scholarly journals More sustainable tomorrow’s transport systems based on Big Data concerning travel preferences

2020 ◽  
Author(s):  
Grzegorz Sierpiński
Keyword(s):  
Big Data ◽  
Transport Systems

scholarly journals BIG DATA FOR RISK ANALYSIS: THE FUTURE OF SAFE RAILWAYS

2016 ◽  
Author(s):  
Miguel Figueres Esteban
Keyword(s):  
Big Data ◽  
Risk Analysis ◽  
Safety Management ◽  
New Technology ◽  
Data Sources ◽  
Transport Systems ◽  
Text Documents ◽  
Railway Systems ◽  
Wide Range ◽  
Three Components

New technology brings ever more data to support decision-making for intelligent transport systems. Big Data is no longer a futuristic challenge, it is happening right now: modern railway systems have countless sources of data providing a massive quantity of diverse information on every aspect of operations such as train position and speed, brake applications, passenger numbers, status of the signaling system or reported incidents.The traditional approaches to safety management on the railways have relied on static data sources to populate traditional safety tools such as bow-tie models and fault trees. The Big Data Risk Analysis (BDRA) program for Railways at the University of Huddersfield is investigating how the many Big Data sources from the railway can be combined in a meaningful way to provide a better understanding about the GB railway systems and the environment within which they operate.Moving to BDRA is not simply a matter of scaling-up existing analysis techniques. BDRA has to coordinate and combine a wide range of sources with different types of data and accuracy, and that is not straight-forward. BDRA is structured around three components: data, ontology and visualisation. Each of these components is critical to support the overall framework. This paper describes how these three components are used to get safety knowledge from two data sources by means of ontologies from text documents. This is a part of the ongoing BDRA research that is looking at integrating many large and varied data sources to support railway safety and decision-makers.DOI: http://dx.doi.org/10.4995/CIT2016.2016.1825

scholarly journals MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems

2020 ◽  
Vol 4 (3) ◽  
pp. 17 ◽  
Author(s):  
Suriya Priya R. Asaithambi ◽  
Ramanathan Venkatraman ◽  
Sitalakshmi Venkatraman
Keyword(s):  
Big Data ◽  
Intelligent Transportation Systems ◽  
Consumer Satisfaction ◽  
Smart Cities ◽  
Transportation Systems ◽  
Transport Systems ◽  
Hybrid Architecture ◽  
Smart Transportation ◽  
Transport Services ◽  
Data Architecture

Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes a Microservice-Oriented Big Data Architecture (MOBDA) incorporating data processing techniques, such as predictive modelling for achieving smart transportation and analytics microservices required towards smart cities of the future. We postulate key transportation metrics applied on various sources of transportation data to serve this objective. A novel hybrid architecture is proposed to combine stream processing and batch processing of big data for a smart computation of microservice-oriented transportation metrics that can serve the different needs of stakeholders. Development of such an architecture for smart transportation and analytics will improve the predictability of transport supply for transport providers and transport authority as well as enhance consumer satisfaction during peak periods.

Geographies of transport II

2016 ◽  
Vol 41 (3) ◽  
pp. 355-364 ◽  
Author(s):  
Tim Schwanen
Keyword(s):  
Big Data ◽  
Network Analysis ◽  
Complex Networks ◽  
Network Science ◽  
Complexity Science ◽  
Transport Systems ◽  
Transport Geography ◽  
Space And Time ◽  
Critical Reflexivity ◽  
Mixed Blessing

Geographical scholarship on transport has been boosted by the emergence of big data and advances in the analysis of complex networks in other disciplines, but these developments are a mixed blessing. They allow transport as object of analysis to exist in new ways and raise the profile of geography in interdisciplinary spaces dominated by physics and complexity science. Yet, they have also brought back concerns over the privileging of generality over particularity. This is because they have once more made acceptable and even normalized a focus on supposedly universal laws that explain the functioning of mobility systems and on space and time independent explanations of hierarchies, inequalities and vulnerabilities in transport systems and patterns. Geographical scholarship on transport should remain open to developments in big data and network science but would benefit from more critical reflexivity on the limitations and the historical and geographical situatedness of big data and on the conceptual shortcomings of network science. Big data and network analysis need to be critiqued and re-appropriated, and examples of how this can be done are starting to emerge. Openness, critique and re-appropriation are especially important in a context where transport geography decentralizes away from its Euro-American core, and the development pathways of transport and mobility in localities beyond that core deserve their own, unique explanations.

