Parking Occupancy Research Articles

Institute of Transportation Engineers Guidelines versus Actual Trip and Parking Generation for a Transit-Oriented Development in an Auto-Oriented Region

The Institute of Transportation Engineers (ITE) guidelines serve as the most widely used reference for trip and parking generation estimates for any new development in the U.S.A. However, recent empirical studies question the efficacy of ITE guidelines in forecasting trip and parking generation in transit-oriented developments (TODs). Following the methodology of studies on seven TODs across the U.S.A., this study focuses on Dallas (TX), as a more auto-oriented American city, to explore the trip and parking generation at Mockingbird TOD. It finds that, with the exception of Station Park in Salt Lake City (which is really a transit-adjacent development rather than a TOD), the Mockingbird TOD has the lowest walk mode share (13.6%), the lowest bike mode share (0.22%), the lowest bus transit mode share (1.09%), and by far the lowest rail transit mode share (5.9%) of all the other seven TODs. Similarly, the Mockingbird TOD ranks first in relation to the driving mode share, with about 80% of its daily trips generated by driving. This is almost twice as many driving trips as the average of the other six TODs, which makes Mockingbird an auto-oriented TOD located in an auto-oriented region where more than 96% of commuting trips are by automobile. Still, the total automobile trip generation rate in Mockingbird is about 12% lower than the ITE estimates. Similarly, while the parking supply in Mockingbird TOD is less than 48% of the ITE recommended supply rate, its peak parking occupancy is only about 55% of the TOD supply.

PARKING OCCUPANCY ESTIMATION ON SENTINEL-1 IMAGES

Abstract. This paper presents a method to estimate the occupancy ratio of parkings from SAR satellite images. The algorithm takes as input a series of Sentinel-1 images along with a mask indicating where the parking is located and returns for each image an occupancy ratio. The method is generic and can easily be extended. We validate our results in two parts. First, we have created a dataset of Sentinel-1 GRD image time series where each image is associated to a ground truth parking occupancy ratio. This ground truth is estimated thanks to a surveillance camera that permanently films and records the parking. We observe a strong correlation between the estimated occupancy rate and the ground truth occupancy rate. Secondly, we estimate the occupancy ratio of the 250 largest retail parkings in France from January 2018 to April 2020. We observe that weekly and seasonal patterns are consistent with consumer and economic trends. Parking occupancy estimations also plummet during the COVID-19 containment measures.

A Battery Energy Storage Sizing Method for Parking Lot Equipped With EV Chargers

Parking lots (PLs) equipped with electric vehicle (EV) chargers will be the most convenient places for EV users to charge their cars. However, there is a high degree of unpredictability around how car parks are used, especially shopping center parking lots (SCPLs). Moreover, the cluster charging of EVs in PLs may destabilise the grid. The application of a battery energy storage system (BESS) in PLs is a potential way to reduce the impact of EV charging on the grid. This article proposes an approach for estimating the size of a stationary BESS for PLs in a constrained grid. At first, the intermittent charging demand of EVs is determined by considering the travel patterns, charging needs, and driver behavior of EVs. Then, a region reduction method combined with nonlinear optimization is proposed to estimate the optimal size of the BESS such that the capital cost and operational cost of the SCPL are minimized. The proposed sizing method 1) ensures the maximum utilization of the stationary BESS; 2) avoids over/undersizing; 3) reduces operational costs; and 4) maintains; the reliability of the system. The applicability of the proposed method is shown by simulating different cases, characterized by real household travel survey data and shopping center car park occupancy data.

