A Big Data Demand Estimation Framework for Multimodal Modelling of Urban Congested Networks

2018 ◽  
pp. 139-146
Author(s):  
Guido Cantelmo ◽  
Francesco Viti
Keyword(s):  
Big Data ◽  
Demand Estimation ◽  
Congested Networks

scholarly journals A Big Data Demand Estimation Model for Urban Congested Networks

2020 ◽  
Vol 21 (4) ◽  
pp. 245-254
Author(s):  
Guido Cantelmo ◽  
Francesco Viti
Keyword(s):  
Big Data ◽  
Classical Problem ◽  
Demand Estimation ◽  
Simulation Software ◽  
Added Value ◽  
Sensor Data ◽  
Mobile Phone Data ◽  
Second Step ◽  
Floating Car Data ◽  
Congested Networks

AbstractThe origin-destination (OD) demand estimation problem is a classical problem in transport planning and management. Traditionally, this problem has been solved using traffic counts, speeds or travel times extracted from location-based sensor data. With the advent of new sensing technologies located on vehicles (GPS) and nomadic devices (mobile and smartphones), new opportunities have emerged to improve the estimation accuracy and reliability, and more importantly to better capture the dynamics of the daily mobility patterns. In this paper we frame this new data in a comprehensive framework which estimates origin-destination flows in two steps: the first step estimates the total generated demand for each traffic zone, while the second step adjusts the spatial and temporal distribution on the different OD pairs. We show how mobile data can be used to obtain OD matrices that reflect the aggregated movements of individuals in complex and large-scale instances, while speed information from floating car data can be used in the second step. We showcase the added value of big data on a realistic network comprising Luxembourg’s capital city and its surrounding. We simulate traffic by means of a commercial simulation software, PTV-Visum, and leverage real mobile phone data from the largest telco operator in the country and real speed data from a floating car data service provider. Results show how OD estimation improves both in solution reliability and in convergence speed.

scholarly journals A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yunlin Guan ◽  
Yun Wang ◽  
Xuedong Yan ◽  
Haonan Guo ◽  
Yu Zhou
Keyword(s):  
Big Data ◽  
Clustering Algorithms ◽  
Demand Estimation ◽  
Accurate Estimate ◽  
Data Driven ◽  
Support Vector ◽  
Regression Problem ◽  
Vector Machines ◽  
Zone Division ◽  
Parking Demand

Parking planning is a key issue in the process of urban transportation planning. To formulate a high-quality planning scheme, an accurate estimate of the parking demand is critical. Most previous published studies were based primarily on parking survey data, which is both costly and inaccurate. Owing to limited data sources and simplified models, most of the previous research estimates the parking demand without consideration for the relationship between parking demand, land use, and traffic attributes, thereby causing a lack of accuracy. Thus, this study proposes a big-data-driven framework for parking demand estimation. The framework contains two steps. The first step is the parking zone division method, which is based on the statistical information grid and multidensity clustering algorithms. The second step is parking demand estimation, which is extracted by support vector machines posed in the form of a machine learning regression problem. The framework is evaluated using a case in the city center in Cangzhou, China.

scholarly journals A Methodology Based on Computational Patterns for Offloading of Big Data Applications on Cloud-Edge Platforms

2020 ◽  
Vol 12 (2) ◽  
pp. 28
Author(s):  
Beniamino Di Martino ◽  
Salvatore Venticinque ◽  
Antonio Esposito ◽  
Salvatore D’Angelo
Keyword(s):  
Big Data ◽  
Human Life ◽  
Fog Computing ◽  
Smart Devices ◽  
Additional Stress ◽  
European Project ◽  
Local Resources ◽  
Big Data Applications ◽  
Base Concept ◽  
Congested Networks

Internet of Things (IoT) is becoming a widespread reality, as interconnected smart devices and sensors have overtaken the IT market and invaded every aspect of the human life. This kind of development, while already foreseen by IT experts, implies additional stress to already congested networks, and may require further investments in computational power when considering centralized and Cloud based solutions. That is why a common trend is to rely on local resources, provided by smart devices themselves or by aggregators, to deal with part of the required computations: this is the base concept behind Fog Computing, which is becoming increasingly adopted as a distributed calculation solution. In this paper a methodology, initially developed within the TOREADOR European project for the distribution of Big Data computations over Cloud platforms, will be described and applied to an algorithm for the prediction of energy consumption on the basis of data coming from home sensors, already employed within the CoSSMic European Project. The objective is to demonstrate that, by applying such a methodology, it is possible to improve the calculation performances and reduce communication with centralized resources.

The path most traveled: Travel demand estimation using big data resources

2015 ◽  
Vol 58 ◽  
pp. 162-177 ◽  
Author(s):  
Jameson L. Toole ◽  
Serdar Colak ◽  
Bradley Sturt ◽  
Lauren P. Alexander ◽  
Alexandre Evsukoff ◽  
...  
Keyword(s):  
Big Data ◽  
Travel Demand ◽  
Demand Estimation

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

“Big Data in Psychology: Methods and Applications”

2017 ◽  
Vol 225 (3) ◽  
pp. 287-288
Keyword(s):  
Big Data

An associated conference will take place at ZPID – Leibniz Institute for Psychology Information in Trier, Germany, on June 7–9, 2018. For further details, see: http://bigdata2018.leibniz-psychology.org

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