Investigating computational techniques for micro-level and macro-level transportation problems on urban road networks

Abstract

Transportation is a fundamental task in modern-day civilization. Examples of transportation in our daily lives include going to the workplace, returning home after work, etc. In this thesis, we investigate computational techniques for Micro-level and Macro-level transportation problems on urban road networks. Micro-level transportation problems involve transportation of a single individual. Whereas, in the case of Macro-level transportation problems, multiple individuals need to be transported to their individual or common destination(s). In our work on Micro-level transportation problems, we consider Constrained Path Optimization for the use-cases of finding Navigable Paths and Safe Paths on road networks. The concept of Navigable paths has the potential to add value to the state-of-art navigation systems, so they can be easily used in developing nations. Likewise, the concept of Safe Routing has a high societal relevance, especially in the developing nations where the lack of infrastructure such as street lights, may contribute to higher crime rates. We devise algorithmic solutions that focus on the systems-oriented perspective, and also build a Navigation system for these application domains of Constrained Path Optimization. Our work on Macro-level transportation problems revolves around Task Assignment in Spatial Crowdsourcing. We consider the use-case of a taxi-hailing service, and propose algorithmic solutions for task assignment that focus on the systems-oriented perspective. Unlike most of the works in this domain, we consider the egalitarian version of the problem, meaning that we optimise the expectation of all entities of the Spatial Crowdsourcing platform.