Harnessing Big Data for the Sharing Economy in Smart Cities

Motivated by the imbalance between demand (i.e., passenger requests) and supply (i.e., available vehicles) in the ride-hailing market and severe traffic congestion faced by modern cities, this dissertation aims to improve the efficiency of the sharing economy by building an agent-based methodological framework for optimal decision-making of distributed agents (e.g., autonomous shared vehicles), including passenger-seeking and route choice. Furthermore, noticing that city planners can impact the behavior of agents via some operational measures such as congestion pricing and signal control, this dissertation investigates the overall bilevel problem that involves the decision-making process of both distributed agents (i.e., the lower level) and central city planners (i.e., the upper level). First of all, for the task of passenger-seeking, this dissertation proposes a model-based Markov decision process (MDP) approach to incorporate distinct features of e-hailing drivers. The modified MDP approach is found to outperform the baseline (i.e., the local hotspot strategy) in terms of both the rate of return and the utilization rate. Although the modified MDP approach is set up in the single-agent setting, we extend its applicability to multi-agent scenarios by a dynamic adjustment strategy of the order matching probability which is able to partially capture the competition among agents. Furthermore, noticing that the reward function is commonly assumed as some prior knowledge, this dissertation unveils the underlying reward function of the overall e-hailing driver population (i.e., 44,000 Didi drivers in Beijing) through an inverse reinforcement learning method, which paves the way for future research on discovering the underlying reward mechanism in a complex and dynamic ride-hailing market. To better incorporate the competition among agents, this dissertation develops a model-free mean-field multi-agent actor-critic algorithm for multi-driver passenger-seeking. A bilevel optimization model is then formulated with the upper level as a reward design mechanism and the lower level as a multi-agent system. We use the developed mean field multi-agent actor-critic algorithm to solve for the optimal passenger-seeking policies of distributed agents in the lower level and Bayesian optimization to solve for the optimal control of upper-level city planners. The bilevel optimization model is applied to a real-world large-scale multi-class taxi driver repositioning task with congestion pricing as the upper-level control. It is disclosed that the derived optimal toll charge can efficiently improve the objective of city planners. With agents knowingwhere to go (i.e., passenger-seeking), this dissertation then applies the bilevel optimization model to the research question of how to get there (i.e., route choice). Different from the task of passenger-seeking where the action space is always fixed-dimensional, the problem of variable action set emerges in the task of route choice. Therefore, a flow-dependent deep Q-learning algorithm is proposed to efficiently derive the optimal policies for multi-commodity multi-class agents. We demonstrate the effect of two countermeasures, namely tolling and signal control, on the behavior of travelers and show that the systematic objective of city planners can be optimized by a proper control

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

Solving Multi-objective Integer Programs using Convex Preference Cones

Esta encuesta tiene dos objetivos: en primer lugar, identificar a los individuos que fueron víctimas de algún tipo de delito y la manera en que ocurrió el mismo. En segundo lugar, medir la eficacia de las distintas autoridades competentes una vez que los individuos denunciaron el delito que sufrieron. Adicionalmente la ENVEI busca indagar las percepciones que los ciudadanos tienen sobre las instituciones de justicia y el estado de derecho en Méxic

Routing choices in intelligent transport systems

Road congestion is a phenomenon that can often be avoided; roads become popular, travel times increase, which could be mitigated with better coordination mechanisms. The choice of route, mode of transport, and departure time all play a crucial part in controlling congestion levels. Technology, such as navigation applications, have the ability to influence these decisions and play an essential role in congestion reduction. To predict vehicles' routing behaviours, we model the system as a game with rational players. Players choose a path between origin and destination nodes in a network. Each player seeks to minimise their own journey time, often leading to inefficient equilibria with poor social welfare. Traffic congestion motivates the results in this thesis. However, the results also hold true for many other applications where congestion occurs, e.g. power grid demand. Coordinating route selection to reduce congestion constitutes a social dilemma for vehicles. In sequential social dilemmas, players' strategies need to balance their vulnerability to exploitation from their opponents and to learn to cooperate to achieve maximal payouts. We address this trade-off between mathematical safety and cooperation of strategies in social dilemmas to motivate our proposed algorithm, a safe method of achieving cooperation in social dilemmas, including route choice games. Many vehicles use navigation applications to help plan their journeys, but these provide only partial information about the routes available to them. We find a class of networks for which route information distribution cannot harm the receiver's expected travel times. Additionally, we consider a game where players always follow the route chosen by an application or where vehicle route selection is controlled by a route planner, such as autonomous vehicles. We show that having multiple route planners controlling vehicle routing leads to inefficient equilibria. We calculate the Price of Anarchy (PoA) for polynomial function travel times and show that multiagent reinforcement learning algorithms suffer from the predicted Price of Anarchy when controlling vehicle routing. Finally, we equip congestion games with waiting times at junctions to model the properties of traffic lights at intersections. Here, we show that Braess' paradox can be avoided by implementing traffic light cycles and establish the PoA for realistic waiting times. By employing intelligent traffic lights that use myopic learning, such as multi-agent reinforcement learning, we prove a natural reward function guarantees convergence to equilibrium. Moreover, we highlight the impact of multi-agent reinforcement learning traffic lights on the fairness of journey times to vehicles