scholarly journals Longitudinal Performance Assessment of Traffic Signal System Impacted by Long-Term Interstate Construction Diversion Using Connected Vehicle Data

2021 ◽  
Vol 11 (04) ◽  
pp. 644-659
Author(s):  
Enrique D. Saldivar-Carranza ◽  
Margaret Hunter ◽  
Howell Li ◽  
Jijo Mathew ◽  
Darcy M. Bullock
Author(s):  
Marija Ostojic ◽  
Archak Mittal ◽  
Hani S. Mahmassani

Connected environments offer more information, improved data availability and quality which can lead to better decision making; new meaningful information adds new functionalities and opportunities to advance operational efficiency. Can traffic signal system efficiency and mobility be measured and enhanced in innovative and meaningful ways by combining two data sources - connected vehicle-generated and controller event logs? This paper develops a comprehensive signal systems performance assessment framework that aims to offer better understanding of current traffic signal practices and standards and add new functionalities and opportunities to enhance signal systems operations. Its core is a novel performance metric that provides a holistic representation of the system which traditional metrics do not offer. To develop and demonstrate the concept, the study used simulation data in a format that corresponds to high resolution data (signal status and vehicle positions) on a tenth of a second level. Vehicle trajectory information is processed, fused with control data, synthesized to produce "information" required to develop a signalized approach performance estimation method. The data analysis platform presented in this study is intended to comprehensively characterize the state of the signalized system and help identify causes of inferior intersection performance by defining a set of visual and quantitative success indicators. The practicality of this method is reflected in reducing the time and effort required by the existing signal design/retiming practice, since trajectory-signal signatures distinguish between incidents and retiming opportunities caused by changing traffic conditions.


Author(s):  
S.U. Dampage ◽  
T.D. Munasingha ◽  
W.D.K Gunathilake ◽  
A.G. Weerasundara ◽  
D.P.D Udugahapattuwa
Keyword(s):  

2019 ◽  
Vol 145 (5) ◽  
pp. 04019034 ◽  
Author(s):  
G. Granello ◽  
C. Leyder ◽  
A. Frangi ◽  
A. Palermo ◽  
E. Chatzi

2018 ◽  
Vol 20 (6) ◽  
pp. 513-527
Author(s):  
Alexander M. Soley ◽  
Joshua E. Siegel ◽  
Dajiang Suo ◽  
Sanjay E. Sarma

Purpose The purpose of this paper is to develop a model to estimate the value of information generated by and stored within vehicles to help people, businesses and researchers. Design/methodology/approach The authors provide a taxonomy for data within connected vehicles, as well as for actors that value such data. The authors create a monetary value model for different data generation scenarios from the perspective of multiple actors. Findings Actors value data differently depending on whether the information is kept within the vehicle or on peripheral devices. The model shows the US connected vehicle data market is worth between US$11.6bn and US$92.6bn. Research limitations/implications This model estimates the value of vehicle data, but a lack of academic references for individual inputs makes finding reliable inputs difficult. The model performance is limited by the accuracy of the authors’ assumptions. Practical implications The proposed model demonstrates that connected vehicle data has higher value than people and companies are aware of, and therefore we must secure these data and establish comprehensive rules pertaining to data ownership and stewardship. Social implications Estimating the value of data of vehicle data will help companies understand the importance of responsible data stewardship, as well as drive individuals to become more responsible digital citizens. Originality/value This is the first paper to propose a model for computing the monetary value of connected vehicle data, as well as the first paper to provide an estimate of this value.


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