Innovative Approaches for the collection of road transport statistics


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By extracting data from Enterprise Resource Planning (ERP) and Transport Management (TM) systems, particularly larger companies can easily generate data for official reporting obligation and directly transfer it to the National Statistical Institution (NSI).

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Innovative Approaches for the collection of road transport statistics

  1. 1. Innovative data collection methods for road freight transport statistics
  2. 2. 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  2
  3. 3. Current situation Quality 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 trips  respondents assumed to minimize efforts, and report a vehicle to be out-of-order during the sample week.  Limited accuracy of cargo types i  Respondents have limited information regarding cargo moved („mixed cargo“)  Assignment of goods to NST/R classification by respondents leads to incoherent results Page  3
  4. 4. 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 Europe Page  4
  5. 5. Goals and Results of the Project Project Goals  Further reduction of the respondents efforts through automation  Return to a larger sample to meet national requirements  Increase of data quality and actuality  Reduction of required ressources for preparation and processing of the data Page  5 Project deliverables  Prototypic and fully functional implementation of the connection between data (from companies) to the XML-Interface  Test the applicability of automatic data collection technologies and algorithms to obtain precise measurements.  Legal, economic and methodical evaluation of the results with respect to the road freight transport statistic
  6. 6. Research Objectives  Prove the technical feasibility  building such a working prototype  develop a sufficiently generic standard interface  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
  7. 7. International coordination Identify similar European initiatives  Identification of similar projects in Europe  Learn from experience and best practice  Implications of easier data exchange for survey design and sampling EUROSTAT  International harmonization of electronic data exchange  Potential requirements for national RF surveys (data collection)  Standards and architectures Software providers (ERP, TMS)  Statistical microdata exporter as standard software component  Economies of scale targeting the EU marketplace  Talks with SAP, Navision, TransIT, Sauer, … Page  7
  8. 8. International responses EUROSTAT: Joint approach of data collection  Metadata – enrichment as early as possible  Joint, comparable Method (for consolidation)  Collection of fuel use (CO²-emission) CBS (NL) is a guide for InnoRFDat-X  Sustains good contacts with SW-industry for realisation of XML-based reports  Uses algorithms for ease of input (goods classification, route validation) Experiences of the Ministère du Développement Durable (FR)  Hesitant Respondents  Heavily fragmented company landscape with litte IT use  No strategy for integration of SW-industry Page  8
  9. 9. International responses Trafik Analys (Sweden)  At the moment with traditional questionnaires only with manual recording  Interested in InnoRFDat-X approach Kraftfahrt-Bundesamt Flensburg (Germany)  Survey: Increasing use of standardised software but lack of citical mass of one provider  Search for alternative data sources for groups of goods, origin & destination Danske Statistik (Denmark)  Legal requirement to replace paper as used medium by the end of 2012 -> Web-questionnaire with manual input  Attempts on using TMS/ERP-data Page  9
  10. 10. Target Solution Architecture  Data interfaces are in the public domain and provided to the Software- and System developers  Completion of missing data and corrections are performed by the respondent  Different information entities are consolidated according to the data model specifications and business logic  Based on the transfer-format the specific required structure for the respective country questionaire is generated
  11. 11. Stakeholders & expected benefits Respondents  Carriers  Freight Forwarders  Companies with own fleet Users  Public sector decision makers  Interest groups  General public Increase of data quality Producers  National Statistics Institutes  Ministries of Transportation Page  11 Reduce cost Lower production cost  No need to collect data manually  Reduction of paper-based work  No follow-up calls from NSI‘s  Increased accuracy  Increased coverage  Less correction, completion efforts  Reduce, eliminate paper based work
  12. 12. Information elements collected 4 XML-based interface specifications are provided FleetMasterData FleetStatusData ConsignmentData PositionData Data on lorries and trailers (capacity, axles, odometer, age, license, etc.) Information on specific lorries at certain times (driven distance, fuel usage, etc.) order related data containing information on goods, packaging, origin and destination … GPS readings, country/ZIP codes, activity (loading, border crossing) Page  12
  13. 13. The information model  0 or more journeys performed during the observation period per vehicle can be reported.  0 to 3 trailers per journey can likewise be reported as well as 0 or more different shipments per journey.  0 or more combined transports per journey can be reported. class PIM Ov erv iew query response  0 or more transit countries (international journeys) per journey can be reported (not shown in the diagram). query::j ourney:: combinedTransport query::j ourney::shipment::commodity:: dangerous 1 0..* 1..* query::motorVehicleNotification query::j ourney 0..* 0..* 1..* 1  0 or more shipments per journey may be reported; each shipment can be associated with 0 or more containers. query::motorVehicle  A shipment can comprise 1 or more commodities; each commodity can be classified to be a dangerous good if applicable. 0..1 0..3 query::j ourney:: trailer query::j ourney:: shipment 0..2 query::j ourney:: shipment::container query::j ourney:: 1..* shipment::commodity
  14. 14. Data Collection Service Protoytped process deployed to move data from IT systems to eQuest eQuest Webapplication API ERP/ TSM Insert your own text here  Questionnaires as XML files are generated by the eQuest system run by Statistics Austria.  These Questionnaires contain the selection criterias for the data export. Database Access Data export Selection criteria XMLQuestionnaire Exported XML data  The data export extracts the information out of the ERP/TMS-systems and saves them as XML-files (4 predefined formats).  These files are uploaded to the „SGVSKonsole“ web-application.  The respondent can now revise the data. Web application „SGVS Console“ Questionnaire with data  The web-application generates a „completed questionnaire“ and uploads this to the eQuest system.  In the eQuest system the report is finished. Page  14
  15. 15. Web-Application Page  15
  16. 16. Web-application Page  16
  17. 17. 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
  18. 18. Automatic classification of goods Official statistics  In compliance with national regulations all transported goods are classified by the NST/R Respondents  Hauliers and forwarding agents often use free text in their operative data. (i.e. 10 ldm bathtubs, granite, etc.)  Assignment to NST/R classification is conducted manually. Experiences  Assignment of goods to NST/R classification through respondents leads to incoherent results (DE)  Hauliers provide free texts, classification is done by NSO (NL) InnoRFDat-X  Development of a model for automatic classification according to NST/R categories for all free texts. Page  18
  19. 19. Automatic classification - experiences Based on an algorithm trained on classification texts and applied to a sample of 1000 cargo descriptions from transport orders Correct categorization  Expect improvements when trained using respondents texts Autoteile , Coils Stahl und Blech Bleche Bleche max . Bleche Überbreite Coils kompl . Profile Leitschienen nahtlose Stahlrohre Profile Profile 12,2 m Profile lt . Beilage Rohre Sonderfahrt , Profile Stabstahl Stahl Stahl ( S320GD+Z275MB) , 2 Coil Stahl Rohre Stahl Vg . 1/7 Stahlbleche Page  19 Insufficiently discriminating Imprecisions  Pervasive use of product codes in one case;  Find a balance between „rote learning“ and the capability to correctly classify new descriptions  Transport provider has insufficient information („45 parcels“) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug) 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
  20. 20. 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 preclassifications of a sample of respondents  Encourage consigners to provide more descriptive data of their goods  Provide functions to manually override misclassifications Page  20
  21. 21. Route & Event Detection Protoytped process deployed to generate trips using GPS data Objective •Timestamp •GPS-Coordinates Speed gradient Geographic change Detect Stops Loading point order info: (ZIP-Code) Spatial distance •Stop-Position •Stop-Duration Mandatory rest period Distance to point of loading Classify Stops Distance to motorway services, etc. Resting stop Page  21 Loading stop  Position data file containing the geographic details of every tour where  a tour starts with the loading of an empty lorry  ends with the unloading of the last cargo  Two-Step Heuristics applied  First to detect all stops in the data file  Second to eliminate nonloading/unloading stops
  22. 22. Route & Event Detection: Prototype Implementation architecture Implementation  GPS data input (via ad hoc XML file)  Consignment data input using interface definition  Linkage between GPS Data and consignment data via postcodes (  Result fed into the Position.Data interface definition Page  22
  23. 23. Event detection performance  Heuristics gave 100% Recall but only 28% Precision  Heuristics + Order details (ZIP-Codes) gave 100% Recall and 85% Precision
  24. 24. Live Test Run Experiences / Feedback Use of unfamiliar terms which did not always correspond with specific business practices Usability problems with handling the web-service prototype (method to complex) Interface was tested successfully and is usable Inconsistencies and errors in the application were corrected Journeys could be reconstructed based on position data and associated with certain orders Automatic classification of transported goods possible Page  25
  25. 25. TMS Software penetration in Austria 12 Methodology 32 40 14  Contacts to 55 larger companies, o.w. 29 responded and provided information  Significant proportion has outsourced transport 14 15  Respondents represented 7,3 % of all trucks registered in Austria (SGVS 2007, Q4)  5.230 / 72.000 = 7,3 % Sauer Hypersoft, -sped Helpten C-Logistic Page  26 Bespoke SW No TMS COSware Transporeon (?)
  26. 26. 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  A large set of position data should reveal patterns of loading/unloading locations to better determine between rest and load/unload stops  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  New regulations to mandate the collection and production methodologies  Development towards a mode-integrative approach  Obligation/encouragement of NSI‘s to utilize available technologies for the collection of raw data
  27. 27. 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 our methodology is expected to significantly increase both quality as well as the quantity 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  It is also expected to further raise the efficiency in the statistical production process leading to cost reductions and time savings.