Deep learning versus traditional methods for parking lots occupancy classification

<span>Due to the increase in number of cars and slow city developments, there is a need for smart parking system. One of the main issues in smart parking systems is parking lot occupancy status classification, so this paper introduce two methods for parking lot classification. The first method uses the mean, after converting the colored image to grayscale, then to black/white. If the mean is greater than a given threshold it is classified as occupied, otherwise it is empty. This method gave 90% correct classification rate on cnrall database. It overcome the alexnet deep learning method trained and tested on the same database (the mean method has no training time). The second method, which depends on deep learning is a deep learning neural network consists of 11 layers, trained and tested on the same database. It gave 93% correct classification rate, when trained on cnrall and tested on the same database. As shown, this method overcome the alexnet deep learning and the mean methods on the same database. On the Pklot database the alexnet and our deep learning network have a close resutls, overcome <br /> the mean method (greater than 95%).</span>

Macroscopic parking dynamics modeling and optimal real-time pricing considering cruising-for-parking

Network traffic congestion is known to be partially caused by vehicles cruising for parking. In this paper, we quantify and assess the effect of cruising-for-parking by developing a macroscopic parking dynamics model for a parking-dense neighborhood with limited parking supply, where cruising-for-parking is explicitly considered in conjunction with the interactions between on- and off-street parking. The model is mainly built upon the system dynamics of different families of vehicles in the neighborhood, which is governed by mass conservation equations utilizing the concept of macroscopic or network fundamental diagram (MFD or NFD). To reduce parking congestion and improve the overall system performance, two real-time parking pricing strategies are developed and integrated with the parking model: (i) a feedback-based reactive pricing strategy driven by the parking occupancy; and (ii) a model-based predictive or proactive pricing strategy that explicitly aims to minimize the expected aggregate cruising delay. Extensive numerical experiments have been conducted to compare the performance of the two strategies applied to both on- and off-street parking. The results provide new insights into how a parking system shall be better managed, with key implications for policy making summarized.

Stochastic modelling of electric vehicle behaviour to estimate available energy storage in parking lots

The increasing penetration of electric vehicles (EVs) brings challenges and opportunities for power systems. One particular opportunity concerns the use of parked EVs to provide energy and associated services to the grid. In this work, the potential energy storage capacity of parking lots (PLs) of EVs is computed using the proposed stochastic model which considers the sporadic nature of the EV’ behaviours (i.e. arrival/departure, battery degradation, travel pattern, charge/discharge rates). The analysis was performed for two types of PLs with very different occupancy distributions, i.e. a shopping centre PL, and a workplace PL. In both cases, the available energy storage capacity of EVs was estimated hourly using real household travel data, i-MiEV data and car park occupancy records. The results show that the aggregated energy storage capacity closely follows the occupancy of EVs in the PLs, and is substantial, with little sensitivity to charging rate. The proposed stochastic modelling considered the variations in energy consumption, battery degradation, and user behaviour, predicted 13.4% less peak capacity than deterministic modelling. Moreover, the authors conclude that the shopping centre PL is a viable energy resource to the grid, with their scale and throughput compensating for the relatively low occupancy.

Smart Parking Detection System using IOT

With the increase in vehicle production and world population, more and more parking spaces and facilities are required. In this paper a new parking system called Smart Parking detection System is proposed to assist drivers in a residential building or office where all the owners or employee will be allotted with a particular parking space to know the occupancy of the parking space if any wrong car is parked in their slot. The new system uses infrared sensors to detect either car park occupancy or improper parking actions. Features of smart parking detection system include detection of car occupancy in a parking slot , getting information of the parked vehicle if it doesn’t matches with the car number registered and notify the details of the car parked by sending message to a registered phone number. This paper also describes the use of a metal rack to protect the sensors from external damage.

A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale

Parking issues have been receiving increasing attention. An accurate parking occupancy prediction is considered to be a key prerequisite to optimally manage limited parking resources. However, parking prediction research that focuses on estimating the occupancy for various parking lots, which is critical to the coordination management of multiple parks (e.g., district-scale or city-scale), is relatively limited. This study aims to analyse the performance of different prediction methods with regard to parking occupancy, considering parking type and parking scale. Two forecasting methods, FM1 and FM2, and four predicting models, linear regression (LR), support vector machine (SVR), backpropagation neural network (BPNN), and autoregressive integrated moving average (ARIMA), were proposed to build models that can predict the parking occupancy of different parking lots. To compare the predictive performances of these models, real-world data of four parks in Shenzhen, Shanghai, and Dongguan were collected over 8 weeks to estimate the correlation between the parking lot attributes and forecast results. As per the case studies, among the four models considered, SVM offers stable and accurate prediction performance for almost all types and scales of parking lots. For commercial, mixed functional, and large-scale parking lots, FM1 with SVM made the best prediction. For office and medium-scale parking lots, FM2 with SVM made the best prediction.

