Proposal and Application of Parking Area Performance Measurement Methodology

Angus×Hereford steers (n = 42) and heifers (n = 21) were ranked by gender and BW on d 0 of the experiment and randomly assigned to 1 of 3 treatments: 1) no transport and full access to feed and water (CON); 2) continuous road transport for 1,290 km (TRANS), or 3) road transport for 1,290 km, with rest stops every 430 km (STOP; total of 2 rest stops). Treatments were applied from d 0 to 1 of the experiment. Cattle from TRANS and STOP treatments were transported in separate commercial livestock trailers, within a single 2.1×7.2 m compartment, but through the exact same route. During each rest stop, STOP cattle were unloaded and offered mixed alfalfa-grass hay and water for ad libitum consumption for 2 h. Upon arrival of STOP and TRANS on d 1, cattle were ranked by sex and BW within each treatment and assigned to 21 feedlot pens (7 pens/treatment; 2 steers and 1 heifer/pen). Full BW was recorded before (d -1 and 0) treatment application and at the end of experiment (d 28 and 29). Total DMI was evaluated daily from d 1 to 28. Blood samples were collected on d 0 (before loading of TRANS and STOP cattle), 1 (immediately after unloading of TRANS and STOP cattle), 4, 7, 10, 14, 21, and 28. Body weight shrink from d 0 to d 1 was reduced (P<0.01) in CON compared to TRANS and STOP, and reduced in STOP compared to TRANS. Mean ADG was greater (P<0.05) in CON compared to TRANS and STOP, but similar (P=0.68) between TRANS and STOP. No treatment effects were detected (P≥0.18) on hay, concentrate, and total DMI. Mean G:F was greater (P=0.05) in CON compared to STOP, tended to be greater (P=0.08) in CON compared to TRANS, and similar (P=0.85) between TRANS and STOP. Plasma cortisol concentrations were greater (P≤0.04) in TRANS compared to CON and STOP on d 1, and greater (P=0.04) in TRANS compared to CON on d 4. Serum NEFA concentrations were greater (P<0.01) in TRANS compared to CON and STOP on d 1, and greater (P≤0.05) in TRANS compared to CON on d 4 and 7. Mean plasma ceruloplasmin concentrations were similar (P=0.19) among treatments. Plasma haptoglobin concentrations were greater (P≤0.04) in TRANS compared to CON and STOP on d 1, and in STOP compared to CON on d 1. In conclusion, inclusion of rest stops during a 1,290-km transport prevented the increase in circulating cortisol and alleviated the NEFA and haptoglobin response elicited by transport, but did not improve feedlot receiving performance of transported cattle.

Parking space is becoming a serious problem due to the day-by-day increase in number of vehicles on the road. Particularly, in cities with large population, or in places where sports or artistic events are scheduled, looking for parking space is a major problem and finding a parking spot can be a frustrating experience. In order to combat this problem, some parking lots have introduced sensors to detect when a car enters or leaves a parking lot in order to track capacity and alert drivers if they are full. This is a partial solution that allows drivers to determine if a parking lot has open parking spots, but not the exact location of those spots. In this paper, we present a prototype of smart parking system using wireless sensor technology and networks. Using a Wireless Sensor Network (WSN), parking spot statuses (occupied or idle) are detected and transmitted to a database. This information then be accessed by users through website or mobile app (application) to receive real-time updates. This system should provide users with near instantaneous updates of available parking spots while the WSN allows for flexibility of sensor placement. With the successful implementation of smart parking, the economical and time costs associated with traffic jams, cost associated with wasted gas fuel, and time looking for an empty parking space that are caused by inefficient parking will be significant reduced.

Parking problems are getting increasingly serious in the urban area. However, the parking spots in the urban area are underutilized rather than really scarce. There is a large number of private spots in the residential areas that have the potential of being shared. Due to its private nature, shared parking is usually operated by a profitable mode. To study the utilization of shared parking and its impact on the morning commute, this paper proposes an evolution model. The supply side is a profit-chasing manager who decides on the selling prices and the business scale, while the demand side refers to travellers who respond to costs and choose the trip mode. By analysing the behaviour (strategy) of both sides, the study covers: 1 - the attraction and competition between parking lots and trip modes, 2 - the utilization and user composition of the parking lots. By inducing two numerical examples, the conclusions are that 1 - managers can achieve maximum profit and optimal allocation through price adjustment and quantity control; 2 - publicity (system cost minimization) and profitability (profit maximization) are consistent under certain threshold conditions; 3 - competition exists between parking lots as well as trip modes; some parking lots are even in short supply; profitable management does not create a market monopoly.

Nowadays, finding a place for vehicle’s parking space has become a big challenge especially, in downtown of metropolises. Parking places are as one of main part of the modern urban transportation system, having the crucial role of removing static traffic from streets, which can cause to reduce streets’ parking spots, improve vehicles movement and increase the width of streets indirectly. Allocations of public parking spaces have been done in traditional way by visiting the location until few years ago without considering all effective parameters in allocation of public parking spots. The only parameter that has been considered, being the price of land. Geographic Information System (GIS) takes into account a suitable system for specifying optimum location to develop the public parking space. Using the GIS has the merit of increasing accuracy in comparison with the traditional method. This research presents proper method for locating of public parking place by consideration all effective parameters and by using GIS. The effective parameters have been weighed by AHP method. Then these parameters have been combined by fuzzy algebraic sum. This method has been used for locating suitable parking spots in region1 of Tehran as the case study.

