AP-G84/04 Best practice in road use data collection, analysis ... - WIM

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Accessed by AR - ARRB TRANSPORT RESEARCH on 04 Feb 2005 Austroads 2004 — 54 — Best Practices in Road Use Data Collection, Analysis and Reporting 9. Missing data – it is good practice to edit and provide missing data. For estimating missing data, it is necessary to refer to historical and current information from previous and subsequent weeks and available data for the pattern relating to weekdays flows. The management of missing data still requires substantially more research. A vital point is to flag amended data and ensure that the original raw data sets can be recovered should better estimation techniques become available. 10. Short duration counts - Counts of very short durations of a few hours or even 12 hours are not reliable for traffic forecasting. With automatic counting equipment now commonly deployed, the extra cost of collecting data on more days should be minimal. The best way to ensure consistency amongst RAs is for each RA to collect robust data sets, i.e. collecting data over a consecutive period of, say, seven days at Short-term Stations. 11. Weigh-in-motion data - WIM site selection and equipment calibration ensure data accuracy and are requirements of good practice. The potential impact of weather conditions on some pavement surfaces should be noted. As long as all lanes of traffic are monitored at a WIM site, the traffic count and WIM data should add extra value to a counting program. 12. Stakeholder consultation - good practice is achieved by adopting a corporate road use data collection framework and engaging stakeholders. Many activities require road use data and a diverse group of stakeholders are involved. As such, road use data is a corporate asset that requires its share of corporate planning and management. 13. Future research - the following areas are identified: − Determine the threshold values for automatic vehicle classification system by lengths; − Develop a model for the correlation of WIM data with classified counts; and − Develop a policy or business model on data integration, accessibility and pricing.
Accessed by AR - ARRB TRANSPORT RESEARCH on 04 Feb 2005 REFERENCES Austroads 2004 — 55 — Best Practices in Road Use Data Collection, Analysis and Reporting ARRB Transport Research Ltd (2003). Express Weigh AASHTO (1992). Guidelines for traffic data programs. American Association of Highway and Transport Officials, Washington, DC. Australian Bureau of Statistics (1963). Survey of Motor Vehicle Usage. Catalogue No. 9202.0. ABS, Sydney. Austroads (1988a). Guide to Traffic Engineering Practice Part 3 – Traffic Studies. Report No. AP-11.3/88. Austroads, Sydney. Austroads (1988b). Guide to Traffic Engineering Practice Part 2 – Roadway Capacity. Report No. AP-11.2/88. Austroads, Sydney. Austroads (2001). The Australian and New Zealand road system and road authorities - national performance indicators 2000. Report No. AP-43/01. Austroads, Sydney (and earlier annual editions: 43/96, 43A/96, 43/98, 43/99, 43/00). Austroads (2004). Guide to Traffic Engineering Practice Part 3 – Traffic Studies. Report No. AP-G11.3/04. Austroads, Sydney. Bennett, D. (2002). Assessment of national highway traffic counting. Contract Report RC2219-1. ARRB Transport Research, Vermont South. Botterill, R and Luk, J.Y.K. (1998). Investigation of a new method of estimating vehicle kilometres of travel. Contract Report RC6028. ARRB Transport Research, Vermont South. Daltrey, R. (2002). Data integrity. Discussion Paper, Roads and Traffic Authority, Sydney. Donnelly, J., Chan, D., Duncan, J., Keighley, T. and Angelovski, K. (2003). SSOFTRAC – a statistical software package for detection and correction of corrupt traffic counts. User’s Manual Version 2.2. CMIS Report No. 03/157. CSIRO Division of Mathematical and Information Sciences, North Ryde. Federal Highway Administration (2001). Traffic monitoring guide. US Department of Transportation, FHWA, Office of Highway Policy Information, Washington DC Federal Highway Administration (2003). Bicycle and pedestrian detection. Final Report prepared by SRF consulting Group, Minneapolis. Golden River Traffic Ltd (2001). M410 Bicycle Counter Mixed Traffic counting. Karl, C.A., Powell, T. and Luk, J.Y.K. (2003). Development of a predictive travel time model for Singapore based on GLIDES. Proc. 21 st ARRB and 11 th REAAA Conf. 18-23 May 2003, Cairns. Koniditsiotis, C. (2000). Weigh-in-motion technology. Publication No. AP-R168/00. Austroads, Sydney. Leschinski, R. (1994). Bicycle detection at signalised intersections. Research Report ARR No. 245, Australian Road Research Board, Vermont South.
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