1. Urban Freight Data Collection 1 Jeffrey Wojtowicz wojtoj@rpi.edu VREF Center of Excellence for Sustainable Urban Freight Systems

2. Introduction  The development of freight demand models is difficult due to:  Lack of proper balance: knowledge, models and data  Poorly understood system  Complexity of the freight system:  Multiple interacting agents with partial views  Multiple metrics to measure freight  Links between participants  Functions performed  Modes/vehicles used  Levels of geography 2

3. Partial view of the freight system 3 Notes: (1): Only of the cargo that they handle. (2): For all the cargo they receive. No single agent can provide a complete picture of the system

4. Multiplicity of metrics 4 Base 1 2 3 4 5 Loaded vehicle-trip Commodity flow Notation: Consumer of cargo (receiver) Empty vehicle-trip

5. Data Needs and Sources

6. Data required by modeling techniques 6

7. Data sources  Primary data sources (in the USA)  Commodity flow survey (CFS) data  Zip code business patterns (ZCBP)  Surveys + interviews + travel diaries …  Secondary sources  GPS data  Experts  Data and Freight Demand Synthesis  Fill in gaps, could provide good estimates  Reduce data collection costs but may introduce an error 7

8. Data gaps identified (United States) 8 Most data needed must be collected from scratch

9. Data collection  Types of data collection techniques or surveys depend on how the sampling frame is defined:  Establishments at origin or destination of the shipment  Truck traffic  Delivery tour  Shipment  This leads to data collection methods that focus on:  Origin or destination of the cargo  En-route, as in a truck intercept survey  Along the supply chain 9

10. Surveys  Data collection methodologies vs. sampling frame:  Establishment-based surveys  Shipper, receiver, and carrier based  Trip intercept based surveys  Roadside interviews  Vehicle based surveys  Travel diaries, and surveys assisted by GPS  Tour based surveys  Longitudinal surveys  Freight volumes data collection techniques 10

11. GPS and freight data collection  Global Positioning Systems track routing patterns  Spatial and temporal  Cannot provide data collected by traditional surveys  e.g., commodity type, shipment size, trip purpose  Need other data sources/methods  Good complement to more traditional freight data collection procedures  Commercially available GPS data are likely to be biased and difficult convert into a representative sample 11

12.  Advantage: Engine status (Ignition off, Ignition On) and travel status (start, stop)  Assumption: Apart from warehouse and truck centers, a vehicle will only turn the engine off for deliveries at stores. This helps identify delivery stops. Event Based GPS Data 12 LabelDate / TimeAddressLatitudeLongitudeEvent 9284/3/2012 21:50521 Park Ave, New York, NY, 1006540.763525-73.9692138Travel Stop 9284/3/2012 21:501 Central Park S, New York, NY, 1001940.76478-73.9737944Travel Start 9284/3/2012 21:55937 7th Ave, New York, NY, 1001940.76668-73.9790527Drive 9284/3/2012 22:0098 W 53rd St, New York, NY, 1001940.761666-73.9790111Drive 9284/3/2012 22:0365 W 56th St, New York, NY, 1001940.763447-73.9769638Travel Stop 9284/3/2012 22:0465 W 56th St, New York, NY, 1001940.763447-73.9769638Ignition Off 9284/3/2012 22:0470 W 57th St, New York, NY, 1001940.763825-73.9768972Ignition On 9284/3/2012 22:0668 W 55th St, New York, NY, 1001940.762497-73.9772Travel Start 9284/3/2012 22:0862 W 57th St, New York, NY, 1001940.763788-73.9768055Travel Stop 9284/3/2012 22:0847 W 56th St, New York, NY, 1001940.763569-73.9765194Ignition Off 9284/3/2012 22:3442 W 56th St, New York, NY, 1001940.762877-73.9767305Ignition On

13. Sample GPS route data 13

14. Sample GPS data 14

15. Sample analysis from GPS data 15

16. Sampling frames and data 16

17. Summary  There is no magic answer for getting freight data  Relationships must be cultivated  Patience must be practiced  Asking for too much data can be a disadvantage  Request needs to be defensible  Generally willing to collaborate if requests are within reason 17

18. Thank you! Questions? 18 Jeffrey Wojtowicz Sr. Research Engineer Assistant Director of Administration VREF CoE-SUFS Rensselaer Polytechnic Institute Troy, NY 12180 wojtoj@rpi.edu