scholarly journals Integrative analysis of multimodal traffic data: addressing open challenges using big data analytics in the city of Lisbon

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Carlos Lemonde ◽  
Elisabete Arsenio ◽  
Rui Henriques
Keyword(s):  
Big Data ◽  
City Council ◽  
Urban Traffic ◽  
Integrative Analysis ◽  
Context Aware ◽  
Traffic Data ◽  
Situational Context ◽  
Transport Modes ◽  
The City ◽  
Urban Data

AbstractWorldwide cities are establishing efforts to collect urban traffic data from various modes and sources. Integrating traffic data, together with their situational context, offers more comprehensive views on the ongoing mobility changes and supports enhanced management decisions accordingly. Hence, cities are becoming sensorized and heterogeneous sources of urban data are being consolidated with the aim of monitoring multimodal traffic patterns, encompassing all major transport modes—road, railway, inland waterway—, and active transport modes such as walking and cycling. The research reported in this paper aims at bridging the existing literature gap on the integrative analysis of multimodal traffic data and its situational urban context. The reported work is anchored on the major findings and contributions from the research and innovation project Integrative Learning from Urban Data and Situational Context for City Mobility Optimization (ILU), a multi-disciplinary project on the field of artificial intelligence applied to urban mobility, joining the Lisbon city Council, public carriers, and national research institutes. The manuscript is focused on the context-aware analysis of multimodal traffic data with a focus on public transportation, offering four major contributions. First, it provides a structured view on the scientific and technical challenges and opportunities for data-centric multimodal mobility decisions. Second, rooted on existing literature and empirical evidence, we outline principles for the context-aware discovery of multimodal patterns from heterogeneous sources of urban data. Third, Lisbon is introduced as a case study to show how these principles can be enacted in practice, together with some essential findings. Finally, we instantiate some principles by conducting a spatiotemporal analysis of multimodality indices in the city against available context. Concluding, this work offers a structured view on the opportunities offered by cross-modal and context-enriched analysis of traffic data, motivating the role of Big Data to support more transparent and inclusive mobility planning decisions, promote coordination among public transport operators, and dynamically align transport supply with the emerging urban traffic dynamics.

LIVE Singapore! The Urban Data Collider

Transfers ◽  
2014 ◽  
Vol 4 (3) ◽  
pp. 117-121
Author(s):  
Carlo Ratti ◽  
Matthew Claudel
Keyword(s):  
Big Data ◽  
Distributed Computing ◽  
Research Design ◽  
Credit Card ◽  
Public Transit ◽  
Physical Space ◽  
The World ◽  
The City ◽  
Enormous Quantity ◽  
Urban Data

The world is urbanizing at an unprecedented rate, and its cities are transformed by technology and distributed computing. With every photograph, Twitter post, public transit ride, and credit card swipe, we leave digital traces in physical space. The enormous quantity of information, or Urban Big Data, that humanity generates each day is beginning to off er new possibilities for research, design, and systems optimization on the city scale, but the first step toward our urban future is finding new ways of understanding and visualizing Big Data—revealing invisible dimensions of the city.

Application research of urban subway traffic mode based on behavior entropy in the background of big data

2021 ◽  
pp. 1-14
Author(s):  
Wanxin Hu ◽  
Fen Cheng
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Rapid Development ◽  
Urban Transportation ◽  
Transportation System ◽  
Urban Traffic ◽  
Time Optimization ◽  
The City

With the development of society and the Internet and the advent of the cloud era, people began to pay attention to big data. The background of big data brings opportunities and challenges to the research of urban intelligent transportation networks. Urban transportation system is one of the important foundations for maintaining urban operation. The rapid development of the city has brought tremendous pressure on the traffic, and the congestion of urban traffic has restricted the healthy development of the city. Therefore, how to improve the urban transportation network model and improve transportation and transportation has become an urgent problem to be solved in urban development. Specific patterns hidden in large-scale crowd movements can be studied through transportation networks such as subway networks to explore urban subway transportation modes to support corresponding decisions in urban planning, transportation planning, public health, social networks, and so on. Research on urban subway traffic patterns is crucial. At the same time, a correct understanding of the behavior patterns and laws of residents’ travel is a key factor in solving urban traffic problems. Therefore, this paper takes the metro operation big data as the background, takes the passenger travel behavior in the urban subway transportation system as the research object, uses the behavior entropy to measure the human behavior, and actively explores the urban subway traffic mode based on the metro passenger behavior entropy in the context of big data. At the same time, the congestion degree of the subway station is analyzed, and the redundancy time optimization model of the subway train stop is established to improve the efficiency of the subway operation, so as to provide important and objective data and theoretical support for the traveler, planner and decision maker. Compared to the operation graph without redundant time, the total travel time optimization effect of passengers is 7.74%, and the waiting time optimization effect of passengers is 6.583%.

scholarly journals On how to incorporate public sources of situational context in descriptive and predictive models of traffic data

