Through the vast adoption and application of emerging technologies, the intelligence and autonomy of smart mobility can be substantially elevated to address more diversified demands and supplies. Along with this trend, a systematic collaboration among three essential elements of smart mobility services, namely devices, data and functions, is being studied to comprehensively break down the intrinsic barriers that existed in current solutions, to support the integration of connectable devices, the fusion of heterogeneous data, the composability of reusable functions, and the flexibility in their cooperations. To enable such a collaboration, this paper proposes a federated platform, called Future Mobility Sensing Advisor (FMSA), which can 1) manage the three elements through standardized interfaces separately and uniformly; 2) create a fully connected knowledge graph to orchestrate the three elements efficiently and effectively; 3) support the client-server interaction in centralized and federated modes to handle service requests and edge resources with various availability and accessibilities jointly and adaptively; and 4) accommodate various mobility services to foster harmonious and sustainable mobility tenderly and invisibly. Moreover, the efficiency and effectiveness of the platform are also tested through a performance evaluation, and a pilot supported at the Great Boston Area, respectively. As a result, it shows that FMSA can 1) achieve high performance by using the two interaction modes selectively, and 2) renovate smart mobility towards sustainability through personalized services that can measure user preferences and system objectives mutually.

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In this chapter, we present a methodological approach for Smart Mobility that integrates three key features: prediction, optimization, and personalization. They are integrated in such a way that when a travel menu is offered, predicted conditions are considered in the attributes of alternatives and optimized system-level policies are maintained. Similarly, user-level estimations and updates are used by prediction and optimization methods at the system-level in order to represent the population with most up-to-date behavioral estimates. Furthermore, a simulation-based evaluation methodology enables to validate the performance of prediction, optimization, and personalization before Smart Mobility is implemented in real-life. Two case studies are presented based on the proposed methodologies together with platforms that facilitate their application. Potential benefits of the proposed methodologies are evaluated which can be classified into user-level and system-level benefits. User-level benefits include consumer surplus, waiting times, etc., and system-level is concerned with congestion, throughput, system-wide travel time, etc. As there is normally a tradeoff between the individual decision-making and system-wide decision-making, Smart Mobility bridges them together with appropriate methodologies on each end. For example, for our Flexible Mobility on Demand case study, we observe 10%–20% reduction in volume-to-capacity ratio as a system-level benefit. Moreover, we see that the tradeoff between consumer surplus and operator profit can be managed with an appropriate objective function.@en

The present work demonstrates how drug-loaded mesoporous silica nanoparticles (MSNPs) can be prepared by a sequential flash nanoprecipitation (FNP) technique. A sequential FNP technique is developed relying on a combination of two multi-inlet vortex mixers (MIVM), by which a continuous process that involves the formation of micelle-based templates followed by an in situ formation of MSNPs is achieved. Moreover, a widely used biological nematicide, abamectin (Abm), is added during the formation of micelles, ultimately leading to Abm-loaded MSNPs with high encapsulation efficiency. The obtained Abm-loaded MSNPs show excellent stability and inhibition activity against the livability of Meloidogyne incognita. Importantly, the parameters of the resulting MSNPs, such as silica shell thickness and inner cavity size of MSNPs, can be easily controlled by tuning the compositions of the reactant streams. We believe that such a simple approach towards direct preparation of drug-loaded MSNPs would find promising up-scale applications in various fields, such as drug delivery, bioimaging, and formulation technology.

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Stated preferences surveys are most commonly used to provide behavioral insights on hypothetical travel scenarios such as new transportation services or attribute ranges beyond those observed in existing conditions. When designing SP surveys, considerable care is needed to balance the statistical objectives with the realism of the experiment. This paper presents an innovative method for smartphone-based stated preferences (SP) surveys leveraging state-of-the-art smartphone-based survey platforms and their revealed preferences sensing capabilities. A random experimental design generates context-aware SP profiles using user specific socioeconomic characteristics and past travel data along with relevant web data for scenario generation. The generated choice tasks are automatically validated to reduce the number of dominant or inferior alternatives in real-time, then validated using Monte-Carlo simulations offline. In this paper we focus our attention on mode choice and design an experiment that considers a wide range of possible existing mode alternatives along with a new alternative on-demand mobility service that does not exist in real life. This experiment is then used to collect SP data or a sample of 224 respondents in the Greater Boston Area. A discrete mode choice model is estimated to illustrate the benefit of the proposed method in capturing current context-specific preferences in response to the new scenario.

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The urban mobility landscape is witnessing widespread changes with the emergence of several disruptive technologies including mobility-as-a-service and automated vehicles. The convergence of these two developments in the form of automated mobility-on-demand (AMoD) services (i.e., a system of shared driverless taxis) is receiving growing interest from industry, governments and researchers worldwide as a promising solution to meet mobility needs in the future in a sustainable manner. However, there is a large degree of uncertainty surrounding the potential adoption of these systems, and their impact on individual travel/activity patterns and the transportation system as a whole. In this context, this paper attempts to gain insights into behavioral preferences and attitudes towards AMoD through a novel context-aware app-based stated preferences survey conducted in Singapore. The SP survey leverages a state-of-the-art smartphone-based platform (Future Mobility Sensing) and its ability to collect revealed preference (RP) and contextual data. Logit mixture models accounting for inter-person heterogeneity and panel effects were estimated on a sample of 2500 observations from 350 respondents. The results indicate the presence of heterogeneity in the valuation of in-vehicle travel time and out-of-vehicle travel time and significant differences across demographic categories. An analysis of price elasticity of demand for AMoD indicates a higher elasticity for AMoD taxi followed by AMoD shared19 taxi and AMOD mini-bus. The importance of modeling inertia in switching from the currently used mode is also highlighted. The results have important policy implications and the models have applications within detailed activity-based microsimulation models to examine the impact of AMoD in future scenarios. @en

In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a <q>special report in 2018 on the impacts of global warming of 1.5 °C  above pre-industrial levels and related global greenhouse gas emission pathways</q>. In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from impacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the impacts of additional warming up to 1.5 °C , including a potential overshoot and long-term impacts up to 2299, and comparison to higher levels of global mean temperature change (based on the low-emissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the climate effects based on the same climate scenarios while accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and in particular differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0. With the aim of providing the scientific basis for an aggregation of impacts across sectors and analysis of cross-sectoral interactions that may dampen or amplify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact models across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiversity).

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