A Parking Pricing Model with Parking Behavior in City

Transmission congestion is one of the key elements affecting spike price and local market power. Reducing the demand and increasing the generation in distribution network may lead to relive the congestion of a transmission line. Plug-in hybrid electrical vehicle (PHEVs) parking lots and emergency demand response programmes (EDRP) are considered as two main options to manage the demand of a transmission bus. In this paper, a novel approach is proposed to determine the optimal capacity of EDRP and PHEV parking lots in order to relieve transmission congestion in a least-cost manner. The behaviour of EDRP participants and available capacity of the parking lot are mathematically modelled. Genetic algorithm (GA) with binary variables is employed to solve the optimisation problem. Simulation is carried out on a 14-bus transmission network. Results demonstrate the optimal contribution of parking lots and EDRP has a significant effect on mitigating congestion and decreasing the cost of the system.

With the rapid development of China's economy and the further expansion of the city's size, people's living conditions have improved, and their spending power has continued to increase. The city's transportation demand has also continued to increase, bringing about the problem of people's parking difficulties. The most direct way to solve the problem of urban parking is to increase the number of parking spaces by constructing a parking lot. This method is difficult to implement due to funding problems or urban space constraints. This article takes the driver 's parking behavior influencing factors as the basis for research, Based on the study of the drivers' influencing factors, this paper analyzes the parking factors and analyzes the parking characteristics by observing the parking lot of Suning Plaza in Wuhu City, analyzing the factors affecting the parking behavior, optimizing the utilization efficiency of existing parking lots, and parking schemes. And management methods to promote the improvement of urban parking difficulties <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> .

Operators of parking guidance and information (PGI) systems often have difficulty providing the best car park availability information to drivers in periods of high demand. A new PGI configuration model based on the optimized combination method is proposed by analyzing parking choice behavior. This paper firstly describes a parking choice behavioral model incorporating drivers perceptions of waiting times at car parks based on PGI signs. Then relationships are developed for estimating the arrival rates at car parks based on driver characteristics, car park attributes and car park availability information displayed on PGI signs. A mathematical program is formulated to determine the optimal display PGI sign configuration to minimize total travel time. A genetic algorithm is used to identify solutions that reduce queue lengths and total travel time. These procedures were applied to a PGI system operating in Deqing Town near Shanghai, producing significant reductions in total travel time of parking vehicles.

The imbalance of rural parking supply and demand has a great impact on traffic congestion and environmental pollution, which has attracted the attention of many scholars as well as policymakers. However, most of the current research on parking choice focuses on urban business and residential areas rather than on rural parking choice behaviour, and focuses on the analysis of observable factors, ignoring the internal relationship with potential variables. This study considers the heterogeneity of individuals and uses the random forest (RF) algorithm to construct a model of rural residents’ willingness to choose parking with both latent and explicit variables, to explore how much and in what ways individual characteristics and parking characteristics affect rural residents’ parking choices, and to explore parking planning programmes and strategies that are truly applicable to rural areas. The results of the study suggest that the safety and convenience of the parking environment are key factors influencing the parking choice behaviour of rural residents, and can greatly improve the predictive accuracy of the parking willingness model. Upon comparison, it is found that the application of the RF algorithm is also significantly better than the logit model in terms of prediction effect, indicating that there is a nonlinear effect among the factors influencing the parking choice behaviour of rural residents and that the RF model with the addition of latent variables provides a better explanatory ability for the study of the parking choice behaviour of rural residents.

With the rapid increase of vehicles in China, traffic jams and parking problem have been becoming the major concern of the urban cities. A large number of studies have shown that parking fees, as economic leverage, can play a vital role in regulating the dynamic traffic demands. This paper proposes a pricing model that applies the principal-agent theory in parking pricing problem. A real-world case of a public parking lot in Shanghai is presented to show the reasonability and applicability of the policy.

Optimal Parking Pricing in General Networks with Provision of Occupancy Information

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.

