Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets
Exploring the use of Corporate Data for AusRAP/ANRAM Data Sets
ABN 68 004 620 651
Victoria 80A Turner St Port Melbourne VIC 3207 Australia Use of Main Roads WA Corporate Data for P: +61 3 9881 1555 F: +61 3 9887 8104 Road Safety Risk Data Sets [email protected]
2017-011 Western Australia 191 Carr Place Leederville WA 6007 Australia P: +61 8 9227 3000 F: +61 8 9227 3030 [email protected]
New South Wales 2-14 Mountain St Ultimo NSW 2007 Australia P: +61 2 9282 4444 F: +61 2 9280 4430 [email protected] for Mark Parr (Main Roads WA) Queensland 21 McLachlan St Fortitude Valley QLD 4006 Australia P: +61 7 3260 3500 F: +61 7 3862 4699 [email protected]
South Australia Level 11, 101 Grenfell Street Adelaide SA 5000 Australia P: +61 8 7200 2659 F: +61 8 8223 7406 [email protected] Reviewed
Project Leader
Anna Brett
Quality Manager
Lisa Steinmetz
PSS17081-1 September 2018
Commercial in confidence September 2018
SUMMARY
This project explored the suitability and applicability of using corporate data extracted from the Main Roads WA corporate database to supplement and ultimately replace the traditional (manually coded data) approach for developing AusRAP and ANRAM data sets.
Data collected during the WARRIP TSD trial was used in the comparison of Main Roads corporate data against the traditional approach. An analysis of the differences between the data sets found similarities in the coding of results of some attributes but substantial differences in many others. It is suggested that corporate data could be used for some, although not currently all, attributes for producing AusRAP / ANRAM data sets. With further investigation and fine tuning, additional attributes could potentially be extracted from the corporate database. It is noted that additional work would need to be undertaken to improve alignment and to ensure confidence in the corporate data. It is recommended that for attributes that require review and refinement, traditional (manual) rating continue in parallel to the (evolving) corporate extraction to facilitate ongoing comparison, analysis and refinement of these until there is confidence that the corporate data can be consistently and accurately extracted.
While use of corporate data is anticipated to deliver relatively small cost savings over the short-term, further cost saving opportunities would arise over time.
A number of key challenges, observations and lessons were noted during this project, including:
▪ There are challenges in aligning data from different sources.
▪ Differences in terminology and understanding / familiarity with different data within ARRB and Main Roads WA can lead to miscommunication and errors. ▪ For some attributes, corporate data may provide more accurate information than the AusRAP traditional (manual) rating approach.
▪ Assumptions relating to the accuracy and / or currency of information in the corporate database, and / or interpretation of these attributes may be erroneous. Although the Report is believ ed to be correct at the time of publication, ▪ Use of corporate data will require consideration of new issues such Australian Road Research Board, to the as deterioration of assets over time. extent lawf ul, excludes all liability f or loss ▪ Differences in interpretation or categorisation of attributes (between (whether arising under contract, tort, corporate and traditional approach) need to be understood statute or otherwise) arising f rom the (particularly if these lead to differences in star rating outcomes). contents of the Report or f rom its use. Where such liability cannot be excluded, ▪ Recognising that some attributes are (currently) difficult to it is reduced to the f ull extent lawf ul. systematically or automatically extract from the corporate database. Without limiting the f oregoing, people However, new and evolving techniques and technologies will help should apply their own skill and improve accuracy of data attributes and will ultimately lead to judgement when using the inf ormation automatic classification of these. contained in the Report.
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ACKNOWLEDGEMENTS The authors would like to acknowledge the large contributions to this project by key data management staff from both Main Roads WA and ARRB.
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CONTENTS
1 INTRODUCTION ...... 1
1.1 Background...... 1
1.2 Purpose for Project...... 1
1.3 Method Overview...... 2
1.4 Comment on Other Similar Projects ...... 2 2 METHOD ...... 3
2.1 AusRAP Data Sets ...... 3 2.1.1 Traditional Coded Data Set (Based on TSD Data)...... 3 2.1.2 Corporate Data Set (Extracted from Main Roads WA Corporate Asset Database) ... 3
2.2 ANRAM Data Sets...... 4
2.3 Analysis ...... 5 2.3.1 Data Set Comparison ...... 5 2.3.2 AusRAP Results Comparison ...... 15 2.3.3 ANRAM Results Comparison...... 21 3 FINDINGS AND DISCUSSION ...... 23
3.1 Differences in Coding and Risk Assessment Results...... 23
3.2 Key Challenges, Observations and Lessons Learnt...... 24
3.3 Comment on Efficiencies and Value for Money...... 26
3.4 Next Steps and Future Technology ...... 27 REFERENCES...... 29 APPENDIX A DERIVING IRAP AND ANRAM ATTRIBUTES FROM IRIS ...... 30 APPENDIX B EXAMPLE ROAD SEGMENT LENGTH APPROACH ...... 60 APPENDIX C CODING COMPARISON BETWEEN DATA SETS...... 63 APPENDIX D CODING COMPARISON BY J KARPINSKI...... 80 APPENDIX E GLOSSARY ...... 81
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TABLES
Table 2.1: Calibration factors ...... 4 Table 2.2: Data set comparison (length & rows) ...... 6 Table 2.3: Data set comparison overview ...... 8 Table 2.4: Critical attributes in crash risk estimation of different crash types ...... 14 Table 2.5: AusRAP star rating results...... 16 Table 2.6: AusRAP star rating comparison ...... 18 Table 2.7: AusRAP star rating detailed comparison...... 18 Table 2.8: ANRAM results summary (traditional vs corporate) ...... 22
FIGURES
Figure 2.1: Corporate data: frequency of segment lengths ...... 6 Figure 2.2: AusRAP star rating map ...... 17 Figure 2.3: SRS correlation results ...... 20
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
1 INTRODUCTION 1.1 Background The Safe System approach establishes an ethical position that no one should die or be seriously injured on the road. To fulfil this vision there is a requirement to identify parts or segments of the road network that require treatment.
The traditional approach to identify crash risk is through crash history (reactive approach). Given that human error may occur at any time on the road network, and that the human tolerance to impact forces may be exceeded on many sections of the network, a proactive approach is valuable. A proactive approach enables Main Roads to be more confident in determining locations that will maximise the number of lives and serious injuries saved for dollars invested; this directly links with Main Roads Safety Policy Statement, the Western Australia State Government’s Road Safety Strategy Towards Zero and the National Road Safety Strategy.
AusRAP and ANRAM can provide insights into identifying crash risk across the road network, and ultimately provide a network-based approach to guide severe crash risk reduction programs: ▪ AusRAP focusses on SRS outputs presented as star ratings. ANRAM has a greater focus on reporting future crash risk in terms of expected FSI crashes and also gives users greater control over treatment selection. ▪ AusRAP uses the total number of fatalities and serious injuries and crash types across the road network to calibrate the fatality estimation model. It does not rely on the spatial location of crashes. ▪ ANRAM uses crash history across the road network within the Crash Validation Module as part of calculations to estimate expected crashes. The spatial location of crashes is used to achieve a more accurate estimate of expected fatal and serious injury crashes for a given road network or route. ANRAM provides enhanced fatality and serious injury estimates for road sections, given infrastructure and operation information (e.g. sealed shoulder width, lane width, speed limit, traffic volume) and observed crash data. ANRAM also provides the ability to ‘test’ treatment options and estimate the reduction in fatal and serious injury crashes given the proposed countermeasures. 1.2 Purpose for Project Main Roads has an extensive and sophisticated asset database. This project looked to explore the suitability and applicability of using corporate data extracted from this database for AusRAP and ANRAM assessments. More specifically, the project looked to: ▪ provide / explore the level of confidence on attributes that may be drawn from the Main Roads WA corporate data set ▪ opportunities to deliver increased efficiency and value for money through using corporate data for multiple purposes (in this case use of asset data to inform crash risk on the network).
The project also builds on Main Roads WA and ARRB’s experience and knowledge in relation to application of ANRAM and sources of data, and also fosters close collaboration to improve and share knowledge for Main Roads WA and Australian road agencies more widely.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
1.3 Method Overview Data collected during the WARRIP TSD1 trial was used in the comparison of Main Roads corporate data against the traditional approach (manually coded data). A comparison of the results was undertaken whereby challenges with particular variables were identified and addressed with the view to quantifying the accuracy, efficiency and cost of using corporate data for this type of analysis.
This project seeks to provide greater clarity on attributes that may be drawn from the Main Roads corporate data set for use in ANRAM or AusRAP assessments. To facilitate this, the reporting / collection for the corporate and manually coded data were from similar time periods (meaning the data should be comparable from a time perspective). As part of the investigation, there was strong collaboration with key Main Roads staff, particularly in preparation of the data sets in order to identify, manage and resolve issues as they arose, in order to leverage and maximise the technical expertise residing within ARRB and Main Roads. 1.4 Comment on Other Similar Projects Previous studies have demonstrated the potential for reduced coding requirements with respect to manual risk assessment ratings (Karpinski 2014; ARRB internal communication) through the integration of corporate data. This project aims to apply lessons learnt from previous analyses against a high-level quality-controlled data set.
Appendix D shows the coding comparison undertaken by J Karpinski between traditionally coded AusRAP (manual coding) and corporate data sets.
Jan Karpinski (2014) noted: ▪ ‘Care needs to be taken when using inventory for the preparation of these data sets, as some inventory may be more up to date than the video used for rating purposes’. ▪ ‘Providing the inventory is current and accurate (which is difficult to quantify), the creation of data from inventory removes rater bias for’ attributes that may have a level of subjectivity. It was noted that manually rated data tended to regularly code road sections with patching as having ‘medium’ or ‘poor’ road condition, when the measured road condition data indicated that these segments were ‘good’ (in terms of roughness and rutting). ▪ ‘Depending on the methodology used to assemble the [corporate] data’ other biases may be evident or introduced ‘based on assumptions used to create the [corporate] data’, and such biases may result in systematically and consistently higher or lower measurements (for instance).
The above observations are true, although it is also noted that ARRB’s experience has also found that: ▪ on occasion, aspects of corporate databases may be outdated themselves, if that part of the corporate database has not been kept up to date ▪ other bias or subjectivity may be evident in any data set, particularly in relation to data that involves human interpretation or input. This may be present in the corporate data set or traditionally coded data set.
1 The TSD is now known as iPAVE in Australia.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
2 METHOD
The project involved: ▪ preparation of AusRAP data sets — creation of traditional data set – coding of TSD video data — creation of data set based on Main Roads WA corporate asset data ▪ preparation of ANRAM data sets ▪ analysis — comparison of ‘coding standard’ of the two data sets — AusRAP and ANRAM analysis of each data set — interpretation of the outcomes. 2.1 AusRAP Data Sets As part of preparation of the AusRAP data sets: ▪ quality checks were undertaken ▪ extensive work was undertaken to ensure alignment of the data sets ▪ traffic volume data was incorporated and the data sets uploaded to ViDA to obtain the AusRAP results.
2.1.1 Traditional Coded Data Set (Based on TSD Data) The previously collected TSD data was referenced to the road distance2 as used by Main Roads. Liaison with Main Roads was required to ensure the referencing lined up correctly with existing road distance and data to ensure the coded data matched the corporate data.
The video data with the corrected referencing was coded in accordance with the accepted AusRAP coding procedures (iRAP 2014).
Quality and sense checks were conducted on coding results as per the requirements of the AusRAP quality assurance (QA) guideline requirements.
2.1.2 Corporate Data Set (Extracted from Main Roads WA Corporate Asset Database) A second data set was created that incorporates Main Roads WA corporate data for the agreed fields. The choice of categories incorporated and the mechanism for extracting the data was determined in consultation with Main Roads, outcomes from the previous analysis (Karpinski 2014) and ARRB knowledge.
Preparation of this data set involved close collaboration between Main Roads WA and ARRB to build the data set. This included: ▪ Discussion and agreement on attributes that would be drawn from the corporate database versus adoption of default values, and some instances where coded data values were to be used in the corporate data set.
2 For this project, ‘road distance’ was referenced to Main Roads WA ‘True Distance’.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
▪ A large amount of work was required to test and ensure alignment of the data. Multiple iterations of producing versions of the corporate data set were required. In doing this, part of the data needed to be dropped from the comparison because alignment could not be achieved for those sections. ▪ Early in the project it was noted that differences in terminology and understanding / familiarity with different data within ARRB and Main Roads WA was leading to miscommunication and errors. These issues were identified and resolved.
AADT data (provided by Main Roads) was incorporated into both data sets prior to final completeness checks / QA being carried out, processing and data upload to ViDA. Where AADT was not available, Main Roads provided default vehicle flows to be applied.
Appendix A provides an outline of the attributes and the underlying approach for extracting the relevant information from the corporate data base by Main Roads WA. As the project progressed, the approach for corporate data extraction needed to be revised. The code for extracting the corporate data was produced using the corporate statistical program SAS. 2.2 ANRAM Data Sets Data was exported from ViDA and final ANRAM sense checks applied to both data sets. Fatal and serious injury (FSI) data (provided by Main Roads) was incorporated and the data sets were translated to ANRAM format.
This project benefited from refinements over the last few years that have been incorporated into the ANRAM translation process developed by ARRB to improve the quality and consistency of AusRAP and ANRAM data sets.
FSI crash data (for a five-year period) were then assigned to each ANRAM road section. The crash data period was selected to reflect the infrastructure data (i.e. corresponding to the ANRAM input data set).
ANRAM calibration values were prepared for the road network, using the five-year crash data provided for this project.
Crash data was also used to calibrate ANRAM (2014, ANRAM v1.04) for the rural undivided road stereotype. The calibration factors used in this project are shown Table 2.1 .
Table 2.1: Calibration factors
Crash type Rural undivided Rural undivided Traditionally coded data Corporate data Run-off-road 0.75 1.53 Head-on 1.01 0.69 Intersection 0.95 2.17 Other 1.45 1.73
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
2.3 Analysis An analysis was undertaken to compare results between the two approaches, using the traditional coded data set as the baseline to further refine the methods and identifying lessons learnt.
2.3.1 Data Set Comparison A review of the differences between the data sets was undertaken. This included checking for differences between the number of rows, the length (km) and coding differences: ▪ Number of rows: the ‘traditionally coded’ data set and ‘Main Roads WA corporate’ data set files (in both AusRAP upload file and ANRAM data file format) have the exact same number of rows of data (6252), as well as the corresponding crash data file (see Table 2.2). ▪ Volumes: It was noted that the ‘traditionally coded’ data set had old AADT data. The traffic volumes in the ‘traditionally coded’ data set were updated to reflect the volumes in the ‘Main Roads WA corporate’ data set. ▪ Coding differences: The section below (‘Coding differences’) provides an overview of the coding differences between the two data sets, while Appendix C includes a more detailed breakdown between the coding of the two data sets. The ‘Coding differences’ section also provides further investigation of critical attributes. ▪ Length (km): It is noted that the ‘length’ field in the traditionally coded data is consistently 100 m. In comparison, the corporate data set length field varies from 0.003 (i.e. 3 m) to 0.203 (i.e. 203 m) (see Table 2.2). The Main Roads WA team was advised of this discrepancy at the time that the data sets were being collated. Because the road distance was used to extract data broken down by segment, a key learning is that this method does not result in producing 100 m segment lengths (which may affect results). Note, the Main Roads WA team advised that the short lengths were likely to be related to where the road goes from a single carriageway to dual or similar. A histogram showing the frequency of different segment lengths in the corporate data shows that the vast majority (99.6%) of the segment lengths were within the range of 90 to 110 m (Figure 2.1). Therefore, while segment lengths in the data do vary from 3 m to 203 m, this range will have a small effect on results, since these extremes form a very low proportion of segment lengths in the overall data set.
