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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.

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).