In modern prosperous cities, there is a great need for advanced parking assistant systems to reduce the hustle for the drivers. Such systems are a kind of intelligent transportation system, which provides drivers with parking information. Existing parking information systems usually ignore the parking price factor and do not automatically provide optimal car parks matching drivers' demand. Currently, the parking price has no negotiable space; consumers lose their bargaining position to obtain better and cheaper parking for their purposes. This paper takes into account the negotiable space on parking prices, makes a strategic decision by adopting the intelligent agent system, and then selects the optimal car park for the driver. These modern intelligent agents have the capability of planning, mobility, execution monitoring, coordination, etc. These characteristics can be utilized to build an integrated parking assistant system. The autonomous coordination activities challenge traditional approaches and call for new paradigms and supporting middleware. An agent-based coordination network was proposed to truly bring benefit to drivers and carpark operators.

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.

Impact of pricing policy change on on-street parking demand and user satisfaction: A case study in Nanning, China

In this paper we develop a dynamic model for the parking management of connected autonomous vehicles (CAVs) in a given area with multiple parking lots. Motivated by the well-known Lotka-Volterra equations, a mathematical model is developed to incorporate the interactions among those parking garages. Parking space availability is considered as the system state, while the price of parking is used as the control variable chosen by the parking garage operators from the admissible set. By regulating parking rates, the demand can be properly distributed among the set of parking lots under consideration, which could potentially reduce traffic congestion as well as fuel consumption of CAVs. Further, we formulate an optimal control problem with the objective of maintaining the availability of each parking lot at a desired level. The optimization problem is addressed using Pontryagin's minimum principle which determines the optimum parking pricing policy. A series of numerical experiments is conducted to show the effectiveness of the proposed approach. Since the mathematical model is fairly general and can be easily modified, it is believed that the procedures presented here will be useful for the parking management of CAVs in the near future.

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.

This study analyzes the parking behavior at route ends of fixed-route paratransit in Metro Manila called "jeepney", focusing on variation with passenger volume during peak and off-peak hours. In most cases (90.3%), jeepneys park on-street at the route-ends while waiting for passengers. This parking behavior can cause street congestion. Waiting time varies with passenger demand such that parking duration is longer during peak hours. The study will propose a simulation model which can explain the hourly variation of parking duration and volume of parked jeepneys. The appropriateness of the model will be tested using data gathered from a survey.

The parking cruising problem can be attributed to the unbalanced spatial parking space occupancy in the parking lot with multiple trip purposes and high traffic volumes, and it has already become an obstacle to improving travel efficiency. This study aims to investigate the reasons for the unbalanced occupancy of parking spaces and develop strategies to achieve spatial equilibrium by reducing parking cruising time. A field survey is first conducted to analyze the factors influencing parking space choice behavior, such as searching distances, walking distances, parking price, and parkers’ attributes. Based on the survey, a mixed logit model is calibrated to reveal parking space choice patterns of different parkers. Afterwards, the parking lot is divided into three zones based on searching distances and walking distances, using the K-means algorithm. According to the results, a simulation-based optimization model is then established, for minimizing the variance of average occupancy. An optimal pricing strategy is obtained at last. A case study in Nanjing, China, is conducted to verify the feasibility and rationality of the proposed method. The difference between weekdays and weekends is explored. The results show that, with the optimal strategies, the standard deviation of average occupancy in each zone decreases by 39.52% during weekends and 42.29% during weekdays. Finally, political implications and recommendations on the layout design of facilities and the adoption of differential parking pricing strategies are provided.

The improvement of parking lots and their management contribute to revitalizing the commercial function of an central area in a local city. First, we investigate the preference structure of users for parking lots by using the measurement techniques such as the analytical hiararchy process (AHP) and a conjoint method (CJM). A questionnaire survey was carried out against users of parking lots in the central area of Nagaoka City. Both of the measurement reveal that the distance from a parking lot to destination and parking fees are the most important factors for users. Secondly, we analize the relationship between the priority index by the AHP and the operational efficiency of parking lots to introduce a demand forecasting model for a parking lot.