scholarly journals Estimation of Travel Demand Models with Limited Information: Floating Car Data for Parameters’ Calibration

2021 ◽  
Vol 13 (16) ◽  
pp. 8838
Author(s):  
Antonello Ignazio Croce ◽  
Giuseppe Musolino ◽  
Corrado Rindone ◽  
Antonino Vitetta
Keyword(s):  
Big Data ◽  
Transport System ◽  
Systems Engineering ◽  
Travel Demand ◽  
Transport Systems ◽  
Limited Information ◽  
Sources Of Information ◽  
Floating Car Data ◽  
Demand Models ◽  
System Configurations

This paper attempts to integrate data from models, traditional surveys and big data in a situation of limited information. The goal is to increase the capacity of transport planners to analyze, forecast, and plan passenger mobility. (Big) data are a precious source of information and substantial effort is necessary to filter, integrate, and convert big data into travel demand estimates. Moreover, data analytics approaches without demand models are limited because they allow: (a) the analysis of historical and/or real-time transport system configurations, and (b) the forecasting of transport system configurations in ordinary conditions. Without the support of travel demand models, the mere use of (big) data does not allow the forecasting of mobility patterns. The paper attempts to support traditional methods of transport systems engineering with new data sources from ICTs. By combining traditional data and floating car data (FCD), the proposed framework allows the estimation of travel demand models (e.g., trip generation and destination). The proposed method can be applied in a specific case of an area where FCD are available, and other sources of information are not available. The results of an application of the proposed framework in a sub-regional area (Calabria, southern Italy) are presented.

scholarly journals Big Data Architecture for Intelligent Transport Systems

2019 ◽  
Vol 8 (9) ◽  
pp. 1281-1286
Keyword(s):  
Big Data ◽  
Transport System ◽  
Traffic Management ◽  
Structured Data ◽  
Transport Systems ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Related Data ◽  
Data Architecture ◽  
Smart Traffic

Day by day as the volume of data is being generated massively, storing of data and processing of data becomes a ever growing challenge in intelligent transport system (ITS). In intelligent transport system there are different areas to concentrate like smart parking systems, dynamic toll charging, smart traffic management etc. This paper is mainly focused on big data architecture for intelligent transport system for dynamic toll charging, traffic management and traffic analysis related data collection from various sources. The data collected from various sources can be in the form of structured data, semi structured data and unstructured data. Because of verity of data collected, this paper gives an idea about which data model is appropriate depending on data collected for transportation system.

A Big Data Application in Intelligent Transport Systems

2015 ◽  
Vol 734 ◽  
pp. 365-368 ◽  
Author(s):  
Hui Jun Yu ◽  
Zhi Gang Wang ◽  
Xiao Yan Liu ◽  
Dong Hu
Keyword(s):  
Big Data ◽  
Traffic Management ◽  
Business Performance ◽  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Data Application ◽  
Future Extension ◽  
Large Complex System ◽  
Big Data Application

Intelligent Transport Systems (ITS) theory is developed nearly a dozen years which is focused on building integrated transportation system, its success will inevitably have a fundamental change in the way the current traffic works. ITS is a large complex system, with integrated multidisciplinary knowledge to implement. The paper explores a new way of integrating big data insights with automated and assisted processes related to ITS. We show how innovation from China Si Chuan Province Traffic Management Bureau under our big data implementation to improve their business performance. With the new big data algorithm, we could predict drivers' behavior, and ultimately understand which factors are influencing the most. The architecture and implementation is thoroughly introduced in the paper, and we point out the future extension in the end.

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