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sofia Cerqueira ◽  
Elisabete Arsenio ◽  
Rui Henriques
Keyword(s):  
Data Analysis ◽  
Data Structures ◽  
Predictive Models ◽  
Series Data ◽  
Traffic Data ◽  
Urban Mobility ◽  
Situational Context ◽  
European Cities ◽  
Context Data ◽  
The City

Abstract Background European cities are placing a larger emphasis on urban data consolidation and analysis for optimizing public transport in response to changing urban mobility dynamics. Despite the existing efforts, traffic data analysis often disregards vital situational context, including large-scale events, weather factors, traffic generation poles, social distancing norms, or traffic interdictions. Some of these sources of context data are still private, dispersed, or unavailable for the purpose of planning or managing urban mobility. Addressing the above observation, the Lisbon city Council has already established efforts for gathering historic and prospective sources of situational context in standardized semi-structured repositories, triggering new opportunities for context-aware traffic data analysis. Research questions The work presented in this paper aims at tackling the following main research question: How to incorporate historical and prospective sources of situational context into descriptive and predictive models of urban traffic data? Methodology We propose a methodology anchored in data science methods to integrate situational context in the descriptive and predictive models of traffic data, with a focus on the three following major spatiotemporal traffic data structures: i) georeferenced time series data; ii) origin-destination tensor data; iii) raw traffic event data. Second, we introduce additional principles for the online consolidation and labelling of heterogeneous sources of situational context from public repositories. Third, we quantify the impact produced by situational context aspects on public passenger transport data gathered from smart card validations along the bus (CARRIS), subway (METRO) and bike sharing (GIRA) modes in the city of Lisbon. Results The gathered results stress the importance of incorporating historical and prospective context data for a guided description and prediction of urban mobility dynamics, irrespective of the underlying data representation. Overall, the research offers the following major contributions: A novel methodology on how to acquire, consolidate and incorporate different sources of context for the context-enriched analysis of traffic data; The instantiation of the proposed methodology in the city of Lisbon, discussing the role of recent initiatives for the ongoing monitoring of relevant context data sources within semi-structured repositories, and further showing how these initiatives can be extended for the context-sensitive modelling of traffic data for descriptive and predictive ends; A roadmap of practical illustrations quantifying impact of different context factors (including weather, traffic interdictions and public events) on different transportation modes using different spatiotemporal traffic data structures; and A review of state-of-the-art contributions on context-enriched traffic data analysis. The contributions reported in this work are anchored in the empirical observations gathered along the first stage of the ILU project (see footnote 1), providing a study case of interest to be followed by other European cities.

scholarly journals Visualizing Conflict: Possibilities for Urban Research

2017 ◽  
Vol 2 (1) ◽  
pp. 100-107
Author(s):  
Juan Francisco Saldarriaga ◽  
Laura Kurgan ◽  
Dare Brawley
Keyword(s):  
Big Data ◽  
Everyday Life ◽  
Data Visualization ◽  
Urban Space ◽  
Data Literacy ◽  
Urban Research ◽  
The World ◽  
Differences Of Opinion ◽  
The City ◽  
Urban Data

The Center for Spatial Research (CSR) is undertaking a multiyear project investigating what we have termed Conflict Urbanism. The term designates not simply the conflicts that take place in cities, but also conflict as a structuring principle of cities intrinsically, as a way of inhabiting and creating urban space. The increasing urbanization of warfare and the policing and surveillance of everyday life are examples of the term (Graham, 2010; Misselwitz & Rieniets, 2006; Weizman, 2014), but conflict is not limited to war and violence. Cities are not only destroyed but also built through conflict. They have long been arenas of friction, difference, and dissidence, and their irreducibly conflictual character manifests itself in everything from neighborhood borders, to differences of opinion and status, to ordinary encounters on the street. One major way in which CSR undertakes research is through interrogating the world of ‘big data.’ This includes analyzing newly accessible troves of ‘urban data,’ working to open up new areas of research and inquiry, as well as focusing on data literacy as an essential part of communicating with these new forms of urban information. In what follows we discuss two projects currently under way at CSR that use mapping and data visualization to explore and analyze Conflict Urbanism in two different contexts: the city of Aleppo, and the nation of Colombia.

scholarly journals UTRaLab – Urban Traffic Research Laboratory

2017 ◽  
Vol 3 ◽  
Author(s):  
Karsten Kozempel ◽  
Andreas Luber ◽  
Marek Junghans
Keyword(s):  
Data Collection ◽  
Research Laboratory ◽  
Urban Traffic ◽  
Environmental Data ◽  
Detection Methods ◽  
Traffic Data ◽  
Traffic Sensors ◽  
The One ◽  
The City ◽  
Traffic Detection

The Urban Traffic Research Laboratory (UTRaLab) is a research and test track for traffic detection methods and sensors. It is located at the Ernst-Ruska-Ufer, in the southeast of the city of Berlin (Germany). The UTRaLab covers 1 km of a highly-frequented urban road and is connected to a motorway. It is equipped with two gantries with distance of 850 m in between and has several outstations for data collection. The gantries contain many different traffic sensors like inductive loops, cameras, lasers or wireless sensors for traffic data acquisition. Additionally a weather station records environmental data. The UTRaLab’s main purposes are the data collection of traffic data on the one hand and testing newly developed sensors on the other hand.

