Analysis and operational challenges of dynamic ride sharing demand responsive transportation models

There is a wide body of evidence that suggests sustainable mobility is not only a technological question, but that automotive technology will be a part of the solution in becoming a necessary albeit insufficient condition. Sufficiency is emerging as a paradigm shift from car ownership to vehicle usage, which is a consequence of socio-economic changes. Information and Communication Technologies (ICT) now make it possible for a user to access a mobility service to go anywhere at any time. Among the many emerging mobility services, Multiple Passenger Ridesharing and its variants look the most promising. However, challenges arise in implementing these systems while accounting specifically for time dependencies and time windows that reflect users’ needs, specifically in terms of real-time fleet dispatching and dynamic route calculation. On the other hand, we must consider the feasibility and impact analysis of the many factors influencing the behavior of the system – as, for example, service demand, the size of the service fleet, the capacity of the shared vehicles and whether the time window requirements are soft or tight. This paper analyzes - a Decision Support System that computes solutions with ad hoc heuristics applied to variants of Pick Up and Delivery Problems with Time Windows, as well as to Feasibility and Profitability criteria rooted in Dynamic Insertion Heuristics. To evaluate the applications, a Simulation Framework is proposed. It is based on a microscopic simulation model that emulates real-time traffic conditions and a real traffic information system. It also interacts with the Decision Support System by feeding it with the required data for making decisions in the simulation that emulate the behavior of the shared fleet. The proposed simulation framework has been implemented in a model of Barcelona’s Central Business District. The obtained results prove the potential feasibility of the mobility concept.Postprint (published version

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

An Ontology for Product-Service Systems

Industries are transforming their business strategy from a product-centric to a more service-centric nature by bundling products and services into integrated solutions to enhance the relationship between their customers. Since Product- Service Systems design research is currently at a rudimentary stage, the development of a robust ontology for this area would be helpful. The advantages of a standardized ontology are that it could help researchers and practitioners to communicate their views without ambiguity and thus encourage the conception and implementation of useful methods and tools. In this paper, an initial structure of a PSS ontology from the design perspective is proposed and evaluated

1999 Professorial Address: Nau te rourou, naku te rourou… Māori education: setting an agenda.

Current educational policies and practices in Aotearoa/New Zealand were developed and continue to be developed within a framework of power imbalances, which effects Maori the greatest. An alternative model that seeks to address indigenous Māori aspirations and Treaty of Waitangi guarantees for self determination is presented here. This model suggests how a tertiary teacher education institution might create learning contexts wherein power-sharing images, principles and practices will facilitate successful participation by Māori students in mainstream classrooms. This model constitutes the classroom as a place where young people's sense-making processes (cultures) are incorporated and enhanced, where the existing knowledges of young people are seen as "acceptable" and "official" and where the teacher interacts with students in such a way that new knowledge is co-created. Such a classroom will generate totally different interaction and participation patterns and educational outcomes from a classroom where knowledge is seen as something that the teacher makes sense of and then passes on to students

Penalty and reward contracts between a manufacturer and its logistics service provider

Contracts are used to coordinate disparate but interdependent members of the supply chain. Conflicting objectives of these members and lack of coordination among the members lead to inefficiencies in matching supply with demand. This study reviews different types of contracts and proposes a methodology to be used by companies for analyzing coordinating contracts with their business partners. Efficiency of the contract is determined by comparing the performance of independent companies under the contract to the supply chain performance under the central decision maker assumption. We propose a penalty and reward contract between a manufacturer and its logistics service provider that distributes the manufacturer’s products on its retail network. The proposed contract analysis methodology is empirically tested with transportation data of a consumer durable goods company (CDG) and its logistics service provider (LSP). The results of this case study suggest a penalty and reward contract between the CDG and its LSP that improves not only the individual firm’s objective functions but also the supply chain costs. Compared to the existing situation, the coordination efficiency of the penalty and reward contract is 96.1 %, proving that optimizing contract parameters improves coordination and leads to higher efficiencies

Assessment of Policy Instruments Toward a Sustainable Traffic System -A backcasting approach for Stockhom 2030

Finding strategies for preventing the process of global warming is growing urgent. Our intention is to highlight the future requirements and expectations on transport related sustainability measures (e.g. mobility management services, road tolls, CO2-taxes and renewable fuel systems) assisting the reaching of a long-term sustainability target of greenhouse gas emissions at the year 2030. We will employ the transport demand model SAMPERS and the traffic assignment model EMME/2 in order to investigate the effect from specific changes to the traffic network of Stockholm 2030, e.g. the environmental and socioeconomic impact from reduced number of commute trips, reduced car ownership, and new price structures and restrictions on private vehicle travel. In connection to this, we also quantify negative side effects (so-called rebound effects) coupled to efficiencies in the traffic network. We use an appraisal framework, influenced by backcasting, in order to assess the impact from the specific policies in relation to the United Nation’s (IPCC) requirements for a sustainable level of CO2-emissions. The findings from this study point at the inevitable need for at least a 50% renewable fuel mix in the traffic system if reaching the target 2030. Single-handedly, travel demand measures are insufficient to accomplish the CO2-emission target for 2030. Nevertheless, reducing traffic volumes by just a few percent might contribute to savings in emission costs, accident costs and aggregate travel time costs in the traffic system. Such measures are needed in order to mitigate the transition from fossil- to renewable fuels.