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Modelling empty runs in regional freight models

Modelling empty runs in regional freight models. Ole Kveiborg and Mikal Holmblad Danish Transport Research Institute Technical University of Denmark. COST-WATCH, Torino October 2007. Empty trips – the ”missing” 25 %.

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Modelling empty runs in regional freight models

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  1. Modelling empty runs in regional freight models Ole Kveiborg and Mikal Holmblad Danish Transport Research Institute Technical University of Denmark COST-WATCH, Torino October 2007

  2. Empty trips – the ”missing” 25 % • In Danish National road freight transport statistics empty trips account for 25% • Trips not veh. km • Focus on • Reduction in CO2 emissions and energy use • Externalities • Competition • Congestion • Optimisation • We do not have much knowledge about empty runs • The relation between runs with and without load • Empty running is not the difference between runs in opposite directions • Empty and loaded vehicles cross in opposite directions • What are the causes for the trips without load?

  3. The contents • How can empty trips be modelled? • Empty trips and trip chains • Model estimation results • Two models compared

  4. = Generating empty trips • Freight flows not balanced between A and B • But over long run vehicles return to their base • National trips close to out-return in one day • A larger un-balance lead to more empty trips • Empty trips in both directions

  5. Generating empty trips • Empty running is wasteful • Trucking industry try to get load on all trips • Contracts • Through distribution centresor co-operation with other companies • Trip chaining • Reduced load • Eksternal regulation • Drive/rest legislation • Maximal allowable load • Distance • Competition in trucking industry • Type of goods • Type of vehicle

  6. Modelling empty running • Empty running has not been a focla area in transport models • Simple ad hoc models • Knowledge of empty running only at aggregate level • Influential factors are not known and have only had limited influence in models • The systematic patterns often not modelled • Contrary to the large effort used at modelling trips with load

  7. Modelling empty trips I • A simple model • Empty running is a fixed factor related to trips with load = * • Problem: • Increasing load from A to B increase empty trips from A to B (and not from B to A as desired)

  8. Modelling empty trips II • A simple model (Nortman og van Es, 1978) • Empty running related by a fixed factor on trips in opposite directions = * • Problem: • More load from A to B does not influence empty trips between A and B (a reduction in empty trips) • Empircal works have indicated that total number of trips is independent of loads

  9. = * Modelling empty trips III • A model based on probability I (Hautzinger, 1984) • Empty trips depend on proportion of loads in both directions • Problem: • Assumes that trips are always out- and return trips

  10. Modelling empty trips IV • Freight transport is more complex • Trip chains

  11. Modelling empty trips V

  12. Modelling empty trips VI • A model based on probability II (Holguin-Veras og Thorsen, 2003) • Empty trips between i and j is sum of direct return trips and trips being part of trip chains • Empty trips in trips chains is a conditional probability

  13. Prob. That a trip from h to j continues to i depends on • Distance between i and j • Distance already travelled • The amount of load between zones

  14. An alternative compromise • This model take distance into account, but is independent of zones

  15. Data • Travel diary • Loaded and empty trips (county level) • Total loads • Travelled distance • Vehicle types • Here: solo and articulated

  16. Data – empty trips depending on distance

  17. Data – compared to total number of trips

  18. Results

  19. Results – explanatory power

  20. Results – explanatory power

  21. Results – distance only

  22. Conclusion • Important to focus on empty trips • Most models use simple relations • Possible to include more realism in models using existing data • Apparently good model fit • Large variation in precision on individual observations • Empty running largest on short distances • However, distance non-significant • Mostly included in zonal relations • Large zones means too little variation in distance • Remove zonal relation leads to small influence from distance

  23. Thank youQuestions? Ole Kveiborg OK@DTF.DK

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