scholarly journals Dynamic stop pooling for flexible and sustainable ride sharing

2022 ◽  
Author(s):  
Charlotte Lotze ◽  
Philip Marszal ◽  
Malte Schröder ◽  
Marc Timme
Keyword(s):  
Steady State ◽  
Simple Model ◽  
Travel Time ◽  
Human Mobility ◽  
A Priori ◽  
Trade Off ◽  
Ride Sharing ◽  
Route Length ◽  
Spatio Temporal ◽  
Demand Patterns

Abstract Ride sharing -- the bundling of simultaneous trips of several people in one vehicle -- may help to reduce the carbon footprint of human mobility. However, the complex collective dynamics pose a challenge when predicting the efficiency and sustainability of ride-sharing systems. Standard door-to-door ride sharing services trade reduced route length for increased user travel times and come with the burden of many stops and detours to pick up individual users. Requiring some users to walk to nearby shared stops reduces detours, but could become inefficient if spatio-temporal demand patterns do not well fit the stop locations. Here, we present a simple model of dynamic stop pooling with flexible stop positions. We analyze the performance of ride sharing services with and without stop pooling by numerically and analytically evaluating the steady state dynamics of the vehicles and requests of the ride sharing service. Dynamic stop pooling does a-priori not save route length, but occupancy. Intriguingly, it also reduces the travel time, although users walk parts of their trip. Together, these insights explain how dynamic stop pooling may break the trade-off between route lengths and travel time in door-to-door ride sharing, thus enabling higher sustainability and service quality.

Mathematical modelling of salt purification by recrystallization. Countercurrent arrangement

1986 ◽  
Vol 51 (11) ◽  
pp. 2481-2488
Author(s):  
Benitto Mayrhofer ◽  
Jana Mayrhoferová ◽  
Lubomír Neužil ◽  
Jaroslav Nývlt
Keyword(s):  
Steady State ◽  
Simple Model ◽  
Mathematical Modelling ◽  
Distribution Coefficient ◽  
Mother Liquor ◽  
Equilibrium Temperature ◽  
Operating Conditions ◽  
Limiting Case

The paper presents a simple model of recrystallization with countercurrent flows of the solution and the crystals being purified. The model assumes steady-state operating conditions, an equilibrium between the outlet streams of each stage, and the same equilibrium temperature and distribution coefficient for all stages. With these assumptions, the model provides the basis for analyzing the variation in the degree of purity as a function of the number of recrystallization stages. The analysis is facilitated by the use of a diagram constructed for the limiting case of perfect removal of the mother liquor from the crystals between the stages.

scholarly journals A novel methodology for epidemic risk assessment of COVID-19 outbreak

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Pluchino ◽  
A. E. Biondo ◽  
N. Giuffrida ◽  
G. Inturri ◽  
V. Latora ◽  
...  
Keyword(s):  
Human Mobility ◽  
Risk Index ◽  
A Priori ◽  
Geographical Area ◽  
Central Italy ◽  
Policy Model ◽  
Italian Regions ◽  
Northern Regions ◽  
Seasonal Flu ◽  
Epidemic Risk

AbstractWe propose a novel data-driven framework for assessing the a-priori epidemic risk of a geographical area and for identifying high-risk areas within a country. Our risk index is evaluated as a function of three different components: the hazard of the disease, the exposure of the area and the vulnerability of its inhabitants. As an application, we discuss the case of COVID-19 outbreak in Italy. We characterize each of the twenty Italian regions by using available historical data on air pollution, human mobility, winter temperature, housing concentration, health care density, population size and age. We find that the epidemic risk is higher in some of the Northern regions with respect to Central and Southern Italy. The corresponding risk index shows correlations with the available official data on the number of infected individuals, patients in intensive care and deceased patients, and can help explaining why regions such as Lombardia, Emilia-Romagna, Piemonte and Veneto have suffered much more than the rest of the country. Although the COVID-19 outbreak started in both North (Lombardia) and Central Italy (Lazio) almost at the same time, when the first cases were officially certified at the beginning of 2020, the disease has spread faster and with heavier consequences in regions with higher epidemic risk. Our framework can be extended and tested on other epidemic data, such as those on seasonal flu, and applied to other countries. We also present a policy model connected with our methodology, which might help policy-makers to take informed decisions.