Video-Based Parking Occupancy Detection for Smart Control System

Street lighting is a fundamental aspect of security systems in homes, industrial facilities, and public places. To detect parking lot occupancy in outdoor environments, street light control plays a crucial role in smart surveillance applications that can perform robustly in extreme surveillance environments. However, traditional parking occupancy systems are mostly implemented for outdoor environments using costly sensor-based techniques. This study uses the Jetson TX2 to develop a method that can accurately identify street parking occupancy and control streetlights to assist occupancy detection, thereby reducing costs, and can adapt to various weather conditions. The proposed method adopts You Only Look Once version 3 (YOLO v3, Seattle, WA, USA) based on MobileNet version 2 (MobileNet v2, Salt Lake City, UT, USA), which is area-based and uses voting to stably recognize occupancy status. This solution was verified using the CNRPark + EXT dataset, a simulated model, and real scenes photographed with a camera. Our experiments revealed that the proposed framework can achieve stable parking occupancy detection in streets.

An efficient smart parking pricing system for smart city environment: A machine-learning based approach

Now-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 interested vehicle owners with least overhead becomes an NP-Hard problem bounded by various constraints. In-advance availability of information regarding parking occupancy plays a major role in hassle free trip optimization for motorists. It also facilitates services-cum-profit management for the parking owners. It further helps in curbing congestion by reducing cruising time and hence, helps in controlling pollution of the smart cities. Thus, accurate and timely information regarding parking occupancy and availability has become the basic need in the evolution of the smart cities. Motivated by these facts, an occupancy-driven machine learning based on-street parking pricing scheme is proposed in this paper. The proposed scheme uses machine learning based approaches to predict occupancy of parking lots, which in turn is used to deduce occupancy driven prices for arriving vehicles. In order to train, test, and compare different machine learning models, on-street parking data of Seattle city has been used. To the best of our knowledge, this is the first time that parking occupancy prediction system is used to generate occupancy based parking prices for on-street parking system of the Seattle city. Results obtained using the proposed occupancy driven machine learning based on-street parking pricing scheme demonstrate its effectiveness over other existing state-of-the-art schemes.

Modeling of the need for parking space in the districts of Moscow metropolis by using multivariate methods

The growth of metropolis cities and consequently the number of vehicles cruising within their boundaries create a permanent problem of dissatisfaction with the amount of parking space and its over-occupancy. The results of continuous observation of parking lots in Moscow and data on registered cars in the city districts was the initial basis for this study. The data was processed by IBM SPSS Statistics 20 statistical program to obtain descriptive statistics indicators of parking space in Moscow, the analysis of cause-and-effect relations and subsequent multivariate modeling using regression analysis; log it regression; discriminant analysis; "classification trees" (decision tree). The results clearly show the possibility of applying the methods of multivariate statistics, log it regression and "classification trees". Both models allow for using the explanatory variables "proportion of parking lots with violations" and "number of parking spaces in the street and road network" to analyze the impact on parking lot occupancy. Also, the descriptive statistics analysis revealed that when the number and proportion of parking lots with violations are 2 times higher on average in the districts with over-occupied parking lots versus the districts where the parking lot occupancy is not so high, and the number of paid parking lots is over 10 times less. The increase in the proportion of parking spaces with violations ranging from 0 to 0.2% entails a sharp increase in parking space occupancy (up to 90%), while a further increase in the proportion of parking spaces with violations does not entail a significant increase in the parking occupancy.