The varying segment length issue is understood, and Main Roads WA advised that the data can be easily corrected within the code for extracting the corporate data. Note, this issue does also occur in traditionally coded data sets. Initially, Main Roads WA adopted the approach of extracting the known true distance then trying to match to that provided by ARRB and correlate this to the ARRB GPS coordinate. This proved problematic with lots of variation. Several other methods were trialled producing similar issues. It was finally decided that, to maintain consistency, Main Roads WA would adopt the ARRB GPS coordinates and simply extract the corresponding true distance. The variations in road distance not matching 100 m segments resulted. However, it is important to note that when calculating the actual distance based on the GPS coordinates given using simple trigonometry (see Appendix B), the majority (99.5%) equates to 100 m rounded to the nearest two decimal places. This is also true for the ARRB data set where, based on the above approach, this data set produces the same results. In view of this, Main Roads WA could simply make each road distance segment equate to the 100 m value.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
Table 2.2: Data set comparison (length & rows)
Data set Number of rows Sum of length (km) TSD AusRAP upload file 6252 (+ a heading row) 625.2 Main Roads WA corporate 6252 (+ a heading row) 625.566 AusRAP upload file TSD ANRAM data file 6252 (+ a heading row) 625.2 Main Roads WA corporate 6252 (+ a heading row) 625.566 ANRAM data file ANRAM crash data file 6252
Figure 2.1: Corporate data: frequency of segment lengths
Frequency
Segment length
When data was processed in iRAP’s ViDA platform, a couple of issues were identified: ▪ TSD AusRAP upload file: H005 distance 16.165 changed to carriageway type undivided (code value = 3) (this did not need to be corrected for Main Roads WA corporate data set). ▪ AusRAP download files may re-order data rows. There is a need to be mindful of this if comparisons between data sets are being undertaken (this issue was encountered during processing of the AusRAP data for this project).
Coding differences: Table 2.3 summarises the correlation between corporate and manually coded attributes (% correct column). The legend below indicates recommendations in relation to the use of corporate data for building data sets, as well as other relevant comments relating to each attribute. As noted above, Appendix B includes a more detailed breakdown between the coding of the two data sets.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
This project primarily focussed on rural routes, which meant that default values for some attributes (particularly relating to pedestrians) could be relatively confidently applied. For more urbanised assessments, the recommendation would be not to apply this philosophy.
Legend Recommend Use of Recommend Use of Recommend further Recommend data be Main Roads WA elected Corporate Data Corporate Data – Some analysis of results, manually coded / to apply default values minor adjustment / application of Corporate Corporate Data not for this project. Main ongoing assessment Data to be determined available Roads WA could required investigate drawing these data from corporate database in the future
Table 2.3 also includes: ▪ Commentary on what may be the reason for the differences, and recommendations on where to focus efforts to potentially achieve greater alignment between manually coded and corporate data. ▪ Associated attributes (which are used as a cross-reference during the QA process). Note that this list does not capture every associated field but does indicate the critical ones. ▪ The iRAP quality benchmark as a useful reference. Note, that this is used during the final step of the QA process, where a sample of data is visually reviewed against the coding results for all attributes. The quality benchmark figure indicates the minimum required to satisfy iRAP requirements.
Table 2.3 lists AusRAP attributes that are rated during the traditional coding process. Other attributes (not included in this table) are the ‘post coding’ attributes, where default values may be applied, where appropriate: ▪ Attributes that may be provided by the client: AADT, Motorcycle %, pedestrian peak hour flow across and along the road, bicycle peak hour flow, and operating speed (85%ile and mean speed). Note that default values for some of these attributes may be applied (if not available in the corporate data) ▪ Attributes for which default values are applied include (as currently not used in Australia): roads that cars can read and star rating policy targets.
It is noted that the coding comparison project undertaken in 2014 (Karpinski 2014, see Appendix D) achieved greater alignment between manually coded and corporate for many attributes, although it is noted that some attributes achieved a similar or better correlation in this current project. Differences in these outcomes may be due to a variety of factors, such as the method used for extraction of corporate data (and other refinements applied) and the location that the data is drawn from (i.e. greater alignment may be more easily achieved for certain road types).
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
Table 2.3: Data set comparison overview
% match Field / between iRAP quality Field linked to Comments / recommendations category Corp and benchmark for QA Traditional For the corporate data, speed has been used as the primary factor in Area Type 86.1% 90% determining area type. Manual coding also considers residential density. Speed Limit Recommend review of corporate methodology based on the above. Investigation may be undertaken into where the variations occurred to determine cause (i.e. was this related to data alignment). Variation may also have occurred where there is a change of speed mid segment. Where coded Speed Limit 95.7% 95% Area Type manually, the higher speed would be applied for that segment. Recommend reviewing methodology applied by Main Roads WA where speed changes mid segment. Recommend investigating further to determine cause for minor misalignment Carriageway (would expect this attribute to be 100% match, although difference could be 99.8% 100% Median Label due to differences in start and end points of segments between the two approaches). Default value of 1 (none) applied. Motorcycle Main Roads WA advised that unable to record due to how Austroads vehicle 99.9% 95% Flow Obs classification applied. May be able to extract using raw data based on min axle spacing. Need to be mindful of micro cars e.g. Smart for 2 wheelbase 1.8m Bicycle Flow Default value of 1 (none) applied. 100% 95% Obs Pedestrian Default value of 1 (none) applied. Flow Obs – 99.8% 95% LHS Pedestrian Default value of 1 (none) applied. Flow Obs – 100% 95% RHS Pedestrian Default value of 1 (none) applied. Flow Obs – N/A 95% Crossing Road Recommend review results and methodology. Expect this field to have good alignment. There is a high variation between manually coded data and No. of Lanes 26% 98% corporate data, especially relating to one and two lanes. Main Roads WA can confirm there is a potential issue with the code producing erroneous results for this attribute. Manual coding approach records the narrowest lane width from within the Lane Width 97.9% 95% segment. Recommend review of corporate methodology to further refine approach. Main variation where manually coded considered narrow, whilst corporate data indicates wide. Recommend review results especially in relation to undivided Paved roads. Methodology for corporate data is basing width on RHS whereas for Sidewalk Shoulder Width 59.8% 95% manually coded data, consideration given to both sides with the highest risk Provision – LHS (i.e. least wide) result being recorded. Main Roads WA can confirm there is a potential issue with the code producing erroneous results for this attribute, as it is related to No. of Lanes code.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
% match Field / between iRAP quality Field linked to Comments / recommendations category Corp and benchmark for QA Traditional Paved Refer above. Sidewalk Shoulder Width 51% 95% Provision – RHS Shoulder Recommend investigation to determine trends for variance. 70.5% 98% Rumble Strips Most variations between the data sets are only one steps different (i.e. straight vs moderate). Review curve radius applied to determine if adjustment improves results. Small number of outliers, investigate to determine if there is a pattern for the variance. The traditional rating approach for classification of the curvature attribute involves a hybrid of measured data inputs (from network survey vehicles) and Speed Limit Curvature 82.5% 95% rater input (note the rater input is generally to ensure that road segments with Quality of features such as a roundabout are not classified as a curve, etc) Curve Main Roads WA data is drawn from actual known curve radius information, then allocates a category based on the iRAP coding manual values for each group. However, Main Roads WA code doesn't account for the use of a speed value in addition to the radius, which may have an impact category allocation if incorporated. Likely mainly due to differences in coding for curvature attribute. Further investigation may be required after curvature attribute achieves better alignment. Based on the iRAP coding manual content, Main Roads WA considers that this attribute has a high degree of subjective interpretation and is reliant on how an individual perceives the safety of the approach to the curve. This will be largely impacted on driver experience. Main Roads WA code is based on comparing the calculated horizontal safe speed a vehicle can travel to the actual sign Curvature Quality of posted speed limit. The posted speed limit used may not include any advisory 81.9% 90% Sight Distance Curve speed signs (e.g. yellow cautionary speed signs) which may impact on the results if included. Delineation The iRAP coding manual approach is based on signage provision, and ARRB coders also take into consideration sight distance and delineation. ARRB notes that the two approaches for determining curve quality are different, and therefore may lead to variations in results. Where a jurisdiction elects to adopt a different approach to determining attribute classification, iRAP requires this variation to be clearly documented (with an understanding in differences in coding outcomes). Grade 99.9% 90% Sight Distance 100% 90% Default value of 1 (Adequate) applied. Curvature Review of corporate methodology required, consider broadening requirements (i.e. inclusion of rumble strips). Quality of Delineation 21.7% 90% Main Roads WA to review code to establish if rumble strips and other line Curve types incorporated in delineation attribute.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
% match Field / between iRAP quality Field linked to Comments / recommendations category Corp and benchmark for QA Traditional Corporate classification for road condition seems to be more stringent than the iRAP model. Consideration required for how this will impact star rating results. Main Roads WA advised that this attribute is calculated based on three tiers of Road Condition 43.6% 90% customer facing levels of service (LOS) relating to ride quality and the underlying levels of roughness using the International Roughness Index (IRI). There is likely to be significant differences between data extracted and a subjective visual interpretation. Corporate methodology for determining skid resistance to be reviewed further with ongoing analysis of results – results not well-aligned with iRAP (visual rating) approach. Variations in results may be due to differences in equipment Skid measured vs visual ratings, currency of SCRIM / BPM or video data, etc. 31.4% 90% Resistance Main Roads WA advised that as similar to road condition, the skid resistance attribute is based on a technical approach using calculations relating to the type of surface (e.g. asphalt, sprayed seal, unsealed) Again, there is likely to be significant differences between data extracted and a subjective visual interpretation. Land Use – Default value of 5 (Not Recorded) applied. 0% 90% LHS Land Use – Default value of 5 (Not Recorded) applied. 0% 90% RHS Vehicle Parking 98.1% 95% Default Value of 1 (None) applied. Recommend review of methodology applied for corporate data extraction. Further analysis comparison required. Median Type 34.7% 95% Carriageway Main Roads WA can confirm there is a potential issue with the code producing erroneous results for this attribute. Recommend reviewing results noting that manual coding assigns ‘present’ where rumble strip is in evidence for the full (or almost full) length of the segment (excluding where an intersection occurs). It would appear Main Roads WA code is assigning values based on defined Centreline road distance ranges, rather than looking up a data set to establish if centreline 90.1% 98% Rumble Strips rumble strips (CRS) are present for this attribute. It may be that it was established in an earlier piece of work that the defined segments had CRS. It is also noted that this piece of code also allocates a median as type 14 for the same road segments. This may explain some of the differences both this and the median type attributes. Default value of 1 (Not Present) applied. Main Roads WA appears to only hold street lighting data for freeways. Street Lighting 89.5% 98% Information needs to be sourced from the relevant utility owners to provide an extract of assets preferably with GPS locations to enable integration into the data set.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
% match Field / between iRAP quality Field linked to Comments / recommendations category Corp and benchmark for QA Traditional Recommend investigating how corporate data accounts for commercial access points.
Property Main Roads WA suggested that this can do with additional refinement. No 88.2% 95% Land Use Access Points inclusion is provided for residential driveways from a known residential database. It appears the code has been written to look at unknown road number ‘Z’ through median opening or u-turns. Although the % hit is fairly high, it requires further investigation. ARRB believes roadside object and distance is currently difficult to accurately code due to complexity of these attributes, i.e. the nearest object that is likely to cause the greatest injury outcome is the item coded for the segment. This is particularly challenging as a corporate database may not be able to capture every roadside object, and in some cases the roadside object may be on private property (e.g. trees). It is important to note that Main Roads WA utilised the Roadside Hazard Rating Object (RHR) data set for ‘Object Distance’ and ‘Roadside Object’ for both LHS and 47.4% 95% Distance – LHS RHS. The RHR information was formulated using similar principles to that of iRAP in visually identifying the various types of roadside hazardous objects, categorising them and measuring their distance from the roadside. The information is nearly 10 years old (2009) and there may be issues in the underlying methodology used that results in differences being introduced when compared to iRAP. Additionally, the application of the worst-case scenario may require further analysis in reviewing the coding and methodology. More investigation is required in the development and application of these attributes. Roadside See above 30% 95% Median Object – LHS Object See above Distance – 40.6% 95% RHS Roadside See above 33.9% 95% Median Object – RHS Facilities for Default value of 4 (None) applied. 82.4% 98% Bicycles Pedestrian Default value of 7 (No Facility) applied. Crossing 99.2% 98% Intersections Facilities Pedestrian Default values of 3 (Not Applicable) applied. Crossing 99.3% 90% Quality
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
% match Field / between iRAP quality Field linked to Comments / recommendations category Corp and benchmark for QA Traditional Default value of 5 (None) applied. Note, much of the difference is due to manual coding of ‘informal path’. Main Roads WA believes that ARRB assessors are misinterpreting the majority of these roads to include informal paths incorrectly. It appears that raters are Sidewalk confusing the unsealed road edges as informal paths. There is generally no Paved Provision – 5.9% 98% evidence of pedestrians along the sides of these roads. A similar result was Shoulder LHS found when reviewing older AusRAP data for Albany Hwy in a previous project. Main Roads WA suggests this be raised as a training point for overseas raters. ARRB has reviewed the commentary in the iRAP coding manual and concurs that a review of methodology is required for this attribute moving forward. Sidewalk Default value of 5 (None) applied. Note, much of the difference is due to Paved Provision – 28.4% 98% manual coding of ‘informal path’. May be worth reviewing how this attribute is Shoulder RHS interpreted.
