University PI: Shashi Shekhar 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 Open problems: A Supply-Demand ratio aware broker for on-demand services On-demand Service broker for moving consumers and moving service providers Other possible ideas (Sharing Economy workshop)

Problem Definition: On-demand Service Propositions (Moving consumers, Fixed Providers) Input: A set of service providers. Each provider is defined using: Location coordinates Service rate over the day (e.g. 5/hr in rush hours, 10/hr in non-rush hours) A set R of consumer requests arriving dynamically R = {ri = (time, current location li, direction of motion, service type, max. travel distance, max. time before service)} k: number of required propositions ttimeout : timeout interval length Output: k service provider(s) propositions and estimated service/pick-up time(s) for each ri ϵ R Objective: Maximize number of matched requests Constraints: Recommended service provider should meet max time before service and max. travel distance constraints for each request Propositions from a given providers should not violate provider’s service rate Other considerations: keeping the eco-system alive and real-time response

Contributions Formally defined the problem of On-demand Spatial Service Propositions. Proposed several matching heuristics for meeting the conflicting requirements of the broker, consumers and service providers including a new category of service provider-centric policies. Our experimental results show that our proposed heuristics (Least Accepted First) result in: a larger number of matched requests for balanced supply and demand scenarios a larger number of matched service providers with a more balanced provider assignment particularly when the available supply exceeds the available demand. Thus, a supply demand ratio and that a supply-demand ratio-aware broker is needed to select the best matching policy.

Problem 1: A Supply-Demand Ratio Aware Broker for On-demand Services Supply-demand ratio exhibits spatio-temporal heterogeneity Balanced Zone: A zone where Demand ≈ Supply Jammed Zone: A zone where Demand >> Supply Sparse Zone: A zone where Supply >> Demand A zone type can also change over time In Jammed zones, main focus is maximizing matched requests and maybe shifting demand to sparser zones In balanced zones, main focus is making consumers happier (since we should be able to handle most requests) In sparse zone, main focus is keeping eco-system alive until demand rises again or shifts from jammed zones OR shifting (moving) providers to jammed zones Busy, hustling, congested, crowded, jammed, crammed Sparse:, quiet,

Problem 1: A Supply-Demand Ratio Aware Broker for On-demand Services Apply a supply-demand ratio aware matching approach: Step 1: Spatial Region is divided into zones (e.g. census blocks) Step 2: Zones are classified into zone types according to their supply-demand ratio over a recent moving horizon. Classification may change over time Step 3: A separate queue for arriving consumer requests is maintained for each zone where the matching policy for the requests in the queue is selected based on zone type. For example: Jammed → broker-centric policy Sparse → service provider-centric policy balanced → consumer-centric policy

A Supply-Demand Ratio Aware Broker for On-demand Services: Requests crossing zone boundaries Two types of requests: local: max travel distance extends only inside the origin zone focal: max travel distance extends beyond origin zone For Local request: sort the candidate propositions based on matching policy of the origin zone For focal requests: Step (1): candidate propositions in each neighboring zone to the origin are sorted based on the policy of that zone.  Step (2): sort propositions from different cells (may always put local propositions first) Strategy (A): Based on historical zone types: sparse then balanced then jammed Strategy (B): Favor neighbor zones with smaller number of current (real-time) originated requests

Problem 1: A Supply-Demand Ratio Aware Broker for On-demand Services Computational Challenges/Contributions: Learning best zone type for each supply-demand ratio what are the boundary values for switching to a different matching policy Validation Challenges: How to show improvement over a fixed policy given that new heuristics have comparable matching size to NN, with better engagement of service providers at the expense of larger travel distances and waiting times. Need to model people leaving the system to influence matched requests: more appropriate for moving service providers Need to model demand/provider shifting between zones. (e.g. consumers not in a hurry) However, if better balancing among providers occurs mainly when supply highly exceeds demand, one may argue that system may benefit from having some providers leave the system.

