Advanced Information Services for Cognitive Behaviour of Travellers

Smart transportation is essentially leveraged by decision making of humans, especially behaviour of travellers. The behaviour (movements; information management) and the advanced information services are mutually entangled. The travellers and the ICT (integrated infocommunication systems of transportation) is considered as an undecomposable set, which has new cognitive capabilities. These capabilities are to be used for mobility related decisions in order to improve sustainability of transportation. In order to reveal, how these capabilities coelvolve with smart transportation comprehensive system and process-oriented scientific research had been launched. Herewith the basic definitions, the architecture and the operation of the integrated system of smart transportation and the model of the smart traveller have been presented following top-down approach of system engineering

Are Transportation Solutions Doomed to Fail Climate-Change Actions? A Book Review

I review a New York Times best-seller book, Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming, edited by Paul Hawken. Drawdown provides many interesting solutions, descriptions, and arguments regarding the global impacts of climate change. Indeed, the book sets forth around 80 solutions and 20 coming to attractions (future options for combating climate change). In this review, however, I focus primarily on the book’s transport solutions. Overall, the book comes short of offering innovative and cost-effective solutions, in contrast to other sectors’ solutions. I believe the reason is the book’s narrow view regarding the overall impacts of transportation and latent opportunities in the sector

PUBLIC TRANSPORT USERS' PREFERENCES AND WILLINGNESS TO PAY FOR A PUBLIC TRANSPORTATION MOBILE APP IN MADRID

[EN] Today, smart cities are presented as a solution to achieve a more sustainable urban development while increasing the quality of life of its citizens through the use of new technologies (Neirotti, 2013). Smart Mobility is based on innovative and sustainable ways to provide transport for the inhabitants of cities, enhancing the use of fuels or vehicle propulsion systems that respect the environment, supported by technological tools and a proactive behaviour of citizenship (Neirotti, 2013). In urban mobility, the purpose of the Smart Cities is to develop flexible systems for real-time information to support decisionmaking in the use and management of different transport modes, generating a positive impact, saving users time and improving efficiency and quality of service. In this context, several solution types are being introduced in the world’s cities. They enable the improvement of the abovementioned factors acting on the demand side resulting in more efficient journeys for individual travelers, and improved satisfaction with the service. (Skelley et Al., 2013) with a lower level of investment than that of infrastructure deployment or an increase in the level of service. One of the most extended solutions is the use of mobile apps for providing the user with contextualized -static and real time- transport information. The study is based on a survey carried out among users of public transport in Madrid under the European OPTICITES project of the 7th Research Framework Programme. The survey contained items on their transportation habits, their level of skills and technological capabilities, and their main expectations about the possibility of using a new application, the main desired capabilities and willingness to pay for use. The study results show the preferences of users of public transport capacity, static, real-time search and in-app services for a multimodal real-time application and willingness to pay for this service, all analyzed by different Slicers users. The results also establish the basis for an estimate of the usefulness of these applications for users of public transport.Velázquez Romera, G.; Monzón, A. (2016). PUBLIC TRANSPORT USERS' PREFERENCES AND WILLINGNESS TO PAY FOR A PUBLIC TRANSPORTATION MOBILE APP IN MADRID. En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 2248-2266. https://doi.org/10.4995/CIT2016.2015.3498OCS2248226

Útvonal értékelõ eljárás személyre szabott utastájékoztatáshoz

A mobilitási igények jellemzőinek és a minőségi elvárások változása miatt a közlekedők egyre inkább igénylik a helyváltoztatásra és annak előkészítésére fordított idő csökkentését, a helyváltoztatás kellemessé tételét, és a megbízható adatokon alapuló utazástervezést.Ezért a nemzetközi és a hazai kutatások is kiterjedten foglalkoznak az utazástervezés optimalizálásával. Léteznek példaértékű alkalmazások, azonban ezek személyre szabott jellege esetleges és csekély mértékű.. A bemutatott kutatás újdonságereje abban rejlik, hogy pontos fizikai jellemzőket és a felhasználói elvárásokat figyelembe véve valósághű az értékelés