Pedestrian Default value of 7 (No Facility) applied. Intersection Crossing on 99.5% 95% Type Side Road For the corporate data set school zones where identified based on roadside School Zone signs being present or not. However, on reviewing the code, it does not 100% 95% Warning account for ‘flashing beacons’ or variable road signs. Code review required. Note this is coded blue as the data sets for this project had no school zones. School Zone Default value of 3 applied. Crossing 100% 95% Supervisor Pedestrian Default value of 1 applied. 100% 95% Fencing Further review / analysis of results recommended. Determine if alignment Intersection between the data sets contributed to variations. Quality / Intersection There is a lot of complex code surrounding how this attribute is determined and 92.6% 98% Channelization Type populated in the corporate data set. An audit of the differences could be and Road undertaken with a view of establishing mistakes in either ARRB or Main Roads Volume WA allocations and refining the code if required. Intersection Recommend further review into alignment of intersections between data sets. Intersection 92.5% 90% Quality Type Recommend further analysis of methodology and results. Corporate data Intersecting Intersection 92.2% 98% source likely to provide more accurate results. Variations in results expected Road Volume Type but will need to be quantified. Some review / further refinement to methodology recommended to ensure fields align. Main Roads WA Methodology indicates measured between adequate / poor and not applicable whilst the manual coding measure between not present / present and not applicable. Intersection Intersection 94.5% 98% This attribute relates to intersection type. There is a lot of complex code Channelization Type surrounding how this attribute is determined and populated for the corporate data set. An audit of the differences could be undertaken with a view of establishing mistakes in either ARRB or Main Roads WA allocations and refining the code if required.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
% match Field / between iRAP quality Field linked to Comments / recommendations category Corp and benchmark for QA Traditional Default Value of 2 (Medium) applied. Upgrade Costs 99.8% 95% Note, not used in risk (including SRS) calculations. Roadworks 94.1% 95% Default value of 1 (No Road Works) applied. Service Road 99.9% 95% Default value of 1 (Not Present) applied. Traffic Calming Default value of 1 applied. / Speed 100% 90% Management Expect this field to closely align with the results of speed limit. Review indicates differences due to methodology applied for the corporate data matching posted limits including in 110 km/hr zones (truck speed limits are 100 km/h). Adjusting Truck Speed this would achieve 95.7% match. 13.1% 98% Limit Main Roads WA can confirm there is a potential issue with the code producing erroneous results for this attribute. Main Roads WA has simply made this attribute equal the ‘speed limit’ attribute. A simple adjustment to code would rectify this.
Critical attributes Current network-level crash risk assessment tools such as ANRAM or AusRAP require significant amount of data coding. In total, 72 attributes need to be coded for each 100 m in order to populate the input data file. Of the 72, 46 affect the risk scores with others being identifiers and non-critical data items.
This high number of attributes is driven by the tools’ treatment-focussed design, rather than crash risk estimation only. For example, the current approach requires that in order to recommend linemarking improvement as a safety treatment, information on its presence and quality needs to be collected first.
The number of attributes that has the greatest contribution to crash risk and crash estimation is fewer than the full set. Previous work undertaken by ARRB looked to identify attributes critical to estimation of risk, i.e. the key variables influencing risk scores and crash estimation in AusRAP and ANRAM. These factors are presented in Table 2.4.
A breakdown of the coding for each of the following attributes is also presented in Appendix C.1: ▪ AADT – same coding in both data sets. ▪ Mean speed – speed limit has been compared here instead. 95.7% of the coding is the same between the data sets. Where the coding is different, the corporate data set often has a higher speed limit. Main Roads WA can confirm there is a potential issue with the code producing erroneous results for this attribute. Main Roads WA has simply made this attribute equal to the ‘speed limit’ attribute. A simple adjustment to the code would rectify this. The same applies to ‘operating speed (85th percentile)’.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
▪ Pavement (road) condition – 43% same coding. Traditional data set has much more coded as ‘1’ (good), while corporate data set has a large proportion coded ‘2’ or ‘3’ (medium or poor). This difference in coding is not necessarily wrong; however, it should be recognised that this will have an impact on results. For the corporate data set, this attribute is calculated based on three tiers of customer facing levels of service (LOS) relating to ride quality and the underlying levels of roughness using the International Roughness Index (IRI). There is likely to be significant differences between data extracted and a subjective visual interpretation. ▪ Grade – 99.9% same. ▪ Pavement width – a combination of lane width and shoulder width. Lane width is 97.9% the same; however, paved shoulder is 51% on drivers’ side and 59.8% on passengers’ side. For the shoulder measurements, where the coding is different, a large proportion is coded as narrower than the corporate data (large proportion of traditional (manual) data coded 3 (medium (≥ 1.0 m to < 2.4 m) when corporate data coded 2 (narrow (≥ 0 m to < 1.0 m)). The Main Roads WA team suspects the difference in coding results here may be partly due to the approach taken for extraction and assigning categories for the corporate data set. Main Roads WA can confirm there is a potential issue with the code producing erroneous results for this attribute. ▪ Curvature – 82.5% same. ▪ Roadside distance and severity (object) – quite a large difference in the coding of these attributes between the data sets. ▪ Intersection type – 92.6% same. ▪ Intersection volume – 92.2% same. ▪ Median type – 34.7% same. A large proportion of corporate data is coded 14 (wide centreline) when traditional (manual) data is coded 1 (centreline). It is also noted that a substantial proportion of corporate data is coded 11 (centreline) when traditional (manual) data is 1, 2, 3, 4, 5, 6,17 (barrier or physical median of different types / widths). The Main Roads WA team suspects the difference in coding results here may be due to the approach taken for extraction and assigning categories for the corporate data set.
Table 2.4: Critical attributes in crash risk estimation of different crash types
Run-off-road Run-off-road Property Attribute Passenger-side Driver-side Head-on Intersection Access Mean speed* ✓ ✓ ✓ ✓ ✓ AADT ✓ ✓ ✓ ✓ ✓ Pavement condition** ✓ ✓ ✓ ✓ Grade ✓ ✓ ✓ ✓ Pavement width*** ✓ ✓ ✓ Curvature ✓ ✓ ✓ Roadside severity – ✓ passenger-side distance
Roadside severity – ✓ passenger-side object
Roadside severity – driver-side ✓ distance
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Run-off-road Run-off-road Property Attribute Passenger-side Driver-side Head-on Intersection Access
Roadside severity – driver-side ✓ object
Intersection type ✓ ✓ Intersecting road volume ✓ Property access points ✓ Median type ✓ ✓ * Mean speed can be adopted from probe data, or assumed to be a derivative of the Speed limit AusRAP attribute. ** Pav ement condition is a combination of Road condition and Skid resistance attributes in AusRAP. *** Pav ement width is a combination of Number of lanes, Lane width and Paved shoulder width attributes in AusRAP.
Source: ARRB internal document (2015).
2.3.2 AusRAP Results Comparison Star rating results comparison Table 2.5 and Figure 2.2 show the star rating results from iRAP’s ViDA platform for the two data sets. While the proportion of the network is similar for two-star roads, the proportions for most other star rating categories is quite different. Figure 2.2 shows that the AusRAP star rating results were sometimes different (between the traditional and corporate coding approaches) for the same sections of road (see also Table 2.6 and Table 2.7). This aspect was investigated further in the ‘SRS results comparison’ section.
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Table 2.5: AusRAP star rating results
Traditional (TSD) coded data set Main Roads WA corporate data set Smoothed Star Ratings (smoothed by length)
Raw Star Ratings
Source: Tables created using ViDA software, ©iRAP (2018).
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Figure 2.2: AusRAP star rating map
(a) Traditional (TSD) coded data
(b) Main Roads WA corporate data
Source: Maps created using ViDA software, ©iRAP (2018), map data © google.
A closer review of the AusRAP data output files (looking at the results for each 100 m segment of the data) for the traditionally coded vs corporate data sets found that 51% of the smoothed star rating results and 43% of the raw results were the same (Table 2.6).
Table 2.7 shows a further breakdown of the traditional and corporate data ratings (green shaded cells highlight same results). Where star ratings are different between the two methods, systematic differences in the star rating results do not show a particular trend for consistently higher or lower results.
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Table 2.6: AusRAP star rating comparison
Smoothed star ratings Raw star ratings Count % Count % Same 3219 51% 2701 43% ±1 2706 43% 2585 41%
> 1 327 5% 966 15% Total 6252 6252
Table 2.7: AusRAP star rating detailed comparison
Raw star results comparison Main Roads WA corporate Traditional 1 2 3 4 5 Total 1 246 434 365 21 11 1077 2 280 903 944 46 38 2211 3 159 504 1456 117 126 2362 4 27 69 217 59 81 453 5 2 9 93 8 37 149
Total 714 1919 3075 251 293 6252 Smoothed star results comparison Main Roads WA corporate
Traditional 1 2 3 4 5 Grand Total 1 660 71 10 741 2 142 1878 1152 77 3249
3 80 529 1312 165 50 2136 4 39 57 27 123
5 1 2 3 Total 222 3106 2592 280 52 6252
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SRS results comparison Some star rating bands (see Table 2.5) do have similar proportions over the road network in this project (for example, the AusRAP Raw Star Ratings results show 35% of the network being 2-star based on the traditional approach compared to 31% for the corporate approach); however, these 2-star ratings have not necessarily been allocated to the same sections of road by the two approaches (see Figure 2.2, Table 2.6 and Table 2.7). In addition, different risk scores may be within the range covered by a single iRAP band. In order to understand this better, the relationship between SRS results for each data set was reviewed (Figure 2.3). This helped to provide an insight into how well the risk scoring matched between the traditional and corporate approaches. If the two approaches delivered identical results (i.e. same SRS – or very similar – for each segment), then: ▪ the blue line would be the same as the orange line (i.e. the equation of best fit (the blue line) through the data (the blue dots) would fit the 1:1 relationship (the orange line)) ▪ the blue dots would tend to follow the blue line, rather than being dispersed away from the blue line (this is reflected by the R2 value).
The correlation for smoothed and raw SRS scores, as well as for each crash type reported in AusRAP was found to be low (see Figure 2.3): ▪ Most of the figures show that the equation of the line of best fit (blue line) is not close to a 1:1 relationship between the results produced by the two data set methods (i.e. for each segment, SRS corporate (plotted on the X-axis) should equal SRS traditional (plotted on the Y-axis) if the two data sets generate the same results. ▪ Most of the figures return a very low R2 value. R2 is a statistical measure of how close the data are to the fitted regression line (in this case the blue dashed line in each graph). A high R2 value (> 0.85) indicates a strong relationship. A low R2 value (< 0.7) indicates a very low connection.
It was noted that: ▪ The equation of the line of best fit (blue line) was close to a 1:1 relationship for ‘vehicle SRS total smoothed’ (i.e. was close to the orange line). However, the low R2 value (i.e. –0.2) indicates a very poor fit between the data and the line of best fit (essentially, it is a random dispersion around the blue line). ▪ The R2 value for head-on overtaking SRS is high (0.88), which indicates that the scores (corporate and traditional) have little spread around their line of best fit. However, the relationship that this line of best fit indicates is a clear demonstration that the two methods produce different scores (if they were producing the same scores, the line of best fit would be a 1:1 relationship). The current corporate data extraction method produces scores that are related to, but appear to be a large multiple of, the traditional scores. Further investigation of this relationship could show a way that the corporate method may, with modification, produce results acceptably close to those produced using the AusRAP method for head-on overtaking SRS results only.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
Figure 2.3: SRS correlation results
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
2.3.3 ANRAM Results Comparison Table 2.8 provides a summary of the ANRAM results for the two data methods (traditional and corporate). The summary shows that the Predicted and ANRAM FSI results were quite different for the two data methods, and the proportion of risk attributed to each key crash type was also quite different. The traditional method attributed approximately 50% of the FSI crash risk to run-off-road crash types, compared to approximately 30% for the corporate data set.
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Table 2.8: ANRAM results summary (traditional vs corporate)
Traditional (TSD) coded Main Roads WA data set corporate data set Observed FSI crashes Run-off-road 100 (48% ) 100 (48% ) Head-on 12 (6% ) 12 (6% ) Intersection 42 (20% ) 42 (20% ) Other 55 (26% ) 55 (26% ) Total (excl ped crashes) 209 (100% ) 209 (100% ) Predicted FSI crashes Run-off-road 72.91 (56% ) 51.69 (33% ) Head-on 7.99 (6% ) 16.58 (11% ) Intersection 19.98 (15% ) 22.85 (15% ) Other 30.15 (23% ) 65.64 (42% ) Total 131.03 (100% ) 156.76 (100% ) ANRAM FSI crashes Run-off-road 73.05 (53% ) 51.98 (34% ) Head-on 7.9 (6% ) 15.97 (10% ) Intersection 23.68 (17% ) 26.1 (17% ) Other 32.43 (24% ) 59.45 (39% ) Total 137.06 (100% ) 153.51 (100% )
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
3 FINDINGS AND DISCUSSION
This project sought to provide greater clarity on attributes that may be drawn from the Main Roads corporate data set. This section presents the project findings, including a discussion on the differences in coding and risk assessment results, key challenges as well as observations and lessons learnt, opportunities to deliver increased efficiency and value for money, and finally next steps the impact of technology developments. 3.1 Differences in Coding and Risk Assessment Results An analysis of the differences between the data sets found similarities in the coding of results of some attributes, but substantial differences in many others. Section 2.3.1 shows that (during this project), the corporate data coding: ▪ was similar to the traditionally coded data set for five attributes (green), and that corporate data could be used ▪ should be suitable to use following some minor adjustment of the corporate extraction method for another five attributes (blue) ▪ may be suitable following a review, analysis and refinement of the corporate extraction method for another 12 attributes (yellow).
The Main Roads WA team noted that coding differences for attributes such as median type and, potentially, pavement (lane and shoulder width) may be due to the approach that was adopted for extraction and assigning corporate information to the attribute categories. Further investigation and refinement of corporate approach should provide closer alignment with the visual rating approach.
The Main Roads WA team noted that for the road condition attribute the traditional (visual rating) approach had no sections assigned as category 3 (poor) and only few category 2 (medium) results compared to the large proportion of category 2 and 3 for this attribute in the corporate data set. For the corporate data, Main Roads WA used a combination of road roughness and rutting in conjunction with standards to determine the classification for both road condition and skid resistance attributes. It would be useful to explore this issue further (for both the road condition and the skid resistance attribute). ARRB notes that for attributes such as these, corporate data may be more accurate than the traditional (visual) rating approach.
It is also noted that for the traditionally coded (manual) rating approach, a subjective visual interpretation is needed for some attributes which may have less reliability than measured data.
The comparison of AusRAP results found that while the proportion of the network is similar for two-star roads, the proportions for most other star rating categories is quite different. A closer review of the AusRAP data (results for each 100 m segment of the data) found that only 51% of the smoothed star rating results and 43% of the raw results were the same. Where star ratings were different, there did not seem to be an indication of a trend for consistently higher or lower results.
An investigation to understand the correlation of AusRAP risk scores for the same road segments found that the traditional and corporate approaches tended to have quite different results (i.e. for smoothed and raw SRS scores, as well as for each cash type reported in AusRAP, the correlation was found to be low). It was noted that for ‘head-on overtaking’ type crashes, the current corporate data extraction method produces scores that are related to, but appear to be a large multiple of, the traditional scores. Further investigation of this relationship could show a way that the corporate method may, with modification, produce results acceptably close to those produced using the AusRAP method for head-on overtaking results only.
