Suchergebnisse

Connected and autonomous vehicles (CAV) have recently attracted policymakers, manufacturers, and customers' attention. Despite their numerous benefits, CAVs still have to overcome many challenges related to the implementation and market penetration. When not dominated by financial constraints, the CAV adoption heavily depends on how policymakers and the government address the other challenges, including public perception, rules, and regulations. This study aims at formulating recommendations to support decision-makers in choosing the most appropriate and sustainable strategy to implement CAV technology. To do so, key barriers were first identified based on the literature review and discussions with decision-makers. Moreover, long-term adoption of CAV technologies in alternative future scenarios is developed. Multicriteria decision-making analysis was conducted to weigh these barriers and rank different strategies of CAV implementation. The transportation system of the Sultanate of Oman was used as a study case. It was found that the lack of technical skills and policies/regulations are the main barriers to the adoption of CAV technologies. To overcome these barriers, suggested strategies include establishing low-cost and short-term solutions, providing training to transportation professionals, and investing in statewide radio communications/IoT for emergency responses. ; The authors acknowledge the research support provided by IMOB Hasselt University Belgium, Middle East College-Oman, the German University of Technology in Oman, Directorate General of Traffic, Royal Oman Police (ROP), and Muscat Municipality for their support and providing the data that makes this research viable and effective.

Technological advancement in automobile and infrastructure sector encourages more and longer distance travel and the way people travel to perform their daily tasks creates environmental problems. Activity-travel behavior of individual have a significant potential to be influenced to reduce environmental issues, however, the underlying factors need to be investigated. This paper investigates pro-environmental activity-travel behavior using recorded GPS-based travel-activity diaries and individual personal traits using online questionnaire and estimating a structure equation model borrowed from theory of planned behavior. The results of the study verified that individual mobility decisions were highly influenced by the attitude one has about specific travel behavior. The results are helpful in devising effective behavioral intervention. (C) 2019 The Authors. Published by Elsevier B.V. ; This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 689954. This paper reflects the authors views. The European Commission is not liable for any use that may be made of the information contained therein. The authors would also like to thank Acadia University and Natural Sciences and Engineering Research Council (NSERC) of Canada for funding the publication of this paper. ; Adnan, M (reprint author), Hasselt Univ, Transportat Res Inst IMOB, B-3590 Diepenbeek, Belgium. muhammad.adnan@uhasselt.be

This paper presents a comprehensive framework that guides in setting up an effective intervention design to promote active school travel keeping in view the air quality. As lack of physical activities are observed in school going children all over the world so various programs have been launched to encourage active commuting among school going kids using different intervention strategies. So far no effort was found in which the pollutant exposure aspect is taken into account. The primary objective of this study is to develop a computational model that helps in detecting walking/cycling school routes at an individual level that enhance physical activity alongside lesser exposure to air pollutants. The computations involved in this process is dependent on a variety of data sets such as individual's school travel dairy, street level pollutant concentration data, public and active transport network data. The output generated gives exposure and physical activity involved in the current and suggested school routes. It is taken into consideration that the changes identified are healthy (having low exposure to pollutants) and encourage active school travel. These changes can be considered as alternatives/suggestions to develop the customized information package that can be used as a behavioural intervention tool. ; This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 689954. This paper reflects the authors views. The European Commission is not liable for any use that may be made of the information contained therein. We would also like to thank Flemish Institute for Technological Research (VITO) for providing us with the running annual means of NO2 concentration for entire Flanders at fine spatial resolution of 100 m2.

This paper presents the cost optimization model which plans a charging strategy for an electric vehicle. In case of time dependent electric prices an intelligent planner is required which plans the charging strategy only at cheaper moments and places to keep the vehicle charged enough to complete its scheduled travels. This model estimates the required charging energy to travel by the electric vehicle. Then using the time dependent electric prices and available power at each period of the time suggests a charging pattern for the electric vehicle which ensures the cheapest charging cost and fulfills the constraints of battery state of the charge. According to the current market share of electric vehicles, a fraction of daily agendas created by the large scale activity-based model are used to test the proposed framework. A central power tracker is introduced which keeps track of available and required power at each period of the day. It also manages the charging requests from electric vehicles. Moreover, an experiment has been set up, it makes use of wind and solar energy production data. Price signal is derived from available power as an indicator of relative cost. ; The research leading to these results has received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement nr 270833.

