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1. 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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2. Optimal capacity sizing of park‐and‐ride lots with information aware commuters

   https://doi.org/10.1111/poms.14053  

   Wang, Xinchang; He, Qie (November 2023, Production and Operations Management)

   We study capacity sizing of park‐and‐ride lots that offer services to commuters sensitive to congestion and parking availability information. The goal is to determine parking lot capacities that maximize the total social welfare for commuters whose parking lot choices are predicted using the multinomial logit model. We formulate the problem as a nonconvex nonlinear program that involves a lower and an upper bound on each lot's capacity, and a fixed‐point constraint reflecting the effects of parking information and congestion on commuters' lot choices. We show that except for at most one lot, the optimal capacity of each lot takes one of three possible values. Based on analytical results, we develop a one‐variable search algorithm to solve the model. We learn from numerical results that the optimal capacity of a lot with a high intrinsic utility tends to be equal to the upper bound. By contrast, a lot with a low or moderate‐sized intrinsic utility tends to attain an optimal capacity on its effective lower bound. We evaluate the performance of the optimal solution under different choice scenarios of commuters who are shared with real‐time parking information. We learn that commuters are better off in an average choice scenario when both the effects of parking information and congestion are considered in the model than when either effect is ignored from the model. 

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

   Xiao, J.; Lou, Y. (January 2018, 2018 Transportation Research Board 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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5. Stretched exponential decay for subcritical parking times on

   https://doi.org/10.1002/rsa.21001  

   Damron, Michael; Lyu, Hanbaek; Sivakoff, David (February 2021, Random Structures & Algorithms)

   null (Ed.)

   In the parking model on ℤd, each vertex is initially occupied by a car (with probability p) or by a vacant parking spot (with probability 1−p). Cars perform independent random walks and when they enter a vacant spot, they park there, thereby rendering the spot occupied. Cars visiting occupied spots simply keep driving (continuing their random walk). It is known that p=1/2 is a critical value in the sense that the origin is a.s. visited by finitely many distinct cars when p<1/2, and by infinitely many distinct cars when p≥1/2. Furthermore, any given car a.s. eventually parks for p≤1/2 and with positive probability does not park for p>1/2. We study the subcritical phase and prove that the tail of the parking time τ of the car initially at the origin obeys the bounds exp(−C1tdd+2)≤ℙp(τ>t)≤exp(−c2tdd+2) for p>0 sufficiently small. For d=1, we prove these inequalities for all p∈[0,1/2). This result presents an asymmetry with the supercritical phase (p>1/2), where methods of Bramson--Lebowitz imply that for d=1 the corresponding tail of the parking time of the parking spot of the origin decays like e−ct√. Our exponent d/(d+2) also differs from those previously obtained in the case of moving obstacles. 

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