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Cruising trajectories for vehicles parked in on‐street parking spaces (a) Long‐distance parkers, (b) Short‐distance parkers
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Parking problems caused by a lack of parking spaces have exacerbated traffic congestion and worsened environmental pollution. An analysis of the cruising process for parking can provide new perspectives to reduce cruising. Based on a parking survey conducted in Beijing, the authors collected a large amount of trajectory data of cruising vehicles. T...
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Nowadays traffic congestion has become significantly worse. Not only has it led to economic losses, but also to environmental damages, wastage of time and energy, human stress and pollution. Generally, traffic congestion is a ripple effect of a road congestion on neighboring roads. When congestion occurs, it will propagate through the road network...
Citations
... Scalability extends beyond physical or digital realms to intellectual domains, as seen in [22], where systems discern management policy weaknesses and suggest improvements. Similarly, [23] proposes solutions for potential parking issues in On-street systems, adaptable to standard smart parking systems. ...
... Processing the streaming data coming from different sensors and cameras deployed in the system is the main process for triggering paramount decisions in [10], [24], [15], [27], [23], and [16]. Sensor data can be used in raw form, as proposed in [15], where decisions are triggered when a certain sensor measurement exceeds a predefined threshold. ...
... An investigative analysis to extract knowledge from sensor data was used in [16], where statistical implication methods and machine learning were employed to deeply understand the data, extract knowledge, detect problems, and recommend suitable solutions. Moreover, computer vision techniques were employed in [27] and [23] to assist in making critical decisions. ...
This paper delves into a crucial yet overlooked facet of smart parking systems: critical decision-making. While existing research extensively covers routine processes such as car routing and scheduling, the focus on pivotal decisions has been lacking. Despite numerous studies, only 23 papers directly address critical decision-making, often not as their primary focus. This scarcity underscores the untapped potential for exploration in this domain. By conducting a comprehensive review, this paper not only highlights the existing research landscape but also identifies significant gaps. Moreover, it serves as a pioneering effort to shed light on the importance of critical decision-making in smart parking systems. By providing valuable insights and analysis, this review lays the groundwork for future research endeavors, encouraging scholars to delve deeper into this unexplored territory. In summary, this paper fills a notable void in the literature, paving the way for further advancements and understanding in the realm of smart parking system decision-making.
... Researches show that cruising for parking spaces results in a peak increase of about 25-40% in the traffic flow [1], the average time spent finding a parking space is 3.5-14 min [2] and cruising for parking spaces wastes gasoline and produces more pollution emissions every year [3]. In other words, cruise for parking has become a common phenomenon in periods and areas with dense parking demand, which not only increases travellers' overall travel cost, but also adds additional congestion and emissions [4]. To tackle this issue, numerous parking management and guidance systems have been developed in the past decades. ...
Parking occupancy prediction is an important reference for travel decisions and parking management. However, due to various related factors, such as commuting or traffic accidents, parking occupancy has complex change features that are difficult to model accurately, thus making it difficult for parking occupancy to be accurately predicted. Moreover, how to give appropriate weights to these changing features in prediction becomes a new challenge in the era of machine learning. To tackle these challenges, a parking occupancy prediction method called time series decomposition–long and short‐term memory neural network (LSTM)–temporal pattern attention mechanism, which consists of three modules, namely 1) time series decomposition: modelling parking occupancy changes by extracting features such as trend, period, and effect; 2) encoder: extracting temporal correlations of feature sequences with LSTM; 3) temporal pattern attention mechanism: assigning attention to different features, are proposed. The evaluation results of 30 parking lots in Guangzhou city show that the proposed model 1) improves accuracy over the baseline model LSTM by 9.14% on average; 2) performs outstanding in four prediction time intervals and six types of parking lots, proving its validity and generality; 3) demonstrates its rationality and interpretability through ablation experiments and Shapley additive explanation.
... Therefore, whether to release all the available urban parking spaces as much as possible has become a critical topic, and we also found that some studies have made efforts to investigate this. For example, providing parking guidance [4,5] through prediction technology to help drivers find parking spaces on the side of the road more quickly, or improving prediction algorithms based on sensor data to evacuate real-time traffic flow faster [6,7]. Recently, with the evolution of the neural network model, some new predictive models that mix the first two features and can handle more complex data have also appeared [8,9]. ...
This study mainly focuses on the estimation calculation of urban parking space. Urban parking has always been a problem that plagues governments worldwide. Due to limited parking space, if the parking space is not controlled correctly, with the city’s development, the city will eventually face the result that there is nowhere to park. In order to effectively manage the urban parking problem, using the dynamic parking fee pricing mechanism combined with the concept of shared parking is an excellent way to alleviate the parking problem, but how to quickly estimate the total number of available parking spaces in the area is a big problem. This study provides a fast parking space estimation method and verifies the feasibility of this estimation method through actual data from various types of fields. This study also comprehensively discusses the changing characteristics of parking space data in multiple areas and possible data anomalies and studies and explains the causes of data anomalies. The study also concludes with a description of potential applications of the predictive model in conjunction with subsequent dynamic parking pricing mechanisms and self-driving systems.
... Based on clustering technology in data mining, regular behaviours of targets can be mined from massive historical trajectory data. At present, trajectory data mining technology has become a hot topic in both military and civil fields [6][7][8][9][10]. It has important practical significance for target behaviour pattern analysis [11,12], task intention prediction [7,13], abnormal behaviours detection [14,15] and realizing intelligent situation awareness [16,17]. ...
