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Controlled parking systems in cities provide designated parking zones and allow citizens to easily find parking spaces increasing comfort and potentially reducing traffic and pollution. However, illegally occupied parking spaces can negatively affect the accuracy of these systems, diminishing their benefits. This work presents a semi-supervised deep learning approach for fine-grained parking violation rate prediction using graph neural networks, which can capture both the spatial and temporal dynamics of parking systems and driver behavior, providing a valuable tool for controlled parking systems. The proposed method addresses several challenges faced in this task, including the need to appropriately construct temporal graph-based training datasets and design a model that can handle missing values. Additionally, the method includes a novel semi-supervised data augmentation and smoothing technique to mitigate the effect of the small amount of annotated data and/or missing data in the training dataset. The proposed method was evaluated using a large-scale dataset from Thessaloniki's on-street public parking system and found to significantly improve the accuracy of parking violation prediction compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The largest public space in Cirebon City, Indonesia, is Bima Sports Center, with conditions that could be more conducive, resulting in the conversion of sidewalks that are not suitable for their function into areas for street vendors and illegal parking, which hurts the interests of pedestrians. This study aims to provide design recommendations (prototype) based on PU regulation no. 02/SE/M/2018 about pedestrian facility standards. The methods used in this research are the Pedestrian Level of Service (PLOS) approach in evaluating the performance of pedestrian facilities, the Customer Satisfaction Index (CSI) approach in assessing pedestrian satisfaction, and providing design recommendations based on the results of the CSI analysis. The research results show that the PLOS value in existing conditions varies from A - to F, and the main variables that determine pedestrian satisfaction are cleanliness, safety, and lighting facilities. Based on the analysis of the results, the design recommendation is to adjust the geometry according to standards and emphasize the Provision of cleaning facilities (bins), lighting, and safety fences along pedestrian paths. [ABSTRACT FROM AUTHOR]

Double parking has many negative effects on traffic indicators such as traffic congestion, traffic flow conditions, and traffic safety. Double parking includes parameters that affect drivers' behavioral and traffic habits. Various inspection activities and penal sanctions are implemented to prevent parking violations. Within the scope of this study, it is aimed to detect double parking with the YOLOv8 model, one of the deep learning algorithms. In this direction, a data set consisting of a total of 891 images was created, taking into account the streets with high traffic density in İzmit and Erzurum. As a result of the YOLO model, the measurement parameter F1 score value was obtained as 0.83. The mAP@0.5 values of the model for double parking, normal parking and the entire data set were obtained as 0.851, 0.922 and 0.886, respectively. When other performance parameters were examined, it was concluded that the model successfully detected the double parking situation. According to the model performance results, 89% of double and normal parking situations were detected correctly. A data set infrastructure has been created for studies on the detection of double parking. With this study, the initial work of the systems for automatic detection of parking violations and instant warning of drivers was carried out. [ABSTRACT FROM AUTHOR]

Taxila intersection is facing severe traffic issues includingcongestion, delays, increased travel times and inadequate infrastructure. The aim of the study is to highlight the real problems at Taxila intersection related to road users, vehicles and road infrastructure. Traffic data was collected during peak hours through traffic observing video cameras at the unsignalized intersection. Subsequently, traffic violation measurements and their comparisons between morning and evening peak hours, as well as between weekend and week days were developed. The study also identifies the involvement in traffic violations by different drivers, age groups and vehicle types. Comparing morning and evening peak hours reveals variations in traffic patterns and congestion levels throughout the day, helping to create customized plans. Similarly, investigating disparities between weekends and weekdays uncovers unique factors influencing traffic conditions on different days. Results showed that highest percentage of violations at the intersection resulted from 'illegal crossing of road by pedestrian' followed by 'illegal parking' at the intersection. While the lowest percentage of violation observed was 'wrong way driving' at the intersection. In conclusion, this research provides a comprehensive understanding of the traffic issues at the Taxila intersection and offers evidence-based strategies to improve traffic flow, mitigate congestion, and enhance road safety in the area. [ABSTRACT FROM AUTHOR]

