1. Integrated Commodity Flow Survey with Advanced Technology
Moshe Ben-Akiva
August 2015
Workshop FHWA – new CFS national-wide

2. Outline
Future Mobility Sensing
MIT
Integrated approach

3. Future Mobility Sensing
MIT
Integrated approach

4. July 2, 2015 | Presentation to MoT
Future Mobility Sensing
Automated travel survey that leverages
increasingly pervasive smartphone ownership
advanced sensing technologies
machine learning techniques
to deliver previously unobtainable range of behavioral data and insights.
July 2, 2015 | Presentation to MoT

5. Automated and integrated travel survey system

6. User Interfaces Non-intrusive iOS and Android apps
User friendly activity diary that users can edit and provide additional information

7. Field Test in Singapore
LTA conducted Household Interview Travel Survey (HITS) with ~10,000 households.
More than 1500 HITS respondents also participated in FMS demonstration project (October 2012 – September 2013)
Known issues in traditional method:
Short activities under-reported
Over-estimated travel times for short trips
Reporting of a simple (typical) day
FMS delivers richer, higher resolution, multi-day travel and activity dataset

8. HITS vs FMS: An example

9. Recent Developments Enhanced technology Additional capabilities
Event based on-phone surveys
Happiness
Transit quality
Context specific SP
Commercialization

10. Future Mobility Sensing
MIT
Integrated approach

11. Motivation Toll roads (Perez and Lockwood 2006)
30-40% of new urban expressway mileage in the US
150 new centerline miles expected per year
Heavy trucks on typical toll road (S&P 2005)
10% of traffic flow
25% of revenue
Toll road forecasts biased and with high variance (Bain 2009)

12. This study survey of truck route choice
data collected directly from drivers
two phases:
Phase I – Driver questionnaires with route choice stated preferences (SP)
Phase II – GPS-based revealed preferences (RP) data

13. SP study: Effect of tolls

14. Phase I – Key findings
Wide variability in preferences towards toll roads and tolls
Route choices depend on multiple factors
Travel time, tolls, delays
Toll bearing terms
Driver compensation method
Shipment characteristics
For more details: Moshe Ben Akiva, Hilde Meersman, Eddy van de Voorde (eds), Freight Transport Modelling, Emerald Books, May 2013

15. Phase II – RP data collection
(adaptation of FMS)
Remove battery level

16. GPS logger Trucks equipped with off-the-shelf loggers (SANAV CT-24)
Monitor all trips continuously
Transmit data in real-time to server
Collects:
Location data
Speed
Timestamp
Report Intervals
Time intervals
Minimum distance

17. Backend Algorithms
Applied to the data received by the backend (MIT server):
Trace creation (FMS)
Stop detection (FMS)
Map Matching (Open Street Map)
Toll detection (Open Street Map)

18. Web interface Validate and correct movement information
Collect additional information
Pick-up & delivery schedules
Cargo type
Tolls, methods of paying
Exit survey
Personal information
Context specific SP

19. Web interface

20. Exit Survey

21. Data collection process
Over the phone using lists of trucking companies
At truck stops and rest areas
Indiana
Massachusetts
Texas
Ontario

22. Driver type: Long tour

23. Driver type: Short tour

24. Driver type: ‘Gypsy’

25. Same driver, different route
This is a map of the Austin area, which has two north-south routes passing through it – I-35 (free) and SH-130 (tolled). This driver went south from Dallas to San Antonio in the morning peak, using SH-130.

26. Same driver, different route
Two days later, he came back on a Saturday morning via the free road, I-35.

27. Same day, different route
This is a similar example from the Chicago area. The driver is based in the southeastern corner of the map, went up to Milwaukee via the Indiana Toll Road and Chicago Skyway, then returned the same day via a different toll road, I-294 (to the west of Chicago).

28. Truck Drivers’ Survey in Singapore
System setup for data collection in Singapore
New questionnaires designed for urban freight

29. Truck Telematics - OBD Devices
Use the On-Board Diagnostic (OBD) port to connect to vehicle’s engine
Data collected (second-by-second):
GPS location
vehicle speed
fuel consumption
other engine parameters (engine rpm,
air intake temperature, etc.)
Able to track route, stops, driver behavior, idling, fuel use and emissions

30. Sample OBD Data from a Truck
Single trip sample OBD data logged
Logged truck trips in a single day
Idling as % of trip time = 51%
Idling as % of fuel use = 25%

31. Future Mobility Sensing
MIT
Integrated approach

32. Integrated approach integrated survey design establishments
carriers/drivers
innovative technology
FMS
tracking/tracing of vehicles and shipments
urban CFS and nationwide CFS

33. Integrated approach (cont’d)
Business Establishments
Tablet-based questionnaire
Needs and capacity, storage, parking, loading and unloading, fleet size, etc.
Commodities
Tracking shipment
RFID tags attached to shipments
Truck Drivers
GPS logger
Web-based or tablet-based verification
Carriers
Web-based questionnaire and GPS loggers for drivers

34. Operational flow Establishment and Driver survey 2a. Producer
Questionnaire
Tag shipments
3. Truck driver
Pick-up/delivery
1. Surveyor
2b. Retailer, etc.
Questionnaire
Tag shipments
5. Truck driver
(hired or owned)
Verify stop purpose and commodity type
4. Carriers
Web-based questionnaire
Carrier and Driver survey

35. Integrated technology

36. Establishments and carrier surveys
Tablet-based questionnaires and shipment tracking
TRACKING SHIPMENTS
WEB- TABLET-
BASED SURVEYS
GIS data & POI
Raw data
Survey Data
Server

37. Thank You!