Discussion on Parking Management System Based on Parking Behavior

With the development of the city, the parking trouble is becoming an outstanding social and transport problem. A reasonable parking pricing scheme makes contribution to improve the use efficiency of parking and to reduce traffic congestion. The paper employs the theories and methods of behavior science to analyze parking behavior spatial structure, and establish the parking pricing model based on parking choice behavior. An algorithm based on the method of restricted capacity and incremental assignment was designed. Parking plays an important role in the traffic system of most major cities. With the developing city, the parking trouble is becoming an outstanding social and transport problem, such as motorists have been observed spending a significant percentage of their total trip time searching for a car park space. Within the city, this can deteriorate the status of traffic congestion and environmental quality. At the same time, unreasonable parking pricing will cause an unreasonable usage of the parking lot. Hence, the parking pricing model based on parking choice behavior is being a noteworthy problem in the traffic field. For solved parking problem in city, there are three stages in the developed country (GUAN, 2002). First is increase capacity of car parking lots, so that increase supply of parking for parking demand. Second is control capacity of parking lots in urban center, and control traffic demand. Three is make use of parking policy to adjust traffic structure of the city. In China, the most of city are actively planning and building parking lots to increase parking supply. But this method of resolved parking problem is unfeasible because there are many conditionality ingredients, such as land, capital, and so on. So it is a necessary choice that using policy and control means to achieve basic spatial-temporal equilibrium between supply and demand in the traffic system. For coming true this goal, it is one of possible approaches that made a reasonable parking fee to regulate the demand of parking. Many studiers already brought forward many parking pricing models, but these models cannot reflect the relationship between the parking fee and the parking choice behavior. This paper describes a parking pricing model based on parking behavior, and an algorithm of this model is designed.

In big cities, there are plenty of parking spaces, but we often find nowhere to park. For example, New York has 1.4 million cars and 4.4 million on-street parking spaces, but it is still not easy to find a parking place near our destination, especially during peak hours. The reason is the lack of prediction of parking behavior. If we could provide parking behavior in advance, we can ease this parking problem that affects human well-being. We observe that parking lots have periodic parking patterns, which is an important factor for parking behavior prediction. Unfortunately, existing work ignores such periodic parking patterns in parking behavior prediction, and thus incurs low accuracy. To solve this problem, we propose PewLSTM, a novel periodic weather-aware LSTM model that successfully predicts the parking behavior based on historical records, weather, environments, and weekdays. PewLSTM has been successfully integrated into a real parking space reservation system, ThsParking, which is one of the top smart parking platforms in China. Based on 452,480real parking records in 683 days from 10 parking lots, PewLSTM yields 85.3% parking prediction accuracy, which is about 20% higher than the state-of-the-art parking behavior prediction method. The code and data can be obtained fromhttps://github.com/NingxuanFeng/PewLSTM.

Towards a behavioural approach to city centre parking

A machine learning and simulation-based dynamic parking choice model for airports

Vehicle cruising for a destination-nearby parking lot has been a problem to drivers in cities. This problem causes drivers to consume unexpected time and resources to compete for a parking lot. Conventional parking schemes are in place to predict or reserve the parking spaces. Some of these schemes optimize the total cost only for locating parking spaces and road traffic condition is usually ignored. In this investigation, key road traffic conditions, such as vehicle queueing at the intersection of roads and parking fee information, are considered since they influence time and cost in the parking process. As such, a new comprehensive parking scheme is needed in place to assist drivers to identify the appropriate parking lot, estimate the transportation time to reach the parking lot, as well as the parking expenses. In essence, the total journey time and the total cost are important factors. However, former research lacked a harmonious consideration of these two factors. In this paper, a new parking model (PM) is developed for a holistic optimization of time and cost. The performance is measured by a newly defined indicator, referred to as a figure of merit, which is a fusion of time and cost. The analysis reveals that the incorporation of PM improves the performance by 24% ~43%.

