Demand Hotspots Research Articles

Comprehensive Analysis of Taxi Service Distribution, Demand Hot spots, and Air Pollution Impact: A GIS and Big Data Approach

As urban transportation demand increases, optimizing taxi services is critical for improving traffic flow and reducing environmental impact. This study investigates the spatial distribution of taxi services, demand hotspots, and their relation to air pollution using Geographic Information Systems (GIS), big data, and land use analysis. After collecting and processing taxi GPS data, we identified high-demand areas such as airports and commercial centers. We then overlaid this data with air pollution monitoring information to analyze potential correlations. The analysis revealed significant overlaps between regions of frequent taxi operation and elevated pollution levels. Using spatio-temporal models, we predicted demand patterns across different periods and explored the connection between land use (commercial, residential, and industrial zones) and traffic congestion. The results show that taxi services contribute significantly to air quality deterioration in densely populated and commercially active areas. These findings suggest that optimized taxi dispatch and fleet redistribution strategies could not only enhance operational efficiency but also alleviate air pollution in urban centers. This study provides actionable insights for policymakers and taxi companies to develop data-driven approaches to urban transportation management, reducing air pollution while improving service efficiency.

A methodological framework for geospatial modelling of hydrogen demand in cities

Urban energy system planning is vital for cities shifting towards a more sustainable and integrated energy system. Hydrogen is considered one of the most promising solutions in future energy systems. Previous work on hydrogen energy systems predominantly analysed hydrogen models on a national level or only parts of the mobility sector. This indicates a research gap for geospatial models that include multiple sectors in which hydrogen can be used. These models can be used to support decision-making processes around the hydrogen economy in cities. This study presents a holistic model addressing the geospatial modelling of hydrogen demand in urban areas. It proposes a method that integrates a variety of open source data, including geodata, earth observation data and energy data to estimate hydrogen demand top down for the industrial feedstock (steel, ammonia, organic chemistry), process heating, and mobility (buses, trucks, trains, airplanes, ships) sectors. The proposed method can also be extended to different sectors. The method is validated by modelling the hydrogen demand in all German cities and benchmarking it with national studies. This study’s results are within the same range as the results of national studies. For this paper, the method is applied for two case studies in Freiburg im Breisgau and Frankfurt am Main. Applying this method in urban areas shows potential hydrogen demand hotspots in these areas. The model’s results help policymakers and industry stakeholders make informed decisions about the development of hydrogen infrastructure and facilitate the adoption of hydrogen as a low-carbon energy carrier. Future research could explore the temporal aspects of hydrogen demand and the spatial influence of hydrogen demand on future hydrogen production facilities such as electrolysers.

RedPacketBike: A Graph-Based Demand Modeling and Crowd-Driven Station Rebalancing Framework for Bike Sharing Systems

Bike-sharing systems have been deployed globally. One of the key issues for high-quality bike-sharing systems is to rebalance city-wide stations to maintain bike availability. Traditional strategies usually operate in fixed paths and limited capacities, lacking the flexibility to cope with the highly dynamic and context dependent riding demands, and usually suffer from high costs and long delays. In this work, we propose RedPacketBike, an incentive-driven, crowd-based station rebalancing framework to effectively recruit participants from hybrid fleets based on the accurate forecast of bike demand leveraging deep learning techniques. First, we propose a spatiotemporal clustering method to extract bike demand hotspots from fluctuating bike usage data. Then, we build a context-aware deep neural network named BikeNet to forecast the trends of bike demand hotspots, simultaneously modeling the spatial correlations. Finally, we propose a reinforcement-learning-based method to find optimal station rebalancing schemes by generating station rebalancing tasks with an integer linear programming (ILP) algorithm and allocating tasks to participants from hybrid fleets with dynamic incentive designs and reward expectations. Experiments using real-world bike-sharing system data validate the performance of our framework, achieving a demand forecast error below 4.171 measured in MAE, and a 17.2% improvement of station availability, outperforming the state-of-the-art baselines.

