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The popularity of on-demand ride pooling is owing to the benefits offered tocustomers (lower prices), taxi drivers (higher revenue), environment (lowercarbon footprint due to fewer vehicles) and aggregation companies like Uber(higher revenue). To achieve these benefits, two key interlinked challengeshave to be solved effectively: (a) pricing -- setting prices to customerrequests for taxis; and (b) matching -- assignment of customers (that acceptedthe prices) to taxis/cars. Traditionally, both these challenges have beenstudied individually and using myopic approaches (considering only currentrequests), without considering the impact of current matching on addressingfuture requests. In this paper, we develop a novel framework that handles thepricing and matching problems together, while also considering the futureimpact of the pricing and matching decisions. In our experimental results on areal-world taxi dataset, we demonstrate that our framework can significantlyimprove revenue (up to 17% and on average 6.4%) in a sustainable manner byreducing the number of vehicles (up to 14% and on average 10.6%) required toobtain a given fixed revenue and the overall distance travelled by vehicles (upto 11.1% and on average 3.7%). That is to say, we are able to provide an idealwin-win scenario for all stakeholders (customers, drivers, aggregator,environment) involved by obtaining higher revenue for customers, drivers,aggregator (ride pooling company) while being good for the environment (due tofewer number of vehicles on the road and lesser fuel consumed).

Future Aware Pricing and Matching for Sustainable On-demand Ride Pooling