The identification of mistakes in road accident records: Part 1, locational variables

Pergamon Accid. Anal. and Rev., Vol. 27, No. 2, pp. 261-276, 1995

Copyright 0 1995 Elsevier Science Ltd Printed in the USA. All rights reserved

OCO14575/95 $6.00 + .OO

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THE IDENTIFICATION OF MISTAKES IN ROAD ACCIDENT RECORDS: PART 1,

LOCATIONAL VARIABLES

KEVIN AUSTIN*

Institute for Transport Studies, University of Leeds, Leeds, LS2 9JT United Kingdom

(Accepted 6 June 1994)

Abstract-The current method of checking police-reported road accident data involves a rigorous process of manual and computer validation, with the objective of removing all the errors that exist on the accident report forms. This paper shows how a geographic information system (GIS) can be used to identify mistakes that exist in several locational variables once this process has been undertaken. It compares items contained on the accident report form with accurate highway feature information obtained from other sources. There were less than 10% of mistakes for the variables of district, speed limit, road class, and road number; less than 20% for junction control, junction detail, and pedestrian crossing facilities; and over 20% for carriageway type. If highway data were routinely entered onto a GIS by all British highway authorities, the above variables might not need to be entered by the police on their accident report forms.

Keywords-Geographic information system, Mistakes, Road accident, Highway inventory data

1. INTRODUCTION

The ultimate objective of road safety engineering and education is to provide for the safe movement of people and goods throughout the highway network. The information from personal injury accidents collected by the police is used to identify locations and population groups that have higher levels of accidents than the norm. To be confident that the correct remedial schemes are undertaken, the accident data need to be of good quality, that is, the items are recorded correctly with few missing values. If this is not the case, then assessments of safety problems relating to certain highway features or locations could be inaccurate, leading to an inefficient alloca- tion of resources.

Several investigations have been undertaken to assess the validity of accident records. In the United States, Shinar, Trent, and McDonald (1983) compared the information on 124 police records with that collected by multi-disciplinary accident investigation (MDAI) teams. They found the most inaccurate highway feature data were gradient, speed limit, surface composition, and curvature. In South Aus-

*The author’s current address is Halcrow Fox and Associ- ates Ltd., Vineyard House, 44 Brook Green, London W6 7BY, United Kingdom.

261

tralia, Howard, Young, and Ellis (1979) found that controls upon the road, intersection type, traffic conditions, and gradient were the variables most often incorrectly recorded. Questionnaire studies of U.K. local authorities (Ibrahim and Silcock 1992; Austin 1993), though, have shown the location of the accident to be the most inaccurately defined variable.

The mistakes made by the police must be expected given the situation they face at the scene of a road traffic accident, but provided a validation system can identify all of them, then these mistakes will not affect the overall quality of the data, although any that remain can have a substantial impact on safety investigation. Those accidents that are coded incorrectly will alter the total number of accidents relating to a certain feature, whilst those that are wrongly located will alter the number of accidents at certain sites, reducing the validity of site- investigation studies.

A comprehensive computerised safety recordkeeping system (Chatfield et al. 1985) has been suggested (which includes linking accident and highway inventory files) as a way of identifying and moni- toring the high accident frequency sites and features. The adoption of this system to identify mistakes in the accident records was not proposed, possibly because the non-spatially represented data files

262 K. AUSTIN

make it difficult to identify errors. The utilization of a geographic information system (GIS) should resolve this problem, and the objective of this paper is to show how it can be used to identify mistakes in several locational variables that remain after the current validation process has been completed. A GIS is a computer program that identifies the location of an object and provides information about it, enabling spatial and statistical analyses to be performed. This project used PC ARC/INFO (Environ- mental Systems Research Institute 1990), which is the most popular GIS used by local authorities in Great Britain with a market share of around 22%, almost double that of the next most popular package, produced by Alper Systems (Campbell and Masser 1991). Digitised road centre-lines were used to repre- sent the map information in this study and consisted of Ordnance Survey Centre-line Alignment of Roads (OSCAR) data for a five-kilometre square of north Hull. Between 1987 and 1991 a total of 1,884 accidents occurred within this area. Certain items coded onto the accident records were compared with the same items taken from road network data, so that mistakes in the coding of these items and in the locating of accidents could be identified.