Problem 2: On-demand Service Broker – Moving Consumers and Moving Service Providers (1/5) Importance: Modeling mobile providers such as food trucks, delivery vehicles, and consumers as service providers (e.g. sharing jumper cables) Problem: Input: Consumers with known real-time locations (and future trajectories?) Service providers with known real-time locations Output: Matched consumers and service providers Objective: Maximize number of matched requests constraints: Consumers: maximum distance detour, max. waiting time Service providers: Meeting service times and locations of previously matched consumers

Moving Consumers and Moving Service Providers: Related Work (2/5) Multiple Weapon Multiple Target Assignment Problem (MWMTAP) Multiple Traveling Salesmen Problem with Moving targets (MTSPMT) (Stieber et al, Optimization Letters, 2015) Problem Formulation: V is set of nodes (cities in MTSPMT, targets in MWMTAP) A is set of arcs V x V W is set if salesmen/weapons Ci,j,k(t): time/distance between I and j when salesman k arrives at j at time t Goal: assign exactly one salesman from W to each node in V such that sum of travelled distance of all salesmen is minimized xli,j,k : binary decision variable =1 if salesman k is sent from i to j, arriving at j at time step l.

Moving Consumers and Moving Service Providers: Related Work (3/5) An integer linear programming formulation: Objective: Subject to constraints: Each node must be visited exactly once by exactly one salesman At each point in time,, each salesman can do at most one trip We can limit number of visits per salesmen within a certain time period (e.g. weapon battery has to be recharged, salesmen need resupply) Problem is NP-hard

Moving Consumers and Moving Service Providers: Related Work (4/5) Proposed 3 Heuristics to get a starting feasible solution (upper bound) – FCFS for targets: Least Time: Assign salesman that an reach target at the earliest possible point in time, even if it has to travel a longer distance than nearby salesmen. No further assignments are made for that salesman until he reaches target destination. Least Distance: Check for each salesman at what point in time does he have to travel the least distance to intercept target. Select the salesman which can travel the least distance. Parameterized heuristic: Use a scoring function for balancing the above 2 heuristics A branch and bound algorithm is used to improve solution.

Moving Consumers and Moving Service Providers: Related Work (5/5) Limitations of related work: Focuses on minimizing traveled distances rather than maximizing number of intercepted targets All targets are either intercepted or no feasible solution is found May not scale for large problem instances. Evaluated 36 instances with 1-3 salesmen and 6-24 moving targets. Targets do not specify constraints unlike consumers Resupply for all salesmen/weapons (if it exists) is assumed to occur at a single origin, unlike delivery trucks which can get resupplied at different store locations Certain locations can be preferred for intercepting consumers versus targets (e.g. rockets) which can be intercepted at anytime.

Other Ideas: Last Mile Delivery Problem (1/2) Leverages spatial & social network information for sharing excess capacity to reduce the GHG emissions, distances associated with the “last-mile” package delivery from online purchases, particularly in low population regions. Compares: Current door-to-door delivery model (CDS) Package pickup location system (e.g. kiosk at a store/public transit platform) (PLS) Social network package pickup (SPLS) CDS: A traveling salesman approach PLS, SPLS: emissions relative to detour required for pickup Likelihood of social network assistance as function of: Social relation Willingness score Spatial closeness

Other Ideas: Last Mile Delivery Problem (2/2) Interesting Variation: Identify mobile persons who will come in contact with both the service provider and the consumer at some points during their commute travel. i.e. each fixed provider can turn into a mobile provider but with known independent trajectories rather than starting from same origin as in delivery trucks (i.. Service provider location) Limitations: Focuses on minimizing distances and emissions rather than maximizing matching size Packages not picked by SPLS are delivered by CDS Only a small number of actors (120) was simulated Only one pickup location was simulated Used Euclidean distances rather than network distances

Other Ideas (cont’d) Sharing economy workshop: Last mile delivery problem how many will be owners and how many will be renters?, lookup other questions. Modeling profits, price changes and group recommendations Preferences learning Proposing up to K propositions

Thank you.

Basic Concepts 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).