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The comparison of the ANRAM results found that the predicted and ANRAM FSI results, as well as the proportion of risk attributed to each key crash type, were quite different for the two data methods. The traditional method attributed approximately 50% of the FSI crash risk to run-off-road crash types, compared to approximately 30% for the corporate data set. 3.2 Key Challenges, Observations and Lessons Learnt A number of key challenges, observations and lessons were noted during this project: ▪ Data alignment – Challenges exist in aligning data from different sources. For this project there were challenges in matching road distance versus using driven video data and the method as to how it was collected. There were some additional challenges for this project because of the recycling of trial TSD data. Because the TSD data was collected for trial purposes (focussed on demonstrating the TSD technology), distance was not aligned to the road distance to the same level of accuracy as would be for a normal project. This distance issue led to matching distances between corporate and TSD data for this project. This would not be an issue for a future data set which would employ ARRBs normal quality processes and procedures. ▪ Terminology – It was recognised that differences in terminology and understanding / familiarity with different data within ARRB and Main Roads WA led to miscommunication and errors early in the project (similar issues have been encountered with other jurisdictions). This may be resolved by ensuring liaison between staff who are intimately familiar with the data (from both ARRB and Main Roads WA) to circumvent communication issues. ▪ Data accuracy / currency — Assumptions relating to the accuracy and / or currency of information in the corporate database, and / or interpretation of these attributes may trigger inaccurate error alerts in the manually coded data. Clear communications on how false positive alerts are managed through the quality assurance stage would be required. For example, if corporate speed limit data was used, the assumption would be that this data is accurate. However, if it were not consistently accurate, issues related to associated attributes may be falsely flagged (e.g. an incorrect 50 km/h speed limit in a rural area). For other attributes, the age (currency) of the data in the corporate database may need to be taken into consideration. — For some attributes, corporate data may provide more accurate information than the AusRAP traditional (manual) rating approach. A key example is pavement quality / skid resistance, where the AusRAP visual rating approach may not provide accurate ratings, and where corporate data may be more accurate. Further investigation would be valuable to confirm whether the data extraction approach used for this project is appropriate, or whether further refinement may be required. This will also largely depend on how Main Roads WA considers the best way to measure road condition and skid resistance. At present a very simplistic approach is taken. However, it may be more beneficial to include other elements to provide a better gauge (more aligned to a visual assessment) of these attributes (especially road condition). — For the traditionally coded (manual) rating approach, a subjective visual interpretation is needed for some attributes which result in less reliability than measured data. It should be remembered that bias or subjectivity may be evident in any data set, particularly in relation to data that involves human interpretation or input. This may be present in the corporate data set or traditionally coded data.
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▪ Data interpretation — For some attributes, during the interpretation and categorisation of attributes, Main Roads WA may have a higher (or potentially lower in some cases) stringency (i.e. when an attribute’s quality may be considered good or poor). This difference in interpretation may not be wrong (and in fact, the road agency’s interpretation may be considered better), but this would have an influence on coding results across the network (and may lead to differences in star ratings). This may introduce challenges (or at least issues to be aware of) if comparing risk score (AusRAP or ANRAM) results where data sets may utilise different approaches (particularly national comparisons). — Future work should also take into account whether interpretation of the data is applicable in different road environments. This project focussed on primarily rural routes. The approach for classification of some attributes may be applicable across urban and rural road environments, while the approach may need to be refined for urban environments. Similarly, default values may be relatively confidently adopted for some attributes in rural environments (particularly relating to pedestrians – as pedestrians are generally not present on rural roads). For more urbanised assessments, this approach would need to be reviewed. — For some attributes, Main Roads may elect to adopt a different approach to determining attribute classification to the approach outlined in the iRAP coding manual. A difference in approach may be appropriate (and preferred) for a jurisdiction; in such cases, iRAP requires this variation to be clearly documented (with an understanding in differences in coding outcomes). ▪ Asset condition – Use of corporate data will require consideration of new issues such as deterioration of assets over time (and whether this is currently (or will be) captured in corporate data, or whether a rule may need to be applied). For attributes that experience deterioration over time (e.g. pavement quality, linemarking etc.), consideration may need to be given to issues such as currency of corporate data, and whether that information is likely to reflect conditions. Drawing these attributes from the corporate database should take into consideration (1) the currency of the information in the corporate database for that attribute (e.g. linemarking installed on x date, or network survey undertaken on x date indicating quality level for that attribute), and (2) the likely deterioration of that asset over time (e.g. should the linemarking be assumed to be poor after x years). Main Roads WA may want to consider an approach for assuming conditions for these types of attributes (for example, if recent data on condition is not available in corporate database, perhaps an assumed level of deterioration over time may be adopted). ▪ Roadside hazards — Some attributes would be extracted from the corporate database, for example roadside hazard type and distance. The iRAP manual requires the most dangerous hazard within each 100 m segment be identified, and then the distance noted. (The iRAP process nominates the order for the roadside hazard severity, for example cliff precedes tree ≥10 cm diameter) ARRB and Main Roads WA recognises that there is difficulty for assessing roadside hazard type and distance especially when taking into account roadside object distance. The Main Roads WA team noted the need to question and / or review the accuracy of their Road Side Hazard (RHR) rating data sets including the methodology used. The Main Roads WA team indicated that it may be valuable to undertake a detailed comparison of the AusRAP approach for roadside hazard object and distance (e.g. through visual audits on a sample of the network) and compare this with the approach applied to determine these attributes from the corporate data in this project. This might provide some insights as to improvements
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that could be made to one or both approaches (traditional rating vs corporate database approach). It is unknown exactly how close the Main Roads WA RHR is aligned to the worst-case principles how closely it corresponds to the iRAP attribute categorisations for the roadside object dataset. Unless a rater is 100% diligent during the entire rating process, it is noted that there is potential for errors to be introduced at this point in the traditionally rated data set. A rater reviews each 100 m segment then decides which object is considered the worst case, based on guidance in the iRAP coding manual and relying on an intricate knowledge and memorisation of roadside object risk hierarchy order. Coding errors may occur (e.g. selection of the incorrect ‘worst’ roadside object’) due to repetitive nature of reviewing long stretches of road that have similar objects such as in WA with roads lined with trees. The QA process looks to identify and resolve such issues. Newer technologies (e.g. use of LiDAR information) are being developed and research is continuing, which could assist in reducing the human element in identifying these related attributes. — It is noted that, in a different project (ARRB internal communication 26 June 2018), ARRB undertook an analysis of 2600 crashes to ascertain the correlation between the object hit in a crash (as recorded in police reports) compared to the hazard identified in AusRAP coding. This analysis found that vehicles often did not hit the object identified in the AusRAP coding (e.g. for the particular data set reviewed, the percentage of crashes where object hit (in the crash data) matched the AusRAP coding was 52% for trees, 44% for ‘no object’, 26% for guardrail and 19% for non-frangible sign, post or pole). This raises the question whether roadside rating is an accurate predictor of hazards of concern. 3.3 Comment on Efficiencies and Value for Money The project also looked to identify opportunities to deliver increased efficiency and value for money through using corporate data for multiple purposes (in this case, use of asset data to inform crash risk on the network): ▪ With a small amount of adjustment, there are 10 attributes (highlighted in green / blue, Table 2.2) that can easily be drawn from the Main Roads WA corporate database. ARRB anticipates that projected initial cost savings for the 10 attributes identified (at a network-level assessment) would be in the order of 5%. ▪ A further 12 attributes (highlighted yellow, Table 2.2), with some further analysis and refinement in the method of corporate data extraction, should be able to be drawn from the Main Roads WA corporate database. ARRB anticipates that cost savings associated with use of the 22 identified attributes (green, blue & yellow) would be in the order of 10%. ▪ These anticipated savings are relatively low due to (1) a large amount of QA effort still required, and (2) complexities in integrating and aligning the two data sets. However, further cost saving opportunities would arise over time as processes relating to the alignment and integration of two independent data sets matured (due to mature processes delivering efficiencies in alignment and integration). In addition, over time, as the number of attributes that may be drawn from corporate database increases (due to refinements in corporate extraction techniques, new information available in corporate databases and / or the influence of new technologies), savings may be compounded.
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Main Roads WA noted that there were some attributes where default values were adopted for this project (due to time constraints and for ease of coding) that could be drawn from the corporate database in the future. These attributes include: ▪ land use – LHS ▪ land use - RHS ▪ street lighting ▪ facilities for bicycles ▪ pedestrian crossing facilities ▪ pedestrian crossing quality ▪ sidewalk provision – LHS ▪ sidewalk provision – RHS ▪ school zone warning ▪ pedestrian crossing on side road ▪ pedestrian fencing.
There are a number of key points to be aware of, which provide further insight into complexities relating to building a data set from two sources (manual and corporate): ▪ Potential cost reduction with a reduction in the number of fields being manually coded over time. Achieving cost reduction requires removing multiple associated fields. Consideration should be given to how categories are grouped (i.e. using corporate source for intersection type and intersecting volume but traditional (manual coding) for intersection channelization would still require coding in relation to intersections). ▪ It should be noted that some fields may require ongoing manual coding in the short-medium term to test and refine changes to corporate extraction methodology to achieve consistent results. This may need to be tested in different road environments, different road stereotypes, and different regions (to gain understanding in accuracy and repeatability in results in these different road configurations, and from different sections of the corporate database). ▪ It should be noted that some fields may require ongoing manual coding for the purpose of QA even where corporate data meets requirements (difficulties arise if associated fields are not drawn from the same data source. In such instances ARRB would propose parallel data coding (both corporate and manual coding). When both associated attributes can be confidently quantified from corporate data, manual coding of these would no longer be required (delivering cost dividends). 3.4 Next Steps and Future Technology The ARRB team suggests that corporate data could be used for some, although currently not all, attributes for producing AusRAP / ANRAM data sets. With a bit more investigation and fine tuning, additional attributes could potentially be extracted from the corporate database and would cover most of the critical attributes relating to crash risk and crash estimation outlined in Table 2.4 (likely attributes include speed, grade, curvature, skid resistance, pavement condition, lane and shoulder width, intersection type and median type). It is noted that additional work would need to be undertaken to improve alignment and to ensure confidence in the corporate data; for instance, continuing to rate these attributes to facilitate ongoing comparison, analysis and refinement of the corporate extraction approach until there is confidence that the corporate data can be consistently
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and accuratey extracted. While use of corporate data is anticipated to deliver relatively small savings over the short-term, further cost saving opportunities would arise over time. It is also noted that roadside hazard distance and object attributes (also critical attributes relating to crash risk and crash estimation) are currently difficult to systematically or automatically ascertain; however, it is likely to become possible as technology in this space continues to evolve.
New and evolving technologies will continue to lead to improvements in accuracy of coding of data attributes (in both traditionally coded and corporate data sets), as well as efficiencies in producing such data. Computer recognition, LiDAR technology and machine learning may be used to accurately train available data to predict the target variable. For instance, categorisation of roadside object type and distance measurement (as well as many other attributes such as lane width, delineation and access points) will eventually be able to replace the visual assessment for these attributes. Work is currently being undertaken in both the private and public sectors in Australia and overseas to progress this (for example, HERE’s work on mapping the road environment to assist automated vehicle navigation would be relevant and translatable for preparation of AusRAP and ANRAM data sets). ARRB’s @Lab team is currently undertaking preliminary work in this space with promising results. Next steps for this work would look to use larger data sets for training the machine learning algorithms, as well as undertaking testing to ensure all required categories can be reliably identified through this process (including in different road environments).
Main Roads WA requested ARRB to indicate a ballpark estimate for AusRAP and ANRAM coding for the remainder of the Main Roads network. Assuming a network carriageway length of up to 19 000 km, and less than 10% of the network being urban, the ballpark figure for AusRAP and ANRAM coding would be in the order of $1 million. Note, this does not include data collection (however, data is being collected as part of the iPave network data collection).
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
REFERENCES ANRAM 2014, version 1.04, Australian National Risk Assessment Model software. iRAP 2018, ViDA software, iRAP, London, UK. iRAP 2014, iRAP star rating and investment coding manual, August 2014, International Road Assessment Progamme (iRAP), London, UK.
Karpinski, J 2014, ‘Main Roads trial of Austroads National Risk Assessment Model (ANRAM)’, ARRB conference, 26th, 2014, Sydney, NSW, ARRB Group, Vermont South, Vic, 15 pp.
AusRAP maps and tables throughout this report were created using VIDA software by iRAP. Copyright © iRAP. All rights reserved. For more information about VIDA software, please visit
Map data - Google Maps (2018), ‘Western Australia’, map data, Google, California, USA.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
APPENDIX A DERIVING IRAP AND ANRAM ATTRIBUTES FROM IRIS
This appendix outlines the underlying approach employed by Main Roads WA for extracting the relevant information from the corporate data base.
Table A 1: Deriving IRAP and ANRAM attributes from IRIS
iRAP Coding Attribute Datatype Description
IRIS
Domain
iRAP ColumniRAP Ref
iRAP Attribute Attribute No. iRAP
ANRAM Column ANRAM Ref iRAP Coding iRAPManualRef 1 A N/A 4.1 Coder Name Y VARCHAR(4) N ▪ Default to Main Roads WA. CODER_NAME ▪ Subject to further consideration if video coding is used. 2 B N/A 4.2 Coder date Y DATE(DD/MM/ N ▪ Date at which Segment (data) was created or modified. CODING_DATE YYYY) ▪ Update Coder date on change to underlying network or asset data (as specified within this extract). Note; existing IRIS methodology will regenerate data for the entire road and not incrementally (i.e. only Segments where changed has occurred). ▪ Subject to further consideration if video coding is used. 3 C N/A 4.3 Road survey date Y DATE(DD/MM/ N ▪ Equals Coder date. SURVEY_DATE YYYY) ▪ Subject to further consideration if video coding is used. 4 D BG 4.4 Image reference Y VARCHAR(4) N ▪ Default to NULL. IMAGE_REF ▪ Subject to further consideration if video coding is used. 5 E A 4.5 Road name Y VARCHAR(4) N ▪ Main Roads road number. ROAD_NO ▪ Note, this extract is limited to the Main Roads network only (i.e. where NE_NT_TYPE = ‘M’). It is not applicable to the Local Road, Proposed or Principal Shared Path networks. 6 F B 4.6 Section Y VARCHAR(30) N ▪ The iRAP Coding Manual indicates Section could be a name to differentiate between ‘sections of SECTION_NO a road’, i.e. if the road agency has some internal naming or referencing method. Optionally, we could use State Link Number/name or some another means to identify/group Segments within a Section.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
▪ Doesn’t have to be a unique number. Can also be words to differentiate segments of road (e.g. Perth to Williams or Perth to Albany, Link 37 etc”. ▪ Should be relevant to the direction of travel. Useful for divided carriageways. ▪ This attribute is used by ViDA to determine how it will handle the smoothing of data by referring to the “Section” or by “Length” as defined in the project data set setup paramenters within ViDA. ▪ Different rules apply depending on the selection type.
▪ Unique number for each Segment within the extract, maintained on change to data (as per Coding date), will not persist over time and will not be sequential (within a road or data set).
