A Data-Driven Traffic-Responsive Signal Control for a Smart City Road Network Under Uncertainty

Data-driven intelligent transportation systems (D2ITSs) have drawn significant attention lately. This work investigates a novel multi-agent-based data-driven distributed adaptive cooperative control (MA-DD-DACC) method for multi-direction queuing strength balance with changeable cycle in urban traffic signal timing. Compared with the conventional signal control strategies, the proposed MA-DD-DACC method combined with an online parameter learning law can be applied for traffic signal control in a distributed manner by merely utilizing the collected I/O traffic queueing length data and network topology of multi-direction signal controllers at a single intersection. A Lyapunov-based stability analysis shows that the proposed approach guarantees uniform ultimate boundedness of the distributed consensus coordinated errors of queuing strength. The numerical and experimental comparison simulations are performed on a VISSIM-VB-MATLAB joint simulation platform to verify the effectiveness of the proposed approach.

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Data-Driven Approach for Incident Management in a Smart City

Applied Sciences ◽

10.3390/app10228281 ◽

2020 ◽

Vol 10 (22) ◽

pp. 8281

Author(s):

Luís B. Elvas ◽

Carolina F. Marreiros ◽

João M. Dinis ◽

Maria C. Pereira ◽

Ana L. Martins ◽

...

Keyword(s):

Data Mining ◽

Smart City ◽

City Planning ◽

Smart Cities ◽

Data Driven ◽

Incident Management ◽

Cascading Effects ◽

Data Mining Approach ◽

Industry Standard ◽

Data Driven Approach

Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens’ quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better.

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Integration of Model-Based Signal Control and Queue Spillover Control for Urban Oversaturated Signalized Intersection

Mathematical Problems in Engineering ◽

10.1155/2019/3149275 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Haitao Xu ◽

Jing Chen ◽

Jie Xu

Keyword(s):

Predictive Control ◽

Traffic Congestion ◽

Road Network ◽

Signalized Intersection ◽

Signal Control ◽

Control Approach ◽

Model Based ◽

Real Time Traffic ◽

Overall Performance ◽

Improved Model

An improved model-based predictive control approach integrating model-based signal control and queue spillover control is proposed in this paper, which includes three modules: model-based signal control, queue spillover identification, and spillover control to deal with the problem of traffic congestion for urban oversaturated signalized intersection. The main steps are as follows. First of all, according to the real-time traffic flow data, the green time splits for all intersections will be solved online by the model-based signal control controller whose optimization model is based on model-predictive control (MPC) strategy. Second, the queue spillover identification module will be used to detect the potential queue spillover. If potential queue spillover is detected, the spillover control module will be activated to prevent vehicles from the upstream link of the link with possible spillover from entering the downstream link to avoid traffic congestion. The experiment is performed on a simulated road network. The results verify that the proposed scheme can significantly decrease the delay which reflects the overall performance of the studied intersection.

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Human-Centric Data Science for Urban Studies

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8120584 ◽

2019 ◽

Vol 8 (12) ◽

pp. 584 ◽

Cited By ~ 2

Author(s):

Bernd Resch ◽

Michael Szell

Keyword(s):

Urban Studies ◽

Smart City ◽

Data Science ◽

City Planning ◽

Data Driven ◽

Data Sets ◽

Use Of Technology ◽

New Perspective ◽

Research Initiatives ◽

Adequate Data

Due to the wide-spread use of disruptive digital technologies like mobile phones, cities have transitioned from data-scarce to data-rich environments. As a result, the field of geoinformatics is being reshaped and challenged to develop adequate data-driven methods. At the same time, the term "smart city" is increasingly being applied in urban planning, reflecting the aims of different stakeholders to create value out of the new data sets. However, many smart city research initiatives are promoting techno-positivistic approaches which do not account enough for the citizens’ needs. In this paper, we review the state of quantitative urban studies under this new perspective, and critically discuss the development of smart city programs. We conclude with a call for a new anti-disciplinary, human-centric urban data science, and a well-reflected use of technology and data collection in smart city planning. Finally, we introduce the papers of this special issue which focus on providing a more human-centric view on data-driven urban studies, spanning topics from cycling and wellbeing, to mobility and land use.

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