Static traffic (parking of vehicles) is one of the most problematic areas of transport in urban areas. In particular, parking areas for heavy freight vehicles in city areas cause problems connected, for example, with insufficient capacity or inadequate equipment. In the Czech Republic the regional concepts for the location of parking lots for trucks have not been developed - rest areas are built mainly on highways. Drivers are forced into other roads to search for alternative parking spaces and thus jeopardize the safety of the cargo to be transported. Because of the lack of such parking areas, drivers are forced to violate the European Agreement Concerning the Work of Crews of Vehicles Engaged in International Road Transport (AETR) or, due to this agreement, to park the truck before the driver’s work shift ends, thereby reducing the efficiency of full driver usage. The paper deals with the issue of rest areas location for road freight vehicles in the selected area. The first part of the paper characterizes variant solutions of parking areas in a particular selected area, which were evaluated on the basis of an analysis of the current conditions. The second part of the paper introduces the evaluation of individual variants using the TOPSIS method, which was chosen as the most appropriate method of multicriteria decision making process.

In Hamadera Park, bicycle parking areas are not defined except for provisional bicycle parking spaces. Hence, bicycle users park their bicycles at various places in the park. In this research, we analyzed the parking patterns and parking areas of bicycle users in Hamadera Park, and clarified the characteristics of parking behaviors under different conditions. We aimed to derive a person’s parking characteristics in this park and thereby obtain knowledge that contributes to the correction of bicycle problems in urban parks. To this end, observation surveys were carried out in summer and autumn, weekdays and holidays, and in the mornings. Our study revealed the parking patterns and characteristics of the parking area in summer and autumn, and confirmed that there were seven types of parking patterns in the park. Many bicycles were seen near the park entrances, near intersections, and around provisional bicycle parking facilities for attracting customers. We noticed that the parking distribution tends to be different depending on the season. The survey results show that bicycle parking lots are preferable in places where usage is expected to be high. It is also desirable to set parking lots in places with little traffic.

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.

With the increasing severity of the malignant tumors situation worldwide, the impacts of climate on them are receiving increasing attention. In this study, for the first time, all-malignant tumors were used as the dependent variable and absolute humidity (AH) was innovatively introduced into the independent variable to investigate the relationship between all-malignant tumors and meteorological factors. A total of 42,188 cases of malignant tumor deaths and meteorological factors in Wuhu City were collected over a 7-year (2014–2020) period. The analysis method combines distributed lagged nonlinear modeling (DLNM) as well as generalized additive modeling (GAM), with prior pre-analysis using structural equation modeling (SEM). The results showed that AH, temperature mean (T mean) and diurnal temperature range (DTR) all increased the malignant tumors mortality risk. Exposure to low and exceedingly low AH increases the malignant tumors mortality risk with maximum RR values of 1.008 (95% CI: 1.001, 1.015, lag 3) and 1.016 (95% CI: 1.001, 1.032, lag 1), respectively. In addition, low and exceedingly low T mean exposures also increased the risk of malignant tumors mortality, the maximum RR was 1.020 (95% CI: 1.006, 1.034) for low T mean and 1.035 (95% CI: 1.014, 1.058) for exceedingly low T mean. As for DTR, all four levels (exceedingly low, low, high, exceedingly high, from low to high) of exposure increased the risk of death from malignant tumors, from exceedingly low to exceedingly high maximum RR values of 1.018 (95% CI: 1.004, 1.032), 1.011 (95% CI: 1.005, 1.017), 1.006 (95% CI: 1.001, 1.012) and 1.019 (95% CI: 1.007, 1.031), respectively. The results of the stratified analysis suggested that female appear to be more sensitive to humidity, while male require additional attention to reduce exposure to high level of DTR.

Highways that cross natural reserves are an intrusion with a nonnegligible negative impact on the behavior of wild animals and have numerous and diverse ecological impacts on wildlife near road areas. Field experiments were carried out to collect traffic flow data on the Qinghai‐Tibet Highway on the Qinghai‐Tibet Plateau, China, and the behavior of the Tibetan antelope crossing the highway was observed. The relationships between the percentage of antelopes successfully crossing the highway and the different traffic flows were analyzed. The results demonstrate that the traffic volume is the main factor affecting the success rate of Tibetan antelopes when crossing the highway, displaying a nonlinear negative correlation. Furthermore, the behavioral responses of the Tibetan antelopes within 500 m of the Qinghai‐Tibet Highway before and after different parking behaviors were observed and the proportions of the different behaviors exhibited by the Tibetan antelopes affected by different driver parking behaviors were analyzed. Parking behaviors were found to have the most significant effect on Tibetan antelope behavior within 400 m of the highway, where parking with somebody getting out having the most prominent impact. The results of this study can guide engineering measures to protect wildlife in the plateau region.

