Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

Ruiz-Rosero, Juan; Ramírez-González, Gustavo; Khanna, Rahul

doi:10.3390/electronics8121501

Cited by 5 publications

(3 citation statements)

References 79 publications

(88 reference statements)

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“…Big data can gather, store, and process large amounts of heterogeneous, large-scale data to assist regulators, cities, transport operators, and travelers to improve the efficiency, regulation enforcement, and sustainability of their mobility solutions. So far route planning (e.g., Masivo model [48]) and public transport timetable optimization [49] are based on simulation models which can greatly benefit from the incorporation of big-data analysis into their models. Additional big-data applications are personalized route planning and smart taxation (based in the polluters-pay principle) such as dynamic tolling depending on the specific CO 2 footprint of cars and their usage (kilometers) in city centers, where air quality has one of the highest impacts on people's health.…”

Section: Big Data and Sustainable Urtmentioning

confidence: 99%

Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit

Taboada

Han

2020

Electronics

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This paper deals with the efficiency and sustainability of urban rail transit (URT) using exploratory data analytics (EDA) and data envelopment analysis (DEA). The first stage of the proposed methodology is EDA with already available indicators (e.g., the number of stations and passengers), and suggested indicators (e.g., weekly frequencies, link occupancy rates, and CO2 footprint per journey) to directly characterize the efficiency and sustainability of this transport mode. The second stage is to assess the efficiency of URT with two original models, based on a thorough selection of input and output variables, which is one of the key contributions of EDA to this methodology. The first model compares URT against other urban transport modes, applicable to route personalization, and the second scores the efficiency of URT lines. The main outcome of this paper is the proposed methodology, which has been experimentally validated using open data from the Transport for London (TfL) URT network and additional sources.

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Section: Big Data and Sustainable Urtmentioning

confidence: 99%

Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit

Taboada

Han

2020

Electronics

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“…The following macroscopic modeling programs are most widely used: CUBE Voyager, TransCAD, VISUM, Strada, Metacor, Emme, Transyt, KRONOS, etc. (Hourdos & Michalopoulos, 2008;Ruiz-Rosero et al, 2019;Fierek & Zak, 2012;Ullah et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Principles of creating a public transport network model for regions

RANCEVA,

UŠPALYTĖ-VITKŪNIENĖ

2023

The 12th International Conference ENVIRONMENTAL ENGINEERING 12th ICEE SELECTED PAPERS

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This article analyzes one of the steps of the transportation system model - network modeling. The network model consists of: zoning of the considered region, selection of network connections and routes of public transport lines, choosing the demand time series of the model. The model created for the region has its own specificities, such as uneven population density, big differentiation of public transport demand in terms of place and time. Therefore, the development of a public transport model for a region is very different from a public transport model for a city. The purpose of this article is to determine the specifics of creating a regional network model. To achieve this goal, the article analyses the best features and principles of the components of the such kind network model.

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“…The following macroscopic modeling programs are most widely used: CUBE Voyager, TransCAD, VISUM, Strada, Metacor, Emme, Transyt, KRONOS, etc. [10,12].…”

Section: Introductionmentioning

confidence: 99%

Specifics of Creating a Public Transport Demand Model for Low-Density Regions: Lithuanian Case

Ranceva,

Ušpalytė-Vitkūnienė

2024

Sustainability

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A transport model usually consists of a demand model and an available transport network model. The purpose of this article is to identify the key specifics for the development of a regional public transport (PT) demand model and to point out the differences from the urban PT demand model. The traditional four-step transport planning demand model consists of trip generation, trip distribution, modal split, and assignment. This article consists of PT model development, calibration, and validation. A PTV VISUM macroscopic modeling program is used for this research. As a result, this article presents basic suggestions for how a PT demand model should be developed in regions. The presented suggestions for developing a PT demand model can be applied to any low-density region. The rest of the article is structured as follows: (1) Background: presents a literature analysis of the four-step model, modal splits, and the features of the PTV VISUM program; (2) Methods: describes the considered region of Lithuania and the data of the developed model; describes the four-step model, which is adapted to the Lithuanian region; (3) Results: presents the results and main suggestions for creating a PT demand model; and (4) Conclusions: presents the main conclusions of the study.

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Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

Cited by 5 publications

References 79 publications

Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit

Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit

Principles of creating a public transport network model for regions

Specifics of Creating a Public Transport Demand Model for Low-Density Regions: Lithuanian Case

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