▪ Ref to attribute 79. NUMBER 7 G D 4.7 Distance Y NUMBER(5) N ▪ Distance from start of road. Distance is a running chainage not road distance, it is consecutive SEGMENT_CHAINAGE with no gaps. Distance Breaks that have a length > 0 must be taken into consideration so that each Segment is continuous. ▪ Each road will start from 0. ▪ Start True (not SLK) of the Segment ▪ iRAP coding manual specifies that a Segment ‘should not be less than 0.1km’ however a Segment may break at a change in carriageway (e.g. from A to U or B to U and vice versa) hence may be less than 0.1km and each segment will have a unique Section. The last Segment on a road or at change in Carriageway may not equal 100m. ▪ Otherwise, if continuous 100m segments are required, then derive Carriageway based on first occurrence. 8 H E 4.8 Length Y NUMBER(3,2) N ▪ Length of Segment in kilometres. SEGMENT_LENGTH ▪ Segment length (report interval) = 100m. 9 I F 4.9 Latitude Y NUMBER(8,6) N ▪ Latitude of Segment start in WGS84. LATITUDE 10 J G 4.9 Longitude Y NUMBER(9,6) N ▪ Longitude of Segment start in WGS84. LONGITUDE 11 K H 4.10 Landmark Y VARCHAR(100 N ▪ May allow locations to be referenced relative to landmarks or points of interest. LANDMARK ) ▪ Use precedence rules as below; − If bridge is located within Segment, report Bridge Number and Description as where Description is Bridge name otherwise Crossing name. Use single location of Bridge and not Bridge Length.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
− Otherwise, return intersecting road names, exclude name of road being processed. Is Intersection Number of any value? − If any Landmark is coincident with start or end of a Segment, report in first. 12 L AU 4.11 Comments Y VARCHAR(100 N ▪ Default to NULL. COMMENT ) 13 M C 4.12 Carriageway Y NUMBER(1) Y ▪ Main Roads left carriageway = 1 (Carriageway A) , Main Roads right carriageway = 2 CWAY (Carriageway B) Otherwise = 3 (Carriageway U or undivided – Main Roads single carriageway). ▪ Code 4 and 5 are not used. ▪ Refer to Distance. As the output Segments are 100m in length it is foreseeable than a Segment will span a change in Carriageway. iRAP specifies that the first occurrence of an attribute value should be used. Use the first occurrence of Carriageway in network connectivity order when processing Left and Single. For Right, further discussion required. ▪ Recommend moving away from iRAP specification and splitting all Segments at change in Carriageway, then determine if unique Section is required for each part that will make up the 100m Segment. Code Category Main Roads Carriageway 1 Carriageway A of a divided carriageway road Left 2 Carriageway B of a divided carriageway road Right 3 Undivided road Single 4 Carriageway A of a motorcycle facility 5 Carriageway B of a motorcycle facility
14 N AT 4.13 Upgrade cost N NUMBER(1) Y ▪ Records the influence that the surrounding land-use, environment and topography will have on the UPGRADE_COST cost of major works. ▪ Default value is 2 (ARAM User Guide). Code Category
1 Low 2 Medium 3 High
15 O N/A 4.14 Motorcycle observed flow N NUMBER(1) Y ▪ Records the number of motorcycles in use within a 100m Segment. MOTORCYCLE_FLOW ▪ Default to 1 (Main Roads WA Not currently considering, unless able to measure).
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
Code Category
1 None 2 1 motorcycle observed 3 2 to 3 motorcycles observed 4 4 to 5 motorcycles observed 5 6 to 7 motorcycles observed 6 8+ motorcycles observed
16 P K 4.15 Bicycle observed flow N NUMBER(1) Y ▪ ANRAM Attribute name; Bike flow − Bike flow. BICYCLE_FLOW ▪ Records the number of bicyclists observed within a 100m Segment. ▪ Default to 1 (Main Roads WA Not currently considering, unless able to measure). ▪ Some Metropolitan roads do not permit cyclists. ▪ Future consideration. Code Category 1 None 2 1 bicycle observed 3 2 to 3 bicycles observed 4 4 to 5 bicycles observed 5 6 to 7 bicycles observed 6 8+ bicycles observed
17 Q L 4.16 Pedestrian observed flow across the road N NUMBER(1) Y ▪ Records the number of pedestrians crossing or about to cross the road within a 100m Segment, Pedestrian flow across the road acknowledged as a random sample. PEDESTRIAN_FLOW ▪ Coding manual acknowledges this is a random sample. ▪ Some sections of Metropolitan Highways prohibit pedestrians. ▪ Default to 1 (Main Roads WA Not currently considering, unless able to measure). ▪ Future consideration. Code Category 1 None
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2 1 pedestrian crossing observed 3 2 to 3 pedestrians crossing observed 4 4 to 5 pedestrians crossing observed 5 6 to 7 pedestrians crossing observed 6 8+ pedestrians crossing observed
18 R M 4.17 Pedestrian observed flow along the road N NUMBER(1) Y ▪ Records the number of pedestrians walking along the right side of the road within a 100m driver-side Segment, acknowledged as a random sample. Pedestrian flow along the road driver-side ▪ Default to 1 (Main Roads WA Not currently considering, unless able to measure). PEDESTRIAN_FLOW_DRIVER ▪ Some Metropolitan roads do not permit cyclists. ▪ Future consideration. Code Category 1 None 2 1 pedestrian along driver-side observed 3 2 to 3 pedestrians along driver-side observed 4 4 to 5 pedestrians along driver-side observed 5 6 to 7 pedestrians along driver-side observed 6 8+ pedestrians along driver-side observed
19 S BO 4.18 Pedestrian observed flow along the road N NUMBER(1) Y ▪ Records the number of pedestrians walking along the left side of the road within a 100m Segment, passenger-side acknowledged as a random sample. PEDESTRIAN_FLOW_PASSENGER ▪ Default to 1 (Main Roads WA Not currently considering, unless able to measure). ▪ Some Metropolitan roads do not permit cyclists. ▪ Future consideration. Code Category
1 None 2 1 pedestrian along passenger-side observed 2 to 3 pedestrians along passenger-side 3 observed
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4 to 5 pedestrians along passenger-side 4 observed 6 to 7 pedestrians along passenger-side 5 observed 6 8+ pedestrians along passenger-side observed
20 T AE 4.20 Land use – driver side N NUMBER(1) Y ▪ Records the type of roadside development that is observed on the right side of the road. LANDUSE_DRIVER ▪ Default to 5. Code Category 1 Undeveloped areas 2 Farming and agricultural 3 Residential 4 Commercial 5 Not Recorded 6 Educational 7 Industrial and manufacturing
21 U AD 4.19 Land use – passenger side N NUMBER(1) Y ▪ Records the type of roadside development that is observed on the right side of the road. LANDUSE_PASSENGER ▪ Default to 5. Code Category
1 Undeveloped areas 2 Farming and agricultural 3 Residential 4 Commercial 5 Not Recorded 6 Educational 7 Industrial and manufacturing
22 V N 4.21 Area type N NUMBER(1) Y ▪ Defines the level of roadside development through which the road is passing AREA_TYPE ▪ Could be based on speed limit. Option might be to coarsely define Urban/Rural using speed limit held in IRIS then review with intention to store in IRIS.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
▪ Town site boundary data set reviewed but not considered appropriate. Code Category 1 Rural / open area 2 Urban / rural town or village
23 W P 4.22 Speed limit Y NUMBER(2) Y ▪ Posted numerical speed limit. SPEED_LIMIT ▪ Return max (SPLI.IIT_SPEED_LIMIT) for Segment. Code Category 1 <30km/h 2 35km/h 3 40km/h 4 45km/h 5 50km/h 6 55km/h 7 60km/h 8 65km/h 9 70km/h 10 75km/h 11 80km/h 12 85km/h 13 90km/h 14 95km/h 15 100km/h 16 105km/h 17 110km/h 18 115km/h 19 120km/h 20 125km/h
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
21 130km/h 22 135km/h 23 140km/h 24 145km/h 25 >=150km/h
24 X BE 4.23 Motorcycle speed limit Y NUMBER(2) Y ▪ Posted numerical speed limit for motor cycles. SPEED_LIMIT_MOTORCYCLE ▪ Equals Speed limit. ▪ Same List of Values as Speed limit. 25 Y BF 4.24 Truck speed limit Y NUMBER(2) Y ▪ Posted numerical speed limit for trucks. SPEED_LIMIT_TRUCK ▪ Same List of Values as Speed limit. ▪ If Speed limit = 110 km/h then set to 100km/h, otherwise equals Speed limit. ▪ Or alternatively; − if a Speed Limit Condition exists on any RAV Network (as stored in IRIS), return this value. Where more than one condition is present, return the lowest speed − otherwise, as above. 26 Z BB 4.25 Differential speed limits Y NUMBER(1) Y ▪ Records the difference in either the operating speed or speed limit between cars and trucks when SPEED_LIMIT_DIFFERENTIAL it exceeds 20 km/h. ▪ If Speed limit – Truck speed limit > 20 then Differential speed limit is Present (2). ▪ Otherwise Not present (1). Code Category 1 Not present 2 Present
27 AA AS 4.26 Median Type Y NUMBER(1) Y ▪ Records road infrastructure feature that separates the two opposing traffic flows. MEDIAN_TYPE ▪ Medians, barriers and line marking held in IRIS, just need to review and agree on business logic to map data to codes. ▪ Return first occurrence within Segment; − If road is a Ramp or Rotary then return One way (13). Note; assuming a Left or Right carriageway is not considered one way. − If MEDI.IIT_MEDIAN_TYPE is Kerbed or Raised or Free Draining or Depressed then Median Type code is 3, 4, 5, 6 or 7 as determined by MEDI.IIT_WIDTH.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
− If MEDI.IIT_MEDIAN_TYPE is Painted or Other Level Treatment and MEDI. IIT_WIDTH > 1.0m then code is Central hatching (10). − If LINE.LINE_MARKING is not Edge Line then a Centreline is present (11). − Cannot recall many instances where a physical divider is used to separate opposing traffic flows and none of the above exists? GEH back to back kerb? Code Category 1 Safety barrier – metal 2 Safety barrier – concrete 3 Physical median width >= 20.0m 4 Physical median width >= 10.0m to < 20.0m 5 Physical median width >= 5.0m to < 10.0m 6 Physical median width >= 1.0m to < 5.0m 7 Physical median width >= 0m to < 1.0m 8 Continuous central turning lane 9 Flexipost 10 Central hatching (>1m) 11 Centre line 12 Safety barrier – motorcycle friendly 13 One way 14 Wide centre line (0.3m to 1m) 15 Safety barrier – wire rope
28 AB AY 4.27 Centreline rumble strips N NUMBER(1) Y ▪ Textured markings running along the centre of the road whose function is to warn drivers crossing CENTRELINE_RUMBLE the median. ▪ Default to 1 (JK)? Code Category 1 Not present 2 Present
29 AC AN 4.30 Roadside severity – driver-side distance N NUMBER(1) Y ▪ Distance to nearest object to the edge line likely to be reached which could result in serious or fatal injury to road users.
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SEVERITY_DISTANCE_DRIVER ▪ Objects were determined and stored in IRIS (Road Hazard Rating – RHRA) as part of the last rating exercise. The object, which side of road it is located, and offset were recorded. ▪ The suitability of this data and its currency needs to be assessed in the context of significant network changes or maintenance. ▪ The below commentary in D11#120567 summarises the broad findings from the previous rating exercise. Offset Comment Within the RHR data there are few objects within this range other than 0 to <1 m barriers. The majority of high severity hazards on the Main Roads WA network are 1 to <5 m within this range. 5 to <10 m Approximately 25% (by length) of RHR data has hazards within this offset. As part of the RHR typically hazards were only recorded to 10 m due to >=10 m limitation of accuracy of measured.
Code Category 1 0 to <1m 2 1 to <5m 3 5 to <10m 4 >= 10m
30 AD AO 4.30 Roadside severity – driver-side object N NUMBER(2) Y ▪ Nearest object likely to be reached which could result in serious or fatal injury to road user. SEVERITY_OBJECT_DRIVER ▪ Similar comments as per Roadside severity – driver-side distance, the business needs to consider what is the cost/benefit of utilising the previous rating data set, undertaking a gap filling exercise or resurvey. Code Category 1 Safety barrier – metal 2 Safety barrier – concrete 3 Safety barrier – motorcycle friendly 4 Safety barrier – wire rope 5 Aggressive vertical face
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
6 Upwards slope – rollover gradient 7 Upwards slope – no rollover gradient 8 Deep drainage ditch 9 Downwards slope 10 Cliff 11 Tree >=10cm dia. 12 Sign, post or pole >= 10cm dia. 13 Rigid structure/bridge or building 14 Semi-rigid structure or building 15 Unprotected safety barrier end 16 Large boulders >=20cm high 17 None
31 AE AL 4.28 Roadside severity – passenger-side distance N NUMBER(1) Y ▪ See Roadside severity – driver-side distance. SEVERITY_DISTANCE_PASSENGER 32 AF AM 4.29 Roadside severity – passenger-side object N NUMBER(2) Y ▪ See Roadside severity – driver-side object. SEVERITY_OBJECT_PASSENGER 33 AG V 4.31 Shoulder rumble strips Y NUMBER(1) Y ▪ Textured markings running along a road whose function is to warn drivers leaving the travelled SHOULDER_RUMBLE way on the left side of the roadway. ▪ For each Segment, if inventory type LINE. IIT_EDGELINE_PRESENT = 1 exists (Audio tactile edge line), then Shoulder rumble strip is Present (2), otherwise 1. ▪ Note, the edgeline present inventory does not need to exist along the entire Segment (it can be partial and return the Code 2). Code Category
1 Not present 2 Present
34 AH T 4.33 Paved shoulder – drivers’ side Y NUMBER(1) Y ▪ Refers to the safe and drivable section of the road to the side of the edge line on the right-hand SHOULDER_DRIVER side of the carriageway. ▪ Measured from the centre of the shoulder marking (edge line) to the edge of the paving.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
▪ If no edge line, then no paved shoulder exists. Refer iRAP Coding Manual 4.32. ▪ Return value of PASH. IIT_WIDTH and assign Code as per below table. ▪ For Carriageway A or Left (1) use XSP = R for sealed shoulder. ▪ For Carriageway B or Right (2) use XSP = L for sealed shoulder. ▪ For Carriageway U or Single use XSP = R for sealed shoulder. ▪ Note; ‘safe drivable section’ has been interpreted as only the sealed portion of the shoulder measured from the centre of the edgeline to the edge of the paving. If an unsealed shoulder is to be included then the above logic will alter. Similarly, are we prepared to accept that a sealed shoulder will predominantly have an edge line or do we incorporate the IRIS line marking inventory? Code Category
1 Wide (>= 2.4m) 2 Medium (>= 1.0m to < 2.4m) 3 narrow (>= 0m to < 1.0m) 4 None
35 AI V 4.32 Paved shoulder left – passenger side Y NUMBER(1) Y ▪ Refers to the safe and drivable section of the road to the side of the edge line on the left-hand side SHOULDER_PASSENGER of the carriageway. ▪ Measured from the centre of the shoulder marking (edge line) to the edge of the paving. ▪ If no edge line, then no paved shoulder exists. Refer iRAP Coding Manual 4.32. ▪ Refer Paved shoulder right – drivers’ side. ▪ For Carriageway A use XSP = L for sealed shoulder. ▪ For Carriageway B use XSP = R for sealed shoulder. ▪ For Carriageway U use XSP = L for sealed shoulder. 36 AJ AP 4.34 Intersection type Y NUMBER(2) Y ▪ Records presence and type of intersection. INTERSECTION_TYPE ▪ Subject to determination of business rules, high confidence that this could be derived from IRIS using a mix of intersection data, where ramps meet through roads, gaps in medians, rail crossings and traffic sign data. Code Category 1 Merge lane 2 Roundabout
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
3 3–leg (unsignalised) with protected turn lane 4 3–leg (unsignalised) with no protected turn lane 5 3–leg (signalised) with protected turn lane 6 3–leg (signalised) with no protected turn lane 7 4–leg (unsignalised) with protected turn lane 8 4–leg (unsignalised) with no protected turn lane 9 4–leg (signalised) with protected turn lane 10 4–leg (signalised) with no protected turn lane 11 Do not use this code 12 None 13 Railway Crossing – passive (signs only) 14 Railway Crossing – active (flashing lights / boom gates) 15 Median crossing point – informal 16 Median crossing point – formal 17 Mini roundabout
37 AK BN 4.35 Intersection channelization ▪ Raised or coloured islands that designate intended vehicle path. INTERSECTION_CHANNELISATION ▪ Turn pockets or slip lanes. Code Category
1 Adequate 2 Poor
3 Not applicable
38 AL AR 4.37 Intersection quality N ▪ Could use existence of signage from IRIS. INTERSECTION_QUALITY 39 AM AQ 4.36 Intersecting road volume Y ▪ Could use mix of Main Roads and Local Government traffic data where available. INTERSECTING_ROAD_VOLUME ▪ For many rural intersection values will be low, perhaps use road hierarchy to estimate vehicles per day, maybe consider Special Use Category or whether intersecting road is sealed (surface type)?
