Integrated Commodity Flow
Survey with Advanced
Technology
Moshe Ben-Akiva
August 2015
Outline
1. Future Mobility Sensing
2. Truckers @ MIT
3. Integrated approach
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1. Future Mobility Sensing
2. Truckers @ MIT
3. Integrated approach
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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
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Automated and integrated travel survey system
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User Interfaces
Non-intrusive iOS and
Android apps
User friendly activity
diary that users can
edit and provide
additional information
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Field Test in Singapore
• LTA conducted Household Interview Travel Survey (HITS)
2012 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
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HITS vs FMS: An example
Recent Developments
1. Enhanced technology
2. Additional capabilities
• Event based on-phone surveys
- Happiness
- Transit quality
• Context specific SP
3. Commercialization
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1. Future Mobility Sensing
2. Truckers @ MIT
3. Integrated approach
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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)
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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
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SP study: Effect of tolls
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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
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Phase II – RP data collection
(adaptation of FMS)
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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
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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)
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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
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Web interface
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Exit Survey
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Data collection process
• Over the phone using lists of trucking companies
• At truck stops and rest areas
- Indiana
- Massachusetts
- Texas
- Ontario
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Driver type: Long tour
Time Space Diagram
User 59
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Driver type: Short tour
Time Space Diagram
User 66
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Driver type: ‘Gypsy’
Time Space Diagram
User 141
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Same driver, different route
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Same driver, different route
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Same day, different route
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Truck Drivers’ Survey in Singapore
• System setup for data collection in Singapore
• New questionnaires designed for urban freight
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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
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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%
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1. Future Mobility Sensing
2. Truckers @ MIT
3. Integrated approach
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Integrated approach
•
integrated survey design
- establishments
- carriers/drivers
•
innovative technology
- FMS
- tracking/tracing of vehicles and shipments
•
urban CFS and nationwide CFS
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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
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Operational flow
Establishment
and Driver
survey
2a. Producer
- Questionnaire
- Tag shipments
Pick-up/delivery
3. Truck
driver
1. Surveyor
2b. Retailer, etc.
- Questionnaire
- Tag shipments
Carrier and
Driver survey
4. Carriers
- Web-based
questionnaire
5. Truck driver
(hired or owned)
- Verify stop purpose
and commodity type
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Integrated technology
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Establishments and carrier surveys
• Tablet-based questionnaires and shipment tracking
TRACKING
SHIPMENTS
WEB- TABLETBASED SURVEYS
GIS
data &
POI
Raw
data
Server
Survey
Data
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Thank You!
mba@mit.edu
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