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In order to commute by carpooling, individuals need to communicate, negotiate and coordinate, and in most cases adapt their agenda (daily schedule) to enable cooperation. Through negotiation, agents (individuals) can reach complex agreements in an iterative way which meet the criteria for the successful negotiation. The procedure of negotiation and trip execution in the carpooling consists of a number of steps namely; (i) explore the social network, (ii) negotiate agenda to reach complex agreements, and (iii) long term trip execution. This paper presents a conceptual design of an organizational-based and agent-based model (ABM) of a set of candidate carpoolers that serves as a proof of concept and is an extension of a simple negotiation model for carpooling. The proposed model is used for simulating the interactions of autonomous agents with their agenda and explore social networks to initiate agent communication to trigger the negotiation process. The schedule adaptation depends on the preferences among feasible schedules of the individuals, generally depends on both the time of day and on the duration of the participation. In this simulation of the evolution we consider a daily schedule that consists of three activities (home, work and home) and a chain of two intermediate trips (home-to-work and work-to-home) only. We carried out a validation study of our results with real data collected in Flanders, Belgium. From the simulation's discussions, it is possible to understand the causes why people can adapt their daily schedule to enable cooperation in order to carpool. The future research will mainly focus on enhancing the mechanism for negotiation between agents. ; The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement nr 270833

Electric power demand for household-generated traffic was estimated as a function of time and space for the region of Flanders, Belgium. An activity-based model was used to predict traffic demand. Electric vehicle (EV) type and charger characteristics were determined on the basis of car ownership and on the assumption that the market shares of EV categories would be similar to the current ones for internal combustion engine vehicles published in government statistics. Charging opportunities at home and work locations were derived from the predicted schedules and the estimation of the possibility to charge at work. Simulations were run for several levels of EV market penetration and for specific ratios of battery-only electric vehicles (BEVs) to pluggable hybrid electric vehicles. A single car was used to drive all trips in a daily schedule. Most of the Flemish schedules could be driven entirely by a BEV even after the published range values were reduced to account for range anxiety and for the overestimated ranges resulting from tests in accordance with standards. The current overnight period for low-tariff electricity was found to be sufficiently long to allow for individual cost optimizing while minimizing the peaks for overall power demand.

Electric power demand for household generated traffic is estimated as a function of time and space for the region of Flanders. An activity-based model is used to predict traffic demand. Electric vehicle (EV) type and charger characteristics are determined on the basis of car ownership and by assuming that EV categories market shares will be similar to the current ones for internal combustion engine vehicles (ICEV) published in government statistics. Charging opportunities at home and work locations are derived from the predicted schedules and by estimating the possibility to charge at work. Simulations are run for several EV market penetration levels and for specific BEV/PHEV (battery-only/pluggable hybrid) ratios. A single car is used to drive all trips in a daily schedule. Most of the Flemish schedules can be driven entirely by a BEV even after reducing published range values to account for range anxiety and for the over-estimated ranges resulting from tests according to standards. The current low tariff electricity period overnight is found to be sufficiently long to allow for individual cost optimizing while peak shaving overall power demand.

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.

Objectives: There is a growing trend in development and application of real-time crash risk prediction models within dynamic safety management systems. These real-time crash risk prediction models are constructed by associating crash data with the real-time traffic surveillance data (e.g. collected by loop detectors). The main objective of this paper is to develop a real-time risk model that will potentially be utilized within traffic management systems. This model aims to predict the likelihood of crash occurrence on motorways. Methods: In this study, the potential prediction variables are confined to traffic related characteristics. Given that the dependent variable (i.e. traffic safety condition) is dichotomous (i.e. "no-crash" or "crash"), a rule-based approach is considered for model development. The performance of rule-based classifiers is further compared with the more conventional techniques like binary logistic regression and decision trees. The crash and traffic data used in this study were collected between June 2009 and December 2011 on a part of the E313 motorway in Belgium between Geel-East and Antwerp-East exits, on the direction towards Antwerp. Results: The results of analysis show that several traffic flow characteristics such as traffic volume, average speed and standard deviation of speed at the upstream loop detector station, and the difference in average speed on upstream and downstream loop detector stations significantly contribute to the crash occurrence prediction. The final chosen classifier is able to predict 70% of crash occasions accurately while it correctly predicts 90% of no-crash instances, indicating a 10% false alarm rate. Conclusions: The findings of this study can be used to predict the likelihood of crash occurrence on motorways within dynamic safety management systems. ; This research was carried out within the framework of the Policy Research Centre on Traffic Safety with the support of the Flemish government and was partly supported by a grant from the Research Foundation Flanders (FWO). The content of this article is the sole responsibility of the authors.

