scholarly journals Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

2021 ◽  
Vol 10 (3) ◽  
pp. 165
Author(s):  
Joerg Schweizer ◽  
Cristian Poliziani ◽  
Federico Rupi ◽  
Davide Morgano ◽  
Mattia Magi
Keyword(s):  
Big Data ◽  
Urban Areas ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Choice Model ◽  
Demand Model ◽  
Agent Based ◽  
Traffic Lights ◽  
Gps Traces ◽  
The City

A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources.

Large-Scale Application of a Combined Destination and Mode Choice Model Estimated with Mixed Stated and Revealed Preference Data

2017 ◽  
Vol 2669 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Michael Heilig ◽  
Nicolai Mallig ◽  
Tim Hilgert ◽  
Martin Kagerbauer ◽  
Peter Vortisch
Keyword(s):  
Large Scale ◽  
Travel Demand ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Revealed Preference ◽  
Demand Model ◽  
Agent Based ◽  
Preference Data ◽  
Mode Choice Model

The diffusion of new modes of transportation, such as carsharing and electric vehicles, makes it necessary to consider them along with traditional modes in travel demand modeling. However, there are two main challenges for transportation modelers. First, the new modes’ low share of usage leads to a lack of reliable revealed preference data for model estimation. Stated preference survey data are a promising and well-established approach to close this gap. Second, the state-of-the-art model approaches are sometimes stretched to their limits in large-scale applications. This research developed a combined destination and mode choice model to consider these new modes in the agent-based travel demand model mobiTopp. Mixed revealed and stated preference data were used, and new modes (carsharing, bikesharing, and electric bicycles) were added to the mode choice set. This paper presents both challenges of the modeling process, mainly caused by large-scale application, and the results of the new combined model, which are as good as those of the former sequential model although it also takes the new modes into consideration.

scholarly journals Understanding policing demand and deployment through the lens of the city and with the application of big data

Urban Studies ◽  
2021 ◽  
pp. 004209802098100
Author(s):  
Mark Ellison ◽  
Jon Bannister ◽  
Won Do Lee ◽  
Muhammad Salman Haleem
Keyword(s):  
Big Data ◽  
Urban Environment ◽  
Urban Areas ◽  
Multilevel Model ◽  
Open Data ◽  
Front Line ◽  
Criminological Research ◽  
The Social ◽  
Generalised Method Of Moments ◽  
The City

The effective, efficient and equitable policing of urban areas rests on an appreciation of the qualities and scale of, as well as the factors shaping, demand. It also requires an appreciation of the factors shaping the resources deployed in their address. To this end, this article probes the extent to which policing demand (crime, anti-social behaviour, public safety and welfare) and deployment (front-line resource) are similarly conditioned by the social and physical urban environment, and by incident complexity. The prospect of exploring policing demand, deployment and their interplay is opened through the utilisation of big data and artificial intelligence and their integration with administrative and open data sources in a generalised method of moments (GMM) multilevel model. The research finds that policing demand and deployment hold varying and time-sensitive association with features of the urban environment. Moreover, we find that the complexities embedded in policing demands serve to shape both the cumulative and marginal resources expended in their address. Beyond their substantive policy relevance, these findings serve to open new avenues for urban criminological research centred on the consideration of the interplay between policing demand and deployment.

scholarly journals Is the Green Wave Really Green? The Risks of Rebound Effects When Implementing “Green” Policies

2021 ◽  
Vol 13 (10) ◽  
pp. 5411
Author(s):  
Elisabeth Bloder ◽  
Georg Jäger
Keyword(s):  
Climate Change ◽  
High Risk ◽  
Urban Areas ◽  
The Other ◽  
Agent Based ◽  
Traffic Lights ◽  
Traffic System ◽  
Rebound Effects ◽  
Car Use ◽  
Reduce Emissions

Traffic and transportation are main contributors to the global CO2 emissions and resulting climate change. Especially in urban areas, traffic flow is not optimal and thus offers possibilities to reduce emissions. The concept of a Green Wave, i.e., the coordinated switching of traffic lights in order to favor a single direction and reduce congestion, is often discussed as a simple mechanism to avoid breaking and accelerating, thereby reducing fuel consumption. On the other hand, making car use more attractive might also increase emissions. In this study, we use an agent-based model to investigate the benefit of a Green Wave in order to find out whether it can outweigh the effects of increased car use. We find that although the Green Wave has the potential to reduce emissions, there is also a high risk of heaving a net increase in emissions, depending on the specifics of the traffic system.