High-level Problem Description Input: Historical trajectories and real-time locations of consumers and service providers Consumer Calendars, wish lists, gift-registries, shopping lists Historical mobile commerce transactions Output: Near-future or real-time mobile commerce opportunities in the ephemeral network by matching producers and consumers 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 Not predicting future location (but destination may be given). Might infer user preferences to reduce the amount of input needed at real-time

Example 1: Fixed Providers - Moving Consumers C may be matched to P1 or P2 But P1 not ready! C max. waiting time will be exceeded Match to P2 and Detour P1 C P2 Service Providers Consumers P1, P2: rush hours: 4 req/hour otherwise: 8 req/hour C: Shopping list, max waiting time (at provider) = 3 min, max detour = 20%

Example 2: Fixed Providers - Moving Consumers C may be matched to P1 or P2 P2 might not satisfy the customer’s max waiting time before service (e.g. C is very hungry) Therefore, C is matched to P1 P1 P2 c Service Providers Consumers P1, P2: 10 req/hour C1: Hair salon or have lunch, max time before service = 10 min, max waiting time = 0 min, max detour = 10%

Real-time/Online Ridesharing Systems (1/2) Passenger constraints: (src, dst, earliest departure time, latest arrival time) Driver constraints: (src, dst, departure time, deviation distance tolerance) Pairs only (2 sec/query) (Online) Ride sharing, Uber * Maximum cardinality matching in bi-partite graph of passengers/drivers.

Real-time/Online Ridesharing Systems (2/2) Passenger constraints: (src, dst, maximum waiting time, % of extra acceptable detour) Driver constraints: only satisfying all current passengers constraints A vehicle can group multiple passengers Problem : Given a set of vehicles and a new passenger request, find the vehicle that minimizes the overall trip cost for the augmented trip schedule. Approach: materializing and pruning valid trip schedules (4 to 12 ms/query) (Online) Ride sharing, Uber * Maximum cardinality matching in bi-partite graph of passengers/drivers.

Discussion: Problem Formulation 1: Fixed Providers, Moving Consumers (cont’d) Grouping consumers for the same server: e.g. If service provider announces a groupon, consumers may go to further service providers or wait more. (money constraint, ranking constraints) Should we assume that supply can change at real-time? e.g. based on restaurant occupancy But has to at least satisfy already accepted requests What about recommending multiple services for a single user along his route?

Discussion Should destination always be part of the consumer request? Maybe yes, because if consumer is not on the road, the value of suggesting a specific service provider on his way decreases as many nearby options become feasible. But users maybe willing to go to a specific suggested provider (without having a prespecified destination) if it has zero waiting time at their arrival (e.g. shopping bag ready to pickup, lunch ready on table or to pickup) What does the Maximum service waiting time constraint indicate? Maximum time before service is received (regardless if it was spent driving or waiting at provider)?

Discussion How can we make use historical trajectories? Why not just match real-time locations? (recurring vs. non-recurring rendezvous)

Preferences Learning

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 Wardrop Equilibrium Under equilibrium conditions, traffic arranges itself in congested networks in such a way that no individal trip maker can reduce his path costs by switching routes. Under equilibrium conditions traffic arranges itself in congested networks such that all used routes between an O-D pair have equal and minimum costs, while all unused routes have greater or equal costs OD Matrix: a matrix that displays the number of trips going from each origin to each destination.  Vehicles decide route based on their characteristics and info acquisition capability. Telematics: the branch of information technology that deals with the long-distance transmission of computerized information

Dynamic Congestion Pricing (1/3) Increasing pricing certain periods of time when peak demand exceeds supply. Economic concept: “charge a price to allocate a scarce resource to its most valuable use evidenced by user’s willingness to pay for the resource” Regulates demand to manage congestion without increasing supply Users become aware of imposed costs Encourages redistribution of demand in space or time Types: A cordon area / area-wide pricing: Pay to enter a restricted area within a city center Urban corridors and toll rings: e.g. charge on freeway by the distance traveled. Corridor or single facility pricing: e.g. bridges, tunnels, using high-occupancy vehicle lanes if toll is paid Concerns: Pricing the poor off the road, and to public transit Difficult to set accurate prices due to imprecise speed-flow curves

Dynamic Congestion Pricing (2/3) Theory of marginal cost pricing: A user equilibrium flow pattern can be shifted toward a system optimal flow pattern by imposing the congestion tolls exactly equal to the difference between marginal societal cost and marginal private cost in the system optimal situation. Literature Taxonomies: (1) Transportation network: First-best tolling: tolls every arc Second-best tolling: tolls a subset of arcs (bottlenecks) (2) Parameters: Deterministic (mostly) Uncertain: e.g. confidence intervals for demand (3) Tolls: Time-varying based on pre-determined price levels Triangular shaped toll: only need to specify max toll Lessons learned of value pricing: Alternatives assessed Effectiveness of outreach efforts and public perception Effectiveness of enforcement Net revenues Equity implications Impacts on travel behavior and air quality standards Travel time savings Impacts on local business.

