Development of New Paradigms of Freight Origin-Destination Synthesis that Use GPS Data

Transcription

1 1 Development of New Paradigms of Freight Origin-Destination Synthesis that Use GPS Data José Holguín-Veras, Professor Director of the Center for Infrastructure, Transportation, and the Environment Rensselaer Polytechnic Institute Phone:

2 JHV s thesis 2 We need comprehensive approaches to ensure a solid foundation for freight transportation modeling and analysis The real challenge we face is not lack of data The real challenge is lack of knowledge about how to explain the functioning of the freight system in mathematical models

3 We are facing a paradox 3 Part 1 of the paradox: GPS and ITS technology have generated a data tsunami Data are abundant, though still major gaps exist Most of the data remain unused Part 2 of the paradox: Our ability to make sense of data and transform the data into usable models could improve a lot We have some data much more than before though we are not able to synthesize it to produce information The hopes for a technological solution to the data problem remain unfulfilled

4 Freight data are not the only thing we need 4 Imagine that by a trick of magic we could obtain a full data set depicting all freight activity in a region: Do we have the modeling techniques to take advantage of the data? Do we have sufficient knowledge of how to incorporate the interactions among the freight agents? Do we have models that incorporate the dynamics that determine tour formation? Do we know enough to forecast their behavior?

5 Knowledge, Models, and Data 5 Data do not necessarily lead to Knowledge, D K Models cannot be developed without Knowledge Knowledge /Model inform Data collection: K+M D We need integrative developments: K+M+D Models Knowledge Data

6 In essence 6 We need to: Increase our Knowledge of the freight system Translate knowledge into models, K M We need to fund theoretical modeling research We need to fund promising modeling avenues

7 Implications of the Cha-Cha-Cha Theory 7 Most scientific discoveries occur because: Chance Challenge Charge Observations: Chance is not a method The existence of a Challenge, does not guarantee the development of scientific breakthroughs Charge is the only alternative

8 Integrating GPS/ITS into OD synthesis 8 The need for integrative K-M-D approaches is clear We have some data We do not know much about freight dynamics We have few models to represent these phenomena Tour behavior Trucks make a significant number of long trip chains (3.2 stops/tour in Denver, 5.6 stops/tour in New York City) Integrated trips along a trip chain (tour) in reality vs. the independency assumption of trips in traditional freight travel demand forecasting models WE NEED DIFFERENT MATHEMATICS

9 How to use GPS tracks? 9

10 OD Synthesis Example (super-simplified) 10 Let s say that you know the traffic in this network 1 10 Potential solutions: Etc.. (even small problems have multiple, even infinite solutions

11 Two major families of techniques 11 Unstructured approaches: Use a general theory (e.g., maximum likelihood, least squares) to compute the OD matrices Advantages: Better fit Disadvantages: No connection with a demand model Structured approaches: Use a demand model to reduce the dimensionality The problem reduces to a parameter estimation problem Advantages: The output is a calibrated demand model Disadvantages: Fit to observed traffic counts not as good

12 Key implication 12 The key to improve ODS is to improve freight demand models In essence, the better the model specification and the more realistic the underlying behavioral assumptions, the more accurate the ODS Work done: ODS based on a basic demand model and an approximation to a tour model to estimate empty trips Same as above with multicommodity flows ODS using a tour model It could directly use GPS data, together with counts

13 ODS based on trip interchanges i-j 13 Holguin-Veras and Patil (2007) developed a model that integrates a demand and an empty trip model The estimation error went down by a factor of 30% The model was able to estimate with reasonable accuracy the parameters of all models Holguin-Veras and Patil (2008) developed a multicommodity version Outperfoms the generic commodity version (40% better) Estimable without major issues Both jointly estimate loaded and empty trip models

14 Tour flow model 14 Tour choice model: To estimate the node sequence comprising a tour Tour flow model: To estimate the number of trips traveling along a particular node sequence Tour choice Tour flows 2010/6/15

15 Entropy Maximization Formulations 15 Tour model (Wang and Holguin-Veras, 2009): MAX W = C t T 1 Subject to: C t ( T 2 t2 )... = M a imtm = m=1 M a jmtm = m=1 M cm tm = m=1 O C M T! m= 1 i D j t m! Entropy Trip production constraints Trip attraction constraints Total time (travel, handling) constraint

16 16 This formulation leads to a general form of the gravity model: Traditional Gravity model: t * ij Tour flow model: t * m = AO B i i N * * * = exp( λi aim + β cm) = exp( λi a i= 1 j D j * exp( β c ij ) N i= 1 Impedance parameter im * )exp( β c m ) Origin/destination parameters

17 Application to the Denver DRCOG data Estimated vs. observed tour flows Mean Average Percent Error: 6.71%

18 In summary 18 Developing ODS models that tap into the available GPS/ITS data, necessitates: A better understanding of the system Knowledge Better mathematical models, and improved computational algorithms Models Data to test and apply them Data Focusing on one, and disregarding the others, is not likely to work

19 Questions? 19