An automatic service to match commuting trips has been designed. Candidate carpoolers register their personal profile and a set of periodically recurring trips. The Global CarPooling Matching Service shall advise registered candidates how to combine their commuting trips by carpooling. Planned periodic trips correspond to nodes in a graph; the edges are labeled with the probability for for success while negotiating to merge two planned trips by carpooling. The probability values are calculated by a learning mechanism using on one hand the registered person and trip characteristics and on the other hand the negotiation feedback. The probability values vary over time due to repetitive execution of the learning mechanism. As a consequence, the matcher needs to cope with a dynamically changing graph both with respect to topology and edge weights. In order to evaluate the matcher performance before deployment in the real world, it will be exercised using a large scale agent based model. This paper describes both the exercising model and the matcher. ; The research leading tot these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement Nr 270833.

Informational interventions are considered important to bring positive changes in attitudes and perception about pro-environmental life styles among individuals. In relation to mobility aspects, it is vital to identify relatively easier changes that have potential to reduce negative impacts of mobility on environment and individual health. This paper provides a comprehensive methodological framework and developed a computation algorithm that helps identify such an easy changes in the travel behavior of an individual. The development of algorithm is based on a variety of different data sources such as activity-travel diaries and related constraint information, meteorological conditions, bicycle and public transport supply data. A variety of rules that are part of the computational algorithm are taken from the transport modelling literature, where constraints and factors were examined for various activity-travel decisions. Three major aspects of activity-travel behavior such as lesser car use, cold start of car engines and participation in non-mandatory outdoor activities are considered in assessing pro-environmental potential. The algorithm is applied to data collected, using citizens from Hasselt and their pro-environmental potential is determined, which has been found significant. ; This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 689954.

Detailed data on regional goods production and consumption are traditionally the starting point to model freight transport on a nationwide scale. The conversation of those goods afterwards into various vehicle load types and the different logistics operations needed to deliver the requested goods type and quantity, follow from that starting point in the modeling process. In this paper, a demand driven microsimulated supply chain model is presented. The model shall be a first step towards calculating realistic production and consumption matrices. So far, government provided data has been used as the base to model freight transport for Flanders. The methodology used and accuracy level of this data is not clear. Modeling demand and production relations shall constitute a starting point towards an agent based freight model under development. The presented model is a microsimulation model incorporating the main firms(firms) of a traditional supply chain. It is a demand driven model using quantity of goods requested at the consumer side as a starting point. The model distinguishes also between shipping and carrying firms and takes into account raw material suppliers able to supply all or some subcomponents needed by production firms. Economic Order Quantity (EOQ) relations are used with modifications to include transportation and labour costs. The model is based on an optimized universal cost function and different initial conditions can be used to mimic real life firm to firm interactions. A certain good demand scenario is simulated and results are shown. ; The various operations of production, distribution and consumption of goods make up supply chain networks. The usual pre requisite to model freight flows on between geographical zones, is an understanding of intra zonal production and consumption relations. This is in essence an aggregation of all individual firm to firm interactions taking place in form of production and consumption of goods, as well as transportation firms involved in the goods exchange process. In this paper, a demand driven micro-simulated supply chain model is presented. The presented model is a microsimulation model modeling the main firm types present in a traditional supply chain. It is a demand driven model using quantity of goods requested at the consumer side as a starting point. The different interacting firms use a modified Economic Order Quantity (EOQ) model as the basis of the simulation process. The cost function used includes also transportation and labor costs. The model simulates also shipping and carrying firms and takes into account raw material suppliers able to supply all or some subcomponents needed by production firms. Different initial conditions can be used to mimic real life firm to firm interactions. Firm level and zone level scenarios are simulated and results are shown.

This study aims to show an ability of Ontology, which is a formal explicit description of concepts in a domain of interest; in an activity-based microsimulation. Thus, an agent-based carpooling application using ontology techniques is presented as a case study with a focus on three functions of the Ontology. First, Ontology facilitates integrating between heterogeneous databases by defining the relationship between their concepts. Second, Ontology verities the compatibility and consistency between the different angles to combine varied models in a common structure by providing shared knowledge between different domains modelling with the definition of objects and concepts. Lastly, Ontology is useful for modelling agent communication by means of making explicit the parsed message between agents with the shared knowledge. This paper introduces related studies and basic knowledge about using methodologies, and supports an example of using Ontology in an agent-based carpooling simulation. (C) 2013 The Authors. Published by Elsevier B.V. ; The research leading tot these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement nr 270833