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 C1C1 P1 P2P2 ConsumersC 1 : Lunch ProducersP 1 : LunchP 2 : Lunch, Ride Sharing Candidate Opportunities (C 1, P 1 ) ST encounter (C 1,P 2 )

Relation to Colocation/Co-occurrence Mining Problem Sub-time-series Co-occurrence Patterns Periodic Sub-trajectory Co-location Patterns ProblemGiven 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? ApproachEnumeration 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.

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 + t 1 → 4 pm + t 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