Barriers and levers for implementing sustainable Nature-Based Solutions in cities

2021 ◽  
Author(s):  
Chloé Duffaut ◽  
Nathalie Frascaria-Lacoste ◽  
Pierre-Antoine Versini
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Project Managers ◽  
Global Changes ◽  
Human Beings ◽  
Urban Context ◽  
Urban Plan ◽  
Education And Awareness ◽  
Awareness Raising ◽  
The City

<p>Hydro-meteorological risks are increasing and this could be due to global changes. These risks are particularly important in the urban context where most human beings live. Indeed, the impervious surfaces present in cities increase the risk of flooding, for example. Nature-Based Solutions can help to reduce these risks by creating permeable soils or storing water while promoting biodiversity. In this context, it is essential to understand what hinders the development and sustainability of these Nature-based Solutions in the city and what could help to deploy them on a large scale. For this purpose, various professionals working on Nature-Based Solutions in the city in France, were interviewed between 2020 and 2021, both in the academic and operational sectors, or even at the interface between the two: researchers in ecology or hydrology, IUCN (International Union for Conservation of Nature) project manager, project managers at the Regional Biodiversity Agency, director and natural environment manager of a watershed union, agro-economists engineer among others. They were asked what are the barriers and potential opportunities for Nature-Based Solutions implementation and sustainability in city. By analysing their answers, it emerges that the obstacles are more often cultural, political or financial than technical. The potential levers often mentioned are education and awareness-raising at all levels, especially for elected officials and the general public. Regulations such as the PLU (Local Urban Plan) and new funding for more natural spaces in the city also seem to be means of promoting Nature-based Solutions in urban areas. These interviews with diverse professionals directly involved in Nature-Based Solutions in cities allow to give real courses of action to be taken to democratize these Solutions throughout the French territory, or even internationally, and therefore ultimately reduce the risks of hydro-meteorology. This is one of the objectives of the French ANR project EVNATURB (Assessment of ecosystem performance of a renaturation of the urban environment), in which this study has been carried out.</p>

The Use of Social Media for Urban Planning

2019 ◽  
pp. 1049-1070
Author(s):  
Fabian Neuhaus
Keyword(s):  
Social Media ◽  
Big Data ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Environments ◽  
Data Systems ◽  
User Management ◽  
Social Nature ◽  
User Data ◽  
Use Of Social Media

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

scholarly journals Incidence of Photovoltaics in Cities Based on Indicators of Occupancy and Urban Sustainability

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 810 ◽  
Author(s):  
Antonio Barragán-Escandón ◽  
Esteban Zalamea-León ◽  
Julio Terrados-Cepeda
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Local Context ◽  
Social Dimensions ◽  
Country Level ◽  
Urban Energy ◽  
Urban Level ◽  
Energy Emissions ◽  
The Impact ◽  
The City

Previous research has assessed the potential of solar energy against possible demand; however, the sustainability issues associated with the use of large-scale photovoltaic deployment in urban areas have not been jointly established. In this paper, the impact of photovoltaic energy in the total urban energy mix is estimated using a series of indicators that consider the economic, environmental and social dimensions. These indicators have been previously applied at the country level; the main contribution of this research is applying them at the urban level to the city of Cuenca, Ecuador. Cuenca is close to the equatorial line and at a high altitude, enabling this area to reach the maximum self-supply index because of the high irradiation levels and reduced demand. The solar potential was estimated using a simple methodology that applies several indexes that were proven reliable in a local context considering this particular sun path. The results demonstrate that the solar potential can meet the electric power demand of this city, and only the indicator related to employment is positive and substantially affected. The indicators related to the price of energy, emissions and fossil fuel dependency do not change significantly, unless a fuel-to-electricity transport system conversions take place.

scholarly journals Mapping Urban Areas Using a Combination of Remote Sensing and Geolocation Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1470 ◽  
Author(s):  
Nan Xia ◽  
Liang Cheng ◽  
ManChun Li
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Urban Areas ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Human Life ◽  
Human Movement ◽  
Night Time ◽  
Urban Mapping ◽  
The City