2. DATA COLLECTION AND VALIDATION PROCEDURE

In the United Kingdom, the responsibility for collecting road-accident data lies with the police. Information is collected either at the scene of an accident, or later, when an involved party reports the incident at a police station. There are no specific personnel whose duty it is to collect this information, and so all uniformed officers may be called upon to undertake this task. The Road Traffic Act (Depart- ment of Transport 1988) makes certain stipulations upon drivers of motor vehicles involved in road accidents, stating that:

If any other person is injured . . . you must report the accident to the police as soon as possible, and in any case within 24 hours.

For these accidents, the Department of Trans- port requires a form (STATS 19) to be completed (see Appendix A). This form is divided into the sections of Accident Circumstances, Drivers and Vehi- cles, and Casualties. The information contained within the Accident Circumstances section includes highway features, environmental conditions, and time-related data. The Drivers and Vehicles section relates primarily to the vehicle movement and crash impact, although summary details of driver age, gen-

der, and alcohol impairment are included. The casu- alty section is predominately concerned with the movement of any pedestrians involved or the position on or inside the vehicle of other injured parties. Only one value can be entered for each item, which can be limiting if two features are present at a site, for example traffic signals and a give-way. However, the increase in complexity of coding and transcribing the data could not be warranted, given this relatively rare occurrence. The police also collect information concerning the clear language descriptions of the accident location and the accident event, although they are not included on the STATS 19 form.

The data entry and validation process described in this section relates to the county of Humberside, although most highway authorities in the United Kingdom maintain a similar system. The records are first transcribed onto a computer database by the police data-processing team, and mistakes are identified by a manual check. These records are then validated using a computer program (STATS 21), which checks the consistency of the data. This program is composed of both intervariable tests (for example, if the Carriageway type variable is entered as a roundabout then the Junction Detail variable must be entered either as a roundabout or a mini- roundabout) and range checks (for example, the carriageway type code must be between 0 and 9). Any records that do not meet the specified criteria are flagged and subsequently manually corrected.

The data are sent to the highway authority who code the location of the accident from the clear language description. This is characterised by a five- figure grid reference, which pinpoints the accident to within 10 metres of the initial point of impact. This type of locational positioning can sometimes present problems when intersections are in proxim- ity, since it is difficult to identify the number of accidents occurring at each. To resolve this problem, some authorities have developed link-node systems whereby each accident is coded with a unique intersection number. A second manual check is undertaken and the STATS 21 program is rerun to check the variables that have been altered. The data are then sent to the Department of Transport, who compile the national accident statistics (Department of Transport 1993).

The police are also informed of many damage- only accidents, and these are recorded in a separate database managed by the police. In Humberside, they are rarely used for accident investigation and so no grid references are assigned, hence the accuracy of the locational variables relating to these records could not be checked. In some authorities grid references are assigned to these accidents, so the

Mistakes in accident records: locational variables 263

validation procedure could be undertaken for these data.

It is inevitable that mistakes will be made, because the police have many duties to perform at the scene of an accident. For accidents that the police do not attend, but are notified of by an involved party, mistakes are even more likely because most individuals are not trained to collect this information. The mistakes in the STATS 19 will not affect the quality of data, provided a system is available that can identify them. The following section ex- plains an improved system that quantifies the number of mistakes remaining for certain location-based items after the current validation procedure has been undertaken.

3. DEVELOPMENT OF A NEW VALIDATION SYSTEM

3.1 Data sources This section describes the process required to

identify mistakes in the accident report form using a GIS. The locational variables investigated consisted Of:

1. Road class; 2. road number; 3. district; 4. speed limit ; 5. Pedestrian crossing facilities; 6. junction control; 7. junction detail; and 8. Carriageway type and markings.

The different items that can be coded for each of these variables are stated on the STATS 19 form (see Appendixes A and B). The correct< type of feature at each location was obtained from a number of sources. Ordnance Survey maps provided information on road class, road number, and the district boundaries, whilst the location of speed-limit signs, pedestrian-crossing facilities, and junction-control type were obtained from paper maps within the Acci- dent Investigation Section of Humberside County Council. Information on junction detail and carriageway type were also obtained from Ordnance Survey maps, although a field survey was required to check their accuracy. The information contained in these sources was likely to be more accurate than that from the police, because it is taken directly from maps and a more detailed investigation was undertaken to obtain the data. The coding of several items, such as whether an accident occurred at a T or Y junction is open to some interpretation. In this case, only one individual coded the network, so at least

Road number Road class Speed limit Carriageway type Junction detail Junction control

1 Split zones1 I I

, boukHT\

A Code

Fig. 1. The process to create a new highway feature file.

there would be consistency in the recording of these items.