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40 AN AK 4.38 Property access points ▪ ANRAM attribute name; Access points − Access points. ACCESS_POINTS ▪ IRIS stores Control of Access. ▪ For vast majority of rural network intersecting roads could be used. Note; access to mine sites have recently been added to support HVS needs. ▪ Roadside stopping paces to be considered. ▪ Investigating use of property address database or whether Landgate hold access information in any of the topographical data sets. 41 AO O 4.39 Number of lanes Y ▪ Should record the predominant character of the road and changes over short lengths of road (less NUMBER_OF_LANES than 400m) should not be recorded. ▪ Based on single direction of travel or both. Exclude overtaking lanes less than 400m in length and auxiliary lanes such as turn pockets or dedicated bus lanes etc. Assume gravel road has 1 lane in either direction? ▪ Count existence of L1 to L9 or R1 to R9 or LO and RO respectively, return value based on below table. Code Carriageway Category 1 A or B or U One 2 A or B or U Two 3 A or B or U Three 4 A or B or U Four or more 5 U Two and one 6 U Three and two
42 AP S 4.40 Lane width Y ▪ Get the minimum lane width within each Segment for lanes as defined in Number of lanes, LANE_WIDTH assign value as per below table. ▪ Methodology for gravel roads to be considered? Code Category 1 Wide (>= 3.25m) 2 Medium (>= 2.75m to < 3.25m) 3 narrow (>= 0m to < 2.75m)
43 AQ W 4.41 Curvature Y ▪ Records horizontal alignment of road.
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CURVATURE ▪ If road is a roundabout, then set value to 1 (‘straight or gently curving’). ▪ Get smallest curve radius within section and assign value as; Code Category Curve radius (r) 1 Straight or gently curving r >900m 2 Moderate 499m < r 901m 3 Sharp 199m < r <500m 4 Very sharp r < 200m
44 AR X 4.42 Quality of curve Y ▪ How easy it is to judge how sharp a curve is and if it can be driven safely. QUALITY_OF_CURVE ▪ Some historic logic is used based on radius and presence (or lack of) of warming signs, needs clarification. Code Category 1 Adequate 2 Poor 3 Not applicable
45 AJ Z 4.43 Grade Y ▪ Gradient of road. GRADE ▪ Will obtain from new inertial data. Code Category 1 >= 0% to <7.5% 2 Not applicable 3 Not applicable 4 >= 7.5% to <10% 5 >= 10%
46 AT AA 4.44 Road condition Y ▪ ANRAM attribute name; ROAD_CONDITION − Road surface condition. Road surface condition ▪ Ability of road to provide a level, even running surface, free from major defects. ▪ Ignore medium (historic approach) unless new methodology is proposed. ▪ If roughness OR rutting below Main Roads WA intervention level by link category. Roughness: M>3.44, A>3.82, B>4.2, C>5.33, D>5.33. OR Rutting: M/A>20, B/C/D>30 then set value to 3, otherwise 1.
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Code Category 1 Good 2 Medium 3 Poor
47 AV AW 4.45 Skid resistance / grip Y ▪ Could use mix of Textured data (macrotexture) and seal type. SKID_RESISTANCE-GRIP ▪ Use requirements outlined within the “Guidelines for Managing Surface Friction on. ▪ Main Roads in WA” Extract included below; High Speed Roads and Roads Surfaced with a Sprayed Seal This section is applicable to roads with a posted speed limit of 90 km/hr or greater and all roads surfaced with a sprayed seal or microsurfacing (slurry seal) regardless of the posted speed limit. Investigatory levels for texture are shown in the table below based on sand patch measured textured depth (or a SPTD equivalent from laser measured road data). Sections of road below the investigatory level should be assessed using a risk based process. The Road Surface Inspection Sheet included at Appendix A may assist with this process. If there is doubt about which level applies to a section of road the default limit should be 1.0mm. Site Description Investigatory level Manoeuvre-free and relatively flat or low vertical gradient 0.8mm ALL other areas including: Traffic light controlled intersections Roundabout approaches 1.0mm Curves with tight radius ≤ 250 m Gradients 5% and 50 m long Roads Surfaced with Asphalt and with a Posted Speed Limit less than 90 km/hr Where a road is surfaced with asphalt and the posted speed limit is less than 90 km/hr the approach for maintaining surface friction will be to use a skid resistance approach using the SCRIM or BPT. Investigatory levels for different site descriptions of a road are shown in the following table. Sections of road below the investigatory level should be assessed using a risk based process. The Road Surface Inspection Sheet included at Appendix A may assist with this process. Investigatory level of Investigatory level Site Description SRV at 20ºC (SCRIM) of SRV at 20ºC (BPT)
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Manoeuvre-free and relatively flat or low 0.40 40 vertical gradient ALL other areas including: Traffic light controlled intersections Roundabout approaches 0.45 45 Curves with tight radius ≤ 250 m Gradients 5% and 50 m long
Code Category 5 Unsealed poor 4 Unsealed adequate 3 Sealed poor 2 Sealed medium 1 Sealed adequate
48 AV Y 4.46 Delineation Y ▪ Records the road attributes which informs drivers of road conditions to keep them within the driven DELINIATION lane and aware of the road ahead. It is based on a combination of factors including edge lines, guideposts and signage. ▪ Consideration for gravel roads where signage and post may exist but no edge lines. ▪ If line marking types 3 or 7 (separation + edge or separation + lane + edge) exist within Segment the set value to 1, otherwise 2. Code Category
1 Adequate 2 Poor
49 AW AX 4.47 Street Lighting N Code Category STREET_LIGHTING 1 Not present 2 Present
50 AX AG 4.48 Pedestrian crossing facilities N ▪ Could use presence of signals at intersection, or existence of sign in median or presence of PEDESTRIAN_CROSSING_FACILITIES median or pedestrian bridge over road etc. Code Category
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1 Grade separated facility 2 Signalised with refuge 3 Signalised without refuge 4 Unsignalised marked crossing with refuge 5 Unsignalised marked crossing without a refuge 6 Refuge only 7 No facility 14 Unsignalised raised marked crossing with refuge 15 Unsignalised raised marked crossing without refuge 16 Raised unmarked crossing with refuge 17 Raised unmarked crossing without refuge
51 AY AH 4.49 Pedestrian crossing quality N ▪ Three primary factors to be considered: PEDESTRIAN_CROSSING_QUALITY − signing − marking or raised crossing − good sight distance. Code Category 2 Poor 1 Adequate 3 Not applicable
52 AZ BM 4.50 Pedestrian crossing facilities – side road N ▪ Presence of purpose built pedestrian crossing facilities. PEDESTRIAN_CROSSING_FACILITIES- ▪ When crossing at a signalised intersection the pedestrian crossing should only be considered SIDE_ROAD signalised if the signals have a pedestrian phase. Code Category 1 Grade separated facility 2 Signalised with refuge 3 Signalised without refuge 4 Unsignalised marked crossing with refuge 5 Unsignalised marked crossing without a refuge
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6 Refuge only 7 No facility 14 Unsignalised raised marked crossing with refuge 15 Unsignalised raised marked crossing without refuge 16 Raised unmarked crossing with refuge 17 Raised unmarked crossing without refuge
53 BA BL 4.51 Pedestrian fencing Y ▪ Fence present to restrict pedestrian crossing flow. PEDESTRIAN_FENCING ▪ Only required one side of the road. ▪ Wall and fence data stored in IRIS, not certain if applicable or of value? Code Category 1 Not present 2 Present
54 BB BA 4.54 Speed management / traffic calming N ▪ Presence of road infrastructure features that will typically reduce the operating speed by 5 to SPEED_MANAGEMENT-TRAFFIC_CALMING 10lm/h below the speed limit. ▪ Possibly derived from sign data held in IRIS, requires business logic to derive. Code Category 1 Not present 2 Present
55 BC N/A 4.55 Vehicle parking ▪ Extent to which vehicle parking along the side of road is present. VEHICLE_PARKING ▪ Parking spaces. Bus stops and general encroachment on the road within 2m of the outside edge of the drivable lane. Code Category 3 Two sides 2 One side 1 None
56 BD AC 4.57 Sidewalk – drivers’ side N Code Category SIDEWALK–DRIVERS_SIDE 4 Non-physical separation 0m to <1.0m
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3 Non-physical separation 1.0m to <3.0m 2 Non-physical separation ≥ 3.0m 1 Physical barrier 7 Informal path 0m to <1.0m 6 Informal path ≥ 1.0m 5 None
57 BE AB 4.56 Sidewalk – passenger side N Code Category SIDEWALK–PASSENGER_SIDE 4 Non-physical separation 0m to <1.0m
3 Non-physical separation 1.0m to <3.0m 2 Non-physical separation ≥ 3.0m 1 Physical barrier 7 Informal path 0m to <1.0m 6 Informal path ≥ 1.0m 5 None
58 BF AZ 4.58 Service road N ▪ Really? Suspect 99% of network will not have road adjacent to main road. Can recall some SERVICE_ROAD instances on Forest Hwy. Code Category 1 Not present 2 Present
59 BG AJ 4.59 Facilities for motorised two wheelers N ▪ Presence of purpose built facilities for motorcycles and other motorise two-wheelers. FACILITIES_FOR_MOTORISED_TWO_WHEE Code Category LERS 5 Inclusive motorcycle lane on roadway
2 Exclusive one way motorcycle path without barrier 1 Exclusive one way motorcycle path with barrier 4 Exclusive two way motorcycle path without barrier 3 Exclusive two way motorcycle path with barrier 6 None
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60 BH AI 4.60 Bicycle facility N ▪ Could consider determining if width of slow lane (nearest to kerb) is ≥ 4.2m. BICYCLE_FACILITY ▪ Or if PSP is adjacent to road (PSP’s are stored in IRIS), predominantly Metropolitan area. ▪ Lanes dedicated for cyclists are not recorded in IRIS. Code Category 5 Extra wide outside (≥4.2m) 3 On-road lane 2 Off-road path 1 Off-road path with barrier 6 Signed shared roadway 7 Shared use path 4 None
61 BI AV 4.61 Roadworks Y ▪ Major road construction or road works in progress. ROADWORKS ▪ Road side bookings are stored in IRIS but for Metropolitan area only. Code Category 3 Major road works in progress 2 Minor road works in progress 1 No road works
62 BJ BC 4.62 Sight distance N ▪ Horizontal or vertical alignment or physical obstructions such as roadside objects and vegetation SIGHT_DISTANCE that may reduce sight distance to less than 100m. ▪ Very simplistic rating. ▪ Don’t appreciate need to use geometry data (as was past practice). Code Category 2 Poor 1 Adequate
63 BK I 5.1 Vehicle flow (AADT) Y NUMBER(6) ▪ For what year? Need to formalise requirement and methodology to factor previous years based on VOLUME fixed growth rate (previously 2.5% ) or by associating network sections to growth rates derived from Network Performance Sites (NPS).
