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Intelligent parking services can provide parking recommendations and reservations for travelers. They are an effective method for solving the cruising for parking problems in big cities. This research conducted a sequential parking decision behavior survey and analyzed travelers’ parking choices and reservation behaviors at different stages of the...
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... In a study analyzing parking policies in 12 cities on five continents, it becomes clear that parking problems are similar everywhere; however, urban planners frequency, while the most critical factors are determined as the parking cost, parking search time, parking duration, and walking time between the parking spot and the final destination [14]. A study conducted in China found that individuals pay more attention to the walking distance after parking, driving time, and parking price when making their parking choices [29]. Moreover, a study in Israel showed that drivers who park for long durations are less likely to change their behavior in response to parking restriction policies [27]. ...
Parking behavior depends on drivers’ choice of parking type and willingness to pay for parking. Generally, the parking type refers to off-street and on-street parking facilities. The main factors affecting the preference for parking types are driver, vehicle, travel, and parking characteristics. Understanding drivers’ parking type preference behavior and accurately modeling drivers’ tendencies helps develop sustainable parking management policies. This study examines the parking preferences of drivers in Istanbul with binary logit models according to whether they pay for parking. The results of the models show that the number of factors influencing parking type preference is higher for free parking than for paid parking, including driver, vehicle, travel, and parking characteristics. Moreover, some factors in the models affect drivers’ parking type preferences differently for paid and free parking. Namely, low-income individuals tend to use on-street parking when parking is free and off-street parking when it is paid. Conversely, individuals who drive small-size vehicles prefer off-street parking for free parking and on-street parking for paid parking. Individuals who prefer off-street parking for free parking expect shorter walking distances to the final destination and parking duration. On the contrary, individuals who choose on-street parking for paid parking anticipate shorter walking distances to the final destination and parking duration.
... Some researchers have conducted in-depth and detailed studies on parking sequence optimization. Huanmei Qin et al. improved Intelligent Parking Services (IPS) by considering the decision-making behavior of travelers and psychological characteristics, building a parking recommendation model that balances parking resources and enhances the parking experience [34]. [35] introduced a fully automated valet parking system, which comprehensively evaluates the key factors affecting the driver's parking space selection, and rationally allocates parking spaces for automated vehicles. ...
With the continuous improvement of the world’s economic development level, the traffic pressure on the road networks of major cities around the globe has been increasing. Influenced by multiple factors, the traffic pressure on urban road networks is not balanced in different directions and at different times. We evaluated and analyzed the efficiency of traffic operation and pollutant emission levels at different time periods and traffic volume using micro traffic simulation software against the background of real roads in the Nishi-Waseda area of Tokyo. We determined the standard traffic volume from 7:00 a.m. to 8:00 a.m. based on the pollutant emission level and designed three on-street parking scenarios taking into account different driving habits and application scenarios and conducted comparative analysis of different parking scenarios. In terms of pollutant emission levels, the on-street parking of Scenario A reduces carbon monoxide emissions by 2.86% and 0.97%, respectively, compared to the other two parking scenarios. The emissions of NOx and VOC are also less than in the other two scenarios. and the results of the study will provide effective decision-making recommendations for the traffic management department. The contribution of this study is the development of a methodology for assessing traffic operations and pollution emissions based on a real road environment. By designing and comparing different on-street parking schemes, the comparative analysis reveals the efficacy of various parking management strategies in mitigating environmental impacts. Moreover, this study provides a valuable reference for understanding the dynamic changes of traffic pressure on the urban road network and their impact on the environment.
... When Mei et al. studied reserved parking spaces, they found that when the proportion of reserved vehicles is not high, congestion in popular parking lots may intensify [36]. Qin et al. [37] found that 56% of travelers chose to make parking reservations during the travel process. ...
... Conversely, in the latter scenario, the main track remains underutilized while the fast track becomes congested. These findings align with the research conducted by Mei and Qin, with the exception that their vehicle reservations were substituted with passenger reservations [36,37]. ...
... congestion on the main track and exacerbating congestion issues. Conversely, in th scenario, the main track remains underutilized while the fast track becomes con These findings align with the research conducted by Mei and Qin, with the excepti their vehicle reservations were substituted with passenger reservations [36,37]. When travel reservations are employed in conjunction with flow-control fenc effectively alleviate the obstruction of the original passage. ...
This study addresses the challenging problem of increasing passengers’ travel efficiency while lowering the infection transmission risk at metro stations during COVID-19 pandemic. To achieve this objective, we deploy Anylogic software and formulate an infection risk model. As a case study, this study focuses on a transfer metro station in Xi’an, China. Firstly, by utilizing Anylogic software, three distinct strategies are simulated: flow-control fences, travel reservation, and the collaborative use of travel reservations and flow-control fences. Secondly, the passenger density and average dwell time under these strategies are assessed while constructing an infection risk model to quantify the risk faced by passengers. Thirdly, when compared to the absence of any strategy, the results are as follows: (1) The flow-control fences strategy: implementing flow-control fences can effectively reduce the risk of passenger infection when the length of the flow-control fences is fixed at 47.5 m, but comes at the cost of a 20.15% decrease in passenger travel efficiency; however, excessively long flow-control fences will neither alleviate congestion nor reduce the infection risk. (2) The travel reservation strategy: the adoption of travel reservations, along with a fast track for reserved users, when the reservation proportion is 40%, leads to a remarkable 29.05% improvement in travel efficiency and reduces the risk of passenger infection by 67.12%. (3) The combined strategy: the combined utilization of travel reservations and flow-control fences enhances travel efficiency by 15.80% and reduces the risk of passenger infection by 56.77% when the reservation proportion is set at 30%. When the reservation proportion is between 10 and 30%, its infection risk reduction effect is better than that of the travel reservation strategy, but this is not necessarily true for their effects on travel efficiency. Finally, this study was compared to an existing study that proposed a new strategy by combining travel reservations with departure intervals, analyzing the effect of the implementation of the strategy with different departure intervals. The findings from this study have implications for developing appropriate strategies to optimize passenger flow without significantly compromising the transmission of infection risk during the pandemic.
With urban traffic congestion and parking issues becoming increasingly severe, the emergence of smart parking systems offers a solution. This research designs and implements an Android-based intelligent parking reservation management system. It aims to provide convenient and efficient parking services by combining mobile technology with intelligent management. The system includes core functions such as user management, parking facility information retrieval, parking space reservation, and driving navigation, catering to diverse needs in urban parking scenarios. Through rigorous system testing, this solution demonstrates excellent performance in user experience, functionality, and efficiency. The Android-based parking reservation management system proposed in this study offers an innovative solution to address urban parking challenges. It paves the way for new possibilities in smart city transportation management.
In the modern era, the issue of vehicle parking has become a significant concern in substantial investments. The conventional approach of locating available parking spaces by manually searching through multiple lanes has proven to be both time-consuming and labor-intensive. Furthermore, it requires parking safely and securely, eliminating the risk of being towed, and at a reduced cost. To tackle this challenge, a cutting-edge parking control system has been developed. This system incorporates secure devices, parking control gates, time and attendance machines, and car counting systems. These features play a crucial role in ensuring the safety of parked vehicles and effectively managing the fee structure for every vehicle's entry and exit. By leveraging IoT-powered technologies, it simplifies the process of locating available parking spaces by providing real-time information, reducing the manual effort required. With IoT, parking management is revolutionized, offering drivers a seamless and secure parking experience while optimizing operational efficiency for parking operators.