3.2 Coding the highway features Alternative procedures to provide the coded in-

formation were required because of differences in the nature of the data used, and this is highlighted in Fig. 1. The OSCAR data contain digitised links and nodes, so forjunction detail and junction control all nodes relating to junctions were selected and saved to separate files as points, whilst for road class and number, speed limit, and carriageway type all roads were selected and saved to separate files as links. The location of pedestrian-crossing facilities were manually plotted into the GIS (using the digital map as a guide) and stored as separate points.

For each variable the appropriate file was selected and the computer drew a boundary around all the respective links or points. This was necessary because the use of five-figure grid references to lo- cate each accident means that they do not necessarily correspond to the road centre lines or junction nodes of the digital map. All accidents falling within this zone could then be considered to be associated with that feature. For the variables of road class, road number, junction detail, and junction control the radius of the zone was 24 m. This was based on:

264 K. AUSTIN

1. the average distance between the centre-line and the edge of the carriageway (3.6 m);

2. the maximum error in the Ordnance Survey maps (0.4 m);

3. the maximum distance from the edge of the junction for which an accident is coded as at the junction (20 m).

The distance of 24 m was also used for thek variables of carriageway type and speed limit to retain consistency in the investigation method. The Department of Transport specifies that any accident located within 50 m of a pedestrian-crossing facility should have the crossing type coded, even if a pedestrian was not involved in the accident. The radius of the zone around each facility was therefore set at 50 m. This included some roads that were not associated with the road where the facility is located, for example, those that were parallel to it. The sphere of influence was therefore nar- rowed to include only those accidents that occurred within 24 m of the centre line of that road. These polygon-based coverages were then saved to separate files, one for each variable.

With the existence of such a wide boundary, i.e. 24 m, some of the carriageway-type and speed-limit codes could be shown as incorrect even though this was not the case. This would occur when accidents were located on roads that existed within the boundary derived for another road. If a link-node system was adopted, which could automatically snap the accident to the intersection (if the accident was less than 20 m from it) or to the road centre line, a wide zone-boundary would not be needed. Unfortunately, the local authority who partici- pated in this survey had not adopted a link- node system and so a wide zone-boundary was required.

The next stage was to split these polygons at the relevant points. For road class, road number, speed limit, and carriageway type, the zones were divided at the changeover point from one feature to another; although if this was at a junction, the zone was split 24 m from the center of the junction along the joining road. Some of the junctions or pedestrian-crossing facilities were sufficiently close to one another for the zones to merge, so the boundaries were split halfway between the two features. This was undertaken so that any accident located within this zone would be compared with just the code of the nearest junction or pedestrian- crossing facility. The district boundaries were

digitised into the computer as polygons, and no other spatial manipulation was required.

For each variable, the zones were selected and coded with the respective numeric value following the criteria stated on the STATS 19 form, for example, the zone representing an A class road was coded as 3. The areas outside the boundaries were coded as zero. The zone boundaries disaggregated by speed limit for the study area are shown in Fig. 2, and the width of these zones corresponds to the distance of 24 m from the centre lines as explained above.

3.3 Identifying mistakes in the accident variables A flow diagram of the procedure to identify mis-

takes in the accident variables is shown in Fig. 3. The accidents to be validated were selected and saved to a file. They were then located onto a digital map using the grid references stated on the accident record. The point-in-polygon command was used to link the accident and highway feature data so that mistakes in the STATS 19 records could be identified. A graphical representation of how this was achieved is shown in Fig. 4. The accident coverage shows the location of all accidents, each specified with a unique identity number, which is used to relate to the attribute table containing the STATS 19 information. The highway feature boundaries also contain identity numbers so that speed limit from the highway-feature data can be accessed. The point- in-polygon process was undertaken to overlay the points from the accident coverage onto the polygons containing the highway-feature data. Information concerning the polygon that encloses each point was then added to the attribute table of the accident coverage.