Predicting car park availability for a better delivery bay management

Abstract Freight parking is a serious problem in smart mobility and we address it in an innovative manner. We propose an intelligent urban parking management system capable to modify in real time the status of any parking spaces, from a conventional place to a delivery bay and inversely. Its driving force is the parking availability prediction. In this article, we model the parking occupancy by many regression types. We analyze their results, identify their strengths as well as their weaknesses and deduce the most effective one. We calculate the probability of a place being left free by the actuarial method. To do so, we exploit a massive amount of real-time parking availability data collected and disseminated by the City of Melbourne, Australia.

A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources

A deep learning model is adopted for predicting block-level parking occupancy in real time. The model leverages Graph-Convolutional Neural Networks (GCNN) to extract the spatial relations of traffic flow in large-scale networks, and utilizes Recurrent Neural Networks (RNN) with Long-Short Term Memory (LSTM) to capture the temporal features. In addition, the model is capable of taking multiple heterogeneously structured traffic data sources as input, such as parking meter transactions, traffic speed, and weather conditions. The model performance is evaluated through a case study in Pittsburgh downtown area. The GCNN-based model outperforms other baseline methods including multi-layer LSTM and LASSO with an average testing MAPE of 10.6% when predicting block-level parking occupancies 30 min in advance. The case study also shows that, in generally, the prediction model works better for business areas than for recreational locations. We found that incorporating traffic speed and weather information can significantly improve the prediction performance. Weather data is particularly useful for improving predicting accuracy in recreational areas.

An Image Feature-Based Method for Parking Lot Occupancy

The main scope of the presented research was the development of an innovative product for the management of city parking lots. Our application will ensure the implementation of the Smart City concept by using computer vision and communication platforms, which enable the development of new integrated digital services. The use of video cameras could simplify and lower the costs of parking lot controls. In the aim of parking space detection, an aggregated decision was proposed, employing various metrics, computed over a sliding window interval provided by the camera. The history created over 20 images provides an adaptive method for background and accurate detection. The system has shown high robustness in two benchmarks, achieving a recognition rate higher than 93%.

A simulator study of driving behavior and mental workload in mixed-use arterial road environments

Objectives: Mixed-use urban environments, such as arterial roads with adjacent commercial land uses, represent crash locations with the highest risk. These locations are often characterized by high volumes of motor vehicle traffic, on-street parking, and interactions with multiple road user groups such as pedestrians, cyclists, and public transportation. The objective of this study was to investigate previously identified crash risk factors for mixed-use urban environments and assess how parking occupancy, center medians, and cyclist volume influence performance and workload in a driving simulator study.Methods: Thirty participants were recruited for the study. Participants completed 6 drives that presented different combinations of cyclist volume, median condition, and parking occupancy. Incorporated into the simulator drives was a secondary peripheral detection task (PDT) designed to measure mental workload. Participants provided subjective assessments of workload using the Rating Scale Mental Effort (RSME).Results: Mean lateral lane position was found to significantly vary across the 3 independent variables of parking occupancy, cyclist volume, and median conditions. No significant changes were identified for mean speed across the conditions. Subjective and objective measures of workload identified changes due to the presence of cyclists with slower reaction times for the PDT task when cyclists were present.Conclusion: The findings provide insight into the interaction of road design elements in mixed-use urban road environments and demonstrate that increasingly complex environments increase driver demand. This has important road design implications for mixed-use arterial roads, which are often characterized by complex interactions between multiple road user groups.

Utilization Management and Pricing of Parking Facilities Under Uncertain Demand and User Decisions

Excessive search for parking spots in congested areas contributes to additional travel delays and negative socio-economic impacts. While managing parking utilization to address the agency’s objectives, it is often very beneficial to reflect the diversity of users’ behaviors and their travel choices. This paper develops a stochastic dynamic parking management model, under competitive user-agency perceptions and uncertain user demand and parking occupancy, to simultaneously minimize the total travelers’ costs and maximize the parking agency’s revenue. The problem is formulated into a dynamic programming model and solved using a stochastic look-ahead technique based on the Monte Carlo tree search algorithm to determine optimal actions on parking price assignment and spot utilization over time. The numerical experiments on a hypothetical and empirical case study are conducted to show the performance of the proposed algorithm and to draw managerial insights. The results are compared with those of benchmark algorithms, which indicate that the proposed methodology can determine near-optimal solutions efficiently.