Automatic bike sharing system planning from urban environment features

ABSTRACT The planning process for bike sharing systems is often complex, involving multiple stakeholders and several considerations: finding hotspots in the potential demand, and dimensioning the system, requires an intimate knowledge of urban mobility patterns and specific local features of the city. The significant costs associated with dynamic rebalancing of bike sharing systems, i.e. with moving bikes across the city to correct the demand imbalance and ensure that they are available where and when they are needed, make correct planning even more critical for the economic viability of the system. In this work, we consider urban environment data from multiple sources and different cities in Europe and the United States to design an automated planning pipeline to place stations in an area with no direct knowledge of the demand. The first step in the planning is to build models of activity patterns and correlate them with features of the urban environment such as land use and mass transit availability; these statistical models can then be used to expand an existing network or even create an entirely new one in a different city. A use case in New York City shows that our system can effectively plan a bike sharing system expansion, providing a valuable first step for the planning process and allowing system designers to identify gaps in existing systems and the locations of potential demand hotspots.

A method to prioritize and allocate nature-based solutions in urban areas based on ecosystem service demand

Mapping and assessing the demand for ecosystem services (ES) in urban areas can support the allocation of nature-based solutions (NbS) to deliver ES where they are most needed. This study presents a method that combines the spatial assessments of ES demand and numeric scores reflecting the capacity of different typologies of NbS to supply multiple ES. The method was applied in 220 ha of potential NbS sites across the urban area of Valletta, Malta, considering 11 NbS types and 5 priority ES. The proposed approach supports both the prioritization of potential NbS sites and the allocation of the specific NbS types which maximise the benefits by providing the best balance of multiple ES. Results show that urban forest is the most needed NbS type across the study area, being the one with the highest capacity to supply most of the analysed ES. However, there are specific cases in which other typologies are more suitable. These include hotspots of demand for specific ES, such as noise reduction and nature-based recreation; as well as sites where size, shape, or land use constraints hinder the implementation of urban forests. Our approach can be used and adapted to support a variety of planning decisions dealing with the prioritization and spatial allocation of NbS, including the development of performance-based approaches aimed at integrating NbS within urban transformation projects.

Exploring and visualizing spatial effects and patterns in ride-sourcing trip demand and characteristics

The complex demand pattern of ride-sourcing remains to be a challenge to transportation modeling practitioners due to the infancy and the inherently dynamic nature of the ride-sourcing system. Spatial effects exploration and analysis protocols can provide informative insights on the underlying structure of demand and trip characteristics. Those protocols can be thought of as an opportunistic strategy to alleviate the complexity and help specifying the appropriate econometric models for the system. Spatial effects exploration is comparable to point pattern analysis, in which, signals from spatial entities, like census tracts, can be analyzed statistically to reveal whether a specific phenomenon respective signal distribution is a completely random process or if it follows some regular pattern. The results of such analysis help to explore the investigated phenomenon and conceptualize its causal forces. In this paper, we apply spatial pattern analysis edge methods integrated into a visual analytics framework to: (1) test the null hypothesis of system demand complete randomness; (2) further analyze and explain this demand in terms of the origin-destination (OD) flow and trips characteristics, i.e., length and duration; and (3) develop a pattern profile of the demand and trip characteristics to provide potential directions to modeling and predictive analytics approaches. This framework helps explain the ride-sourcing system demand and trip characteristics in space and time to fill the gap in integrating the system in multimodal transportation frameworks. We use the ride-sourcing trip dataset released from the City of Chicago, USA, for the year 2019 to showcase the proposed methods and their novelty in capturing such effects as well as explaining the underlying complexities in a streamlined workflow. The ride-sourcing demand hotspots were explored and identified in the city’s central business district. A novel method to capture and analyze the origin-destination flowlines was developed and implemented. Finally, a complementary trip characteristics pattern analysis was conducted to fully comprehend the system and validate the findings from the system demand points and OD-flowlines.