Dynamic Congestion Pricing (3/3) Example formulations: (Bi-level) Optimization Problem to minimize congestion when flow correspond to dynamic user equilibrium by dynamically adjusting tolls Lower level problem: maximizes travel cost to find worst case demand Upper level problem: minimizes the cost by determining toll price given that demand is in the worst case. Equilibrium constraints Decision variable: toll price Determine a schedule of tolls (over all arcs) that leads to day-to-day dynamic user equilibrium Uses cellular Particle Swarm optimization Decision variable: Variable for which we want to find optimal value. Computational scalability: Around 17 hours for 4 OD pairs, a network with 13 nodes and 18 edges and only 1 tolled edge.

Dynamic Pricing of Inventories (1/3) Dynamic pricing policies in retail based on how much current customers value the product and what future demand will be. To avoid lost sales and excess inventory Types of pricing policies: Posted price (static vs. dynamic) Price discovery (e.g. bidding process such as auctions) Literature taxonomy of dynamic pricing across markets: Replenishment vs. No Replenishment (short/end of life-cycle) of Inventory during product life-cycle (R/NR) Dependent vs. Independent Demand Over Time (D/I) Durable goods vs. non durable frequent goods(e.g. milk and bread) Myopic vs. Strategic Customers (M/S): considering future prices

Dynamic Pricing of Inventories (2/3) Literature focused on: NR-I (e.g. short life-cycle product such as fashion apparel and holiday products) R-I-M (e.g. grocery, produce, pharmaceutical products) (A) No Inventory Replenishment and Independent over Time (NR-I) Goal: How to price products to maximize expected/discounted profits over the (short) selling season? Demand is modeled as a Poisson process with intensity that varies over time, with price or inventory level (occupied shelf space). Pricing policies: Periodic: groups of customers have same price Given that price can at most be modified k times and length of each price period Continuous (as function of time): customers arrive sequentially Select from a discrete set of allowable prices: e.g. Decide optimal timing of price change given the number of price changes, initial price, and set of allowable prices RIM: Myopic because customers cannot wait for price drops due to being necessity items, also large number of customers, and only small expected sales.

Dynamic Pricing of Inventories (3/3) (B) Inventory Replenishment and Independent Demand, Myopic Customers: (R-I-M) Goal: How to coordinate pricing with inventory procurement and production decisions? Models of demand: Uncertain demand, convex production and ordering costs, unlimited production capacity Uncertain demand, Fixed ordering cost, limited production capacity Deterministic demand Need to study multiple stores with different demand patterns simultaneously (2002) i.e. Generating a price schedule per item per store

Alternative Problem Formulations Fixed Providers, Moving Consumers, Destination Specified Fixed Providers, Moving Consumers, Destination Unspecified Fixed Providers, Moving Consumers, Multiple Destinations Importance Mobile commerce for many service providers (e.g. food, retail stores, malls, etc) Mobile commerce for many service providers (e.g. food, retail stores, malls, etc) but limited to complex itinerary requests Related Work Online maximum bipartite matching Multiple destination route planning Novelty Matching moving objects on a road network with thousands of service providers Matching objects on a road network with thousands of service providers. (When destination is not known matching decision is based on fixed current location rather than consumer trajectory (less rich spatial/motion semantics, equivalent to fixed customers) Multiple-destination Route planning literature does not assume: fixed capacities/supply per destination (i.e. is not coupled with matching) Unspecified/Imprecise destinations (only service types are specified) Challenges Scalability, real-time response Deciding most suitable providers for a given request along the route Computational complexity of route planning through multiple unspecific destinations while maximizing matching Contributions Can we use spatial heuristics to maximize matching rather than 1D ranking functions of providers? E.g. assigning providers closer to src vs. dest Can we use spatial heuristics to maximize matching rather than 1D ranking functions of providers? A scalable algorithm with spatial heuristics for matching requests with multiple destinations to service providers while respecting provider and consumer constraints