An enquiry was undertaken to identify those accidents whose coding taken from the highway data was different from that on the accident record. The example in Fig. 4 shows that for the accident with identity number 1, an identical speed limit was recorded on the highway feature and STATS 19 records and so would not be identified as incorrect. The accident with identity number 2 had a different STATS 19 speed-limit code to that revealed by the highway-feature data. This could have been because the speed limit was miscoded by the police or because it was mislocated outside the 30 mph zone. The accident with identity number 3 was located outside a road boundary, so the speed limit recorded in the highway feature file was equal to zero, hence the accident was mislocated. The same procedure was adopted to identify discrepancies in the coding of the other variables, although a more complex enquiry was needed to identify mistakes in road class


rj 30 mph n 60 mph

m 40 mph n 70 mph * N Fig. 2. A representation of the zone boundaries distributed by speed limit.

and number. The reason was that for accidents that occur at junctions, the class and number of the road where the accident occurred and the road that it joins are recorded as separate fields. Hence, if an accident was located within the boundary of a classified road it would be identified as incorrect only if the highway feature code was different to the STATS 19 class or number in both fields.

For each variable investigated, a locational plot (see Fig. 5) and a table (see Table 1) were produced, which included all accidents identified as incorrect. The table contains the accident details including its reference number, the coding of the feature (from

the STATS 19 record and from the highway feature data), and a description of the location (used to de- termine whether the accident was located correctly). The grid references of those accidents that were incorrectly located were adjusted, whilst an incorrect value for an investigated variable was altered to that of the highway-feature data.

3.4 Costs incurred to develop this system The costs involved in developing such a system

include those of buying the hardware and software required and maintaining the data. Hardware costs are rapidly falling, so adequate computer facilities

266 K. AUSTIN

highway feature data

Fig. 3. The process of identifying incorrectly coded or located accidents.

could be provided at little over f2000. The cost of GIS software ranges from approximately 22500 to f10,OOO depending on the level of sophistication. Because this system used the more basic GIS tech- niques, one of the less expensive packages was adequate. The highway-feature data items have to be located and coded manually, which is a time-con- suming and costly process for a large administrative area. However, most authorities in the United King- dom collect highway inventory data. This requires that teams of engineers travel around the road network to code into a computer the location and type of all highway features, including road type, signs, lines, and traffic-control devices. The location of these items could be automatically placed as points over the digital map, which removes the need for additional site visits. The codes for these points were also included, which reduces the amount of manual manipulation to identify and code the items. Unfor- tunately, the highway-inventory data for the area used in this study are stored in a link-node format and were not compatible with the GIS used without an additional piece of software, which could not be made available. However, this type of integration is possible and will mean that this type of application is readily worthwhile. Once the data are contained within the system, the costs of updating the informa-

tion will be minimal. For example, it is unlikely that many more than a dozen new pedestrian crossing facilities would be installed in an average-sized U.K. local authority in a year, and since it would take only a minute or so to enter the data for each facility, these tasks will be performed inexpensively. The cost of adopting such a system in the long run is therefore likely to be affordable to most U.K. local authorities.

4. RESULTS

The mistakes were divided into two categories; those for which the locational text did not correspond to the grid reference position (referred to as mislocated) and those for which the locational text corresponded to the grid location but for which there was a difference in the coding of the item between the STATS 19 records and the highway feature data (referred to as miscoded). For each of the variables the number of accidents that were miscoded and mislocated are shown in Table 2.

The mistakes were less than 10% for the variables of district, speed limit, road class, and road number; less than 20% forjunction control, junction detail, and pedestrian crossing facilities; and over 20% for carriageway type. For all variables, the accidents that were mislocated were randomly distributed throughout the network, but were usually close to the road where they should have been located. This indicates that the mistakes probably arose during the selection of the 10-m grid box in which the accident should have been located. Conversely, those accidents that were miscoded were overrepre- sented in several feature types. The following sections contain a more detailed analysis of the results.

4.1 Road class Of those accidents that were miscoded (see

Table 3), 22 (81.5%) were coded as unclassified and located on the B-class road. This total represents 14.1% of the 156 accidents that actually occurred on this B road and indicates that in many instances the police considered that this road was unclassified.

4.2 Road number There was a total of 82 accidents identified as

incorrect, of which 78 were also identified when the road class variable was checked. In these instances, both road class and road number were incorrectly recorded. This is so because the existing process accepts as correct accidents that were coded as unclassified and containing no road number or those that were coded as classified and containing


MAP PLOT ATtRIBUTE TABLE

Accident coverage

L I I

Highway feature coverage

Merged coverage of accidents and highway feature data

Fig. 4. A graphical representation of the point-in-polygon command.

a road number, even if they were incorrect. The accidents that were miscoded are shown in Table 4, and the same explanations apply to this variable as to road class. For the four additional accidents that were incorrect, road class had been coded correctly, but the number did not relate to the road where the accident took place.

