rates of up to

10 readings/s, with a rate of 1 reading/s being most common. If these data are not

managed properly or if the application that the GPS is being used for does not require

update rates of this rapidity then GPS could be seen as contributing to information

overload. A number of standard data protocols for GPS are available, and the choice of

the most desirable protocol for a given application must be considered, for it provides one

means to counter information overload. One of the most common data format protocols

is the NMEA (National Marine Electronics Association) standard protocol. It is primarily

designed for marine instrumentation, on the basis of communications using an ASCII

sentence library. The ASCII sentences can be transmitted from the receiver sequentially at

rates of up to l/s. In a vehicle tracking context only two sentences are required. The first is

the GGA sentence which gives the most comprehensive information for a GPS fix within


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the standard. It includes variables such as universal time, latitude, longitude, a quality

indicator which determines what type of GPS fix is being used, the number of satellites

(NSAT) used in the fix, the PDOP, the altitude, the geoid ellipsoid separation, and the age

of the differential correction (if applicable). The second is the VTG sentence which gives

direction of travel and speed indicators. This type of data protocol was successfully used

during the travel time surveys mentioned above, for it can provide a large amount of data

frequently (Zito and Taylor, 1994). This protocol is preferred when an analysis is required

on parts of the journey as well as the journey as a whole, as is the case in traffic systems

surveys.

A variation to the NMEA standard is the TAIP (Trimble Standard ASCII Protocol)

protocol. This is configured such that it will only output GPS data when the user sends the

receiver a specific string a specifying what data are required. A useful application of this

type of communication protocol would be in fleet monitoring applications, where the user

is not interested in knowing where the vehicle is continually, but rather what is the status

of the fleet when a new service is required, so that the most appropriate vehicle to perform

the service may be selected. This type of protocol greatly reduces the amount of data

being processed by the control station and so helps to reduce possible information over-

load in, say, fleet management, despatching and scheduling applications.

REAL-TIME IVHS AND GPS

GPS has the ability to collect and store large amounts of useful data. If these data could

be used in real-time then a number of applications in IVHS would be opened up. If data

such as percentage stopped time and speed of a probe vehicle circulating in a network

could be known in real-time, which is eminently feasible given a good communication

system, then assessments can be made as to where congestion levels are highest. This

information could then be relayed to the public as part of a traffic user information system

(e.g. Koutsopoulos and Xu, 1993; Rilett and Van Aerde, 1993; Collier, 1993) for instance

providing drivers with warnings to avoid areas where congestion delays were expected.

How this information could be relayed would depend on the authority that had control

over it. Methods for IVHS applications include the use of advance warning signs and

electronic billboards along highways to display this information, and radio stations

devoted to providing the public with details of current traffic situations.

Public transport could also greatly benefit from GPS. Commuters could be informed of

the likely arrival time of the next buses and also notified of any delays or deviations from

schedules that may have been encountered. If the whole or a major proportion of the

public transport fleet were equipped with GPS then real-time information of traffic con-

gestion could also be obtained. Perhaps this might reduce the need for dedicated probe

vehicles to be used, which might be seen as contributing to congestion themselves. If

government agencies were to fit out their vehicles with GPS then this would be another

source where a transport department could obtain real-time information about congestion

levels. Equipping government vehicles and taxis with transponders for terrestrially-based

location systems has already been undertaken in some places, for instance the ANTTS

system in Sydney (Longfoot and Quail, 1990) has seen road transport agency vehicles and

metropolitan taxis so equipped. In Houston, Texas, a pool of volunteer drivers have had

their vehicles equipped with transponders which then provide information back to a

central controller (Levine and McCasland, 1994). The major issue with this approach lies

in the way to tap and manage the available data so that sense can be made from it.

Understandably, a large fleet equipped with GPS which is constantly sending information

back to a control station is likely to overload the control station. There appears to be

good sense in avoiding the presence of a large number of special-duty probe vehicles,

especially in congested areas if those vehicles themselves contribute to the congestion. On

the other hand if the fleet equipped with GPS is so small that a representative picture of

congestion levels cannot be obtained then the quality of information being obtained must

be suspect. Methods such as aggregating data and selecting only appropriate yet sufficient


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Zito et al.

data are issues to be addressed in the development of advanced traffic control and driver

information systems. Research is also needed on specific issues such as optimum popula-

tion size and distribution for probe vehicles in a network, to obtain satisfactory coverage

whilst not unduly affecting congestion levels or overloading the monitoring system, and

on the combination of vehicle location methods (e.g. GPS in combination with dead-

reckoning or terrestrial location systems) most appropriate for comprehensive coverage in

an urban area.

CONCLUSIONS

Methods for automatic vehicle monitoring (AVM) form an integral component of

IVHS technology, with many IVHS applications requiring information on the real-time

location of vehicles. GPS offers one readily available method, of low cost to users who

need only provide suitable receivers to use the GPS satellite system. The question has

been, just how useful and accurate is GPS information, especially for mobile applications?

This paper has presented some basic findings about the use of GPS in IVHS and other

road traffic-related areas, and has described the results of some extensive field trials,

conducted under a variety of conditions, that suggest that useful traffic and travel data

can be taken from GPS, and that the commonly available GPS information also contains

information on the quality of the received information.