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▪ In addition, define methodology to fill gaps when data is not available (consider filling gap at traffic section level). ▪ Presume directional volumes for A and B Carriageways and combined for U? 64 BL J 5.2 Motorcycle % ▪ Percentage of Vehicle flow classed as motorised 2 wheel or light 3 wheel vehicle MOTORCYCLE_% ▪ Coding manual specifies category values based on class ranges. ▪ If less than 1% then Category 2, 1% ≤ Category 3 ≤ 5% , 5% ≤ Category 4 ≤ 10% , ▪ Analyse NPS data to derive either single rural average or on a road by road basis. Highly unlikely motorcycles comprise more than 10% of traffic. Code Category 10 100% 9 81% – 99% 8 61% – 80% 7 41% – 60% 6 21% – 40% 5 11% – 20% 4 6% – 10% 3 1% – 5% 2 0% 1 Not recorded
65 BM BP 5.3 Pedestrian peak hour flow across the road N ▪ If < 1 then Category 1, If ≥ 1 and ≤ 5 then Category 2, If ≥ 6 and ≤ 25 then Category 3, If ≥ 26 PEDESTRIAN_PEAK_HOUR_FLOW_ACROS and ≤ 50 then Category 4, If ≥ 51 and ≤ 100 then Category 5, If ≥ 101 and ≤ 200 then Category S_THE_ROAD 6, If ≥ 201 and ≤ 300 then Category 7, If ≥ 301 and ≤ 400 then Category 8, If ≥ 401 and ≤ 500 then Category 9, If ≥ 501 and ≤ 900 then Category 10, If > 900 then Category 11. Code Category 11 900+ 10 501 to 900 9 401 to 500 8 301 to 400 7 201 to 300
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6 101 to 200 5 51 to 100 4 26 to 50 3 6 to 25 2 1 to 5 1 0
66 BN BQ 5.4 Pedestrian peak hour flow along the road N ▪ If < 1 then Category 1, If ≥ 1 and ≤ 5 then Category 2, If ≥ 6 and ≤ 25 then Category 3, If ≥ 26 driver-side and ≤ 50 then Category 4, If ≥ 51 and ≤ 100 then Category 5, If ≥ 101 and ≤ 200 then Category PEDESTRIAN_PEAK_HOUR_FLOW_ALONG 6, If ≥ 201 and ≤ 300 then Category 7, If ≥ 301 and ≤ 400 then Category 8, If ≥ 401 and ≤ 500 _THE_ROAD_DRIVER-SIDE then Category 9, If ≥ 501 and ≤ 900 then Category 10, If > 900 then Category 11. Code Category 11 900+ 10 501 to 900 9 401 to 500 8 301 to 400 7 201 to 300 6 101 to 200 5 51 to 100 4 26 to 50 3 6 to 25 2 1 to 5 1 0
67 BO BR 5.5 Pedestrian peak hour flow along the road N ▪ If < 1 then Category 1, If ≥ 1 and ≤ 5 then Category 2, If ≥ 6 and ≤ 25 then Category 3, If ≥ 26 passenger-side and ≤ 50 then Category 4, If ≥ 51 and ≤ 100 then Category 5, If ≥ 101 and ≤ 200 then Category PEDESTRIAN_PEAK_HOUR_FLOW_ALONG 6, If ≥ 201 and ≤ 300 then Category 7, If ≥ 301 and ≤ 400 then Category 8, If ≥ 401 and ≤ 500 _THE_ROAD_ PASSENGER-SIDE then Category 9, If ≥ 501 and ≤ 900 then Category 10, If > 900 then Category 11. Code Category 11 900+
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10 501 to 900 9 401 to 500 8 301 to 400 7 201 to 300 6 101 to 200 5 51 to 100 4 26 to 50 3 6 to 25 2 1 to 5 1 0
68 BP N/A 5.6 Bicycle peak hour flow N ▪ Coding manual acknowledges this is a random sample. BICYCLE_PEAK_HOUR_FLOW ▪ Some sections of Metropolitan Highways prohibit cyclists. ▪ Previous decision was to default value to 1 (1 bicycle observed per 100m)? ▪ If < 1 then Category 1, If ≥ 1 and ≤ 5 then Category 2, If ≥ 6 and ≤ 25 then Category 3, If ≥ 26 and ≤ 50 then Category 4, If ≥ 51 and ≤ 100 then Category 5, If ≥ 101 and ≤ 200 then Category 6, If ≥ 201 and ≤ 300 then Category 7, If ≥ 301 and ≤ 400 then Category 8, If ≥ 401 and ≤ 500 then Category 9, If ≥ 501 and ≤ 900 then Category 10, If > 900 then Category 11. Code Category 11 900+ 10 501 to 900 9 401 to 500 8 301 to 400 7 201 to 300 6 101 to 200 5 51 to 100 4 26 to 50 3 6 to 25 2 1 to 5
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1 0
69 BQ Q 5.7 Operating speed (85th percentile) Y ▪ Speed at which 85% of vehicles are travelling. OPERATING_SPEED_(85TH_ PERCENTILE) ▪ Define methodology when not available, i.e. add 10kmph to speed limit. ▪ Note, if required for Metropolitan area then should peak periods be excluded from the calculation? Code Category 1 <30km/h 2 35km/h 3 40km/h 4 45km/h 5 50km/h 6 55km/h 7 60km/h 8 65km/h 9 70km/h 10 75km/h 11 80km/h 12 85km/h 13 90km/h 14 95km/h 15 100km/h 16 105km/h 17 110km/h 18 115km/h 19 120km/h 20 125km/h 21 130km/h 22 135km/h
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23 140km/h 24 145km/h 25 >=150km/h 31 <20mph 32 25mph 33 30mph 34 35mph 35 40mph 36 45mph 37 50mph 38 55mph 39 60mph 40 65mph 41 70mph 42 75mph 43 80mph 44 85mph 45 >=90mph
70 BR R 5.8 Operating speed (mean) Y ▪ Average operating speed. OPERATING_SPEED_(MEAN) ▪ Define methodology when not available. ▪ Note, if required for Metropolitan area then should peak periods be excluded from the calculation? Code Category 1 <30km/h 2 35km/h 3 40km/h 4 45km/h 5 50km/h
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6 55km/h 7 60km/h 8 65km/h 9 70km/h 10 75km/h 11 80km/h 12 85km/h 13 90km/h 14 95km/h 15 100km/h 16 105km/h 17 110km/h 18 115km/h 19 120km/h 20 125km/h 21 130km/h 22 135km/h 23 140km/h 24 145km/h 25 >=150km/h 31 <20mph 32 25mph 33 30mph 34 35mph 35 40mph 36 45mph
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37 50mph 38 55mph 39 60mph 40 65mph 41 70mph 42 75mph 43 80mph 44 85mph 45 >=90mph
71 BS N/A 5.9 Roads that cars can read ▪ Meets specification for vehicles to be able to recognise the delineation (markings and signs). ROADS_THAT_CARS_CAN_READ ▪ Default “Does not meet specification”. Code Category 1 Meets specification 2 Does not meet specification
72 BT BH 5.10 Car star rating policy target N ▪ Records the minimum policy Star Rating target. CAR_STAR_RATING_POLICY_TARGET ▪ Currently not used in ViDA. However, will be included at a later date. Code Category 1 1 Star 2 2 Star 3 3 Star 4 4 Star 5 5 Star 6 Not applicable
73 BU BI 5.10 Motorcycle star rating policy target N ▪ Records the minimum policy Star Rating target. MOTORCYCLE_STAR_RATING_POLICY_TA ▪ Currently not used in ViDA. However, will be included at a later date. RGET Code Category
1 1 Star
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2 2 Star 3 3 Star 4 4 Star 5 5 Star 6 Not applicable
74 BV BJ 5.10 Pedestrian star rating policy target N ▪ Records the minimum policy Star Rating target. PEDESTRIAN_STAR_RATING_POLICY_TAR ▪ Currently not used in ViDA. However, will be included at a later date. GET Code Category
1 1 Star 2 2 Star 3 3 Star 4 4 Star 5 5 Star 6 Not applicable
75 BW BK 5.10 Bike Bicycle star rating policy target N ▪ Records the minimum policy Star Rating target. BIKE_BICYCLE_STAR_RATING_POLICY_TA ▪ Currently not used in ViDA. However, will be included at a later date. RGET Code Category
1 1 Star 2 2 Star 3 3 Star 4 4 Star 5 5 Star 6 Not applicable
76 BX N/A N/A Annual fatality growth multiplier Number (1) ▪ Default to 1. ANNUAL_FATALITY_GROWTH_MULTIPLIER ▪ As per iRAP Coding Manual. 77 BY N/A 4.52 School zone warning ▪ Presence of flashing signs & speed limits. SCHOOL_ZONE_WARNING ▪ Static signs or road markings.
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Code Category
1 School zone flashing beacons 2 School zone static signs or road markings 3 No school zone warning 4 Not applicable (no school at the location)
78 BZ N/A 4.53 School zone crossing supervisor ▪ Presence of crossing attendant. SCHOOL_ZONE_CROSSING_SUPERVISOR ▪ Refer Police for data source. Code Category
1 School zone crossing supervisor present at school start and finish times 2 School zone crossing supervisor not present 3 Not applicable (no school at the location)
79 CA B N/A Smoothed section ID ▪ ANRAM attribute name: Section − Section. SECTION ▪ This field is an iRAP attribute generated after upload and processing by ViDA. The iRAP data produced from ViDA is then subsequently used within the ANRAM file as the “Section” attribute at column “B”. NOTE: Smoothing ca be variable depending on how the user chooses to smooth the result in iRAP. It can be selected using the default assumptions, or it can be specified break point along the road to manage changes over time to cater for specific long-term projects. BS N/A Jurisdiction Y ▪ Default to 5 (WA)? JURISDICTION BT N/A Road type Y ▪ Subject to definition of business rules. ROAD_TYPE Source: This content produced by Main Roads WA team Notes: ▪ Attribute Descriptiv e Text ATTRIBUTE_NAME (Blue = ANRAM Attribute Name (where diff to iRAP) Red = Mandatory or NOT NULL, Grey = Optional or NULL allowed) ▪ Segment – means the output 100m data
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APPENDIX B EXAMPLE ROAD SEGMENT LENGTH APPROACH
The Main Roads WA proposed approach for addressing the varying segment length issue is illustrated in this appendix.
The segment length for both the traditional and corporate data sets was calculated using the trigonometric formula in Equation A1 below. The results are presented in Table B 1 for sample of segments. Table B 1 shows the calculated segment lengths were similar for both corporate and traditional approaches, and also shows the ‘Applied length’ (which corresponds with the calculated length rounded to one decimal place).
Trigonometric formula = = ACOS(COS(RADIANS(90-LatitudeStart))×COS(RADIANS(90-LatitudeEnd))+SIN(RADIANS(90- A1 for calculating segment LatitudeStart))×SIN(RADIANS(90-LatitudeEnd))×COS(RADIANS(LongitudeStart-LongitudeEnd)))×6371 length
Table B 1: Calculation and comparison of segment length traditional and corporate data sets
Traditional (TSD) coded data set Main Roads WA corporate data set Main Corporate Traditional Roads WA Data set Data set Diff in Applied Image reference Road Name Section Longitud Longitud Code Length Length Latitude Distance Latitude Distance Dist Length e e Extract Using Trig Using Trig Length Formula Formula Great Eastern Hwy Fr Llyod St Frame 97 Great Eastern Highway Perth-Coolgargie –31.8917 116.0134 16.165 –31.89184028 116.0134676 16.159 –0.006 0.088 Great Eastern Hwy Fr Llyod St Frame 107 Great Eastern Highway Perth-Coolgargie –31.89192164 116.0144094 16.265 –31.8920073 116.0144628 16.247 –0.018 0.093 0.096 0.098 0.1 Great Eastern Hwy Fr Llyod St Frame 117 Great Eastern Highway Perth-Coolgargie –31.89209838 116.0154319 16.365 –31.89218163 116.0154945 16.34 –0.025 0.102 0.099 0.099 0.1 Great Eastern Hwy Fr Llyod St Frame 127 Great Eastern Highway Perth-Coolgargie –31.89227322 116.0164619 16.465 –31.89236291 116.0165367 16.442 –0.023 0.099 0.100 0.099 0.1 Great Eastern Hwy Fr Llyod St Frame 137 Great Eastern Highway Perth-Coolgargie –31.89245153 116.0174942 16.565 –31.89253455 116.0175735 16.541 –0.024 0.095 0.100 0.099 0.1 Great Eastern Hwy Fr Llyod St Frame 147 Great Eastern Highway Perth-Coolgargie –31.89263354 116.0185289 16.665 –31.89271382 116.0186077 16.636 –0.029 0.102 0.100 0.100 0.1 Great Eastern Hwy Fr Llyod St Frame 157 Great Eastern Highway Perth-Coolgargie –31.89281779 116.0195549 16.765 –31.89289898 116.0196463 16.738 –0.027 0.1 0.100 0.099 0.1 Great Eastern Hwy Fr Llyod St Frame 167 Great Eastern Highway Perth-Coolgargie –31.89300193 116.0205819 16.865 –31.89308234 116.0206719 16.838 –0.027 0.101 0.099 0.099 0.1 Great Eastern Hwy Fr Llyod St Frame 177 Great Eastern Highway Perth-Coolgargie –31.89318552 116.021607 16.965 –31.8932727 116.0217118 16.939 –0.026 0.106 0.100 0.099 0.1 Great Eastern Hwy Fr Llyod St Frame 187 Great Eastern Highway Perth-Coolgargie –31.89336988 116.0226348 17.065 –31.89345673 116.0227478 17.045 –0.02 0.099 0.100 0.099 0.1 Great Eastern Hwy Fr Llyod St Frame 197 Great Eastern Highway Perth-Coolgargie –31.89354048 116.0236667 17.165 –31.89374714 116.0248308 17.245 0.08 0.104 0.199 0.099 0.2
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APPENDIX C CODING COMPARISON BETWEEN DATA SETS
The tables in this appendix present a coding comparison between the traditionally coded (TSD) and corporate AusRAP data sets. C.1 Critical Attributes Coding comparison for the attributes was considered critical to the estimation of risk (as per Table 2.4). The green shaded cells indicate where the coding is the same.
Table C 1: Speed limit
Main Roads WA corporate Traditional 7 9 11 13 15 17 5 10 7 115 36 23 2 9 47 3 15 11 4 1 323 11 49 30 13 1 2 9 95 33 15 3 237 29 17 4 1 5169 Same: 95.7%, Different: 4.3%.
Table C 2: Road condition
Main Roads WA corporate Traditional 1 2 3 1 2668 2307 1172 2 14 57 34 Same: 43.6%, Different: 56.4%.
Table C 3: Grade
Main Roads WA corporate Traditional 1 2 1 6246 5 4 1 Same: 99.9%, Different: 0.1%.
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Pavement width
Table C 4: Lane width Table C 5: Paved shoulder Table C 6: Paved shoulder drivers’ side passengers’ side Main Roads WA corporate Main Roads WA Main Roads WA corporate corporate Traditional 1 2 1 5440 55 Traditional 1 2 3 Traditional 1 2 3 2 78 679 1 13 5 2 1 834 55 32 Same: 97.9%, Different: 2.1%. 2 1 778 37 2 24 728 72 3 8 2678 2400 3 32 2045 2174 4 9 39 282 4 13 30 213 Same: 51%, Different: 49%. Same: 59.8%, Different: 40.2%.
Table C 7: Curvature
Main Roads WA corporate Traditional 1 2 3 4 1 5054 250 48 2 2 705 105 88 Same: 82.5%, Different: 17.5%.
Table C 8: Roadside distance drivers’ side
Main Roads WA corporate Traditional 1 2 3 4 1 10 27 35 2 12 462 281 1471 3 2 191 458 1407 4 4 135 147 1610 Same: 40.6%, Different: 59.4%.
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Table C 9: Roadside object drivers’ side
Main Roads WA corporate Traditional 1 2 4 6 8 9 10 11 12 15 17 1 31 2 2 1 10 2 36 4 3 6 14 4 2 2 24 1 3 3 36 5 8 6 10 1 2 41 10 4 31 8 65 7 5 5 38 2 1 21 2 26 8 4 6 29 76 6 38 19 128 9 2 11 22 38 306 287 97 1213 386 4 1563 12 30 5 4 12 1 16 57 326 13 1 1 10 15 9 2 1 4 16 4 17 45 1 82 35 47 7 1 72 117 5 673 Same: 33.9%, Different: 66.1%.
Table C 10: Roadside distance passengers’ side
Main Roads WA corporate Traditional 1 2 3 4 1 4 9 1 49 2 6 630 188 963 3 445 621 1338 4 159 128 1711 Same: 47.4%, Different: 52.6%.
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Table C 11: Roadside object passengers’ side
Main Roads WA corporate Traditional 1 2 4 6 8 9 10 11 12 15 17 1 65 3 2 15 2 5 3 4 4 4 3 71 1 10 5 2 18 2 3 2 28 6 2 1 46 4 5 22 17 1 163 7 5 2 33 4 2 11 7 139 8 4 6 27 65 5 39 20 177 9 1 3 11 43 1 56 223 163 85 1 811 152 7 2128 12 49 4 18 4 23 4 50 444 3 442 13 2 3 1 1 11 15 2 1 1 2 3 16 6 17 12 10 3 27 3 24 7 1 369 Same: 30%, Different: 70%.
