Innovative methods to collect road statistics


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Innovative methods to collect road statistics

  1. 1. Innovative data collection methods for road freighttransport statisticsEUROSTAT, Oct 11, 2012
  2. 2. Current situationQuality concerns regarding road transport data Limited relevance of data for Austria’s traffic & transport planners  due to sample errors on county or smaller traffic cell levels Insufficient precision of reported tonne-km  as a result of automated imputation of the distance travelled between origin and destination of a journey using distance matrices, ignoring potential detours in between. Possible over-estimation of empty trips  assuming that distances between places of unloading and subsequent places of loading are empty trips Assumed under reporting of both empty & loaded trips  respondents assumed to minimize efforts, and report a vehicle to be out-of-order during the sample week. Limited accuracy of cargo types  Respondents have limited information regarding cargo moved („mixed cargo“)  Assignment of goods to NST/R classification by respondents leads to incoherent resultsPage  2
  3. 3. Consortium Gebrüder Weiss GmbH Petschl Transporte Österreich GmbH & Co KG Process knowledge and experience of transport companies Paradigma Unternehmensberatung GmbH Austrian Institute of Technology – Department Mobility Technology, data management und electronic data exchange Wirtschaftsuniversität Wien – Inst. f. Transportwirtschaft und Logistik Methods and legal environment of road freight statistics in EuropePage  3
  4. 4. Goals and Results of the Project Project Goals Project deliverables  Further reduction of the  Prototypic and fully functional respondents efforts through implementation of the connection automation between data (from companies) to  Return to a larger sample to meet the XML-Interface national requirements  Test the applicability of automatic  Increase of data quality and data collection technologies and actuality algorithms to obtain precise measurements.  Reduction of required ressources for preparation and processing of the  Legal, economic and methodical data evaluation of the results with respect to the road freight transport statisticPage  4
  5. 5. Research Objectives Prove the feasibility of using data available from transport companies  building a working prototype, using IT data from transportation companies as a source  develop a sufficiently generic standard interface (fleet information, consignment & location data) Assess the organizational impact on the respondents  obtain empirical information on the benefits as well as potential issues Use information about goods from transport booking data, to infer cargo types (NST/R)  Train a Bayes algorithm, part of the KNIME data mining software to classify goods using free text Use GPS location to measure distances travelled and to infer load/unload events.  Obtain route information from GPS readings – infer events and compare with order information Obtain experience with the technical and economic challenges to implement the standard interface  industry software as well as individually developed software
  6. 6. Road Transport Statistics in Austria EU Regulation 70/2012 provides a general legal and methodological framework for the different national surveys (territoriality principle) Stratified quarterly sample comprises 6,500 vehicles per quarter out of a total of about 72,000 registered.  Meets quality criteria described in the regulation  Original sample size of 26000 vehicle weeks per year significantly reduced (since 2006) Local units, operating vehicles and drawn for the sample use paper based questionnaires or an electronic questionnaire (11% in 2009) Efforts to complete the questionnaire have been reduced.  27.3 minutes on average to complete a questionnaire  150 work weeks per annum for the Austrian economy … Main task for completing the questionnaires consists of collecting and preparing the information within their companies.Page  6
  7. 7. Target Solution Architecture Data interfaces are in the public  Completion of missing data and domain and provided to the corrections are performed by the Software- and System developers respondent Different information entities are  Based on the transfer-format the consolidated according to the data specific required structure for the model specifications and business respective country questionaire is logic generated
  8. 8. Information elements collected4 XML-based interface specifications are provided FleetMasterData FleetStatusData ConsignmentData PositionData Data on lorries Information on order related GPS readings, and trailers specific lorries at data containing country/ZIP (capacity, axles, o certain times information on codes, activity dometer, age, lice (driven distance, goods, packaging, (loading, border nse, etc.) fuel usage, etc.) origin and crossing) destination …Page  8
  9. 9. Data Collection ServiceProtoytped process deployed to move data from IT systems to eQuest eQuest Process activities Web- application  Questionnaires as XML files are generated by API the eQuest system run by Statistics Austria.ERP/TSM  These Questionnaires contain the selection criterias for the data export. Database XML- Access Data export Selection criteria Question-  The data export extracts the information out naire of the ERP/TMS-systems and saves them as XML-files (4 predefined formats). Exported XML data  These files are uploaded to the „SGVS- Konsole“ web-application.  The respondent can now revise the data. Web application  The web-application generates a „completed „SGVS Console“ Questionnaire questionnaire“ and uploads this to the eQuest with data system.  In the eQuest system the report is finished.Page  9
  10. 10. Web-ApplicationPage  10
