Urban freight Data collection and modeling · • Future Mobility Sensing (FMS): technology...

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)


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


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


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


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


Data collection approaches (ongoing)

• Commodity flow surveys

• Retailer surveys

• Truck traffic counts

• Truck surveys

• SMART Future Mobility Survey


Traffic counts

• Exploring use of traffic image processing techniques

• LTA expressway and intersection traffic monitoring cameras

(widely placed across island) are potential data sources


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


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


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


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


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


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


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


Progress

• System setup for data collection in Singapore

• New questionnaires designed and implemented for pre-survey

and activity diary


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


Thank you!

[email protected]

[email protected]

mailto:[email protected]

mailto:[email protected]

Urban freight Data collection and modeling · • Future Mobility Sensing (FMS): technology...

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