Urban Freight Data Collection

1. Urban Freight Data Collection 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

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

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

5. Data Needs and Sources

6. Data required by modeling techniques

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

8. Data gaps identified (United States) 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

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

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

12. Event Based GPS Data • 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.

13. Sample GPS route data

14. Sample GPS data

15. Sample analysis from GPS data

16. Sampling frames and data

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

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