4.3 District In total, 17 accidents were identified as incor-

rect and all were miscoded. One of these was located only 2 m from the boundary, and, because of possible errors in the drawing of the district boundaries, one could not be certain that this was incorrect. Only 1 out of the 45 accidents located less than 100 m from the district boundary was coded incorrectly. This figure may be low because large signs have been erected at the boundaries stating the change from one district to another. Most of the mistakes

occurred at locations more than 300 m from the boundary points where the signs would not be visi- ble. These large signs are rarely erected in the rural areas, which may lead to a greater number of mistakes in those regions.

4.4 Speed limit @ The GIS validation system identified 131 acci-

dents as being incorrectly recorded, but after comparison with the locational text, three accidents that occurred on roads subject to a 30-mph speed limit were inside the 40 mph speed-limit boundary and so were correctly coded. Table 5 shows that four of these did not have a standard speed and so an extra enquiry using the current nongeographic system should be included to identify them. This would ac- cept speed limits of 20, 30, 40, 50, 60, and 70 mph only.

The majority of discrepancies (73.5%) were be-

268 K. AUSTIN

7% Accident with STATS 19 and highway data

different for speed limit

Fig. 5. The location of accidents with speed limit identified as incorrect.

Table 1. A sample printout of details from accidents with speed limit identified as incorrect

Accident reference number ST’ATS 19 speed Highway feature speed

limit limit Location

96887 30 40

16988 39590 60

9591 30 159696 30 113591 30 143290 30 47290 40

125187 30 61090 30

40 30

40

40 0

40 0

30

0 0

Fairfax Ave/Bricknell Ave XRDS, Hull A1079 Beverley RdlSculcoates

La/Queens Rd Al 174 About 200 m north of Dunswell

Rbout B1233 Cottingham Rd/Hall Rd/Hotham Rd Thwaite St 50m East JW The Paddock Hull Rd JW Bricknell Ave Cottingham Rd JW Hardy St Cottingham Rd 100 yd west JW Newland

Ave A1079 Beverley Rd JW Pearson Ave, hull A1079 Beverlev Rd JW Grove St. hull


Table 2. The number of accidents miscoded and mislocated for Table 4. A comparison in the coding of road number between several variables on the STATS 19 record STATS 19 records and highway feature data

Variable Number Number Percent miscoded

miscoded mislocated or mislocated

Road class 27 51 4.1 Road number 31 51 4.4 Speed limit 83 45 6.8 District 16 0 0.8 Pedestrian facilities 282 46 15.3 Carriageway type 346 48 20.9 Junction detail 115 121 12.5 Junction control 127 121 13.2

tween 30 and 40 mph roads, which was to be expected given that 97.8% of accidents were subject to a speed limit within this range. The data recorded in Table 5 reveal that the proportion of coding mistakes for accidents subject to 60- and ‘IO-mph speed limits was significantly higher than that for 30- and 40-mph roads (x2 = 85.4 with 1 df, p < .Ol). This indicates the difficulties in coding this variable in the peripheral sections of the urban areas where the speed limit may not necessarily relate to the surrounding land use that usually dictates it.

4.5 Pedestrian crossing facilities A comparison of the coding between the STATS

19 records and the highway feature data is shown in Table 6. The location of refuges were not included in this study because the installation dates were not obtainable. The validation procedure identified 3.2 times as many accidents located within the 50-m boundary but coded as outside than those located outside the 50-m boundary and coded as inside. This bias leads to pedestrian crossing facilities being shown to be safer than they actually are. To correct this, the number of accidents that occurred within 50 m of a pedestrian-crossing facility should increase by 37.9%-this ranges from 11.6% at zebra crossings to 350% at subways. The level of coding for subways was lower probably because they are not at grade and so would be difficult to identify.

Table 3. A comparison in the coding of road class between the STATS 19 records and highway feature data

Highway’ feature road class

STATS 19 road class

A B Unclassified

A B

500 - - 134

5 22

Unclassified

-

1223

Highway feature road number

STATS 19 road number 1079 1165 1174 1233 None

1079 422 1165 - 67 z 1 z 1174 1 - 9 1233 w z None -2 5 q 22 1221 Other 1 - - - 2

An additional 23 accidents were located within 50 m “other light-controlled crossing” (i.e., signals) but were coded as near a refuge. At these sites, the refuges were used to stagger the pedestrian crossings, although the item should be coded as signals since refuges would be coded only if no other controls are present. Thirty accidents that were actually located within 50 m of a signal were recorded as occurring within 50 m of a pelican crossing. This type of control operates in a similar way to a pelican, although pelican crossings would be located away from junctions.