Self-driving vehicles rely on detailed semantic maps of the environment for operating. In this letter, we propose a method to autonomously generate such a semantic map enriched with knowledge of parking spot locations. Our method detects and uses parked vehicles in the surroundings to estimate parking lot topology and infer vacant parking spots via a graph-based approach. We show that our method works for parking lot structures in different environments, such as structured parking lots, unstructured/unmarked parking lots, and typical suburban environments. Using the proposed graph-based approach to infer the parking lot structure, we can extend the estimated parking spots by 57%, averaged over six different areas with ten trials each. We also show that the accuracy of our algorithm increases when combining multiple trials over multiple days. With ten trials combined, we managed to estimate the whole parking lot structure and detected all parking spots in four out of the six evaluated areas.

Finding free slots in the parking area has become a major problem that the modern society is facing nowadays, which also leads to traffic overcrowding at the parking area. As the number of vehicles are increasing at a quick pace find parking slots has become difficult. The proposed paper helps to solve the parking problem. In traditional parking system a person has to find vacant slot or a gate keeper needs to inform about the available parking slot at parking area. To address the issue, a system where the user will be able to know the number of vacant slots in a particular parking area along with total number of slots at the entrance of the parking area. As a critical component of this project, by built a car parking slot detection system which collects data from a camera at parking area. Today's most popular object detection model YOLO, is trained to achieve desired results with good precision scores at real-time speed. The model will be getting all the parking slots that are present in the parking area, to which marking boxes are drawn around for training. Real time processing is performed on the obtained data by the model to find whether the slot is vacant or occupied. Thus, it gives the information of available vacant slots to park the car at parking area. Key Words: YOLO, Object detection, Computer vision, Machine learning.

Parking in heavily populated areas has been considered one of the main challenges in the transportation systems for the past two decades given the limited parking resources, especially in city centres. Drivers often waste long periods of time hunting for an empty parking spot, which causes congestion and consumes energy during the process. Thus, finding an optimal parking spot depends on several factors such as street traffic congestion, trip distance/time, the availability of a parking spot, the waiting time on the lot gate, and the parking fees. Designing a parking spot allocation algorithm that takes those factors into account is crucial for an efficient and high-availability parking service. We propose a smart routing and parking algorithm to allocate an optimal parking space given the aforementioned limiting factors. This algorithm supports choosing the appropriate travel route and parking lot while considering the real-time street traffic and candidate parking lots. A multi-objective function is introduced, with varying weights of the five factors to produce the optimal parking spot with the least congested route while achieving a balanced utilization for candidate parking lots and a balanced traffic distribution. A queueing model is also developed to investigate the availability rate in candidate parking lots while considering the arrival rate, departure rate, and the lot capacity. To evaluate the performance of the proposed algorithm, simulation scenarios have been performed for different cases of high and low traffic intensity rates. We have tested the algorithm on in-city parking facility in the city of Al Madinah as a case study. The results show that the proposed algorithm is effective in achieving a balanced utilization of the parking lots, reducing traffic congestion rates on all routes to candidate parking lots, and minimizing the driving time to the assigned parking spot. Additionally, the proposed algorithm outperforms the MADM algorithm in terms of the selected three metrics for the five periods.

SCADA for the prototype M221 PLC-based multi-area car parking system is a system for managing several separate car parks on line via ethernet. The system is equipped with HMI as an interface to facilitate operators to monitor system conditions in real-time and the ability to display data on the number of vehicles entering and exiting, counting the number of vehicles parked for one day, and calculating revenue throughout the day. Loop sensor is used to detect vehicles that will enter or exit the parking door. Input from the sensor will be processed by the PLC to move the doorstop motor, indicator lights and update the display on the HMI. The results of a multi-area prototype test process for a car parking system for two parking areas have worked well. The system is able to control the opening and closing of the entrance and exit bars, controlling the indicator lights whether or not the parking area is full, showing the animation of the system process to the HMI in real time, calculating the number of vehicles and calculating the total cost of revenue in each parking area.

Model free Reinforcement Learning to determine pricing policy for car parking lots

Smart parking management aims to leverage advanced sensing and monitoring technologies to optimize the usage of parking spots within large car parks and ease parking for drivers by directing them to available parking spots, thereby reducing unnecessary stress and delay that further worsen the traffic conditions. The idea of smart parking development began to make parking easier, faster, with less of a drain on resources which are slowly disappearing. The aim of the Smart Parking Management System (SPMS) proposed in this paper is to assist drivers in alleviating several issues linked to parking. Our SPMS provides a sensing platform supported by a mobile app to enable real-time interaction of drivers with the car park infrastructure in a way that minimizes drivers' search time for an empty parking spot within large multi-storey car parks and facilitates the localization of their parked cars. To validate the efficiency of our SPMS, a small scale test-bed was developed as a proof of concept and tested under five different scenarios. The obtained results highlight its effectiveness and call for its potential integration in real world car parks.

