ZAC: A Zone pAth Construction approach for effective real-time ridesharing

National Research Foundation (NRF) Singapore under SMART and Future Mobilit

Neural Approximate Dynamic Programming for On-Demand Ride-Pooling

On-demand ride-pooling (e.g., UberPool) has recently become popular because of its ability to lower costs for passengers while simultaneously increasing revenue for drivers and aggregation companies. Unlike in Taxi on Demand (ToD) services -- where a vehicle is only assigned one passenger at a time -- in on-demand ride-pooling, each (possibly partially filled) vehicle can be assigned a group of passenger requests with multiple different origin and destination pairs. To ensure near real-time response, existing solutions to the real-time ride-pooling problem are myopic in that they optimise the objective (e.g., maximise the number of passengers served) for the current time step without considering its effect on future assignments. This is because even a myopic assignment in ride-pooling involves considering what combinations of passenger requests that can be assigned to vehicles, which adds a layer of combinatorial complexity to the ToD problem. A popular approach that addresses the limitations of myopic assignments in ToD problems is Approximate Dynamic Programming (ADP). Existing ADP methods for ToD can only handle Linear Program (LP) based assignments, however, while the assignment problem in ride-pooling requires an Integer Linear Program (ILP) with bad LP relaxations. To this end, our key technical contribution is in providing a general ADP method that can learn from ILP-based assignments. Additionally, we handle the extra combinatorial complexity from combinations of passenger requests by using a Neural Network based approximate value function and show a connection to Deep Reinforcement Learning that allows us to learn this value-function with increased stability and sample-efficiency. We show that our approach outperforms past approaches on a real-world dataset by up to 16%, a significant improvement in city-scale transportation problems.Comment: Accepted for publication to the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20

RE-ORG: An online repositioning guidance agent

Ministry of Education, Singapore under its Academic Research Funding Tier 2Demo Paper</p

Online spatio-temporal matching in stochastic and dynamic domains

Spatio-temporal matching of services to customers online is a problem that arises on a large scale in many domains asso-ciated with shared transportation (ex: taxis, ride sharing, su-per shuttles, etc.) and delivery services (ex: food, equipment, clothing, home fuel, etc.). A key characteristic of these prob-lems is that matching of services to customers in one round has a direct impact on the matching of services to customers in the next round. For instance, in the case of taxis, in the sec-ond round taxis can only pick up customers closer to the drop off point of the customer from the first round of matching. Traditionally, greedy myopic approaches have been adopted to address such large scale online matching problems. While they provide solutions in a scalable manner, due to their my-opic nature the quality of matching obtained can be improved significantly (demonstrated in our experimental results). In this paper, we present a two stage stochastic optimization for-mulation to consider expected future demand. We then pro-vide multiple enhancements to solve large scale problems more effectively and efficiently. Finally, we demonstrate the significant improvement provided by our techniques over my-opic approaches on two real world taxi data sets.

Robust influence maximization

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Online spatio-temporal matching in stochastic and dynamic domains

National Research Foundation (NRF) Singapor