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1. A Smartphone-Based Parking Guidance System with Predictive Parking Availability Information

   Xiao, J. ; Lou, Y. ( January 2018 , 2018 Transportation Research Annual Meeting)

   Searching for parking has been a problem faced by many drivers, especially in urban areas. With an increasing public demand for parking information and services, as well as the proliferation of advanced smartphones, a range of smartphone-based parking management services began to emerge. Funded by the National Science Foundation, our research aims to explore the potential of smartphone-based parking management services as a solution to parking problems, to deepen our understandings of travelers’ parking behaviors, and to further advance the analytical foundations and methodologies for modeling and assessing parking solutions. This paper summarizes progress and results from our research projects on smartphone-based parking management, including parking availability information prediction, parking searching strategy, the development of a mobile parking application, and our next steps to learn and discover new knowledge from its deployment. To predict future parking occupancy, we proposed a practical framework that integrates machine-learning techniques with a model-based core approach that explicitly models the stochastic parking process. The framework is able to predict future parking occupancy from historical occupancy data alone, and can handle complex arrival and departure patterns in real-world case studies, including special event. With the predicted probabilistic availability information, a cost-minimizing parking searching strategy is developed. The parking searching problem for an individual user is a stochastic Markov decision process and is formalized as a dynamic programming problem. The cost-minimizing parking searching strategy is solved by value iteration. Our simulated experiments showed that cost-minimizing strategy has the lowest expected cost but tends to direct a user to visit more parking facilities compared with two greedy strategies. Currently, we are working on implementing the predictive framework and the searching algorithm in a mobile phone application. We are working closely with Arizona State University (ASU) Parking and Transit Services to implement a three-stage pilot deployment of the prototype application around the ASU main campus. In the first stage, our application will provide real-time information and we will incorporate availability prediction and searching guidance in the second and third stages. Once the mobile application is deployed, it will provide unique opportunities to collect data on parking search behaviors, discover emerging scenarios of smartphone-based parking management services, and assess the impacts of such systems. 

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2. Driver Behavior-aware Parking Availability Crowdsensing System Using Truth Discovery

   https://doi.org/10.1145/3460200  

   Zhu, Yi ; Gupta, Abhishek ; Hu, Shaohan ; Zhong, Weida ; Su, Lu ; Qiao, Chunming ( July 2021 , ACM Transactions on Sensor Networks)

   null (Ed.)

   Spot-level parking availability information (the availability of each spot in a parking lot) is in great demand, as it can help reduce time and energy waste while searching for a parking spot. In this article, we propose a crowdsensing system called SpotE that can provide spot-level availability in a parking lot using drivers’ smartphone sensors. SpotE only requires the sensor data from drivers’ smartphones, which avoids the high cost of installing additional sensors and enables large-scale outdoor deployment. We propose a new model that can use the parking search trajectory and final destination (e.g., an exit of the parking lot) of a single driver in a parking lot to generate the probability profile that contains the probability of each spot being occupied in a parking lot. To deal with conflicting estimation results generated from different drivers, due to the variance in different drivers’ parking behaviors, a novel aggregation approach SpotE-TD is proposed. The proposed aggregation method is based on truth discovery techniques and can handle the variety in Quality of Information of different vehicles. We evaluate our proposed method through a real-life deployment study. Results show that SpotE-TD can efficiently provide spot-level parking availability information with a 20% higher accuracy than the state-of-the-art. 

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3. A reinforcement learning approach for user-optimal parking searching strategy on a network exploiting network topology

   Xiao, J ; Lou, Y ( January 2019 , 2019 Transportation Annual Conference)

   This paper investigates the idea of introducing learning algorithms into parking guidance and information systems that employ a central server, in order to provide estimated optimal parking searching strategies to travelers. The parking searching process on a network with uncertain parking availability can naturally be modeled as a Markov Decision Process (MDP). Such an MDP with full information can easily be solved by dynamic programming approaches. However, the probabilities of finding parking are difficult to define and calculate, even with accurate occupancy data. Learning algorithms are suitable for addressing this issue. The central server collects data from numerous travelers’ parking search experiences in the same area within a time window, computes approximated optimal parking searching strategy using a learning algorithm, and distributes the strategy to travelers. We propose an algorithm based on Q-learning, where the topology of the underlying transportation network is incorporated. This modification allows us to reduce the size of the problem dramatically, and thus the amount of data required to learn the optimal strategy. Numerical experiments conducted on a toy network show that the proposed learning algorithm outperforms the nearest-node greedy search strategy and the original Q-learning algorithm. Sensitivity analysis regarding the desired amount of training data is also performed. 

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4. An Edge Based Smart Parking Solution Using Camera Networks and Deep Learning

   https://doi.org/10.1109/ICCC.2018.00010  

   Bura, Harshitha ; Lin, Nathan ; Kumar, Naveen ; Malekar, Sangram ; Nagaraj, Sushma ; Liu, Kaikai ( July 2018 , 2018 IEEE International Conference on Cognitive Computing (ICCC))

   The smart parking industry continues to evolve as an increasing number of cities struggle with traffic congestion and inadequate parking availability. For urban dwellers, few things are more irritating than anxiously searching for a parking space. Research results show that as much as 30% of traffic is caused by drivers driving around looking for parking spaces in congested city areas. There has been considerable activity among researchers to develop smart technologies that can help drivers find a parking spot with greater ease, not only reducing traffic congestion but also the subsequent air pollution. Many existing solutions deploy sensors in every parking spot to address the automatic parking spot detection problems. However, the device and deployment costs are very high, especially for some large and old parking structures. A wide variety of other technological innovations are beginning to enable more adaptable systems-including license plate number detection, smart parking meter, and vision-based parking spot detection. In this paper, we propose to design a more adaptable and affordable smart parking system via distributed cameras, edge computing, data analytics, and advanced deep learning algorithms. Specifically, we deploy cameras with zoom-lens and motorized head to capture license plate numbers by tracking the vehicles when they enter or leave the parking lot; cameras with wide angle fish-eye lens will monitor the large parking lot via our custom designed deep neural network. We further optimize the algorithm and enable the real-time deep learning inference in an edge device. Through the intelligent algorithm, we can significantly reduce the cost of existing systems, while achieving a more adaptable solution. For example, our system can automatically detect when a car enters the parking space, the location of the parking spot, and precisely charge the parking fee and associate this with the license plate number. 

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5. Optimizing Curbside Parking Resources Subject to Congestion Constraints

   Dowling, Chase ; Fiez, Tanner ; Ratliff, Lillian ; Zhang, Baosen ( December 2017 , IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes)

   To gain theoretical insight into the relationship between parking scarcity and congestion, we describe block-faces of curbside parking as a network of queues. Due to the nature of this network, canonical queueing network results are not available to us. We present a new kind of queueing network subject to customer rejection due to the lack of available servers. We provide conditions for such networks to be stable, a computationally tractable "single node" view of such a network, and show that maximizing the occupancy through price control of such queues, and subject to constraints on the allowable congestion between queues searching for an available server, is a convex optimization problem. We demonstrate an application of this method in the Mission District of San Francisco; our results suggest congestion due to drivers searching for parking stems from an inefficient spatial utilization of parking resources. 

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