Urban law enforcement agencies are under great pressure to respond to emergency incidents effectively while operating within restricted budgets. Minutes saved on emergency response times can save lives and catch criminals, and a responsive police force can deter crime and bring peace of mind to citizens. To efficiently minimize the response times of a law enforcement agency operating in a dense urban environment with limited manpower, we consider in this paper the problem of optimizing the spatial and temporal deploymentof law enforcement agents to predefined patrol re-gions in a real-world scenario informed by machinelearning. To this end, we develop a mixed integer linear optimization formulation (MIP) to minimize the risk of failing response time targets. Given the stochasticity of the environment in terms of incident numbers, location, timing, and duration, we use Sample Average Approximation (SAA) to finda robust deployment plan. To overcome the sparsity of real data, samples are provided by an incident generator that learns the spatio-temporal distribution and demand parameters of incidents froma real world historical dataset and generates sets of training incidents accordingly. To improve run-time performance across multiple samples, we implement a heuristic based on Iterated Local Search(ILS), as the solution is intended to create deployment plans quickly on a daily basis. Experimental results demonstrate that ILS performs well against the integer model while offering substantial gains in execution time.

Maritime navigational safety is of utmost importance to prevent vessel collisions in heavily trafficked ports, and avoid environmental costs. In case of a likely near miss among vessels, port traffic controllers provide assistance for safely navigating the waters, often at very short lead times. A better strategy is to avoid such situations from even happening. To achieve this, we

a) formalize the decision model for traffic hotspot mitigation including real-istic maritime navigational features and constraints through consultations with domain experts; and

b) develop a constraint programming based scheduling approach to mitigate hotspots. We model the problem as a variant of the resource constrained project scheduling problem to adjust vessel movement schedules such that the average delay is minimized and navigational safety constraints are also satisfied. We conduct a thorough evaluation on key performance indicators using real world data, anddemonstrate the effectiveness of our approach inmitigating high-risk situations.

Aggregation systems (e.g., Uber, Lyft, FoodPanda, Deliveroo) have been increasingly used to improve efficiency in numerous environments, including in transportation, logistics, food and grocery delivery. In these systems,a centralized entity (e.g., Uber) aggregates supply and assigns them to demand so as to optimize a central metric such as profit, numberof requests, delay etc. Due to optimizing ametric of importance to the centralized entity, the interests of individuals (e.g., drivers, delivery boys) can be sacrificed. Therefore, in this paper, we focus on the problem of serving individual interests, i.e., learning revenue maximizing policies for individuals in the presence of a self interested central entity. Since thereare large number of learning agents that arehomogenous, we represent the problem as an Anonymous Multi-Agent Reinforcement Learning (AyMARL) problem. By using the self interested centralized entity as a correlation entity, we provide a novel learning mechanism that helps individual agents to maximize their individual revenue. Our Correlated Learning(CL) algorithm is able to outperform existing mechanisms on a generic simulator for aggregation systems and multiple other benchmark Multi-Agent Reinforcement Learning (MARL) problems.

Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to customers; Ubereats, Deliveroo, FoodPanda etc for matching restaurants to customers. In these online to offline service problems, individuals who are responsible for supply (e.g., taxi drivers, delivery bikes or delivery van drivers) earn more by being at the ”right” place at the ”right” time. We are interested in developing approaches that learn to guide individuals to be in the ”right” place at the ”right” time (to maximize revenue) in the presence of other similar ”learning” individuals and only local aggregated observation of other agents states (e.g., only number of other taxis in same zone as current agent).

Existing approaches in Multi-Agent Reinforcement Learning (MARL) are either not scalable (e.g., about 40000 taxis/cars for a city like Singapore) or assumptions of common objective or action coordination or centralized learning are not viable. A key characteristic of the domains of interest is that the interactions between individuals are anonymous, i.e., the outcome of an interaction (competing for demand) is dependent only on the number and not on the identity of the agents. We model these problems using the Anonymous MARL (AyMARL) model. To ensure scalability and individual learning, we focus on improving performance of independent reinforcement learning methods, specifically Deep Q-Networks (DQN) and Advantage Actor Critic (A2C) for AyMARL. The key contribution of this paper is in employing principle of maximum entropy to provide a general framework of independent learning that is both empirically effective (even with only local aggregated information of agent population distribution) and theoretically justified.

Finally, our approaches provide a significant improvement with respect to joint and individual revenue on a generic simulator for online to offline services and a real world taxi problem over existing approaches. More importantly, this is achieved while having the least variance in revenues earned by the learning individuals, an indicator of fairness.

Community detection has been widely studied in the areas of social network analysis and recommendation system. However, most existing research focus on cases where relationships are explicit or depend on simultaneous appearance. In this paper, we propose to study the community detection problem where the relationships are not based on simultaneous appearance, but time-delayed appearances. In other words, we aim to capture the relationship where one individual physically follows another individual. In our attempt to capture such relationships, the major challenge is the presence of spatial homophily, i.e., individuals are attracted to locations due to their popularities and not because of communications.

In tackling the community detection problem with spatial homophily and delayed responses, we make the following key contributions: (1) We introduce a four-phase framework, which by way of using quantified impacts excludes homophily. (2) To validate the framework, we generate a synthetic dataset based on a known community structure and then infer that community structure. (3) Finally, we execute this framework on a real-world dataset with more than 6,000 taxis in Singapore. Our results are also compared to those of a baseline approach without homophily-elimination.

Electric Vehicles (EVs) are the next wave of technology in the transportation industry. EVs are increasingly becoming common for personal transport and pushing the boundaries to become the main-stream mode of transportation. Use of such EVs in logistic fleets for delivering customer goods is not far from becoming reality. However, managing such fleet of EVs bring new challenges in terms of battery capacities and charging infrastructure for efficient route planning. Researchers have addressed such issues considering different aspects of the EVs such as linear battery charging/discharging rate, fixed travel times, etc. In this paper, we address the issue of waiting times due to limited charging capacity at the charging stations while planning the routes of EVs for providing pickup/delivery services. We provide an exact mathematical model of the problem considering waiting times of vehicle based on their arrival at the charging stations. We further develop a genetic algorithm approach that embeds Constraint Programming to solve the problem.We test our approach on a set of benchmark Solomon instances.

We address the problem of maritime traffic management in busy waterways such as Singapore Straits to increase the safety of navigation by reducing congestion. We model maritime traffic as a large multiagent systems with individual vessels as agents, and the port authority as the regulatory agent. Our main contributions include the development of a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels. We also develop a traffic coordination approach that provides speed recommendation to vessels in different zones. We exploit the nature of collective interactions among agents to develop a scalable policy gradient approach that can scale up to real world problems. Empirical results on synthetic and real world problems show that our approach can significantly reduce congestion while keeping the traffic throughput high.