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Despite the increased research interest in wayfinding assistance systems, research on the appropriate point in time or space to automatically present a route instruction remains a desideratum. We address this research gap by reporting on the results of an outdoor, within-subject design wayfinding study (N ¼ 52). Participants walked two different routes for which they requested spoken, landmark-based turn-by-turn route instructions. By means of a survival analysis, we model the points in space at which participants issue such requests, considering personal, environmental, route-and trial-related variables. We reveal different landcover classes (e.g., densely built-up areas) and personal variables (e.g., egocentric orientation and age) to be important, discuss potential reasons for their impact and derive open research questions.

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Given the ongoing transformation of the transport sector toward electrification, expansion of the current charging infrastructure is essential to meet future charging demands. The lack of fast-charging infrastructure along highways and motorways is a particular obstacle for long-distance travel with battery electric vehicles (BEVs). In this context, we propose a charging infrastructure allocation model that allocates and sizes fast-charging stations along high-level road networks while minimizing the costs for infrastructure investment. The modeling framework is applied to the Austrian highway and motorway network, and the needed expansion of the current fast-charging infrastructure in place is modeled under different future scenarios for 2030. Within these, the share of BEVs in the car fleet, developments in BEV technology and road traffic load changing in the face of future modal shift effects are altered. In particular, we analyze the change in the requirements for fast-charging infrastructure in response to enhanced driving range and growing BEV fleets. The results indicate that improvements in the driving range of BEVs will have limited impact and hardly affect future costs of the expansion of the fast-charging infrastructure. On the contrary, the improvements in the charging power of BEVs have the potential to reduce future infrastructure costs.

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Fast-charging capacities must be sufficiently allocated to meet the charging demand of the growing battery electric vehicle (BEV) fleet. We present a methodology for testing the implementability of a planned charging infrastructure for highway networks in terms of underutilized charging capacities and bottlenecks. A linear optimization model for determining charging activities at a fast-charging infrastructure was developed to accomplish this. Using a bottom-up approach, we modeled the charging activities based on the traffic flow between starting and destination points in the network. The proposed model is applied to a planned fast-charging infrastructure along the highway network in the east of Austria. The obtained results reveal that the charging infrastructure is capable of meeting demand during all observed extreme traffic load and temperature conditions. Thus, no bottlenecks are detected, but locations of charging stations with overestimated capacities are discovered, implying that the local capacities would never be fully utilized. Our findings also highlight the importance of considering the spatio-temporal dynamics of charging activities and the traffic flow when expanding fast-charging infrastructure.

With energy communities and local electricity markets on the rise, the possibilities for prosumers to be actively involved in the energy system increase, creating more complex settings for energy communities. This paper addresses the following research question: Does having knowledge about the future development in energy communities help make better decisions selecting new participants than without consideration of any future developments? Each year, the community is faced with the exit of existing members and a portfolio of possible new entrants with different characteristics. For this purpose, a bi-level optimization model for dynamic participation in local energy communities with peer-to-peer electricity trading, which is able to select the most suitable new entrants based on the preferences of the members of the original community, is extended to a stochastic dynamic program. The community wants to plan a few years ahead, which includes the following uncertainties: (i) which members leave after each period, and (ii) which are the potential new members willing to join the community. This paper’s contribution is a stochastic optimization approach to evaluate possible future developments and scenarios. The focus lies on the contractual design between the energy community and new entrants; the model calculates the duration of contracts endogenously. The results show a sample energy community’s decision-making process over a horizon of several years, comparing the stochastic approach with a simple deterministic alternative solution.