Parking in dense urban areas is a major challenge for last mile logistics. Parking shortage and policies that do not address commercial vehicles' needs often lead these vehicles to park illegally. This paper conducts a scoping literature review on the parking behaviours of commercial freight and service vehicles, methods used to model these behaviours, and factors that determine their outcomes. Thirty-four studies are included in the review. It is found that commercial vehicles' parking behaviours mainly comprise parking location and type choices including illegal parking, parking duration, and parking cruising. Methods used to model these behaviours primarily include discrete-choice modelling, regression analysis, survival analysis and simulation. We identify key knowledge gaps and provide insights on research opportunities in modelling more complex parking decisions, investigating parking cruising of commercial vehicles, evaluating the implications of freight demand management, and developing data fusion techniques. [ABSTRACT FROM AUTHOR]

Minimum parking requirements (MPRs) have been criticized for creating excess parking, degrading urban form, reducing housing affordability, and encouraging automobile dependency. As a result, many American cities have begun to reduce or remove parking minimums in some or all areas. However, existing research on the effects of these policy changes has focused only on the analysis of parking supply. We investigated the broader results of parking reform at a small university city in the Midwest that removed MPRs for downtown and university districts. Our quasi-experimental research found that onsite parking construction in the deregulated zones decreased dramatically, from 108% of the earlier requirement to only 46%, clearly indicating that MPRs had enforced oversupply of parking. Of the 43 new major developments built in the 7 years following the parking reform, 84% provided less parking than previous requirements, including eight developments with zero parking. The reduction in onsite parking led to more efficient use of existing parking stock, both public and private. Furthermore, the removal of MPRs, combined with other policies, helped improve urban form by increasing housing density, promoting active building frontages, and guiding a growing share of new developments to transit-rich and walkable districts. American cities stand to benefit greatly by relaxing or repealing parking requirements. Cities that expect resistance to parking reforms can take a targeted and incremental approach, first removing MPRs in denser areas such as downtowns, transit corridors, and other prime districts where an oversupply of parking is typically being reinforced by uniform MPRs. The case of Champaign (IL) demonstrates that a targeted district-scale reform is likely to encounter minimal opposition. [ABSTRACT FROM AUTHOR]

Taking Mount Wuyi National Park as an example, this paper uses the Willingness to Pay (WTP) and Best-Worst Scaling (BWS) methods, combined with quantitative analysis methods such as linear correlation analysis and binary logit regression analysis, to explore tourists' willingness to pay for Mount Wuyi Park and its influencing factors. Research results show that tourists' overall willingness to pay is concentrated in the range of 61 to 150 yuan, and Maxdiff model shows that tourists' willingness to pay is 77.04 yuan. The willingness to pay of tourists is positively correlated with their satisfaction and daily average consumption in scenic areas. The higher the overall satisfaction of tourists, the higher their willingness to pay. The impact of factors such as daily expenses, number of tourists, and infrastructure of scenic spots on willingness to pay is more significant. Respondents with higher daily expenses are more affected by the number of tourists and the quality of scenic spot services. [ABSTRACT FROM AUTHOR]

Understanding which aspects can have the greatest influence on the performance, comfort and safety of a public transport system can be of great help in improving its operation and promoting more sustainable mobility. To find out more about these aspects, a survey was carried out among more than 180 drivers of urban bus transport systems in seven medium-sized cities in Spain. The drivers' perspective has been insufficiently taken into account in previous research, despite the fact that these workers have a high and direct knowledge of the operation and problems directly affecting the service. The data collected through this survey have been analysed using descriptive statistics and an Ordered Probit model. The results have allowed us to detect specific aspects to improve the performance and comfort of users and drivers such as the correct location and length of bus stops and enforcing regulation to ensure that buses have priority in traffic and that illegal parking does not interfere with their operations. In addition, bus lanes were seen as important by drivers to improve the comfort and safety of the service. Other factors related to safety were generally highly rated and there was little dispersion in the responses about their importance, although aspects such as proper visibility, avoiding illegal parking, and campaigns to encourage bus users to be aware of their surroundings stood out in the answers. Finally, the payment system was highlighted as very relevant to ensure the performance and comfort of users and drivers during the operation at bus stops. Improving this system was identified as the measure that could have the greatest impact on the performance of the service as a whole. [ABSTRACT FROM AUTHOR]

