Data Sources for Traffic Analysis in Urban Canyons—The Comprehensive Literature Review
Source abstract
Abstract
We propose a comprehensive literature review based on big data and V2X research to find promising tools to detect vehicles for traffic research and provide safe autonomous vehicle (AV) traffic. Presented data sources can provide real-time data for V2X systems and offline databases from VATnets for micro- and macro-modeling in traffic research. The authors want to present a set of sources that are not based on GNSS and other systems that could be interrupted by high-rise buildings and dense smart city infrastructure, as well as review of big data sources in traffic modeling that can be useful in future traffic research. Both reviews findings are summarized in tables at the end of the review sections of the paper. The authors added propositions in the form of two hypotheses on how traffic models can obtain data in the urban canyon connected environment scenario. The first hypothesis uses Roadside Units (RSUs) to retrieve data in similar ways to cellular data in traffic research and proves that this source is data rich. The second one acknowledges Bluetooth/Wi-Fi scanners’ research potential in V2X environments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Human Mobility and Location-Based Analysis · Automated Road and Building Extraction · Video Surveillance and Tracking Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Civil Engineering, Wrocław University of Science and Technology (Politechnika Wrocławska), 50-370 Wrocław, Poland
References (100+)
Show source-provided references
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