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In response to an impending demand growth at an existing reactive managed lane system and to provide a timely and more effective temporary hard shoulder activation, this paper presents the development of short-term prediction models. A lane-oriented attribute—namely, the left-lane flow distribution ratio—is introduced to ameliorate the system by capturing the forthcoming stream dynamics and reconfiguring it to be proactive. To assess the impact on the network's performance of implementing a system for hard shoulder running, an exploratory analysis was performed on the basis of data acquired by seven radar sensors located every 500 m along a Swiss freeway section that was not affected by incoming or exiting traffic. A locally weighted regression was employed to provide more accurate insight into traffic behavior by comparing observations derived during the regular operation of the system and a period in which it was suspended, with respect to seasonality patterns. To describe the impending stream motion by examining different time–volume clusters (off-peak and peak hours), two prediction models were specified according to the time range. The preliminary results of the study for several prediction horizons demonstrate an acceptable prediction uncertainty. The hard shoulder activation prediction confirms the analysis of the findings of this research with regard to the impact on operations.

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The aim of this study is to capture a technology's pathway by identifying emerging subdomains in a complex system of economic processes. The objective is to uncover indirect latent relations among agents interacting in a specific techno-economic segment (TES). A methodology, including an "Extract-Transform-Load" (ETL) process preceding the two steps aimed for analysis, is developed to analyse a TES regarding R&D economic processes of the photonics technology. In the first step, economic relevant R&D activities (EU funded projects and patents) are analysed through a multilayer network (MLN) of agents, considering their interactions in three dimensions, which represent occurred and latent relationships: co-participations in economic activities, common geographical location provenance, common use of technological terms. Then communities are detected (Infomap Algorithm for MLN), and their ongoing within and between connections are studied, as potential factors that affect the entire structured technological ecosystem. In the second step, technological subdomains associated with method-oriented and application-oriented activities are identified through topic modelling. Using the MLN structure, the textual information of the corpus of documents describing the aforementioned economic R&D activities is associated to agents, and the topic model (Latent Dirichlet Allocation) uncovers additional potential semantic connections among them. Subsequently, the results of the MLN community detection and of the topic modelling based on the descriptions of economic activities are considered. Hence, the latent relations of agents are mapped.

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