A multiple criteria approach for the evaluation of the rail transit networks in Istanbul

The deficiencies in the Istanbul transportation system have led the local authorities to plan several alternative transportation projects. In this paper three alternative rail transit network proposals are evaluated by using Analytic Hierarchy Process (AHP), a multiple criteria decision support system. The AHP facilitates decision-making by organizing perceptions, experiences, knowledge and judgments, the forces that influence the decision, into a hierarchical framework with a goal, scenarios, criteria and alternatives of choice. Based on this analysis, the decision makers have developed a new alternative as a combination of the most closely competing two alternative rail transit networks. This combination rail transit network is currently under construction.     

Big data in public transportation: a review of sources and methods

ABSTRACT

The collection of big data, as an alternative to traditional resource-intensive manual data collection approaches, has become significantly more feasible over the past decade. The availability of such data, coupled with more sophisticated predictive statistical techniques, has contributed to an increase in attention towards the application of these data, particularly for transportation analysis. Within the transportation literature, there is a growing emphasis on developing sources of commonly collected public transportation data into more powerful analytical tools. A commonly held belief is that application of big data to transportation problems will yield new insights previously unattainable through traditional transportation data sets. However, there exist many ambiguities related to what constitutes big data, the ethical implications of big data collection and application, and how to best utilize the emerging data sets. The existing literature exploring big data provides no clear and consistent definition. While the collection of big data has grown and its application in both research and practice continues to expand, there is a significant disparity between methods of analysis applied to such data. This paper summarizes the recent literature on sources of big data and commonly applied methods used in its application to public transportation problems. We assess predominant big data sources, most frequently studied topics, and methodologies employed. The literature suggests smart card and automated data are the two big data sources most frequently used by researchers to conduct public transit analyses. The studies reviewed indicate that big data has largely been used to understand transit users’ travel behavior and to assess public transit service quality. The techniques reported in the literature largely mirror those used with smaller data sets. The application of more advanced statistical methods, commonly associated with big data, has been limited to a small number of studies. In order to fully capture the value of big data, new approaches to analysis will be necessary.     

The development of a decision making framework for evaluating the trade-off solutions of cleaner seaborne transportation

The general rise in marine fuel prices in combination with ever-more stringent environmental regulations resulting from IMO conventions and EU Directives have become the main industry drivers for seaborne transportation to become cleaner and more energy efficient. Compliance with existing and soon-to-be-enacted regulations requires evaluating the trade-off between often-conflicting options to select the best available technology or fuel source. Although the traditional way of dealing with this issue has been to apply a cost benefit analysis, this kind of analysis does not adequately consider the complexities of the problem, such as incorporating linguistic preferences or interrelations amongst attributes, experts and their preferences.The challenge in such an analysis corresponds to that of a multiple attribute decision-making problem in which a finite number of alternatives are assessed with regards to a finite number of attributes and experts and ranked from the best to the worst.In this paper, a comprehensive and holistic decision-making framework is proposed to overcome the barriers of cost-benefit analysis techniques, facilitating the inclusion of all possible combinations of decision-making parameters and their discrete values, which will eventually help the industry achieve cleaner seaborne transportation.To demonstrate the applicability of the proposed framework, this paper focuses on a real-life study case involving an environmental compliance problem in the Port of Copenhagen, Denmark, in relation to a particular EU Directive. In conclusion, the proposed framework can be applied as a generalised decision-making model to similar compliance issues encountered within other modes of transportation such as rail and road.