Predicting Human Mobility with Reinforcement-Learning-Based Long-Term Periodicity Modeling

2021 ◽  
Vol 12 (6) ◽  
pp. 1-23
Author(s):  
Shuo Tao ◽  
Jingang Jiang ◽  
Defu Lian ◽  
Kai Zheng ◽  
Enhong Chen
Keyword(s):  
Reinforcement Learning ◽  
Human Mobility ◽  
Recurrent Network ◽  
Mobility Prediction ◽  
Learning Framework ◽  
Temporal Features ◽  
Wide Range ◽  
Spatio Temporal ◽  
Historical Trajectory

Mobility prediction plays an important role in a wide range of location-based applications and services. However, there are three problems in the existing literature: (1) explicit high-order interactions of spatio-temporal features are not systemically modeled; (2) most existing algorithms place attention mechanisms on top of recurrent network, so they can not allow for full parallelism and are inferior to self-attention for capturing long-range dependence; (3) most literature does not make good use of long-term historical information and do not effectively model the long-term periodicity of users. To this end, we propose MoveNet and RLMoveNet. MoveNet is a self-attention-based sequential model, predicting each user’s next destination based on her most recent visits and historical trajectory. MoveNet first introduces a cross-based learning framework for modeling feature interactions. With self-attention on both the most recent visits and historical trajectory, MoveNet can use an attention mechanism to capture the user’s long-term regularity in a more efficient way. Based on MoveNet, to model long-term periodicity more effectively, we add the reinforcement learning layer and named RLMoveNet. RLMoveNet regards the human mobility prediction as a reinforcement learning problem, using the reinforcement learning layer as the regularization part to drive the model to pay attention to the behavior with periodic actions, which can help us make the algorithm more effective. We evaluate both of them with three real-world mobility datasets. MoveNet outperforms the state-of-the-art mobility predictor by around 10% in terms of accuracy, and simultaneously achieves faster convergence and over 4x training speedup. Moreover, RLMoveNet achieves higher prediction accuracy than MoveNet, which proves that modeling periodicity explicitly from the perspective of reinforcement learning is more effective.

Single-station seismic monitoring of permafrost on Mt. Zugspitze (Germany) over the past 15 years

2021 ◽  
Author(s):  
Fabian Lindner ◽  
Joachim Wassermann
Keyword(s):  
Travel Time ◽  
Seismic Velocity ◽  
Temperature Record ◽  
Lapse Time ◽  
Resistivity Tomography ◽  
Single Station ◽  
Velocity Variations ◽  
Spatio Temporal ◽  
Velocity Changes ◽  
Permafrost Thawing

<p>Permafrost thawing affects mountain slope stability and can trigger hazardous rock falls. As rising temperatures promote permafrost thawing, spatio-temporal monitoring of long-term and seasonal variations in the perennially frozen rock is therefore crucial in regions with high hazard potential. With various infrastructure in the summit area and population in the close vicinity, Mt. Zugspitze in the German/Austrian Alps is such a site and permafrost has been monitored with temperature logging in boreholes and lapse-time electrical resistivity tomography. Yet, these methods are expensive and laborious, and are limited in their spatial and/or temporal resolution.</p><p>Here, we analyze continuous seismic data from a single station deployed at an altitude of 2700 m a.s.l. in a research station, which is separated by roughly 250 m from the permafrost affected ridge of Mt. Zugspitze. Data are available since 2006 (with some gaps) and reveal high-frequency (>1 Hz) anthropogenic noise likely generated by the cable car stations at the summit. We calculate single-station cross-correlations between the different sensor components and investigate temporal coda wave changes by applying the recently introduced wavelet-based cross-spectrum method. This approach provides time series of the travel time relative to the reference stack as a function of frequency and lag time in the correlation functions. In the frequency and lag range of 1-10 Hz and 0.5-5 s respectively, we find various parts in the coda that show clear annual variations and an increasing trend in travel time over the past 15 years of consideration. Converting the travel time variations to seismic velocity variations (assuming homogeneous velocity changes affecting the whole mountain) results in seasonal velocity changes of up to a few percent and on the order of 0.1% decrease per year. Yet, estimated velocity variations do not scale linearly with lag time, which indicates that the medium changes are localized rather than uniform and that the absolute numbers need to be taken with caution. The annual velocity variations are anti-correlated with the temperature record from the summit but delayed by roughly one month.</p><p>The phasing of the annual seismic velocity change (relative to the temperature record) is in agreement with a previous study employing lapse-time electrical resistivity tomography. Furthermore, the decreasing trend in seismic velocity happens concurrently with an increasing trend in temperature. The results therefore suggest that the velocity changes are related to seasonal thaw and refreeze and permafrost degradation and thus highlight the potential of seismology for permafrost monitoring. By adding additional receivers and/or a fiber-optic cable for distributed acoustic sensing, hence increasing the spatial resolution, the presented method holds promise for lapse-time imaging of permafrost bodies with high spatio-temporal resolution from passive measurements.</p>

scholarly journals Accelerating simulations using REDCHEM_v0.0 for atmospheric chemistry mechanism reduction