Portuguese Sardines - from the ritual to the brand

2017 ◽  
Vol 5 (2) ◽  
pp. 109-120
Author(s):  
Cecília Avelino Barbosa
Keyword(s):  
Social Construction ◽  
Place Branding ◽  
Symbolic Capital ◽  
City Council ◽  
Point Of View ◽  
City Branding ◽  
The Past ◽  
Behavioral Expression ◽  
The City ◽  
City Identity

Place branding is a network of associations in the consumer’s mind, based on the visual, verbal, and behavioral expression of a place. Food can be an important tool to summarize it as it is part of the culture of a city and its symbolic capital. Food is imaginary, a ritual and a social construction. This paper aims to explore a ritual that has turned into one of the brands of Lisbon in the past few years. The fresh sardines barbecued out of doors, during Saint Anthony’s festival, has become a symbol that can be found on t-shirts, magnets and all kinds of souvenirs. Over the year, tourists can buy sardine shaped objects in very cheap stores to luxurious shops. There is even a whole boutique dedicated to the fish: “The Fantastic World of Portuguese Sardines” and an annual competition promoted by the city council to choose the five most emblematic designs of sardines. In order to analyze the Sardine phenomenon from a city branding point of view, the objective of this paper is to comprehend what associations are made by foreigners when they are outside of Lisbon. As a methodological procedure five design sardines, were used of last year to questioning to which city they relate them in interviews carried in Madrid, Lyon, Rome and London. Upon completion of the analysis, the results of the city branding strategy adopted by the city council to promote the sardines as the official symbol of Lisbon is seen as a Folkmarketing action. The effects are positive, but still quite local. On the other hand, significant participation of the Lisbon´s dwellers in the Sardine Contest was observed, which seems to be a good way to promote the city identity and pride in their best ambassador: the citizens.

Treading Angels: Edinburgh and its Festivals

2019 ◽  
Vol 28 (3) ◽  
pp. 290-317
Author(s):  
David McCrone
Keyword(s):  
City Council ◽  
The City ◽  
Public Bodies

How did Edinburgh become ‘festival city’? Despite appearances, it was not always so, and it acquired the accolade by happenstance; in the view of one observer, a ‘strange amalgam of cultural banditry, civic enterprise and idealism’. The official Festival's survival was down to the City Council, and it was funded almost entirely by public bodies. This was the central structure around which The Fringe developed, and The Traverse prospered, along with smaller festivals and events to become Festival City. The story sheds considerable light on how Edinburgh ‘works’, its strengths and weaknesses combined.

What Makes a City Bikeable? A Study of Intercity and Intracity Patterns of Bicycle Ridership using Mobike Big Data Records

2020 ◽  
Vol 46 (1) ◽  
pp. 55-75
Author(s):  
Ying Long ◽  
Jianting Zhao
Keyword(s):  
Big Data ◽  
Average Distance ◽  
Policy Implications ◽  
Rating System ◽  
City Centre ◽  
Average Score ◽  
Policy Makers ◽  
Usage Frequency ◽  
Share Data ◽  
The City

This paper examines how mass ridership data can help describe cities from the bikers' perspective. We explore the possibility of using the data to reveal general bikeability patterns in 202 major Chinese cities. This process is conducted by constructing a bikeability rating system, the Mobike Riding Index (MRI), to measure bikeability in terms of usage frequency and the built environment. We first investigated mass ridership data and relevant supporting data; we then established the MRI framework and calculated MRI scores accordingly. This study finds that people tend to ride shared bikes at speeds close to 10 km/h for an average distance of 2 km roughly three times a day. The MRI results show that at the street level, the weekday and weekend MRI distributions are analogous, with an average score of 49.8 (range 0–100). At the township level, high-scoring townships are those close to the city centre; at the city level, the MRI is unevenly distributed, with high-MRI cities along the southern coastline or in the middle inland area. These patterns have policy implications for urban planners and policy-makers. This is the first and largest-scale study to incorporate mobile bike-share data into bikeability measurements, thus laying the groundwork for further research.

Introduction

2017 ◽  
Author(s):  
George Hoffmann
Keyword(s):  
Chief Editor ◽  
City Council ◽  
The West ◽  
Son Of God ◽  
Warm Summer ◽  
The City

On a warm summer afternoon in 1561, Calvin’s chief editor donned a heavy stole, thick robes, and a gleaming tiara and proceeded to strut and fret his hour upon the stage in a comedy of his own devising. For little more than a century, Christians in the West had celebrated on August 6th Christ’s Transfiguration as the son of God in shining robes. But on this Sunday in Geneva, the city council, consistory, and an audience fresh from having attended edifying sermons at morning service gathered to applaud the transfiguration of the learned Conrad Badius into the title role of ...

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