The following conclusions may be drawn from the experimental program described

here:

(a) GPS can provide useful real-time data on vehicle position and speed, provided that

account is taken of the quality of the signals received in judging the usefulness of the

observed data. In this regard, particular attention needs to be given to those obser-

vations which are taken immediately following a change in the number of satellites

available or the constellation of satellites in view. Significant errors can occur in

such observations;

(b) the choice of GPS receiver capability is important in vehicle monitoring appli-

cations, and preference should be given to those GPS receivers expressly designed

for mobile use. These receivers are not necessarily the most expensive available.

The important feature for a suitable GPS receiver is its ability to provide direct

measurements of the speeds;

(c) GPS direct speed measurements should always be used in preference to speeds

calculated on the basis of vehicle positions over time. Such calculations are likely to

be error prone, whereas the direct speeds correspond closely with those observed

using on-board vehicle instrumentation;

(d) the errors from GPS are dependent on the physical environment (topography and

built form) of the region in which a probe vehicle is operating. Errors increase

in dense urban areas e.g. downtown areas, compared with other environments.

Suburban and rural areas provide the minimum errors. The effects of physical

environment lie mainly in the restrictions that will be placed on the number of

satellites visible to the GPS receiver;

(e) there is evidence that errors in direct speed measurements can be related to GPS

performance parameters (e.g. number of satellites and PDOP) that are provided as

part of the standard GPS information strings. Further work is required to firmly

establish the possible relationships and to test the relative value of alternative signal

quality parameters, but at this stage the occurrence of sudden changes in PDOP and

NSAT may be taken as indications of possible errors in position and speed values at

that point in time. These errors will then dampen down in successive (second by

second) observations.

The general conclusion is thus that GPS has much to offer as a vehicle identification

and monitoring tool for IVHS applications. As described in this paper, the GPS system

has the capability to provide large amounts of individual time, position and speed data at


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rapid update rates. A key issue is thus one of data management, so that a steady string of

reliable and useful information on vehicle position and progress can be obtained. One

promising example shown in the paper is the use of GIS as a display and analysis tool for

the GPS data. Without such tools, GPS might well do little more than add to information

overload. Data management is the key to transforming good data into useful information.

To obtain reliable GPS data several considerations must be taken into account. Firstly

the minimum degree of accuracy needed by the application must be known. Is centimetre

accuracy (as in a land surveying application) required, or is f 5 or * 50 m satisfactory?

Are the data required in real-time, or can they be post-processed? These are some of the

questions to be considered when considering a potential GPS application. The answers to

these questions help to determine the necessary features of suitable GPS receivers for the

application, such as the number of input channels, need for inbuilt memory, and the kind

of GPS processing software. There are also a number of features common to all GPS

receivers: as clearly demonstrated by the field studies both speed and position data can be

degraded in unfavourable physical environments (e.g. the concrete canyons of a CBD).

Then the use of the signal quality indicators should be considered so that assessments can

be made of the reliability of the incoming GPS data.

This area of GPS reliability demands further research at present. GPS is based on “line

of sight” principles. If there is not a clear view to the sky (e.g. in a tunnel or under a

bridge) then readings are not possible. When the view to the sky is restricted, inferior

signals may result in less accurate data observation. Other systems such as “dead

reckoning”, or simply using the last bearing and speed value to calculate a new position,

may have to be used until the GPS position is re-established. The experiences gained in the

Adelaide field trials suggest that these lost signal durations will only be of the order of

seconds under normal operating conditions. Of course, it must also be noted that alter-

native technologies may not be capable of providing the wealth of information available

from GPS at anything like the same update rates. GPS stands ready as a valuable tool for

IVHS applications. given adequate attention to its possible shortcomings.

REFERENCES

Collier C. (1993) An information network for route guidance and travel guidance systems.

Par@ Rim TransTech

Confiwncr~ Volume I. Advanced Technologies. Third 1n1. Co@ on Applications qf Advanced Technologies in

Traruporration Engineering

(Hendrickson C. and Sinha K., Eds), pp.

265-271.

ASCE. New York.

Dailey D. J, Haselkorn M. P. and Lin P. (1993) Traffic information and management in a geographically dis-

tributed computing environment.

Pacific Rim TransTech Conference- Volume I: Advanced Technologies.

Third Ini. Conf. on Applications of’ Advanced Technologies in Transporration Engineering

(Hendrickson C. and

Sinha K.. Eds), pp. 159 165. ASCE, New York.

Drane C. (1992) Positioning Systems: u Un ed Approach. Springer-Verlag, Berlin.

Koutsopoulos H. N. and Xu H. (1993) An information discounting routing strategy for advanced traveller

information systems,

Transpn. Res.-C. 1, 249-264.

Levine S. Z. and McCasland W. R. (1994) Monitoring freeway traffic conditions with automatic vehicle identi-

fication systems.

ZTE Ji 64, 23 .28.

Longfoot J. E. and Quail D. J. (1990) Automatic network travel time measuring system (ANTTS).

Pro

Documents

Gps in Time Domain