Reusing wastewater for agricultural irrigation: a water-energy-food Nexus assessment in the North Western Sahara Aquifer System

The North Western Sahara Aquifer System stands out as one of the water scarcest regions in the world. Moreover, in recent decades agriculture activity has grown exacerbating the pressure on groundwater resources and pumping energy requirements. In this study, a water-energy-food Nexus approach was used to assess the effect of capturing, treating and reusing wastewater for irrigation. GIS-based tools were used to capture the systems spatial dimension, enabling to match wastewater supply and water demand points, identify demand hotspots and evaluate techno-economically viable wastewater treatment options. Moreover, the minimum energy requirements for brackish water desalination were estimated. Seven domestic wastewater treatment technologies and one irrigation tailwater treatment technology were evaluated, making use of a levelized cost of Water methodology to identify the least-cost system. Four scenarios were constructed based on water-consumption behaviour of farmers towards changes in irrigation water pricing. The identified least-cost wastewater treatment technologies showed clear trade-offs, as different technologies were more cost-effective depending on treatment capacity requirements of the spatially distributed agglomerations. The reuse of treated wastewater/tailwater in agricultural irrigation, showed improvement of groundwater stress, reducing on about 49% water abstractions and groundwater stress levels in the best case scenario. However, groundwater stress still fell on the extremely high category, highlighting the critical condition of the aquifer. Furthermore, reuse of wastewater/tailwater decreased dependency on groundwater pumping and the overall energy-for-water requirements, reducing by about 15% the total energy requirements in the best case scenario. However, to effectively preserve water resources and act holistically towards the sustainable development agenda, measures as better water pricing mechanisms, management strategies to improve water productivity and adoption of more efficient irrigation schemes may be needed.

Research on economic function data and entrepreneurship analysis based on machine learning and computer interaction platform

Big data has the characteristics of rapid data flow, massive data scale, dynamic data system, and various data types, and it has become increasingly apparent in improving innovation and entrepreneurship data analysis, trend prediction, and decision support. In this paper, the authors analyze the economic function data and entrepreneurship analysis based on machine learning. The support vector pair is very sensitive to the choice of parameters, and the parameters obtained using the genetic algorithm will greatly improve the accuracy of the model prediction. When using the genetic algorithm to find parameters, the cv method is used for verification. By applying big data technologies and platforms, it can provide strong data support to establish entrepreneurship education; integrate and integrate various types of innovation and entrepreneurship data, improve the quality of data collection.At the same time, through big data mining and analysis, accurately determine market demand hotspots and innovation and entrepreneurship trends, and promote scientific planning of innovation and entrepreneurship strategies. The research results show that this research model can be applied to actual projects in the future, and help investors better understand the changes of market economy.

A Stochastic Model for UAV Networks Positioned Above Demand Hotspots in Urban Environments

Wireless access points on Unmanned Aerial Vehicles (UAVs) are being considered for mobile service provisioning in commercial networks. These UAV access points will carry radio infrastructure and will be temporarily deployed in areas of dense user traffic to deal with excess user data demand. In this paper, we analyze the coverage when UAVs act as access points for users on the ground in an urban area. We consider the impact of several UAV placement strategies, either independent of, or responsive to, the locations of the users on the ground. Our analysis allows us to demonstrate how the density of the UAVs in the network will determine whether the UAVs should position themselves closer to user hotspots to improve the received signal strength, or further away from one another to mitigate interference. In addition, we demonstrate how network design parameters such as the UAV height above ground or the antenna beamwidth impact the coverage probability. In addition to simulations, we provide mathematical expressions for the coverage probability, for the scenario where UAVs are positioned directly above the centers of user hotspots.

Analyzing Spatial Congruencies and Mismatches between Supply, Demand and Flow of Ecosystem Services and Sustainable Development

Ecosystem services (ESs) are increasingly included into decision-making to achieve Sustainable Development Goals (SDGs). Although both concepts consider the interactions between humans and the environment, spatial relationships between ESs and sustainability have been rarely addressed. Therefore, this study aims at analyzing spatial congruencies and mismatches between ESs and sustainability in the greater Alpine region. Using hot spot and overlap analyses, we overlaid maps of supply, demand and flow of eight key ESs with the spatial distribution of sustainability based on 24 indicators. Our results reveal that, in most cases, supply of and demand for ESs are greatly dislocated. These mismatches are reflected also in the spatial distribution of sustainability. In contrast to ES demand hot spots, supply hot spots are generally characterized by high sustainability levels, especially in relation to the environment. However, due to discrepancies in the social and economic dimensions, it cannot be assumed that ES supply hot spots always correspond to high sustainability. Hence, using ES indicators for measuring sustainability provides rather limited insights. We conclude that both concepts should be applied in a complementary way to maximize ecological, social and economic benefits in land management and planning processes.