4.6 Junction detail Private drives joining the highway and other

junctions (such as alleyways) were not coded onto the map, so accidents occurring at these types of junction were not checked for mistakes. Of the 121 accidents that were mislocated, 85.5% were coded as 20 m or less from a junction but were located outside this boundary. This may be lower than the true number of mislocated accidents because if the clear language description was not specific to a location then the accident was considered to be miscoded. This was especially the case for nonjunction accidents, as only one road name is usually included. Table 7 compares the coding of junction detail between the STATS 19 records and the highway feature data and shows that there were over three times as many accidents located within the junction

Table 5. A comparison in the coding of speed limit between STATS 19 records and highway feature data

Highway feature speed limit

STATS 19 speed limit 30 40 60 70

30 1677 3 40 20 2 z 60 2 1 ; - 70 - 1 3 - Other 4 - - -

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Table 6. A comparison in the coding of pedestrian crossing facilities between STATS 19 records and highway feature data

Highway feature pedestrian crossing facilities

STATS 19 pedestrian crossing facilities Zebra Pelican Signal Subway Refuge Outside

Zebra 60 us

- - - 10 Pelican - 30 - - 36 Signal - - 42 - 7 Subway - - - J 1 Refuge - Outside 18 G

23 - 17 - 54 22 - 1349

boundary but coded as “not at a junction” than vice versa. As a result of this bias, the number ofjunction accidents is underestimated by 3%.

T-junctions accounted for 63.5% of the miscoded accidents, which was to be expected because 65.7% of junction accidents occurred at this type of facility. The same comparison can be made for crossroads; this represents 22.4% of junction accidents and 19.1% of those that were miscoded.

There were 44 accidents recorded as occurring at a junction but carrying the incorrect junction code; 66.3% of these codes were discrepancies between crossroads and T-junctions. This highlights the difficulty in distinguishing between these types of junction.

4.7 Junction control The accuracy of the coding of uncontrolled

junctions was not investigated because the majority of accidents (66.9%) occurred at the junction of private drives and the highway or at “otherjunctions.” Table 8 compares the coding of junction control between the STATS 19 records and highway feature data and reveals that all the 71 accidents that were incorrectly coded as inside or outside the function boundary were identified in the junction-detail section.

In addition, 49 accidents coded as “give-way” were within the boundary of traffic signals. Both “give-way” and traffic signals existed at these sites, so all of these accidents must relate to signals as well as to give-ways (except possibly for rear-end shunts on the give-way leg). The manual of the De- partment of Transport (1994) gives no guidance as to which type of junction control should be coded in these circumstances, although, as traffic signals are the most dominant control type, it is probably appropriate to use this value.

4.8 Carriageway type markings A total of 402 accidents were identified as incor-

rect for this variable, although eight of these were correctly coded but located inside the boundary of another carriageway feature. A comparison of the coding between the STATS 19 records and the highway feature data is shown in Table 9 and revealed that the items most often miscoded were dual two- lane, single three-lane, and single four or more lanes. This was so because of difficulties in coding the carriageway type at junctions with turning lanes and along roads with bus lanes.

Forty-three miscoded accidents were located at junctions where the number of lanes were increased to facilitate turning traffic; these included (a) 14 acci-

Table 7. A comparison in the coding of junction detail between STATS 19 records and highway feature data

Highway feature junction detail

Not within STATS 19 junction detail 20 metres

Not within 20 metres 543 Roundabout 1 Mini-roundabout -

T 13 Y -

Slip road -

Crossroads 3 Multiple -

Private drive -

Other -

Mini- Slip Private Roundabout roundabout T Y road Crossroads Multiple drive Other

- 46 - - 8 - - - 69 - - -

1 : _‘zz 1 _ - - - - 756 1 - 11 - - - - - 2s - 3 --- - - I-_ - - - -

- - 17 - - 274 - - - - - - 3-_ - - -

- - --- - - 109 - - 3-_ - - - 9


Table 8. A comparison in the coding ofjunction control between STATS 19 records and highway feature data

Highway feature junction control STATS 19 junction control Give-way Stop Signal Uncontrolled Outside

Give-way 876 - 49 - 13 stop 3-- - Signal 4 - 195 - 4 Uncontrolled - - 142 Outside 52 - -2 - 543

dents coded as single three-lane when the general carriageway type was single two-lane; (b) 13 accidents coded as single four-lane when the general carriageway type was single two-lane; (c) 16 accidents coded as dual three-or-more-lanes each way when the general carriageway type was dual two- lanes each way.

