Connected vehicle simulation framework for parking occupancy prediction (demo paper)
Authors: 
Pierpaolo Resce, Lukas Vorwerk, Zhiwei Han, Giuliano Cornacchia, Omid Isfahani Alamdari, 
Mirco Nanni, Luca Pappalardo, Daniel Weimer, Yuanting LiuAuthors Info & Claims
Pierpaolo Resce, Lukas Vorwerk, Zhiwei Han, Giuliano Cornacchia, Omid Isfahani Alamdari, Mirco Nanni, Luca Pappalardo, Daniel Weimer, Yuanting LiuAuthors Info & ClaimsArticle No.: 60, Pages 1 - 4
Abstract
This paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.
References
[1]
Michael Balmer, Kay W Axhausen, and Kai Nagel. 2006. Agent-based demand-modeling framework for large-scale microsimulations. Transportation Research Record 1985, 1 (2006), 125--134.
[2]
Fabian Bock, Sergio Di Martino, and Antonio Origlia. 2019. Smart parking: Using a crowd of taxis to sense on-street parking space availability. IEEE Transactions on Intelligent Transportation Systems 21, 2 (2019), 496--508.
[3]
Xiao Chen. 2014. Parking occupancy prediction and pattern analysis. Dept. Comput. Sci., Stanford Univ., Stanford, CA, USA, Tech. Rep. CS229-2014 (2014).
[4]
Lara Codecá, Jakob Erdmann, and Jérôme Härri. 2018. A sumo-based parking management framework for large-scale smart cities simulations. In 2018 IEEE Vehicular Networking Conference (VNC). IEEE, 1--8.
[5]
Shuhui Gong, Xiaopeng Mo, Rui Cao, Yu Liu, Wei Tu, and Ruibin Bai. 2021. Spatio-temporal Parking Behaviour Forecasting and Analysis Before and During COVID-19. arXiv preprint arXiv:2108.07731 (2021).
[6]
Heejae Kim, Jiyong Han, Seong-Hwan Kim, Jisoo Choi, Dongsik Yoon, Minsu Jeon, Eunjoo Yang, Nhat Pham, Sungpil Woo, Jeongkyu Park, et al. 2017. IsV2C: An integrated road traffic-network-cloud simulator for V2C connected car services. In 2017 IEEE International Conference on Services Computing (SCC). IEEE, 434--441.
[7]
Ludovic Leclercq, Alméria Sénécat, and Guilhem Mariotte. 2017. Dynamic macroscopic simulation of on-street parking search: A trip-based approach. Transportation Research Part B: Methodological 101 (2017), 268--282.
[8]
Matan Levi, Yair Allouche, and Aryeh Kontorovich. 2018. Advanced analytics for connected car cybersecurity. In 2018 IEEE 87th Vehicular Technology Conference (VTC Spring). IEEE, 1--7.
[9]
Pablo Alvarez Lopez, Michael Behrisch, Laura Bieker-Walz, Jakob Erdmann, Yun-Pang Flötteröd, Robert Hilbrich, Leonhard Lücken, Johannes Rummel, Peter Wagner, and Evamarie Wießner. 2018. Microscopic Traffic Simulation using SUMO, In The 21st IEEE International Conference on Intelligent Transportation Systems. IEEE Intelligent Transportation Systems Conference (ITSC). https://elib.dlr.de/124092/
[10]
Antonio Origlia, Sergio Di Martino, and Yuri Attanasio. 2019. On-Line Filtering of On-Street Parking Data to Improve Availability Predictions. In 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE, 1--7.
[11]
Michal Piorkowski, Natasa Sarafijanovic-Djukic, and Matthias Grossglauser. 2009. CRAWDAD dataset epfl/mobility (v. 2009-02-24). Downloaded from https://crawdad.org/epfl/mobility/20090224.
[12]
Thi Thuy An Vo, Peter van der Waerden, and Geert Wets. 2016. Micro-simulation of car drivers' movements at parking lots. Procedia engineering 142 (2016), 100--107.
[13]
Shuguan Yang, Wei Ma, Xidong Pi, and Sean Qian. 2019. A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources. Transportation Research Part C: Emerging Technologies 107 (2019), 248--265.
Index Terms
- Connected vehicle simulation framework for parking occupancy prediction (demo paper)
Recommendations
A simulation study on prioritizing connected freight vehicles at intersections for traffic flow optimization (industrial paper)
SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information SystemsDue to the importance of road freight, there is a significant cost of delaying freight vehicles on the road. In this work, we focus on freight vehicle optimization by reducing delays at intersections. Our simulation study evaluates the effectiveness of ...
A framework for simulation of surrounding vehicles in driving simulators
This article describes a framework for generation and simulation of surrounding vehicles in a driving simulator. The proposed framework generates a traffic stream, corresponding to a given target flow and simulates realistic interactions between ...
An efficient smart parking pricing system for smart city environment: A machine-learning based approach
AbstractNow-a-days, with the ever increasing number of vehicles, getting parking space at right place and on time has become an inevitable necessity for all across the globe. In this context, finding an unoccupied parking slot by the ...
Highlights- An Optimized Parking Pricing Scheme.
- A Machine learning model for smart ...
Comments
Information & Contributors
Information
Published In
November 2022
806 pages
Copyright © 2022 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 November 2022
Check for updates
Author Tags
Qualifiers
- Demonstration
Funding Sources
Conference
SIGSPATIAL '22
Sponsor:
SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems
November 1 - 4, 2022
Washington, Seattle
Acceptance Rates
Overall Acceptance Rate 257 of 1,238 submissions, 21%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 71Total Downloads
- Downloads (Last 12 months)11
- Downloads (Last 6 weeks)0
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in