  11. 11. Validation Rules (Excerpt) Every lorry or articulated vehicle mentioned in the NSI’s questionnaire must have an entry in the company’s fleet management system. Odometer readings at the beginning and the end of the reporting period must be available, where the latter has to be greater or equal than the former. If multiple odometer readings are available over time, the sequence of readings must be non-decreasing. Every shipment must have been allocated to one or more sections of a journey. If events and activities such as load, unload are reported or inferred from the position data (see below), corresponding sections of journey’s have to be reported as well. Reported sections and journeys of a given vehicle must not overlap
  12. 12. Automatic classification of goods Official  In compliance with national regulations all transported goods are statistics classified by the NST/R  Hauliers and forwarding agents often use free text in their operative Respondents data. (i.e. 10 ldm bathtubs, granite, etc.)  Assignment to NST/R classification is conducted manually.  Assignment of goods to NST/R classification through respondents Experiences leads to incoherent results (DE)  Hauliers provide free texts, classification is done by NSO (NL)  Development of a model for automatic classification according to InnoRFDat-X NST/R categories for all free texts.Page  12
  13. 13. Automatic classification - experiencesBased on an algorithm trained on classification texts and applied to a sample of1000 cargo descriptions from transport orders Correct categorization Insufficiently discriminating Imprecisions  Expect improvements  Pervasive use of product  Find a balance between when trained using codes in one case „rote learning“ and the respondents texts  Transport provider has capability to correctly insufficient information classify new descriptions („45 parcels“)Autoteile , Coils Stahl und Blech 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Bleche 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Bleche max . 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Bleche Überbreite 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Coils 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)kompl . Profile 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Leitschienen 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)nahtlose Stahlrohre 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Profile 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Profile 12,2 m 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Profile lt . Beilage 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Rohre 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Sonderfahrt , Profile 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Stabstahl 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Stahl 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Stahl ( S320GD+Z275MB) , 2 Coil 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Stahl Rohre 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Stahl Vg . 1/7 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Stahlbleche 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)Page  13
  14. 14. Route & Event DetectionProtoytped process deployed to generate trips using GPS data •Timestamp Objective •GPS-Coordinates Speed gradient  Position data file containing the geographic details of Detect Stops Geographic change every tour whereLoading Spatial distance  a tour starts with thepointorder info: •Stop-Position loading of an empty lorry(ZIP-Code) •Stop-Duration  ends with the unloading Mandatory rest period of the last cargo Classify Stops Distance to  Two-Step Heuristics applied point of loading  First to detect all stops Distance to motorway services, etc. in the data file  Second to eliminate non- loading/unloading stops Resting stop Loading stopPage  14
  15. 15. Event detection performance  Heuristics gave 100% Recall but only 28% Precision  Heuristics + Order details (ZIP-Codes) gave 100% Recall and 85% Precision
  16. 16. Lessons learned The collection and use of operational available at transport companies to produce road traffic statistics is feasible  Standardized interfaces can be cost-effectively implemented Research implications  Use patterns in position data to discriminate between rest and load/unload stops (independent of consignment information)  Respondent specific training sets is expected to increase the precision of goods classification  Generation of data from ERP/TMS-system would allow for continuous reporting of transport activities Recommended changes to the legal framework  Enable NSI‘s to utilize available IT assets for the collection of raw data  Adapt statistical sampling to changes in the market place (freight forwarders)  Uniform collection and production methodologies across EU member states
  17. 17. TMS Software penetration in Austria Methodology 32 12  Contacts to 55 larger companies, o.w. 29 responded and provided 40 information 14  Significant proportion has outsourced transport: Strabag, Rail Cargo, Lenzing AG, Magna Steyr, 14 Borealis 15  Respondents represented 7,3 % of all trucks registered in Austria (SGVS 2007, Q4) Sauer Bespoke SW Hypersoft, -sped Keine TMS  5.230 / 72.000 = 7,3 % Helpten COSware C-Logistic Transporeon (?)Page  18
  18. 18. The way forward Solution is not restricted to the Austria territory.  The choice of application architecture and scope has been guided by the vision of a European rather than only a national application. A Europe wide adoption of the approach is expected to increase quality of the data collected by the member states.  applicability of transport statistics all over Europe will be enhanced  comparability of transport statistics across countries will be improved. Piloted process has shown the potential to substantially reduce the administrative burden on reporting companies Expectation to further raise the efficiency of statistical production processes leading to cost reductions and time savings.
  19. 19. Summary Model deployed is capable to automatically classify cargo  Sufficient precision achieved after training with respondent specific datasets  Less effort required from the respondents  Quality improvements as a consequence of consistent classification Implementation aspects  Improve „training phase“ using both descriptions and pre- classifications of a sample of respondents  Encourage consigners to provide more descriptive data of their goods  Provide functions to manually override misclassificationsPage  20