Intersections are considered among the most hazardous points in traffic and can pose significant dangers, especially for pedestrians. The primary aim of this study is to investigate factors affecting pedestrian safety at intersections and to develop effective strategies to enhance pedestrian safety. In this context, various factors influencing pedestrian safety at intersections need to be considered. Within the scope of the research, factors such as pedestrian speed, vehicle speed, traffic signs and signals, intersection geometry, signaling systems, road surface, traffic volume, illegal parking, weather and lighting conditions, pedestrians' ages and clothing choices have been identified as key elements affecting pedestrian safety at intersections. These criteria have a wide range of impacts on traffic safety. The SWARA method was used to determine the priority order of these criteria. As a result of this analysis, vehicle speed was identified as the most significant factor, while pedestrian clothing choice was determined to be the least important factor. Based on the analysis of these criteria, appropriate measures can be taken to ensure safe pedestrian crossings. In conclusion, a holistic approach must be adopted to enhance pedestrian safety. This entails considering all factors affecting pedestrian safety at intersections and developing appropriate strategies. This approach can lead to more effective solutions to intersection safety issues and ensure pedestrian safety. [ABSTRACT FROM AUTHOR]

Artificial intelligence systems based on deep learning architectures are being investigated as decision-support systems for human decision-makers across a wide range of decision-making contexts. It is known from the literature on AI in medicine that patients and the public hold relatively strong preferences in relation to desirable features of AI systems and their implementation, e.g. in relation to explainability and accuracy, and in relation to the role of the human decision-maker in the decision chain. The features that are preferred can be seen as 'protective' of the patient's interests. These types of preferences may plausibly vary across decision-making contexts, but the research on this question has so far been almost exclusively performed in relation to medical AI. In this cross-sectional survey study we investigate the preferences of the adult Danish population for five specific protective features of AI systems and implementation across a range of eight different use cases in the public and commercial sectors ranging from medical diagnostics to the issuance of parking tickets. We find that all five features are seen as important across all eight contexts, but that they are deemed to be slightly less important when the implications of the decision made are less significant to the respondents. [ABSTRACT FROM AUTHOR]

For freight and servicing movements entering a city, parking provides the access enabling service to customers. This will invariably occur on the street in parking spots or in modern buildings in off-street facilities. In light of changing urban planning priorities and as a result, declining on-street loading zone spaces, this paper explores the provision and challenges of off-street loading docks to support freight and servicing task activity in major urban centres. While it may not be fully appreciated, provisions to adequately accommodate a city's generated freight task is critical to urban planner's broader objectives. As a non-discretionary transport task, freight vehicles will continue to enter cities. If good off-street loading dock facilities are not provided, vehicles will seek out legitimate or illegal on-street parking, and urban planner's place making objectives are likely to be compromised. The paper first considers the planning approaches that govern the provision of loading docks. This paper focuses on Sydney but draws on comparisons primarily to the Borough of City of London. Based on recent observations in Sydney, it then considers various stakeholder perspectives towards loading dock provision and use. Modelling approaches to forecast better requirements that could assist to overcome the planning issues faced are then outlined. The paper concludes with actions taken to address the challenges. • Lack of tools for planning loading docks leads to conflict between developers and government. • Poor provision of loading docks in new developments can compromise urban planning objectives. • Regression and clustering analysis techniques allow the capacity of loading docks and their levels of service to be estimated. [ABSTRACT FROM AUTHOR]

