Supply-Demand Ratio and On-Demand Spatial Service Brokers A collaboration between Ford University Research Program and University of Minnesota University PI: Shashi Shekhar Ford PI: Shounak Athavale Eric Marsman

Outline IWCTS Slide Review Date Extensions for the ACM Transactions on Intelligent Systems and Technology (ACM TIST) - Special Issue on Urban Intelligence

Extensions for ACM TIST IWCTS 2016 ACM TIST 2016 Problem Definition Objective: max. matched requests Consumers had 2 constraints: Max. travel distance Max waiting time Proof of NP-hardness Add a “Basic Concepts” subsection Modify consumer constraints Formalize a measure of fairness Proposed Approach Proposed 2 service-provider centric heuristics: Least Accepted First Least appearance as Candidate First Incorporating ratings Supply-demand ratio-aware broker (moving providers?) Add example for each different heuristic Experimental Evaluation Four experiments: % matched requests % matched providers avg. requests/provider Stdev of requests/provider Expand experimental evaluation section by adding more experiments. from original paper tie-breaker heuristics Discussion None Add discussion section (further related work, choice of simulation framework) Special Issue: Urban Intelligence - ACM TIST.   http://tist.acm.org/CFPs/ACM%20TIST%20SI%20on%20Urban%20Intelligence%20CFP.pdf

Basic Concepts A Service Provider: A provider registered in the system is defined using its location and service rate per hour over the day (e.g. 15 requests per hour) A Consumer Request: A request for from a mobile consumer, including the consumer’s current location, max. acceptable travel distance and max. acceptable waiting time before service. A Service Provider Proposition: A quadruple (r, p, d, w) where: r ϵ set of available consumer requests p ϵ set of registered service providers d: distance between r and p w: waiting time before r is served by p Example: (C1, P1, 2 miles, 5 min)

Problem Definition Add computational complexity subsection with proof of NP-hardness Modify consumer constraints Maximum travel time vs. travel distance (longer distance on a freeway can be faster) Maximum waiting time at provider Formalize a measure of fairness: Previously communicated informally as “Keeping the eco-system functioning by engaging many service providers and balancing their assignments as evenly as possible” Need to propose a formal measure

Problem Definition (cont’d) Provider Balance Score: A weighted sum to: Maximize ratio of matched providers Minimize STDEV of number of assigned requests per provider (normalize) Used as a secondary objective function (tie-breaker) Another alternative: Keep a single objective (maximizing number of matched requests), but model providers leaving the system if not matched. (unbalanced assignment → less matched requests) Drawbacks: Makes more sense only in case of moving providers i.e. problem changes to moving providers and fixed consumers. (a reviewer’s comment) No need for several propositions since for moving providers, only a single assignment is enough. Need to simulate a long duration for providers to shift in space or leave system

Problem Definition: On-Demand Spatial Service Propositions Input: A set P of service providers A set R of consumer requests arriving dynamically A number of required propositions K Output: K service provider(s) propositions for each request Objective: Maximize number of matched requests Secondary Objective: Maximize provider-balance score Constraints: Each returned proposition satisfies the consumer’s max. travel time and waiting time constraints and does not violate the provider’s service rate.

Proposed Approach (1/?): Incorporating Ratings Reviewer Comment: “clearly LAF results in higher % matched providers as SDR increases, by its very definition, but this alone does not make it a good choice as a heuristic, as it doesn't consider the possibility that certain providers could be "least accepted" for a reason (e.g., bad reputation, historically-bad service, etc.). Forcing consumers to choose from commonly-unaccepted options solely for the sake of "balance" seems unlikely to be acceptable in certain "spatial service" use cases (such as the restaurant scenario considered).” To avoid bias towards low-rating providers: Define an “Average Consumer Loss” function (ACL): Add ACL to the Provider Balance Score:

Proposed Approach (2/?): Incorporating Ratings Approaches to compare: For least assigned providers, select highest-rated K candidates Challenge: Are we penalizing new providers? For least assigned providers, diversify selected ratings Challenge: May penalize consumers by adding low ratings For least assigned providers, return when average ratings of propositions ≥ avg. ratings of candidates

Interaction between price modeling, incentives, moving providers Proposed Approach (3/?) Reviewer Comments: “The technical contribution is limited to a number of heuristics and a rather straightforward evaluation framework.” “It is unclear why the consumer is travelling in this application framework and not the provider. e.g. for most ridesharing applications, the providers are travelling and the consumers location is fixed (for pickup).” “It is assumed that all providers equally charge for their services; in practice, this is not the case. One could argue that the distance from consumer’s current position to the location of the service provider captures to some extend the consuming cost but not entirely. For example, if the providers are restaurants or supermarkets, a person might decide to travel a bit more instead of paying extra for her meal or her grocery.” Interaction between price modeling, incentives, moving providers

A Supply-Demand Ratio Aware Broker for On-demand Services (4/?) Motivation: 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: a moving horizon is used to infer zone type as time changes Propose a supply-demand ratio-aware broker that changes matching strategies in space and time based on local supply-demand ratios. A separate queue is maintained for each zone and matching strategy is selected based on zone type. Balanced zone: focus on making consumers happier (since we should be able to handle most requests) Apply Consumer-centric heuristics (e.g. NN)

A Supply-Demand Ratio Aware Broker for On-demand Services (5/?) Jammed zone: focus is maximizing matched requests Apply a broker-centric heuristic (i.e. highest matching heuristic) Incentivize consumers: Model hurried vs. relaxed vs. indifferent consumers Suggest longer waiting/travel time to consumers (maybe with lower prices) to shift demand to sparser zones (relaxed) Suggest higher pricing to consumers to get service now (hurried) in case of mobile providers Incentivize (mobile) providers with recently few matches to move to current zone for a higher price. Provider cost model: price > cost of travel to current zone + trip cost Sparse zone: main focus is keeping eco-system alive until demand rises again Apply provider-centric heuristics Mobile providers are more realistic when discussing keeping the eco-system alive. Incentivize (mobile) providers: Suggest higher price for providers to move to jammed zones

A Supply-Demand Ratio Aware Broker for On-demand Services (6/ A Supply-Demand Ratio Aware Broker for On-demand Services (6/?): Requests crossing zone boundaries Challenges: Price modeling Sharing of ride for a number of consumers How to learn the boundary values between different zone types? Need to model (mobile) providers leaving the system to influence matched requests: e.g. if cost of travel time since last ride > expected price of next ride Two types of requests: local: max travel distance extends only inside the origin zone focal: max travel distance extends beyond origin zone For focal requests, may sort candidates in neighbor zones based on their zone types or current number of real-time requests.

Experimental Evaluation Extend experimental evaluation section to include: Evaluating effect of supply-demand ratio on other metrics: Avg. consumer waiting time, Avg. consumer travel time Total execution time Evaluating the effect of varying other parameters: K ttimeout Service times, consumer’s maximum waiting time and travel time constraints Neighborhood size for LLEP Combining heuristics to solve ties and enhance matching quality Evaluate other proposed work

Thank you.