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Ephemeral Network Broker to Facilitate
Future Mobility Business
Models/Transactions
A collaboration between Ford University Research Program and
University of Minnesota
University PI:
Shashi Shekhar
Ford PI:
Shounak Athavale
Outline
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Problem Description
Related Work
Challenges
Example
Relation to previous work
Synthetic Data Generation
Problem Description
• Ephemeral Networks: Groups of people, good and services
that encounter each other in the physical world
– are in close geographic proximity
– during routine activities such as commute, shopping, entertainment
• Goal: Investigate ephemeral network broker that can identify
novel opportunities for Mobile Commerce in Ephemeral
Networks (MCEN).
Problem Statement
• Input:
– Historical trajectories and real-time locations of consumers and service providers
(producers)
– Consumer Calendars, wish lists, gift-registries, shopping lists
– Historical mobile commerce transactions
• Output:
– MCEN near-future or real-time opportunities by matching producer and consumer pairs
• Constraints:
– Physical World: Human life (set of activities) → Activities generate trips which
generate commerce opportunities (supply and demand)
– Activities are not random/independent: Routine/Periodic activity, routine patterns of
life, routine demands/commerce needs
– Modeling MCEN socio-economic semantics: e.g. need, readiness for transactions,
trust)
Related Work
• Social network analysis for long term social relationships
• Ephemeral Social networks
• Sharing economy:
– car sharing, Uber, hotel rooms (Airbnb), Meal sharing, favor
networks for sharing chores
• Trajectory Pattern mining (e.g. flock, meeting patterns)
– Differences: periodicity, road network
Challenges
• Modeling of socio-economic semantics (e.g. supply,
demand, trust)
• Choice of interest measure (tradeoff)
• Scaling to Big Spatio-temporal Data (megacities)
Example
C1
Candidate
Opportunities
P2
(C1, P1)
P1
(C1,P2)
ST
encounter
Consumers
Producers
C1: Lunch
P1: Lunch
P2: Lunch, Ride Sharing
Relation to Colocation/Co-occurrence
Mining Problem
Sub-time-series Co-occurrence
Patterns
Periodic Sub-trajectory
Co-location Patterns
Problem
Given historical trajectory data,
identify the (multi-dimensional) subtime-series that correlates with noncompliant windows (e.g. of
emissions)
Given historical trajectory data,
identify (Producer, Consumer)
pairs that periodically co-locate
Interest
measure
What patterns have a distribution
that is NOT independent from noncompliant events?
Should capture:
• duration/length of encounter
• Periodicity/Return period
• Historical Success rate?
Approach
Enumeration of temporal patterns in
a set of time series
• Enumeration on a spatiotemporal network
Return Period
• A estimate of the likelihood of an event (e.g. earthquake, flood) to
occur.
• Return Period =
number of years on record
number of recorded event occurrence s
• Example:
– If a flood has a return period of 10 years.
Then, its probability of occurring in any one year is 1/100 or 1%
– Could happen more than once in 100 years (independent of
when last event occurred)
• Producer/consumer pairs with small return periods are
more promising.
Synthetic Data Generation (1/3)
Brinkhoff:
– Trip-based short term observations
• Vehicles disappear at destination
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Speed affected if number of moving objects on edge > threshold
Starting node: randomly
Destination node: depends on preferred route length (i.e. time, vehicle)
External events: weather, traffic jams (external objects)
• May lead to re-computation of route
Limitations:
– Does not account for real-world traffic flow and population (in implementation)
– Does not model multiple trips for the same object (historical data)
Synthetic Data Generation (2/3)
BerlinMod:
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Object-based simulation for long-term observations/multiple days
Each object has home node and work node and neighborhood (3 km)
Work/Home nodes: random or using region probability
Trips:
• Home/work: 8 pm + t1 → 4 pm + t2
• 0.4 probability for trips in each spare time block (1 to 3 stops)
» 4 hour after work
» 2 five-hour blocks on a weekend
– Simulates speed changes:
• Accelerate: automatically to reach max speed
• Deceleration/Stop: road crossings, curved edges
Limitations:
– Does not consider edge load (e.g. congestion) and external factors (e.g. weather effect).
– Generation of home and work nodes are independent
Synthetic Data Generation (3/3)
DYNASMART:
– Dynamic Network Assignment-Simulation Model for Advanced Road Telematics
– Designed to model traffic pattern and evaluate network performance under real-time
information systems (e.g. reconstructions).
– Uses OD Matrix to model simulated trips.
– Trip Simulation:
• Assign vehicles initially to (one of k) shortest path (s).
• Recompute path cost
– Congested edges are penalized
• Re-assign vehicles (switching occurs)
• Continue until wardrop equilibrium is reached
– Advanced capabilities:
• Models signalized intersections, ramp entry/exit etc.
• Models driver’s behaviors
– infrequent updates of network route info, fraction of info-equipped drivers