Urban areas are essential to daily human life; however, the urbanization process also brings about problems, especially in China. Urban mapping at large scales relies heavily on remote sensing (RS) data, which cannot capture socioeconomic features well. Geolocation datasets contain patterns of human movement, which are closely related to the extent of urbanization. However, the integration of RS and geolocation data for urban mapping is performed mostly at the city level or finer scales due to the limitations of geolocation datasets. Tencent provides a large-scale location request density (LRD) dataset with a finer temporal resolution, and makes large-scale urban mapping possible. The objective of this study is to combine multi-source features from RS and geolocation datasets to extract information on urban areas at large scales, including night-time lights, vegetation cover, land surface temperature, population density, LRD, accessibility, and road networks. The random forest (RF) classifier is introduced to deal with these high-dimension features on a 0.01 degree grid. High spatial resolution land cover (LC) products and the normalized difference built-up index from Landsat are used to label all of the samples. The RF prediction results are evaluated using validation samples and compared with LC products for four typical cities. The results show that night-time lights and LRD features contributed the most to the urban prediction results. A total of 176,266 km2 of urban areas in China were extracted using the RF classifier, with an overall accuracy of 90.79% and a kappa coefficient of 0.790. Compared with existing LC products, our results are more consistent with the manually interpreted urban boundaries in the four selected cities. Our results reveal the potential of Tencent LRD data for the extraction of large-scale urban areas, and the reliability of the RF classifier based on a combination of RS and geolocation data.

scholarly journals How do green areas influence the temperature of cities? Web map to help local decision making about cities and the climate emergency

2021 ◽  
Vol 2 ◽  
pp. 1-6
Author(s):  
Carla Garcia-Lozano ◽  
Anna Peliova ◽  
Josep Sitjar
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Decision Makers ◽  
Efficient Manner ◽  
Web Map ◽  
Urban Greenery ◽  
Local Decision ◽  
Local Decision Making ◽  
The City ◽  
Positive Effect

Abstract. The positive effect of urban greenery on the city’s microclimate is well known, as is its ability to reduce the ambient temperature in urban areas. Our results show how the areas with the lowest surface temperature clearly coincide with the vegetated areas in the city of Barcelona. This phenomenon demonstrates the importance of increasing the urban greenery in large compact cities, such as the city of Barcelona, in order to regulate the local temperature and mitigate the effects of global warming on a large scale. The web map presented here can be used as a tool for decision makers to identify the warmest areas in the city of Barcelona and to increase greenery in an efficient manner.

scholarly journals Simulating Offender Mobility: Modeling Activity Nodes from Large-Scale Human Activity Data

2020 ◽  
Vol 68 ◽  
pp. 541-570
Author(s):  
Raquel Rosés ◽  
Cristina Kadar ◽  
Charlotte Gerritsen ◽  
Chris Rouly
Keyword(s):  
Human Activity ◽  
Urban Areas ◽  
Large Scale ◽  
Complex Dynamics ◽  
Open Data ◽  
Human Movement ◽  
Urban Structure ◽  
Real World Data ◽  
Activity Data ◽  
Agent Based

In recent years, simulation techniques have been applied to investigate the spatiotemporal dynamics of crime. Researchers have instantiated mobile offenders in agent-based simulations for theory testing, experimenting with crime prevention strategies, and exploring crime prediction techniques, despite facing challenges due to the complex dynamics of crime and the lack of detailed information about offender mobility. This paper presents a simulation model to explore offender mobility, focusing on the interplay between the agent's awareness space and activity nodes. The simulation generates patterns of individual mobility aiming to cumulatively match crime patterns. To instantiate a realistic urban environment, we use open data to simulate the urban structure, location-based social networks data to represent activity nodes as a proxy for human activity, and taxi trip data as a proxy for human movement between regions of the city. We analyze and systematically compare 35 different mobility strategies and demonstrate the benefits of using large-scale human activity data to simulate offender mobility. The strategies combining taxi trip data or historic crime data with popular activity nodes perform best compared to other strategies, especially for robbery. Our approach provides a basis for building agent-based crime simulations that infer offender mobility in urban areas from real-world data.

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