The manual of the Department of Transport (1994) gives no guidance as to whether this item should be coded to include these lanes or not. How- ever, the general carriageway type would be more useful for analysis purposes.

Of the 346 accidents that were miscoded, 192 occurred along a 2.75km stretch of road. This road included three- and four-lane single carriageway in- corporating a bus lane and a section of two-lane single carriageway road without a bus lane due to road width limitations. Only 29.8% of accidents along this stretch of road were coded correctly and the accuracy for each section type was (a) 58.2% for single four-lane; (b) 82.6% for single two-lane; (c) 1.4% for single three lane with a 5-m lane in one direction and two 2.5-m lanes in the opposite direction (one of which was a bus lane).

These results indicate that bus lanes were gener- ally not considered as separate lanes in carriageway type. They should be included because vehicles use these lanes at all times, even though some classes of traffic are prohibited during certain periods.

A further study to investigate the accuracy of the Carriageway Type variable on roads with bus lanes would be useful to ascertain whether the results of this study are unique. Clarification should also be given as to whether turning lanes at junctions should be added to the standard number of lanes.

CONCLUSIONS

This GIS-based validation system has verified mistakes in the coding and locating of accidents not identified by the current system. For the variables of Road Class, Road Number, Speed Limit, and District the level of mistakes were less than 10%. This figure is low probably because the features are unambiguous and are recognised by people with some local knowledge. The level of mistakes for Pedestrian-Crossing facilities, Junction Detail, and Junction Control were between 10% and 20%. All require the estimation of distance, so it is probably inevitable that a greater number of mistakes will be made. Carriageway Type was the variable most often inaccurately coded, mainly due to uncertainty as to whether bus lanes should be coded as additional lanes. This may be a peculiarity of the sample, and a larger study should be undertaken to identify whether this phenomenon is unique.

If all highway authorities adopt this GIS-based system, the variables discussed in this paper could be removed from the police form altogether as they would be added to the computer database automatically once an accident has been placed onto the digital map. This means that over 2.1 million fewer items of data would need to be collected nationally by the police. This estimate is obtained by multi- plying the number of reported accidents occurring in Britain in 1992 (233,025) by the eight variables tested in this system and adding this to the number of junction accidents (143,161) multiplied by 2 (to account for the extra codes of road class and number

Table 9. A comparison in the coding of carriageway type or markings between STATS 19 records and highway feature data

Highway feature carriageway type or markings

STATS 19 carriageway Dual 2 Dual 3 or Single 2 Single 3 Single 4 or type or markings Roundabout One-way lanes more lanes Single track lanes lanes more lanes

Roundabout 71 3

- - - 3 - - One way - - - - 1 - - Dual 2 lanes - - 154 - - 7 2 15 Dual 3 or more lanes - - 21 - - 2 Single track - - - - 3 3

2 2 -

Single 2 lanes 1 4 11 - - 1127 113 52 Single 3 lanes - - 1 - - 26 25 14 Single 4 or more lanes - - - - - 22 32 155 Unknown - - - - - 10 - 1

272 K. AUSTIN

when an accident occurs at a junction). The annual costs of recording changes in the road network are likely to be minimal and the information could be used for other purposes, so this type of system is likely to be worthwhile.

Acknowledgments-The work undertaken in this project would not have been possible without the help of the Director of Techni- cal Services, Humberside County Council, the Chief Constable of Humberside Police, the Ordnance Survey for the use of the OSCAR road centre-line data and ESRI (U.K.) for the use of ARC/INFO GIS. The author would like to thank Peter Shenherd of Humberside County Council and Miles Tight and Howard Kirby of the University of Leeds for their encouragement and helpful suggestions, which have contributed to the work reported here. This work was undertaken under a U.K. research student- ship awarded by the Science and Engineering Research Council.

REFERENCES

Austin, K. P. The collection and use of additional sources of road safety data in highway authorities. Traffic Engi- neering and Control 34: 540-543; 1993.

Campbell, H.; Masser, D. The impact of GIS on local government in Great Britain. Conference of the Associ- ation of Geographic Information, Birmingham, En- gland, May 12-14, 1991.

