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

• 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

P1P2

Consumers C1: Lunch

Producers P1: Lunch P2: Lunch, Ride Sharing

Candidate Opportunities

(C1, P1)

ST encounter

(C1,P2)

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) sub-time-series that correlates with non-compliant 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 non-compliant 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 spatio-temporal network

Return Period• A estimate of the likelihood of an event (e.g. earthquake, flood) to

occur.

• Return Period =

• 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.

soccurrenceevent recorded ofnumber recordon years ofnumber

Synthetic Data Generation (1/3)

Brinkhoff:– Trip-based short term observations

• Vehicles disappear at destination– 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:– 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