Modeling Evacuation Risk Using a Stochastic Process Formulation of Mesoscopic Dynamic Network Loading

One of the actions usually conducted to limit exposure to a hazardous event is the evacuation of the area that is subject to the effects of the event itself. This involves modifications both to demand (a large number of users all want to move together) and to supply (the transport network may experience changes in capacity, unusable roads, etc.). In order to forecast the traffic evolution in a network during an evacuation, a natural choice is to adopt an approach based on Dynamic Traffic Assignment (DTA) models. However, such models typically give a deterministic prediction of future conditions, whereas evacuations are subject to considerable uncertainty. The aim of the present paper is to describe an evacuation approach for decision support during emergencies that directly predicts the time-evolution of the probability of evacuating users from an area, formulated within a discrete-time stochastic process modelling framework. The approach is applied to a small artificial case as well as a real-life network, where we estimate users' probabilities to reach a desired safe destination and analyze time dependent risk factors in an evacuation scenario

Route plan evaluation method for personalised passenger information service

Due to changing expectations of characteristics of mobility demands, public transportation users increasingly require a reduction of both the preparation and travel time, an easier and more pleasant travelling experience as well as route plans based on reliable data. Both international and domestic research is widely concerned with route planning optimization. Exemplary assistance applications are already in operation, but they are only semi-occasionally and slightly personalized. Consequently, there is potential for significant research and development in this area. Our developed method and algorithm evaluates the routes based on the personalised user settings and in this way, the ideal routes can be determined. User preferences are represented in evaluation criteria. The algorithm also manages network modifications and often-changing user preferences. The novelty of our algorithm lies in the more realistic evaluation of the routes appreciably considering both the exact physical properties of the infrastructure and the users’ detailed personal preferences

Personalised information services for bikers

Modal share of cycling is growing; however, information services are to be significantly improved. Our research aim was to reveal, how the available real-time data can be used to support bikers and to ease decision making by provision of personalised information. We have identified the categories of biker information services and then the route planner applications have been analysed in order to point out the exemplary solutions. The attributes of mobility and information management, as well as their correspondences have been revealed in order to develop appropriate information services. We have carried out a questionnaire survey to identify bikers’ habits and expectations towards information services. An analysis method has been elaborated which is applicable to determine the correspondences between mobility and information management attributes. The results are to be applied as bases during development of adaptive, personalised information application aiding decisions

Doctor of Philosophy

dissertationData-driven analytics has been successfully utilized in many experience-oriented areas, such as education, business, and medicine. With the profusion of traffic-related data from Internet of Things and development of data mining techniques, data-driven analytics is becoming increasingly popular in the transportation industry. The objective of this research is to explore the application of data-driven analytics in transportation research to improve the traffic management and operations. Three problems in the respective areas of transportation planning, traffic operation, and maintenance management have been addressed in this research, including exploring the impact of dynamic ridesharing system in a multimodal network, quantifying non-recurrent congestion impact on freeway corridors, and developing infrastructure sampling method for efficient maintenance activities. First, the impact of dynamic ridesharing in a multimodal network is studied with agent-based modeling. The competing mechanism between dynamic ridesharing system and public transit is analyzed. The model simulates the interaction between travelers and the environment and emulates travelers' decision making process with the presence of competing modes. The model is applicable to networks with varying demographics. Second, a systematic approach is proposed to quantify Incident-Induced Delay on freeway corridors. There are two particular highlights in the study of non-recurrent congestion quantification: secondary incident identification and K-Nearest Neighbor pattern matching. The proposed methodology is easily transferable to any traffic operation system that has access to sensor data at a corridor level. Lastly, a high-dimensional clustering-based stratified sampling method is developed for infrastructure sampling. The stratification process consists of two components: current condition estimation and high-dimensional cluster analysis. High-dimensional cluster analysis employs Locality-Sensitive Hashing algorithm and spectral sampling. The proposed method is a potentially useful tool for agencies to effectively conduct infrastructure inspection and can be easily adopted for choosing samples containing multiple features. These three examples showcase the application of data-driven analytics in transportation research, which can potentially transform the traffic management mindset into a model of data-driven, sensing, and smart urban systems. The analytic