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.

The generation of substantial amounts of travel- and mobility-related data has spawned the emergence of the era of big data. However, this data generally lacks activity-travel information such as trip purpose. This deficiency led to the development of trip purpose inference (activity type imputation/annotation) techniques, of which the performance depends on the available input data and the (number of) activity type classes to infer. Aggregating activity types strongly increases the inference accuracy and is usually left to the discretion of the researcher. As this is open for interpretation, it undermines the reported inference accuracy. This study developed an optimised classification methodology by identifying classes of activity types with an optimal balance between improving model accuracy, and preserving activity information from the original data set. A sensitivity analysis was performed. Additionally, several machine learning algorithms are experimented with. The proposed method may be applied to any study area. ; This work was supported by the National Research Foundation of Korea funded by the Korean government (MSIP) under grant NRF-2010-0028693.

Currently walking is a multidisciplinary and emerging point of attention for urban sustainability and for ensuring the quality of pedestrian environments. In order to understand pedestrian behaviour, walkability researches estimate the factors which affect the level of pedestrian satisfaction. Past studies focused on the relationship between environmental factors and pedestrian behavioural outcomes. In this study, we developed pedestrian satisfaction multinomial logit models using various data sets, examining the relative impact of five differently themed sets of attributes: personal, walk-facilities, land-use, pedestrian volumes, and weather-related variables. The results show that the personal variability attributes were selected as the most significant. We investigated the effects of personal variability, such as the spatial cognition level and travel purpose, and detailed effects of environmental features. In addition, crowdedness, land-use types, and residential information were investigated. The results from this study offer contributions by providing evidence of the importance of personal and contextual variables in influencing pedestrian walkability. ; This work was supported by the National Research Foundation of Korea grant funded by the Korea government (MSIP) (NRF-2010-0028693).

With ageing, travel becomes more and more complex. Physical and cognitive abilities may decline, and thus, elderly people may experience several problems that hinder them from travelling independently. One of the most difficult issues when addressing the problem of age-related travel restrictions is the heterogeneity of the ageing population. SOULMATE offers a non-intrusive and personalized mobility package that evolves with the end-user across his/her different life stages to ensure him/her to make secure trips. SOULMATE aggregates three types of mobility support: Indoor virtual training of a route, active routing during trips and monitoring by a coach at a distance during trips. For each individual, the desired or necessary functionalities can be chosen, based on his/her specific abilities and travel needs. The SOULMATE solution will be developed iteratively, in co-creation with end-users and stakeholders, and the package will be tested intensively, and evaluated in three countries (Belgium, The Netherlands and Austria) based on usability, technical and business aspects. ; The SOULMATE project is co-founded by the AAL Programme of the European Union; and by the funding authorities Agentschap Innoveren en Ondernemen (Flanders, Belgium), Austrian Ministry for Transport, Innovation and Technology (Austria) and ZonMw, the Netherlands Organization for Health Research & Development (The Netherlands). The SOULMATE consortium consists of partners from: ABEONAconsult, Eindhoven University of Technology, University of Applied Science FH JOANNEUM GmbH, Activ84Health, Roessingh Research and Development, Happy Aging, Slimmer Leven, GEFAS STEIERMARK, c.c.com Moser GmbH and FRAISS – IT Consulting & Mediendesign. The authors want to thank all partners involved in the consortium.

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