In the military and civilian surveillance domain, it is of great significance to mine regular behaviours of targets for situation awareness and command decision support. Most of the existing trajectory clustering algorithms only consider the similarity of spatial position of the trajectory, without sufficient multi‐dimensional information such as time, course and velocity. Some approaches based on information fusion take these multi‐dimensional information into account, but the features with different dimensions fused by weight coefficients are not robust and universal for different scenarios. In this paper, a regular behaviour mining method based on spatiotemporal trajectory multi‐dimensional features and density clustering is proposed. Firstly, multi‐dimensional Hausdorff similarity is defined to measure spatiotemporal trajectory from different feature dimensionalities. Different from methods based on information fusion, the proposed method defines trajectory density in feature similarity of different dimensions and adaptively determines parameters according to feature distribution in different dimensions. Experimental results in simulated and radar measured trajectory data show that the proposed method can be accurate and robust in clustering evaluation indexes such as Purity, Precision, Recall and Rand Index from different scenarios, which has a good application prospect in intelligent surveillance tasks.
... In clusters 2 and 3, districts have higher parking occupancy during the day rather than during the night, and this phenomenon is more obvious in cluster 2 than cluster 3. Comparing these four clusters to the analytics in Section 4.3 and Figure 10, we can conclude that the two analytics results are consistent. Using clustering methods, spatio-temporal parking patterns can also be found in other cities such as Seattle [50], Munich [41], and Beijing [51]. Even multiple parking behaviors around the globe can be identified through social media [52]. ...
Parking plays an essential role in urban mobility systems across the globe, especially in metropolises. Hong Kong is a global financial center, international shipping hub, fast-growing tourism city, and major aviation hub, and it thus has a high demand for parking. As one of the initiatives for smart city development, the Hong Kong government has already taken action to install new on-street parking meters and release real-time parking occupancy information to the public. The data have been released for months, yet, to the best of our knowledge, there has been no study analyzing the data and identifying their unique characteristics for Hong Kong. In view of this, we examined the spatio-temporal patterns of on-street parking in Hong Kong using the data from the new meters. We integrate the t-SNE and k-means methods to simultaneously visualize and cluster the parking occupancy data. We found that the average on-street parking occupancy in Hong Kong is over 80% throughout the day, and three parking patterns are consistently identified by direct data visualization and clustering results. Additionally, the parking patterns in Hong Kong can be explained using land-use factors. Overall, this study can help the government better understand the unique characteristics of on-street parking and develop smart management strategies for Hong Kong.
... In order to improve the objectivity of index weights and reduce subjective errors, this paper adopts the Spectral Clustering algorithm (SC) [31], which is suitable for highdimensional clustering, adaptable to data distribution and has excellent clustering effect, is used to unsupervisedly classify the expert scoring results by combining the scoring characteristics of different experts. In terms of SC algorithm parameter selection, in order to achieve optimal clustering results, Calinski_Harabaz_score (CH_score) was cho-Agriculture 2022, 12, 815 6 of 15 sen to evaluate the clustering effect, and the sample points were clustered into clusters C(C = {c 1 , c 2 , . . . ...
With the accelerated digital transformation, food security data is exponentially growing, making it difficult to process and analyze data as the primary challenge for food security risk regulation. The promotion of “big data + food” safety supervision can effectively reduce supervision costs and improve the efficiency of risk detection and response. In order to improve the utilization of testing data and achieve rapid risk assessment, this paper proposes a rice security risk assessment method based on the fusion of multiple machine learning models, and conducts experimental validation based on rice hazard detection data from 31 provinces in China excluding Hong Kong, Macao and Taiwan in 2018. The model comparison verifies that the risk assessment model shows better performance than other mainstream machine learning algorithms, and its evaluation accuracy is as high as 99.54%, which verifies that the model proposed in this paper is more stable and accurate, and can provide accurate and efficient decision-making basis for regulatory authorities.
... Based on video data and trajectory extraction software, 240 vehicle trajectories were obtained. The extracted data included the real-time position and driving speed of the passing and cruising vehicles [29]. When the car travelers drive through a distance of 200 m to the destination, their driving speed, that is, cruising speed, is between 10 and 20 km/h. ...
The rapid increase in the number of cars has caused many problems, such as “cruising for parking” and “illegal parking”. In this study, we conducted several on-street parking surveys in Beijing’s business districts. A parking location choice model and decision rules for the cruising process are established. A multi-agent based on-street parking simulation was constructed to explore the effects of time-varying parking prices on parking demand. It is concluded that demand-driven dynamic parking pricing can effectively regulate the distribution of parking demand and ensure the utilization of parking facilities within the desirable range in business districts. A lower desirable range and higher price change for the price adjustment can cause larger fluctuations in parking demand, fewer time intervals within the desirable range, and more price adjustment times. A higher desirable range and higher price change can result in longer cruising and driving times, and lower driving speeds. Considering the effectiveness and operating costs of the pricing schemes, it is recommended that the suitable range for parking occupancy rate is 60%–80% and the price change is 2 Yuan/h. The price adjustment threshold can be set based on the scale of the regional road network. The proposed dynamic parking pricing strategy can balance the distribution of the parking demand and reduce parking and traffic problems. The research conclusions can also provide a reference for the formulation of dynamic parking pricing strategies.
At present, car ownership is expanding, and parking facilities are insufficient. This problem has plagued people’s lives and hindered the development of cities. The stereo garage has become the main way to solve the parking problem. But the existing stereo garage is low in intelligence and low in vehicle entry efficiency. Therefore, in this study, a new vehicle entry strategy for the road stacking stereo garage is designed. GA algorithm is innovatively improved and applied to vehicle strategy optimization. By taking the dual objective function as the fitness function of the algorithm, the access strategy is optimized. Using MATLAB software to simulate and verify each access strategy and its improvement effect. This study provides guidance and data support for seeking the best vehicle access strategy. It has good practical application value for vehicle access in 3D garage.
