scholarly journals Data-Driven Urban Mobility Modeling and Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-2
Author(s):  
Xiaolei Ma ◽  
Guohui Zhang ◽  
Xiaoyue Liu
Keyword(s):  
Data Driven ◽  
Urban Mobility ◽  
Mobility Modeling ◽  
Modeling And Analysis

scholarly journals Mobile Sensing for Data-Driven Mobility Modeling

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Kashif Zia ◽  
Arshad Muhammad ◽  
Katayoun Farrahi ◽  
Dinesh Kumar
Keyword(s):  
Mobile Sensing ◽  
Data Driven ◽  
Mobility Modeling

Advances on Urban Mobility Using Innovative Data-Driven Models

2020 ◽  
pp. 1-38
Author(s):  
Marcelo O. Rosa ◽  
Keiko V. O. Fonseca ◽  
Nádia P. Kozievitch ◽  
Anderson A. De-Bona ◽  
Jeferson L. Curzel ◽  
...  
Keyword(s):  
Data Driven ◽  
Urban Mobility

Urban mobility modeling to reduce traffic congestion in Surabaya: a system dynamics framework

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Erma Suryani ◽  
Rully Agus Hendrawan ◽  
Philip Faster Eka Adipraja ◽  
Arif Wibisono ◽  
Lily Puspa Dewi
Keyword(s):  
System Dynamics ◽  
Public Transport ◽  
Traffic Congestion ◽  
Supply And Demand ◽  
Urban Mobility ◽  
Rapid Transit ◽  
Content Type ◽  
Mobility Modeling ◽  
Delay Performance ◽  
Mobility Performance

Purpose This paper aims to address the urban mobility and traffic congestion problem under environmental dynamics to improve mobility and reduce traffic congestion using system dynamics (SD) simulation and scenarios. Design/methodology/approach SD simulation was used to analyze urban mobility and traffic congestion. Data were collected from the Transportation Department of Surabaya City. Several scenarios to improve urban mobility and reduce traffic congestion were developed by modifying the structures and parameters of the model. Findings Several factors influence urban mobility, including modal split, trip frequency, delay performance and the ratio of public transport supply and demand. Urban mobility, daily traffic and road capacity are some factors that affect traffic congestion. Scenarios can be designed based on the assumptions of the proposed strategy. Research limitations/implications The study was conducted at Surabaya City, East Java, Indonesia, which is the fourth most-congested city in the world. Practical implications By implementing several strategies (mass rapid transit and bus rapid transit development and public transport delay reduction), mobility performance is projected to be improved by 70.34-92.96%. With this increased mobility, traffic congestion is projected to decline by 52.5-65.8%. Originality/value The novel contributions of this research are: formulating relationships between several variables; modeling dynamic behavior of urban mobility and traffic congestion; and building scenario models to improve mobility and reduce traffic congestion in Surabaya. With the increase in urban mobility and the decrease in average daily traffic, traffic congestion could be reduced by a minimum of 57.6% and a maximum of 69%.

Mobility Modeling and Data-Driven Closed-Loop Prediction in Bike-Sharing Systems

2019 ◽  
Vol 20 (12) ◽  
pp. 4488-4499 ◽  
Author(s):  
Zidong Yang ◽  
Jiming Chen ◽  
Ji Hu ◽  
Yuanchao Shu ◽  
Peng Cheng
Keyword(s):  
Closed Loop ◽  
Data Driven ◽  
Mobility Modeling ◽  
Loop Prediction ◽  
Bike Sharing
Sign in / Sign up

Export Citation Format

Share Document