Cooperative parking search: Reducing travel time by information exchange among searching vehicles

Rybarsch, Matthias; Aschermann, Malte; Bock, Fabian; Goralzik, Anne; Köster, Felix; Ringhand, Madlen; Trifunović, Aleksandar

doi:10.1109/itsc.2017.8317697

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…The study in [15] investigated cooperative vehicle parking system via V2V wireless communication to allow vehicles to find and select parking spaces. While the author of [16] investigated the effectiveness of vehicle collaboration during the search for parking spaces in reducing search time and travel time using V2V connections. Li et.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Cooperative Health Emergency Response System in Autonomous Vehicles

Elayan¹,

Aloqaily

Guizani

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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Recent technological advances have reshaped many aspects of our lives, especially modern transportation systems. For instance, AI and B5G Networks have raised the level of automation as autonomous vehicles (AV) become decisionindependent and self-aware. However, in-vehicle health monitoring is still an open issue. Therefore, a cooperative healthcare emergency response framework has been proposed that employs in-vehicle intelligent health monitoring and local networks for AV to minimize the time to receive emergency treatment for passengers with abnormal health conditions. The extensive simulation results show that the framework minimizes the First Emergency Treatment Time by at least 75%, and eliminates hospital waiting time, the Total Time for Emergency Treatment is minimized by at least 93%. Finally, it reduces Travel Time by nearly 50%. All results compared to the autopilot approach.

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Section: Related Workmentioning

confidence: 99%

Intelligent Cooperative Health Emergency Response System in Autonomous Vehicles

Elayan¹,

Aloqaily

Guizani

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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“…Our work is positioned within this scope to investigate decision-making approaches for parking-selection that includes the routing process. The targeted issues varied between cruising time [30,31,32], parking-search space [33,34] and the incurred search complexity [35,36,37]. We propose a stochastic process centred on parking-availability expectations within an actual urban area and using parking-dynamics data aggregated from IoTenabled parking-gateway platforms.…”

Section: Background and Related Workmentioning

confidence: 99%

Internet of Things data analytics for parking availability prediction and guidance

Atif

Kharrazi

Ding

et al. 2020

Trans Emerging Tel Tech

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Cutting-edge sensors and devices are increasingly deployed within urban areas to make-up the fabric of TCP/IP connectivity driven by Internet of Things (IoT). This immersion into physical urban environments creates new data-streams which could be exploited to deliver novel cloud-based services. Connected-vehicles and road-infrastructure data are leveraged in this paper to build applications that alleviate notorious parking and induced traffic-congestion issues. To optimize the utility of parking-lots, our proposed SmartPark algorithm employs a discrete Markov-chain model to demystify the future state of a parking-lot, by the time a vehicle is expected to reach it. The algorithm features three modular sections. First a search process is triggered to identify the expected arrival-time periods to all parking lots in the targeted Central Business District or CBD area. This process utilizes smart-pole data-streams reporting congestion rates across parking-area junctions. Then, a predictive analytics phase uses consolidated historical-data about past parking-dynamics, to infer a statetransition matrix showing the transformation of available spots in a parking-lot over short periods of time. Finally, this matrix is projected against similar future seasonal-periods to figure out the actual vacancy-expectation of a lot. The performance evaluation over an actual busy CBD area in Stockholm (Sweden) shows increased scalability capabilities, when further parking-resources are made available, compared to a baseline case algorithm. Using standard urban-mobility simulation packages, the traffic-congestion aware SmartPark is also shown to minimize the journey duration to the selected parking-lot while maximizing the chances to find an available spot at the selected lot.

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“…With the increasing usage of mobile smart devices, the joint collection of information by volunteers-either opportunistic or participatory-known as crowd-sourcing has an increasing relevance and potential (Heipke 2010). Examples of crowd-sourced information are the measurement of atmospheric data (Muller et al 2015), the measurement of rainfall (Fitzner and Sester 2016), or the availability of parking spaces in an area (Urra and Ilarri 2019;Rybarsch et al 2017) as well as the detection of road traffic congestion (Dimri et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Traffic Regulator Detection Using GPS Trajectories

Golze

Zourlidou

Sester

2020

KN J. Cartogr. Geogr. Inf.

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This paper explores the idea of enriching maps with features predicted from GPS trajectories. More specifically, it proposes a method of classifying street intersections according to traffic regulators (traffic light, yield/priority-sign and right-of-way rule). Intersections are regulated locations and the observable movement of vehicles is affected by the underlying traffic rules. Movement patterns such as stop events or start-and-stop sequences are commonly observed at those locations due to traffic regulations. In this work, we test the idea of detecting traffic regulators by learning them in a supervised way from features derived from GPS trajectories. We explore and assess different settings of the feature vector being used to train a classifier that categorizes the intersections based on traffic regulators; also, we test several experimental setups. The results show that a Random Forest classifier with oversampling and Bagging booster enabled can predict the intersection regulators with 90.4% accuracy. We discuss future research directions and recommend next steps for improving the results of this research.

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Cooperative parking search: Reducing travel time by information exchange among searching vehicles

Cited by 10 publications

References 13 publications

Intelligent Cooperative Health Emergency Response System in Autonomous Vehicles

Intelligent Cooperative Health Emergency Response System in Autonomous Vehicles

Internet of Things data analytics for parking availability prediction and guidance

Traffic Regulator Detection Using GPS Trajectories

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