Chatfield, B. V. et al. Introduction to comprehensive computerised safety recordkeeping systems (CCRS). Transportation Research Circular 293. Washington, DC: Transportation Research Board, National Re- search Council, 1985.

Department of Transport. Instructions for the completion of road accident reports: STATS 20. London: HMSO; 1994.

Department of Transport. Road accidents Great Britain 1992. London: HMSO; 1993.

Department of Transport. The road traffic act. London: HMSO; 1988.

Environmental Systems Research Institute. PC ARC/ INFO reference manual. Redlands, CA: ESRI: 19%.

Howard, B. V.; Young, M. F.; Ellis, J. P. Appraisal of the existing traffic accident data collection and recording system. South Australia. Report number CR6. Can- berra, Australia: Department of Transport; 1979.

Ibrahim, K. ; Silcock, D. T. The accuracy of accident data. Traffic Engineering and Control 33:492-4%; 1992.

Shinar, D.; Treat, J. R.; and McDonald, S. T. The validity of police reported accident data. Accident Analysis & Prevention 15:175-191; 1983.

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276 K. AUSTIN

APPENDIX B

An Explanation of the Codes for the Variables Used

The definitions of the codes and the methods of coding for the variables investigated are specified by the Depart- ment of Transport (Department of Transport 1994). Some of the items such as “one-way” are self-explanatory and so are not mentioned here.

Road class--This relates to the strategic importance of the road. At junctions, the class of the joining road is also included, although if three or more different roads join at a junction, the two roads with the highest class are entered; whilst if all the roads are of the same class, then the two roads with the lowest road number are included.

Road number-This is the unique identifying code for the road and must be included for all roads that are not unclassified.

District-This refers to the local administrative area, of which there are eight within Humberside and is an additional variable not coded onto the national STATS 19 form.

Speed limit-The coding of this variable relates to the permanent speed limit in miles per hour. Ones that are temporarily in force (due, for example, to road works) are not recorded.

Pedestrian crossing facilities If an accident occurs within 50 metres of a pedestrian

crossing, then the code for that type of facility should be included. Where two facilities are within 50 metres of the accident, the code should relate to the one that is nearest to the accident.

A zebra crossing contains broad alternating black and white lines painted on the road and is marked by a flashing beacon. This allows pedestrians to have prece- dence over vehicles when they step into the carriageway .

Pelican crossings are positioned solely for pedestrians and are controlled by lights, which change to indicate the pedestrian’s right of way once a button has been pressed.

Other light controlled crossings refer to junctions with traffic control devices that include an individual light to enable pedestrians to cross safely and are stated as “signal” in this survey.

School crossing patrols relate to a person exhibiting a “stop-children” sign, and when they step into the car-

riageway, vehicles are required to stop. An authorised person is a policeman or traffic warden in uniform who has statutory power to control the traffic. Both of these are not included in the survey because they are not permanent features of the highway.

A refuge is an island, usually placed in the centre of the road which permits pedestrians to concentrate on crossing one stream of traffic at a time.

Junction detail For this variable, any accident occurring within 20 metres

of a junction should have the code for that type of junction included. If two junctions are within 20 metres of the accident then the code should relate to the junction that is nearest to the accident.

A roundabout is stated as a one-way circulatory system around a central island with entry controlled by “Give Way” markings.

A mini-roundabout operates in the same way as a roundabout but the central island is flush or only slightly raised from the carriageway and has a diameter of less than four metres.

The Tand Yjunctions are three arm intersections differen- tiated by the angle of the approaching road, indicated by the appropriate letter.

Slip roads refer to the merging and diverging lanes for grade separated junctions only.

A crossroads is a junction that contains four arms with an uninterrupted alignment.

A multiple junction contains more than four entry arms. A private drive or entrance joining the highway is only

included if one of the vehicles was using it at the time of the accident and not if the accident occurred within 20 metres of one.

The other category refers to an alleyway or layby, and this is only included if one of the vehicles was using it at the time of the accident.

Junction control-A Give-way requires vehicles to yield to the traffic on the other road, although if no vehicles are present than the driver is not compelled to stop. At junctions with a stop sign, vehicles are required to stop regardless of the traffic conditions on the other road. The authorised person category is not included in the study since it is not a permanent highway feature.

Carriageway type or markings-The roundabout category refers to both the roundabout and mini-roundabout items in the junction detail section. Dual carriageways refer to roads where a central reserve is constructed to segregate opposing flows, whereas single carriageways do not contain this.

The identification of mistakes in road accident records: Part 1, locational variables

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