Table C 12: Intersection type
Main Roads WA corporate Traditional 1 3 4 5 7 8 9 12 14 16 1 3 5 6 21 1 3 28 1 1 1 34 4 1 28 60 1 104 1 5 1 1 6 1 1 7 6 3 6 8 1 7 7 17 9 1 1 1 7 10 1 12 19 58 93 2 6 7 5686 1 1 14 1 3 3 15 10 16 2 2 Same: 92.6%, Different: 7.4%.
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Table C 13: Intersecting road volume
Main Roads WA corporate Traditional 1 2 3 4 5 6 7 3 1 1 2 5 4 1 1 23 32 30 71 5 1 1 1 14 22 71 6 1 42 59 7 5 7 28 34 113 5686 Same: 92.2%, Different: 7.8%.
Table C 14: Median type
Main Roads WA corporate Traditional 1 2 3 4 5 6 10 11 14 15 1 5 1 8 4 2 12 4 43 4 3 1 99 4 4 310 13 4 1 47 43 76 1 632 11 5 15 56 8 129 1 6 1 14 32 1 68 2 8 2 3 10 52 11 1 1879 2603 15 1 2 17 42 Same: 34.7%, Different: 65.3%. C.2 Attributes where Coding is the Same The following attributes are coded the same in both data sets (Same: 100%, Different: 0%): ▪ pedestrian fencing ▪ speed management / traffic calming ▪ motorcycle facility ▪ car star rating policy target ▪ motorcycle star rating policy target ▪ pedestrian star rating policy target ▪ bicycle star rating policy target ▪ annual fatality growth multiplier ▪ school zone warning ▪ school zone crossing supervisor ▪ sight distance ▪ AADT (same AADT data – provided by Main Roads WA – used in both data sets).
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C.3 Attributes where Coding is Different
Table C 15: Divided / undivided
Main Roads WA corporate Traditional 1 2 3 1 690 8 2 1019 3 3 4532 Same: 99.8%, Different: 0.2%.
Table C 16: Area type
Main Roads WA corporate Traditional 1 2 1 5143 737 2 131 241 Same: 86.1%, Different: 13.9%.
Table C 17: Speed limit
Main Roads WA corporate Traditional 7 9 11 13 15 17 5 10 7 115 36 23 2 9 47 3 15 11 4 1 323 11 49 30 13 1 2 9 95 33 15 3 237 29 17 4 1 5169 Same: 95.7%, Different: 4.3%.
Table C 18: Truck speed limit
Main Roads WA corporate Traditional 7 9 11 13 15 17 5 10 7 115 36 23 2 9 47 3 15 11 4 1 323 11 49 30 13 1 2 9 95 33 15 7 1 237 5198 Same: 13.1%, Different: 86.9%.
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Table C 19: Median type
Main Roads WA corporate Traditional 1 2 3 4 5 6 10 11 14 15 1 5 1 8 4 2 12 4 43 4 3 1 99 4 4 310 13 4 1 47 43 76 1 632 11 5 15 56 8 129 1 6 1 14 32 1 68 2 8 2 3 10 52 11 1 1879 2603 15 1 2 17 42 Same: 34.7%, Different: 65.3%.
Table C 20: Centreline rumble strips
Main Roads WA corporate Traditional 1 2 1 3597 616 2 2039 Same: 90.1%, Different: 9.9%.
Table C 21: Roadside distance drivers’ side
Main Roads WA corporate Traditional 1 2 3 4 1 10 27 35 2 12 462 281 1471 3 2 191 458 1407 4 4 135 147 1610 Same: 40.6%, Different: 59.4%.
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Table C 22: Roadside object drivers’ side
Main Roads WA corporate Traditional 1 2 4 6 8 9 10 11 12 15 17 1 31 2 2 1 10 2 36 4 3 6 14 4 2 2 24 1 3 3 36 5 8 6 10 1 2 41 10 4 31 8 65 7 5 5 38 2 1 21 2 26 8 4 6 29 76 6 38 19 128 9 2 11 22 38 306 287 97 1213 386 4 1563 12 30 5 4 12 1 16 57 326 13 1 1 10 15 9 2 1 4 16 4 17 45 1 82 35 47 7 1 72 117 5 673 Same: 33.9%, Different: 66.1%.
Table C 23: Roadside distance passengers’ side
Main Roads WA corporate Traditional 1 2 3 4 1 4 9 1 49 2 6 630 188 963 3 445 621 1338 4 159 128 1711 Same: 47.4%, Different: 52.6%.
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Table C 24: Roadside object passengers’ side
Main Roads WA corporate Traditional 1 2 4 6 8 9 10 11 12 15 17 1 65 3 2 15 2 5 3 4 4 4 3 71 1 10 5 2 18 2 3 2 28 6 2 1 46 4 5 22 17 1 163 7 5 2 33 4 2 11 7 139 8 4 6 27 65 5 39 20 177 9 1 3 11 43 1 56 223 163 85 1 811 152 7 2128 12 49 4 18 4 23 4 50 444 3 442 13 2 3 1 1 11 15 2 1 1 2 3 16 6 17 12 10 3 27 3 24 7 1 369 Same: 30%, Different: 70%.
Table C 25: Shoulder rumble strips
Main Roads WA corporate Traditional 1 2 1 3488 245 2 1602 917 Same: 70.5%, Different: 29.5%.
Table C 26: Paved shoulder drivers’ side
Main Roads WA corporate Traditional 1 2 3 1 13 5 2 2 1 778 37 3 8 2678 2400 4 9 39 282 Same: 51%, Different: 49%.
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Table C 27: Paved shoulder passengers’ side
Main Roads WA corporate Traditional 1 2 3 1 834 55 32 2 24 728 72 3 32 2045 2174 4 13 30 213 Same: 59.8%, Different: 40.2%.
Table C 28: Intersection type
Main Roads WA corporate Traditional 1 3 4 5 7 8 9 12 14 16 1 3 5 6 21 1 3 28 1 1 1 34 4 1 28 60 1 104 1 5 1 1 6 1 1 7 6 3 6 8 1 7 7 17 9 1 1 1 7 10 1 12 19 58 93 2 6 7 5686 1 1 14 1 3 3 15 10 16 2 2 Same: 92.6%, Different: 7.4%.
Table C 29: Intersection channelization
Main Roads WA corporate Traditional 1 2 1 5865 281 2 60 46 Same: 94.5%, Different: 5.5%.
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Table C 30: Intersecting road volume
Main Roads WA corporate Traditional 1 2 3 4 5 6 7 3 1 1 2 5 4 1 1 23 32 30 71 5 1 1 1 14 22 71 6 1 42 59 7 5 7 28 34 113 5686 Same: 92.2%, Different: 7.8%.
Table C 31: Intersection quality
Main Roads WA corporate Traditional 1 2 3 1 95 78 206 3 92 95 5686 Same: 92.5%, Different: 7.5%.
Table C 32: Property access
Main Roads WA corporate Traditional 1 2 4 1 48 385 2 34 3 9 198 4 4 58 5516 Same: 88.2%, Different: 11.8%.
Table C 33: Number of lanes
Main Roads WA corporate Traditional 1 2 3 4 1 7 4349 25 2 1 1599 84 14 3 17 5 11 145 Same: 26%, Different: 74%.
Table C 34: Lane width
Main Roads WA corporate Traditional 1 2 1 5440 55 2 78 679 Same: 97.9%, Different: 2.1%.
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Table C 35: Curvature
Main Roads WA corporate Traditional 1 2 3 4 1 5054 250 48 2 2 705 105 88 Same: 82.5%, Different: 17.5%.
Table C 36: Curve quality
Main Roads WA corporate Traditional 1 2 3 1 594 14 290 3 811 18 4525 Same: 81.9%, Different: 18.1%.
Table C 37: Grade
Main Roads WA corporate Traditional 1 2 1 6246 5 4 1 Same: 99.9%, Different: 0.1%.
Table C 38: Road condition
Main Roads WA corporate Traditional 1 2 3 1 2668 2307 1172 2 14 57 34 Same: 43.6%, Different: 56.4%.
Table C 39: Skid resistance
Main Roads WA corporate Traditional 1 2 3 4 1 1867 83 46 18 2 4045 96 94 1 3 2 Same: 31.4%, Different: 68.6%.
Table C 40: Delineation
Main Roads WA corporate Traditional 1 2 1 1274 4885 2 12 81 Same: 21.7%, Different: 78.3%.
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Table C 41: Street lighting
Main Roads WA corporate Traditional 1 1 5594 2 658 Same: 89.5%, Different: 10.5%.
Table C 42: Pedestrian crossing
Main Roads WA corporate Traditional 7 2 6 3 4 4 1 6 41 7 6200 Same: 99.2%, Different: 0.8%.
Table C 43: Pedestrian crossing quality
Main Roads WA corporate Traditional 3 1 46 3 6206 Same: 99.3%, Different: 0.7%.
Table C 44: Pedestrian crossing – side road
Main Roads WA corporate Traditional 7 2 10 6 23 7 6219 Same: 99.5%, Different: 0.5%.
Table C 45: Parking / side friction
Main Roads WA corporate Traditional 1 1 6135 2 115 3 2 Same: 98.1%, Different: 1.9%.
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Table C 46: Sidewalk drivers’ side
Main Roads WA corporate Traditional 5 2 32 3 24 4 11 5 1773 6 51 7 4361 Same: 28.4%, Different: 71.6%.
Table C 47: Sidewalk passengers’ side
Main Roads WA corporate Traditional 5 1 4 2 40 3 26 4 66 5 369 6 1463 7 4284 Same: 5.9%, Different: 94.1%.
Table C 48: Service road
Main Roads WA corporate Traditional 1 1 6244 2 8 Same: 99.9%, Different: 0.1%.
Table C 49: Bicycle facility
Main Roads WA corporate Traditional 4 1 2 2 3 3 1043 4 5149 5 14 6 13 7 28 Same: 82.4%, Different: 17.6%.
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Table C 50: Roadworks
Main Roads WA corporate Traditional 1 1 5882 2 370 Same: 94.1%, Different: 5.9%.
Table C 51: 85th%ile speed and Mean speed
Main Roads WA corporate Traditional 7 9 11 13 15 17 5 10 7 123 35 18 9 53 2 10 11 11 2 326 10 44 25 13 3 7 10 96 24 15 6 234 29 17 14 4 5156 Same: 95.8%, Different: 4.2%. Coding comparison for attributes ‘85th%ile speed’ and ‘mean speed’ were the same.
C.4 Attributes for which Default Values Applied by Main Roads WA The following attributes are coded the same in both data sets (Same: 100%, Different: 0%): ▪ motorcycle % ▪ pedestrian peak hour flow across road ▪ pedestrian peak hour flow along road drivers’ side ▪ pedestrian peak hour flow along road passengers’ side ▪ bicycle peak hour flow.
For the attribute ‘Roads that cars can read’, default values were used for both coding approaches, however different defaults were applied (i.e. Same: 0%, Different: 100%).
Table C 52: Upgrade impact cost
Main Roads WA corporate Traditional 1 1 4307 2 1448 3 497 Same: 99.8%, Different: 0.2%.
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Table C 53: Motorcycle observed
Main Roads WA corporate Traditional 1 1 6244 2 8 Same: 99.9%, Different: 0.1%.
Table C 54: Bicycle observed
Main Roads WA corporate Traditional 1 1 6250 2 2 Same: 100%, Different: 0% (note 2 records different).
Table C 55: Pedestrian observed flow across road
Main Roads WA corporate Traditional 1 1 6250 2 2 Same: 100%, Different: 0% (note 2 records different).
Table C 56: Pedestrian observed flow along road drivers’ side
Main Roads WA corporate Traditional 1 1 6237 2 9 3 6 Same: 99.8%, Different: 0.2%.
Table C 57: Pedestrian observed flow along road passengers’ side
Main Roads WA corporate Traditional 1 1 6220 2 19 3 9 4 2 5 1 6 1 Same: 99.5%, Different: 0.5%.
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Table C 58: Land use driver side
Main Roads WA corporate Traditional 5 1 4036 2 1688 3 311 4 214 7 3 Same: 0%, Different: 100%.
Table C 59: Land use passengers’ side
Main Roads WA corporate Traditional 5 1 4263 2 1531 3 235 4 223 Same: 0%, Different: 100%.
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
APPENDIX D CODING COMPARISON BY J KARPINSKI
The table below shows the coding comparison undertaken by Jan Karpinski between traditionally coded AusRAP and corporate data sets.
Table D 1: Comparison of AusRAP data and Main Roads WA data for Eyre Highway (H003), Great Eastern Highway (H005), Great Northern Highway (H006), Coolgardie Norseman Highway (H010) and Victoria Highway (H011)
Source: Karpinski (2014).
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Use of Main Roads WA Corporate Data for Road Safety Risk Data Sets PSS17081-1
APPENDIX E GLOSSARY
The report assumes the reader has prior knowledge of ANRAM v1.0 and AusRAP and their basic terminology. For those not familiar with either risk assessment system, the following definitions may be of assistance.
ANRAM – the current working version of ANRAM used in this project was v1.04.
AusRAP – the Australian version of iRAP (identical). The current version is v3.02.
ViDA – software used by AusRAP to upload and process AusRAP road attribute data and produce star ratings based on risk scores, road investment programs and program-level safety benefits.
AusRAP Star Rating Score (SRS) – AusRAP SRSs are measure of individual risk of severe casualty crash based on AusRAP risk scores. Available only through ViDA at this time.
ANRAM risk score – risk scores calculated by ANRAM for each of the four vehicle crash types (run-off-road, head-on, intersection and other). They represent the individual road user’s risk of a severe (fatal or serious injury) casualty crash. It is referred to as individual risk. There is a general correlation between ANRAM and AusRAP risk scores although the absolute values are quite different. ANRAM v1.0 also uses slightly older risk algorithms than AusRAP.
ANRAM FSI crashes – estimate of the severe (fatal or serious injury) casualty crashes based on road stereotype, engineering features, speed, potential conflicting traffic and severe crash history. It is determined per road section per 5 years produced for each vehicle crash type and then aggregated. It can be expressed as a rate per km as a standardised measure of collective risk, i.e. risk of severe crashes to all road users on that section (vehicle-based crashes).
Individual risk – a measure of risk to an individual road user expressed per kilometre of travel by a vehicle, or vehicle-kilometres travelled (VKT). It is collective risk adjusted for road user exposure. Individual risk is often measured by fatal and serious injury crash rates, or risk assessment scores. AusRAP SRSs and ANRAM risk scores averaged for a given road section are an approximation of individual risk.
Collective risk – a measure that indicates crash frequency as experienced by the community, that is the number of crashes per unit of time. It can be expressed per section, per kilometre, or per intersection. ANRAM outputs Total ANRAM FSI crashes per road section which is converted to Total ANRAM FSI crashes per kilometre to provide a comparison rate of collective risk.
Road attribute – a characteristic of a road, e.g. sealed shoulder width, curvature, AADT. Each attribute has two or more categories with risk values assigned to each, based on safety research evidence.
Road type – ANRAM v1.04 divides roads into six categories, e.g. urban undivided, urban divided. This determines the algorithms used to estimate FSI crashes.
TSD – Traffic Speed Deflectometer (TSD) is now known as iPAVE in Australia.
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