With the increasing automobiles in China, the parking difficulty has gradually spread over the city. Developing city-wide parking guidance systems (CPGS) has become urgent to many local governments of China nowadays. However, there haven’t been any effective systems available yet, because the economic investments on sensors and the time costs of negotiation with proprietors of parking lots are unaffordable from city-wide perspective. The lack of parking data is the most critical problem in development of CPGS. In this paper, we propose a new lightweight technical solution to implement CPGS by strengthening data mining and utilization under the current conditions. We start with mining the public information of parking lots, to quantify the service capability and pick out some of the most important as the samples. Next, we build a RGAN to generate data for those parking lots without data, patching them based on the samples with the similar surroundings. The technology of data generation will help us rebuild the city-wide parking data at low costs, both time and money. Finally, we design a model of recommendation based on the real-time driving data to implement intelligent parking guidance, featuring active recommendation and automatic adjustment with vehicle moving. The solution makes the best use of the existing data while reducing the reliance on sensors significantly. And we develop an experimental system in Shenzhen, achieving the rather satisfying guidance effect in practice. Hence, it’s a novel and effective exploration of CPGS, breaking through the dilemma of heavy dependence on sensors.

Polycyclic aromatic hydrocarbons (PAHs) can occur at levels of environmental concern in stream sediments from urban and industrial watersheds. Recent studies indicate that coal-tar sealant use on parking lots may have been the major source of PAHs to urban streams in the eastern US over the past 40 years. This study evaluates the spatial distribution of PAHs in urban stream and pond sediments in Galloway Creek in Springfield, Missouri by focusing specifically on parking lots with and without coal-tar sealants as contamination source areas. Multiple-linear regression analysis is used to evaluate the spatial connectivity of contaminated sediment to potential source areas and compare the relative influence of watershed source factors and reach sediment variability on sediment PAH levels. Sediments from coal-tar sealed parking lots and the streams that drain them are enriched in PAHs at concentrations considered toxic to aquatic life, with concentrations that are, on average, 35 and 480 times greater than those of unsealed asphalt and concrete lots, respectively. Moreover, sediment PAH concentrations are strongly correlated with the percentage of sealed parking lot area within the upstream drainage area of the sampling site, in contrast to total parking lot area or sediment composition. Metal and nutrient contaminants are poorly correlated with sealed lot area indicating a wider range of urban source inputs. Finally, parking lots with coal-tar coatings contribute >80% of the total PAH concentration in urban stream and pond sediments in Galloway Creek. If coal-tar sealant use ended, sediment PAH concentrations would probably decrease over time to levels not harmful to sediment-dwelling organisms.

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.

This paper analyses the theoretical mechanism of skill heterogeneity across city size and transportation under the ‘new’ new economic geography (NNEG) framework. We make a heterogeneous selection model from the NNEG framework and a heterogeneous assumption for skilled workers with continuous density function ∫ s _ s ¯ ⁡ f ( s ) ds , in which both iceberg-type transport costs and the value of spatial interactions between workers from across-cities are taken into account in a spatial economy. Our framework shows that the presence of transportation and city size creates a mechanism which always promotes spatial sorting for heterogeneous workers according to skills’ spatial distribution. Agglomeration equilibrium in large cities is the unique equilibrium and it is stable when the cost of trade and the value of spatial interactions between workers exist across-cities. It is obvious that tougher competition in larger cities requires more high-skilled workers, thus becoming more attractive to skilled job seekers.

This paper methodically investigates the influence of inclusive income growth on city size, examining it through the dual lenses of "income" and "distribution." The analysis leverages meticulously collected panel data encompassing 276 Chinese cities at the prefecture level and above, spanning the period from 2005 to 2019. Theoretical analysis indicates that the effect of city size expansion on per capita income adheres to a 'U'-shaped trajectory, while its influence on the urban-rural income gap manifests an 'inverted U' pattern. Moreover, the inclusive income growth stemming from city size demonstrates notable heterogeneity across various geographic locations and city hierarchies. The findings reveal that human capital serves as the primary mechanism through which city size influences inclusive income growth. After decomposing the income inclusiveness index, it becomes evident that the expansion of city size exerts a more potent direct driving effect on the income of urban residents. On the one hand, city size expansion directly increases rural residents' income levels by improving labor productivity. On the other hand, it facilitates leapfrog income development by inducing the rural labor force to move to cities.