In recent years, with the rapid development of China’s automobile industry, the number of vehicles in China has been increasing steadily. Vehicles represent a convenient mode of travel, but the growth rate of the number of urban motor vehicles far exceeds the construction rate of parking facilities. The continuous improvement of parking allocation methods has always been key for ensuring sustainable city management. Thus, developing an efficient and dynamic parking distribution algorithm will be an important breakthrough to alleviate the urban parking shortage problem. However, the existing parking distribution models do not adequately consider the influence of real-time changes in parking demand and supply on parking space assignment. Therefore, this study proposed a method for dynamic parking allocation using parking demand predictions and a predictive control method. A neural-network-based dynamic parking distribution model was developed considering seven influencing factors: driving duration, walking distance, parking fee, traffic congestion, possibility of finding a parking space in the target parking lot and adjacent parking lot, and parking satisfaction degree. Considering whether the parking spaces in the targeted parking lots are shared or not, two allocation modes—sharing mode and non-sharing mode—were proposed and embedded into the model. At the experimental stage, a simulation case and a real-time case were performed to evaluate the developed models. The experimental results show that the dynamic parking distribution model based on neural networks can not only allocate parking spaces in real time but also improve the utilisation rate of different types of parking spaces. The performance score of the dynamic parking distribution model for a time interval of 2–20 min was maintained above 80%. In addition, the distribution performance of the sharing mode was better than that of the non-sharing mode and contributed to a better overall effectiveness. This model can effectively improve the utilisation rate of resources and the uniformity of distribution and can reduce the failure rate of parking; thus, it significantly contributes to more smart and sustainable urban parking management.

Statistics from the Central Statistical Office (CSO) state that the total number of motor vehicles and mopeds in Poland in 2019 was 31,989,313 units. According to the Central Register of Vehicles (CEPiK) maintained by the Ministry of Digitalisation, the number of motor vehicles registered in Poland in 2019 was 23,878.8 thousand units. Among them, 18,302.4 thousand passenger cars were registered. The constantly increasing number of vehicles on the road has a significant impact on the deterioration of road capacity and drivers’ frustration with parking, especially during rush hours in the centres of large cities. Hence, urban logistics, including the country’s parking policy, is extremely important to a significant proportion of citizens. Unfortunately, citizens have different perspectives on parking issues. Most drivers expect to be able to leave their car as close as possible to their destination, while some residents, especially non-motorised ones, are against the conversion of free city space into car parks. In response to the presented issues, a comparative analysis of selected types of car parks was carried out. This analysis was one of the stages of the project entitled: “Analysis of the environmental impact of a vertical parking solution using life cycle assessment at the design stage” carried out at the Silesian University of Technology.

The development of technology has an impact for the transportation sector, one of them is about parking the vehicle. Parking is a state where a vehicle is not moving for a short time because the driver is going for somewhere else. To find an empty parking spot, a driver must look up for a parking lot that has been allowed for parking, it is quite difficult to find a parking spot because the space available for parking is very limited and the amount of vehicles that need parking is very high. It needs a relatively long time for a driver to get a parking spot and can cause a traffic jam in the parking lot because many drivers are trying to find an empty spot for their vehicles. This is the reason why the author develops an application for parking lot using Android Smartphone. This paper uses Rational Unified Process Methodology and the tool that used for this paper is Photoshop CS5, Adobe Dreamweaver, MySQL, and using Unified Modeling Language tools. The programming language that used for this paper is PHP (Hypertext Preprocessor).

Recently, due to the development of automobile industry, the number of vehicles is increasing. The traditional parking lots are unable to meet the growing demands from so many vehicles. Therefore, the current parking lots present many problems. Such as, can't find parking spaces quickly, difficult to manage too many vehicles and manual toll collection costs much time. To solve those problems above, this paper designs a new parking lot system which is based on ZigBee technology and Support Vector Machine (SVM). By the designed parking system, the parking issues can be solved more efficiently and intelligently.

In this paper, we are introducing an intelligent valet parking management system that guides the cars to autonomously park within a parking lot. The IPLMS for Intelligent Parking Lot Management System, consists of two modules: 1) a model car with a set of micro-controllers and sensors which can scan the environment for suitable parking spot and avoid collision to obstacles, and a Parking Lot Management System (IPLMS) which screens the parking spaces within the parking lot and offers guidelines to the car. The model car has the capability to autonomously maneuver within the parking lot using a fuzzy logic algorithm, and execute parking in the spot determined by the IPLMS, using a parking algorithm. The car receives the instructions from the IPLMS through a wireless communication link. The IPLMS has the flexibility to be adopted by any parking management system, and can potentially save the clients time to look for a parking spot, and/or to stroll from an inaccessible parking space. Moreover, the IPLMS can decrease the financial burden from the parking lot management by offering an easy-to-install system for self-guided valet parking.