Sequential Decision Making for Improving Efficiency in Urban Environments

Rapid “urbanization” (more than 50% of world’s population now resides in cities) coupled with the natural lack of coordination in usage of common resources (ex: bikes, ambulances, taxis, traffic personnel, attractions) has a detrimental effect on a wide variety of response (ex: waiting times, response time for emergency needs) and coverage metrics (ex: predictability of traffic/security patrols) in cities of today. Motivated by the need to improve response and coverage metrics in urban environments, my research group is focussed on building intelligent agent systems that make sequential decisions to continuously match available supply of resources to an uncertain demand for resources. Our broad approach to generating these sequential decision strategies is through a combination of data analytics (to obtain a model) and multistage optimization (planning/scheduling) under uncertainty (to solve the model). While we perform data analytics, our contributions are focussed on multi-stage optimization under uncertainty. We exploit key properties of urban environments, namely homogeneity and anonymity, limited influence of individual entities, abstraction and near decomposability to solve ”multi-stage optimization under uncertainty” effectively and efficiently. 1 Problems of Interest and Significance Many decision problems in urban environments can be characterised as requiring a match between limited resource supply and an unpredictable demand for resources. Given below are a few practical real world urban decision problems of interest to us: • Taxi fleets: Resource supply corresponds to the available taxis and demand corresponds to customers needing taxis. The goal in this problem is to increase revenues for taxis (or reduce wait times for customers) by continuously matching available taxis to customer demand or proxies for customer demand (ex: taxi stands). • Bike sharing systems: Resource supply corresponds to available bikes at base stations and demand corresponds to customers needing bikes. The goal in this problem is to reduce lost demand due to unavailability of bikes at base stations. We are focussed on lost demand, as it can lead to customers employing private vehicles, which in turn will lead to increased carbon emissions and traffic congestion. A similar problem is relevant to car sharing systems as well. • Emergency response: Resource supply corresponds to ambulances or fire trucks at base stations and demand corresponds to emergency events. The goal in this problem is to reduce response time for emergency events by dynamically moving the ”right” ambulances to the ”right” base stations. • Traffic patrol and Security: Resource supply corresponds to traffic or security personnel at base locations and demand corresponds to potential for traffic violations or security incidents. The goal in this problem is to prevent traffic violations and security incidents by reducing predictability in patrols of traffic/security personnel without sacrificing on coverage of ”important” locations. • Theme parks: Resource supply corresponds to attractions and demand corresponds to patrons visiting the attractions. The goal in this problem is to reduce wait times by providing decision support to patrons on visiting the ”right” attractions at the ”right” times. We now situate these urban decision problems in the context of existing work in Artificial Intelligence and Operations Research on general resource allocation problems. While there are other factors (offline/online, objectives etc.), we categorise using the following three criterion to precisely highlight differences between existing work and our work : (a) Scale of problems; (b) Cooperative/Competitive nature of decision makers (ones doing matching) or supply or demand; and (c) Deterministic or Non-deterministic (Stochastic and Dynamic) nature of the environment. Figure 1 provides this categorisation identifying specific research threads in a category using names of models/representations/frameworks. We first describe the four categories associated with existing research: 1. Deterministic and Cooperative Problems: In this category, (Distributed) Constraint Satisfaction [Yokoo and Hirayama, 2000] and (Distributed) Constraint Optimization [Pragnesh Jay Modi and Yokoo, 2005] models have been employed to represent problems where values (can represent demand) have to be assigned to variables (resources) so as to Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)