2018 ◽  
Vol 11 (8) ◽  
pp. 3391-3407 ◽  
Author(s):  
Zacharias Marinou Nikolaou ◽  
Jyh-Yuan Chen ◽  
Yiannis Proestos ◽  
Jos Lelieveld ◽  
Rolf Sander
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
A Priori ◽  
Chemical Mechanism ◽  
Climate Modelling ◽  
Mechanism Reduction ◽  
Computational Speed ◽  
Speed Up ◽  
Spatio Temporal ◽  
Skeletal Mechanism

Abstract. Chemical mechanism reduction is common practice in combustion research for accelerating numerical simulations; however, there have been limited applications of this practice in atmospheric chemistry. In this study, we employ a powerful reduction method in order to produce a skeletal mechanism of an atmospheric chemistry code that is commonly used in air quality and climate modelling. The skeletal mechanism is developed using input data from a model scenario. Its performance is then evaluated both a priori against the model scenario results and a posteriori by implementing the skeletal mechanism in a chemistry transport model, namely the Weather Research and Forecasting code with Chemistry. Preliminary results, indicate a substantial increase in computational speed-up for both cases, with a minimal loss of accuracy with regards to the simulated spatio-temporal mixing ratio of the target species, which was selected to be ozone.

scholarly journals Validation of nine years of MOPITT V5 NIR using MOZAIC/IAGOS measurements: biases and long-term stability

2014 ◽  
Vol 7 (11) ◽  
pp. 3783-3799 ◽  
Author(s):  
A. T. J. de Laat ◽  
I. Aben ◽  
M. Deeter ◽  
P. Nédélec ◽  
H. Eskes ◽  
...  
Keyword(s):  
Near Infrared ◽  
Transport Model ◽  
A Priori ◽  
Validation Data ◽  
Chemistry Transport Model ◽  
Large Variability ◽  
Long Term Stability ◽  
Spatio Temporal ◽  
Good Agreement

Abstract. Validation results from a comparison between Measurement Of Pollution In The Troposphere (MOPITT) V5 Near InfraRed (NIR) carbon monoxide (CO) total column measurements and Measurement of Ozone and Water Vapour on Airbus in-service Aircraft (MOZAIC)/In-Service Aircraft for a Global Observing System (IAGOS) aircraft measurements are presented. A good agreement is found between MOPITT and MOZAIC/IAGOS measurements, consistent with results from earlier studies using different validation data and despite large variability in MOPITT CO total columns along the spatial footprint of the MOZAIC/IAGOS measurements. Validation results improve when taking the large spatial footprint of the MOZAIC/IAGOS data into account. No statistically significant drift was detected in the validation results over the period 2002–2010 at global, continental and local (airport) scales. Furthermore, for those situations where MOZAIC/IAGOS measurements differed from the MOPITT a priori, the MOPITT measurements clearly outperformed the MOPITT a priori data, indicating that MOPITT NIR retrievals add value to the MOPITT a priori. Results from a high spatial resolution simulation of the chemistry-transport model MOCAGE (MOdèle de Chimie Atmosphérique à Grande Echelle) showed that the most likely explanation for the large MOPITT variability along the MOZAIC-IAGOS profile flight path is related to spatio-temporal CO variability, which should be kept in mind when using MOZAIC/IAGOS profile measurements for validating satellite nadir observations.

scholarly journals THE RICH AND THE POOR IN A SIMPLE MODEL OF GROWTH AND DISTRIBUTION

2016 ◽  
Vol 20 (7) ◽  
pp. 1934-1952 ◽  
Author(s):  
Kirill Borissov
Keyword(s):  
Economic Growth ◽  
Steady State ◽  
Simple Model ◽  
Disposable Income ◽  
State Equilibrium ◽  
Positional Concerns ◽  
The Poor ◽  
The Rich ◽  
Intertemporal Equilibrium ◽  
Unique Steady State

We consider a model of economic growth with altruistic agents who care about their consumption and the disposable income of their offspring. The agents' consumption and the offspring's disposable income are subject to positional concerns. We show that, if the measure of consumption-related positional concerns is sufficiently low and/or the measure of offspring-related positional concerns is sufficiently high, then there is a unique steady-state equilibrium, which is characterized by perfect income and wealth equality, and all intertemporal equilibira converge to it. Otherwise, in steady-state equilibria, the population splits into two classes, the rich and the poor; under this scenario, in any intertemporal equilibrium, all capital is eventually owned by the households that were the wealthiest from the outset and all other households become poor.

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