Data analytics and optimization for assessing a ride sharing system

Ride-sharing schemes attempt to reduce road traffic by matching prospective passengers to drivers with spare seats in their cars. To be successful, such schemes require a critical mass of drivers and passengers. In current deployed implementations, the possible matches are based on heuristics, rather than real route times or distances. In some cases, the heuristics propose infeasible matches; in others, feasible matches are omitted. Poor ride matching is likely to deter participants from using the system. We develop a constraint-based model for acceptable ride matches which incorporates route plans and time windows. Through data analytics on a history of advertised schedules and agreed shared trips, we infer parameters for this model that account for 90% of agreed trips. By applying the inferred model to the advertised schedules, we demonstrate that there is an imbalance between riders and passengers. We assess the potential benefits of persuading existing drivers to switch to becoming passengers if appropriate matches can be found, by solving the inferred model with and without switching. We demonstrate that flexible participation has the potential to reduce the number of unmatched participants by up to 80%

MOBILITY ANALYSIS AND PROFILING FOR SMART MOBILITY SERVICES: A BIG DATA DRIVEN APPROACH. An Integration of Data Science and Travel Behaviour Analytics

Smart mobility proved to be an important but challenging component of the smart cities paradigm. The increased urbanization and the advent of sharing economy require a complete digitalisation of the way travellers interact with the mobility services. New sharing mobility services and smart transportation models are emerging as partial solutions for solving some tra c problems, improve the resource e ciency and reduce the environmental impact. The high connectivity between travellers and the sharing services generates enormous quantity of data which can reveal valuable knowledge and help understanding complex travel behaviour. Advances in data science, embedded computing, sensing systems, and arti cial intelligence technologies make the development of a new generation of intelligent recommendation systems possible. These systems have the potential to act as intelligent transportation advisors that can o er recommendations for an e cient usage of the sharing services and in uence the travel behaviour towards a more sustainable mobility. However, their methodological and technological requirements will far exceed the capabilities of today's smart mobility systems. This dissertation presents a new data-driven approach for mobility analysis and travel behaviour pro ling for smart mobility services. The main objective of this thesis is to investigate how the latest technologies from data science can contribute to the development of the next generation of mobility recommendation systems. Therefore, the main contribution of this thesis is the development of new methodologies and tools for mobility analysis that aim at combining the domain of transportation engineering with the domain of data science. The addressed challenges are derived from speci c open issues and problems in the current state of the art from the smart mobility domain. First, an intelligent recommendation system for sharing services needs a general metric which can assess if a group of users are compatible for speci c sharing solutions. For this problem, this thesis presents a data driven indicator for collaborative mobility that can give an indication whether it is economically bene cial for a group of users to share the ride, a vehicle or a parking space. Secondly, the complex sharing mobility scenarios involve a high number of users and big data that must be handled by capable modelling frameworks and data analytic platforms. To tackle this problem, a suitable meta model for the transportation domain is created, using the state of the art multi-dimensional graph data models, technologies and analytic frameworks. Thirdly, the sharing mobility paradigm needs an user-centric approach for dynamic extraction of travel habits and mobility patterns. To address this challenge, this dissertation proposes a method capable of dynamically pro ling users and the visited locations in order to extract knowledge (mobility patterns and habits) from raw data that can be used for the implementation of shared mobility solutions. Fourthly, the entire process of data collection and extraction of the knowledge should be done with near no interaction from user side. To tackle this issue, this thesis presents practical applications such as classi cation of visited locations and learning of users' travel habits and mobility patterns using historical and external contextual data

Research and innovation in smart mobility and services in Europe: An assessment based on the Transport Research and Innovation Monitoring and Information System (TRIMIS)

For smart mobility to be cost-efficient and ready for future needs, adequate research and innovation (R&I) in this field is necessary. This report provides a comprehensive analysis of R&I in smart mobility and services in Europe. The assessment follows the methodology developed by the European Commission’s Transport Research and Innovation Monitoring and Information System (TRIMIS). The report critically assesses research by thematic area and technologies, highlighting recent developments and future needs.JRC.C.4-Sustainable Transpor

A framework for the synergistic integration of fully autonomous ground vehicles with smart city

Most of the vehicle manufacturers aim to deploy level-5 fully autonomous ground vehicles (FAGVs) on city roads in 2021 by leveraging extensive existing knowledge about sensors, actuators, telematics and Artificial Intelligence (AI) gained from the level-3 and level-4 autonomy. FAGVs by executing non-trivial sequences of events with decimetre-level accuracy live in Smart City (SC) and their integration with all the SC components and domains using real-time data analytics is urgent to establish better swarm intelligent systems and a safer and optimised harmonious smart environment enabling cooperative FAGVs-SC automation systems. The challenges of urbanisation, if unmet urgently, would entail severe economic and environmental impacts. The integration of FAGVs with SC helps improve the sustainability of a city and the functional and efficient deployment of hand over wheels on robotized city roads with behaviour coordination. SC can enable the exploitation of the full potential of FAGVs with embedded centralised systems within SC with highly distributed systems in a concept of Automation of Everything (AoE). This paper proposes a synergistic integrated FAGV-SC holistic framework - FAGVinSCF in which all the components of SC and FAGVs involving recent and impending technological advancements are moulded to make the transformation from today's driving society to future's next-generation driverless society smoother and truly make self-driving technology a harmonious part of our cities with sustainable urban development. Based on FAGVinSCF, a simulation platform is built both to model the varying penetration levels of FAGV into mixed traffic and to perform the optimal self-driving behaviours of FAGV swarms. The results show that FAGVinSCF improves the urban traffic flow significantly without huge changes to the traffic infrastructure. With this framework, the concept of Cooperative Intelligent Transportation Systems (C-ITS) is transformed into the concept of Automated ITS (A-ITS). Cities currently designed for cars can turn into cities developed for citizens using FAGVinSCF enabling more sustainable cities

TCitySmartF: A comprehensive systematic framework for transforming cities into smart cities

A shared agreed-upon definition of "smart city" (SC) is not available and there is no "best formula" to follow in transforming each and every city into SC. In a broader inclusive definition, it can be described as an opportunistic concept that enhances harmony between the lives and the environment around those lives perpetually in a city by harnessing the smart technology enabling a comfortable and convenient living ecosystem paving the way towards smarter countries and the smarter planet. SCs are being implemented to combine governors, organisations, institutions, citizens, environment, and emerging technologies in a highly synergistic synchronised ecosystem in order to increase the quality of life (QoL) and enable a more sustainable future for urban life with increasing natural resource constraints. In this study, we analyse how to develop citizen- and resource-centric smarter cities based on the recent SC development initiatives with the successful use cases, future SC development plans, and many other particular SC development solutions. The main features of SC are presented in a framework fuelled by recent technological advancement, particular city requirements and dynamics. This framework - TCitySmartF 1) aims to aspire a platform that seamlessly forges engineering and technology solutions with social dynamics in a new philosophical city automation concept - socio-technical transitions, 2) incorporates many smart evolving components, best practices, and contemporary solutions into a coherent synergistic SC topology, 3) unfolds current and future opportunities in order to adopt smarter, safer and more sustainable urban environments, and 4) demonstrates a variety of insights and orchestrational directions for local governors and private sector about how to transform cities into smarter cities from the technological, social, economic and environmental point of view, particularly by both putting residents and urban dynamics at the forefront of the development with participatory planning and interaction for the robust community- and citizen-tailored services. The framework developed in this paper is aimed to be incorporated into the real-world SC development projects in Lancashire, UK

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes