On Demand High Capacity Ride Sharing for Mobility on Demand (MoD) Systems

Mobility-on-demand (MoD) systems are emerging as a novel mode for urban mobility which provide users with a reliable mode of transportation that is catered to the individual needs. Ride sharing services provided by these mobility on demand systems provide not only a very personalized mobility experience but also present immense potential for positive societal impacts with reference to pollution, energy consumption, congestion, and etc. Ride sharing services primarily concern with picking up spatiotemporally distributed mobility demand and delivering it within a pre-specified time window subjected to different constraints. Large scale ride sharing in more sophisticated spatiotemporally distributed mobility demand distributions, require well designed mathematical models and algorithms in order to match riders and vehicle fleets in real time. In this research, the motive is to design and develop a dynamic model for ride sharing which is reactive anytime optimal and can perform dynamic vehicle assignment in an effective and efficient manner while being able to scale well with both sparse and dense spatio-temporal demand distributions.                

Keywords : Ride sharing, Human Mobility, Vehicle Routing, Smart Cities, Intelligent Transport Systems, Mobility on demand.


Customer profiling to improve service and management of mobility on demand systems.

In the modern era of big data, many systems which provide various services thrive to gather data related to the customer-system interaction. Mobility on demand systems can be recognized as a similar type of a system, which gathers a massive amount of data related to customers mobility on a daily basis. Identifying and characterizing different customer profiles within the customer base by analyzing this data related to the customer mobility is quite important for the strategic decision-making process. Hence, this project explores algorithms and techniques that are suitable to model customer related data of “mobility on demand” systems in order to profile customers to support predictive management and service enhancement of the system.   

Key-words : Data-Mining, Mobility on demand systems, Customer profiling, Customer segmentation 


Affect level opinion mining of Twitter Streams

Twitter is a social media platform which is used by millions of users to express their opinions freely. There are about 120,000 active twitter users in sri lanka. Because of the rapidly increasing number of tweets, mining people’s expressed opinions in tweets on interesting topics has attracted more and more attention. Mining of these opinions manually is an impossible task, thus we have to employ automated methods to summarize the opinions. Opinion of a tweet can be summarized at the level of sentiment polarity or more finer level of expressed emotion. In this research our goal is to develop an emotion analysis algorithm which can accurately recognize emotions in a given tweet and provide an approach to identify the emotion intensity for group of tweets related to a single topic.

Keywords: data-mining, opinion-mining, affect, social-media, twitter


Developing a Trip Distribution model for Identified Mobility Groups using Big Data

Transport infrastructure is an important component of the economy and a common tool used for the development. The satisfactory outcome of the transport depends on the effectiveness and the efficiency of infrastructure planning which involved in expensive and time consuming human intervention in current conventional approaches. Ubiquitous mobile usage and the massive data it generates presents new opportunities to assess the demand for this infrastructure, diagnose problems, and plan for the future. These data sources include passively collected data such as mobile phone network data (CDR, VLR data), smartphone GPS data and etc. Further, these newer data sources have the ability to complement conventional data as proven by the previous studies. However, before these benefits can be realized, methods must be found to integrate such new data sources with existing transportation planning frameworks such as widely used travel demand models like four step model and direct demand models. Therefore the current research study is continued to reformulate a comprehensive transport demand model based on new big data inputs

Keywords: Big Data, Machine Learning, CDR


Forecasting Agricultural Crop Yield using Remote Sensing Data & Machine Learning

In recent years, sustainability of the agriculture sector has been threatened due to devastating environmental hazards and severe climate conditions that has occurred all over Sri Lanka. Gathering data of environmental catastrophes and important environmental factors such as temperature, soil moisture & atmospheric humidity etc., estimating effects of them on agriculture are necessarily involved in highly error-prone & time consuming human interventions in current traditional approaches. Policies and decisions taken by authorities of the government and other stakeholders are highly susceptible to flaws of current approaches. The tendency to employ data science and remote sensing techniques together in the field of agriculture is limited in developing countries due to scarcity of remote sensing resources till recent years. The objective of this research is to explore the untouched synergy of the remote sensing and machine learning methodologies in order to enable a data driven policy & decision making culture in agriculture sector.

Keywords: Remote Sensing, Big Data, Machine Learning, Agriculture, Data Driven Decision Making