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Published byDomenic Henderson Modified over 9 years ago
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Integrated Commodity Flow Survey with Advanced Technology
Moshe Ben-Akiva August 2015 Workshop FHWA – new CFS national-wide
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Outline Future Mobility Sensing MIT Integrated approach
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Future Mobility Sensing
MIT Integrated approach
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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
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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) 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
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Recent Developments Enhanced technology Additional capabilities
Event based on-phone surveys Happiness Transit quality Context specific SP Commercialization
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Future Mobility Sensing
MIT 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) Remove battery level
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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
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Driver type: Short tour
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Driver type: ‘Gypsy’
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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.
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Same driver, different route
Two days later, he came back on a Saturday morning via the free road, I-35.
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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).
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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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Future Mobility Sensing
MIT 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 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
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Integrated technology
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Establishments and carrier surveys
Tablet-based questionnaires and shipment tracking TRACKING SHIPMENTS WEB- TABLET- BASED SURVEYS GIS data & POI Raw data Survey Data Server
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Thank You!
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