Mining Customized Bus Demand Spots Based on Smart Card Data: A Case Study of the Beijing Public Transit System

In recent years, to fix the shortcomings of traditional bus service and meet the diversified needs of passengers, a new type of transit system, the customized bus (CB), has been proposed. However, how to define and mine the CB’s demand is still less being addressed. Since the data of bus smart cards can provide more travel information, it makes the mining of potential CB’s demand spots more possible, which can be helpful in CB service design. In order to mine the demand spots more scientifically, this paper, for the first time, quantitatively defines the CB demand characteristics and criteria of selecting potential area, and develops a demand hotspots extraction methodology for CB. The methodology solves two issues primarily. One is how to organize massive smart card data and obtain the space-time pattern and mobility of passenger efficiently; the other is how to mix the CB demand characteristics into the method. This demand spots extraction method can generate multi-style maps, including the heat and origin-destination maps, for spatial cluster of CB’s demand spots in rational areas in terms of the CB demand characteristics based on geographic information system. By using the bus smart card data in Beijing, China, this paper carries out a case study to validate the method. The empirical data mining analysis shows that our proposed method can define demand spots ideally. Our work can provide a valuable reference for decision makers to design CB system.

La investigación de servicios ecosistémicos en Chile: una revisión sistemática

La relación de los ecosistemas con los ecosistemas-sociedad y el bienestar humano, mediante el uso del concepto de servicios ecosistémicos, ha sido abordada con mayor énfasis en los últimos años, tanto en el ámbito científico, como también en el contexto de la gestión ambiental pública y la toma de decisiones. Un gran impulso para este interés lo constituyó la publicación en el año 2005 del reporte internacional Millenium Ecosystem Assessment (Evaluación de los Ecosistemas del Mileno). No obstante, a nivel de políticas e instrumentos de gestión ambiental el vínculo entre la investigación y la aplicación de los conocimientos es un aspecto aún pendiente, especialmente en países en desarrollo. Este trabajo hace una revisión sistemática de la investigación de servicios ecosistémicos en Chile, con el fin de detectar qué y cómo se estudian los servicios ecosistémicos en el país, de manera de servir para respaldar y orientar las necesidades futuras de investigación que puedan ser aplicadas en el contexto de la conservación de la biodiversidad, la gestión ambiental y la planificación territorial. Nuestros resultados muestran un importante incremento en el desarrollo de la investigación científica a partir de 1994, concentrándose especialmente luego de 2005, reflejando la tendencia internacional de incremento de trabajos sobre servicios ecosistémicos post-publicación del reporte ya mencionado. Tras el análisis se observa que los estudios realizados en Chile han utilizado mayoritariamente herramientas de valoración económica por sobre otro tipo de herramientas y que son escasos los ejercicios de modelación y mapeo de servicios ecosistémicos. Asimismo, hay una necesidad evidente de realizar estudios en ecosistemas desérticos (especialmente en aquellos costeros y altoandinos), en ecosistemas de montaña y glaciares, en ecosistemas antropizados de la zona centro-sur, siendo éstos grandes demandantes de servicios ecosistémicos provistos por los ecosistemas naturales cercanos, y en ecosistemas marino-costeros e insulares.

Downward Facing Directional Antennas for Ultra-High Density Indoor Small Cells

The demand for wireless data is predicted to grow exponentially in the next few years, driven by the growing popularity of smartphones and tablet devices, and new data intensive services. The use of small cells in a heterogeneous network structure is a promising way of providing the required capacity in future 5G cellular networks, offering gains in capacity due to higher spatial reuse, better radio propagation conditions and the ability to target demand hotspots. Examples of these small cells include femtocells and metrocells. In this paper, the concept of deploying indoor small cells in higher densities in order to further leverage spatial reuse gains is examined. The issues and challenges involved in deploying these very small cells with coverage range of only a few meters and a novel antenna configuration providing coverage from the ceiling are discussed. Simulations of an LTE deployment are performed indicating that the proposed antenna configuration reduces interference and improves capacity by a factor of 4 compared to using traditional dipole antennas, enabling significant indoor user throughput gains. Simulations also show that the increased densities also can result in very high number of handovers, and techniques for managing mobility in such deployments are needed.