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1Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Urban freight –
Data collection and modeling
Lynette CheahSingapore University of Technology and Design (SUTD)
Fang ZhaoSingapore-MIT Alliance for Research and Technology,
Future of Urban Mobility (SMART FM)
2Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Challenges in urban freight
• A complex socio-technical problem,
impacting:
• urban and transport system planning
• efficiency, cost of supply chains
• environment and safety
• Freight accounts for 35% of world transport energy use
• Quarter of transport-related CO2 in European cities
• Road freight expected to grow 60+% between 2000-2050
Source: DaBlanc 2010
3Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Complexity of urban freight
• Different for every city
• Fragmented industry: 90% of trucks in Asia owned by individuals
• Diversity of commodity flow and transport implications
• chemicals: pick-drop-drop-drop
• materials, e.g. cement: hub & spoke
• fast moving consumer goods (fmcg), food: fixed schedule,
subject to delivery time constraints
• heavy equipment: dynamic based on job portfolio
4Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Hurdles to understanding urban freight
• Lack of knowledge and data on freight flows in cities
• In Singapore, no counterpart of Household Interview Travel Survey
• A few uncoordinated studies, limited geographical and commodity
coverage
• Absence of freight models for policy and governance
Systematic data collection is the initial step
– measure what we want to manage
5Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Urban freight data of interest
• Commodity flows
• production-consumption flows
• Truck movements
• pickup/delivery operations
• truck tours/routing characteristics (including fuel consumption
and engine performances)
• truck drivers’ behaviour
• Interactions amongst agents
• suppliers, carriers, logistics providers, retailers
6Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Data collection approaches (ongoing)
• Commodity flow surveys
• Retailer surveys
• Truck traffic counts
• Truck surveys
• SMART Future Mobility Survey
8Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Traffic counts
• Exploring use of traffic image processing techniques
• LTA expressway and intersection traffic monitoring cameras
(widely placed across island) are potential data sources
9Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Why truck surveys?
• Fleet and driver monitoring
• eco- or safe-driving
• anti-idling
• Optimize routing with known truck tours/trajectories
• Carbon footprint and fuel use assessment
10Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Future Mobility Survey
Next-generation freight data collection
- leverage pervasive smartphone, GPS loggers and OBD
devices, advanced sensing and communication technologies
and machine learning architecture
- deliver previously unobtainable range of data reflecting what
shippers and carriers do, not what they say they did
11Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Problem and Opportunity
• Increasingly difficult to conduct reliable surveys via traditional
approaches
- Response rates are low
- Non-representative samples based on convenience and
intercept sampling
- Short respondent attention span and limited ability to accurately
recall information
• Smartphones, GPS loggers, OBD devices and RFID tags
unobtrusively collect a wealth of valuable information
- Better quality and quantity of data, especially when combined
with information from shippers and carriers
12Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
FMS Solution
• Future Mobility Sensing (FMS): technology developed to
innovate travel behavior surveys
• Machine learning algorithms combined with web-based
user input to extend and validate smartphone sensing data
13Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
FMS
• Underlying technology has been developed, tested and
proven effective
• Field test with LTA HITS 2012
- Yields more detailed and varied data than traditional
travel survey approaches
• US inter-city truck driver survey
- significant variability in driver tour patterns and route
choice behavior
14Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Challenges
• Adaptation of the US truck driver survey (inter-city) to
urban freight environment
- Track a variety of commercial vehicles
- More frequent stops
- More diverse activities
• Challenges
- Lower GPS data quality
- Shorter stop durations
- Denser road network
- Large number of origins and destinations
15Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Solutions
• Enhance questionnaires
– augment pre-survey with frequent stops, routes, trips,
activity types etc. to capture repetitive behavior
– modify stop questions to include more diverse activities
• Improvements to stop/activity detection algorithms
– extend period of observation
– enhance machine learning algorithms to include user
history and Points of Interest (POI) data
16Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Progress
• System setup for data collection in Singapore
• New questionnaires designed and implemented for pre-survey
and activity diary
17Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Next steps
• Enhance machine learning algorithms to include user history and
Points of Interest (POI) data
• Implement new pre-survey and activity diary questions
• Incorporate stated preferences questions
• Testing
18Urban Logistics: E-Commerce & Sustainability Symposium, Nov 28, 2014
Thank you!