An Occupancy Simulator for a Smart Parking System: Developmental Design and Experimental Considerations

This paper presents the development of a parking occupancy simulator to support a smart parking system. The simulator uses an agent-based approach to model drivers who follow activity plans and who may or may not use the smart parking system. We illustrate how the process of developing our simulator helped in the design and implementation of the smart parking system components. The paper also shows how the simulator was used to study the possible usage of the smart parking system in a university campus, foreseeing (1) support for the smart parking system’s overall suitability, (2) reservation guarantee violation problems, and (3) the value of using total traveled distance as a metric for the smart parking evaluation. The experience presented in this paper may prove valuable to teams planning the development of a smart parking system for similar contexts.

Development of parking estimation on rest service area in Malaysia

Roadside rest area also known as rest service area (RSA) is one of the important facilities for expressway users. However, designing an efficient RSA is a challenge to most transport planners since it needs the information on potential and existing traffic volumes to estimate the parking demand and design of rest area facilities are also critical. Planning for these facilities is crucial in order to avoid over-design or under design which will affect the cost in the future for capacity issues. Therefore, this study is to determine the parking occupancy and develop parking estimation on RSA. The study was conducted on 10 sites of RSA along E1 and E2 expressway (Southern-Northern Expressway). The data collection for this study was conducted during the mid-week. Simple count observation and indirect method were used to determine the parking occupancy and traffic characteristics on RSA. The results show the peaking characteristics of space occupied, whereas the highest demand exhibited was during the midday period. The estimation model was found that traffic and site characteristics can be explained to estimate parking occupancy on RSA. The estimation result seems to be applicable.

Can I Park in the City Center? Predicting Car Park Occupancy Rates in Smart Cities

ABSTRACT Finding an available parking space can be difficult in parts of most cities, especially in city centers. In this article, we address the study of parking occupancy data in Birmingham, Glasgow, Norfolk, and Nottingham in the United Kingdom. We test several prediction strategies, such as polynomial fitting, Fourier series, K-means clustering, and time series, and analyze their results. We use cross-validation to train the predictors and then test them with unseen occupancy data. Additionally, we develop a web service to visualize the current and historical parking data in a map, allowing users to consult the occupancy rate forecast up to one week in advance to satisfy their parking needs. We believe that the use of these accurate, intelligent techniques creates user services for citizens living in real smart cities as a way of improving their quality of life, decreasing wait times, and reducing fuel consumption.

Smart Parking: Using a Crowd of Taxis to Sense On-Street Parking Space Availability

Monitoring the occupancy of on-street parking spaces on a city-wide scale is still an open issue. Past research demonstrated the viability of parking crowd-sensing by means of the standard on-board sensors of probe vehicles, foreseeing the use of high-mileage vehicles, like taxis. Nevertheless, the achievable spatio-temporal sensing coverage has never been deeply investigated. In this paper, we investigate the suitability of taxi fleets of different sizes to crowd-sense on-street parking availability. We considered 579 road segments in San Francisco (USA), covered both by sensors of the SFpark project and by the GPS traces of 536 taxis. For each of these segments, we computed the taxi transit frequencies, representing the achievable coverage by vehicles equipped with sensors detecting empty parking spots. By combining these frequencies with parking occupancy data coming from SFpark, we estimated the potential quality of crowd-sensed on-street parking information for different fleet sizes. Moreover, we investigated the impact of different misdetection amounts, and Kalman filters to handle them. The results show that a total of 300 taxis can crowd-sense on-street parking availability with an error of up to ±1 stall in 86% of the cases. Moreover, the quality of the sensors is as important as the fleet size (300 taxis with 10% probability of misreadings provide availability information comparable to 486 taxis with 16% probability), while the use of Kalman filters did not lead to statistically significant improvements. In conclusion, the traffic management authorities should consider parking crowd-sensing via probe vehicles as a promising alternative to the expensive deployment of the static parking sensors.