The parking problem is a common sub-topic of transportation planning and urban planning, and it is increasing its impact day by day as urbanization intensifies. The rapid increase in the number of private vehicles, coupled with the presence of borders and thresholds limiting capacity increases in cities render the demand-supply problem permanent. The parking problem is generally addressed by rapidly generating additional supply to meet the rising demand and is dealt by non-integrated, piecemeal methods. Such micro- and medium-sized solutions only focus on temporarily deferring the larger problem. Hence, there is a need for an approach in which the demand is determined at all scales, evaluated on the basis of sub-centers, and policies and strategies are produced accordingly. In this study, a demand-oriented integrated parking lot planning approach has been generated for Trabzon, where supply is strongly limited due to topography. With the generated approach, policies and principle recommendations have been developed. [ABSTRACT FROM AUTHOR]

A growing population necessitates more transportation, which pressures car parking spots. Parking is a problem for public places in cities, such as theatres, malls, parks, and temples. Even though several techniques have been suggested in publications, manual parking systems are still used in most places. For large locations where it is challenging to find open spaces, traditional parking arrangements need to be more archaic and convoluted. This might lead to heavy traffic, minor mishaps, and widespread accidents. In the modern era of sophisticated parking management systems, an automatic parking spot-detecting system has been introduced in an innovative format. Experts in computer vision are drawn to this emerging field to contribute. The system could tell if the automobile was fully or partially parked. Neither during the process nor afterward, human oversight is required. As parking management enters the modern era, computer vision is becoming increasingly critical. The parking system will not only make it easier for drivers to identify parking spaces but also enhance parking administration and monitoring. Vehicles will be able to observe available parking spots due to technology that monitors parking spaces. India and other emerging nations, as well as industrialized ones, have recently shown interest in smart cities. This article's smart auto parking system was conceived and implemented utilizing a Raspberry Pi and cameras placed in various parking spaces. Using a website and an Android app, this project creates and deploys a real-time system that enables vehicles to efficiently find and reclaim open parking spaces. [ABSTRACT FROM AUTHOR]

Illegal parking during peak hours and on heavily traveled streets can significantly impact the severity of traffic congestion and related problems. A double parking violation is among the most significant instances of illegal parking. Double parking violations reduce road capacity and increase traffic congestion. Intensified traffic congestion also increases travel time and reduces road user satisfaction. Thus, the management of double parking violations is critical to managing traffic congestion in urban streets. In this study, the factors influencing the drivers' tendency to perpetrate a double parking violation were identified using the ordinal logistic regression model in the central area of Mashhad. Then, by redesigning the ordinal logistic regression model and utilizing the identified effective factors, a model was developed to predict the level of drivers' propensity to perpetrate a double parking violation during urban trips. Mashhad is one of the largest cities in Iran, and its central district experiences a high volume of daily traffic. The results indicated that parking duration is the main factor affecting drivers' propensity to perpetrate a double parking violation. Driver education level and driver presence in the vehicle ranked second and third, respectively. The P-value in the goodness-of-fit test was obtained at less than 0.05, indicating the designed model's acceptable accuracy. • The greater the necessity of parking, the greater the likelihood that the driver will commit a double parking violation. • Reducing the educational level of drivers to B.Sc. or less increase the likelihood that they will perpetrate double parking. • If the parking duration is less than 25 min, the driver's visual search distance for parking will be significantly reduced. [ABSTRACT FROM AUTHOR]

During the peak period of urban traffic, about one-third of vehicles are looking for parking spaces, resulting in air and noise pollution, and even traffic accidents due to emotional stress of drivers; one of the reasons is the lack of guidance information for vacant parking spaces, which leads to inefficient parking behavior. The "Guide System" of smart parking can guide drivers to quickly enter the parking space to improve parking efficiency, which can greatly reduce urban air and noise pollution, and even reduce traffic accidents and casualties. This study discusses the technological development of smart parking "navigation system", through the social network analysis formed by patent citation, and then analyzes and discusses the technology status and role of individual companies in the industry. This research uses two analysis indicators of technology overlap and network centrality and uses patent citation networks to analyze the technology status of individual companies. Technology overlap is composed of technology knowledge status (TKS) and technology knowledge reliability (TKR), and social network centrality. This research uses the navigation system of smart parking to explore whether this model can locate the technology role and location of individual companies. Companies with special industries/equipment/unique technologies are scattered in special fields. [ABSTRACT FROM AUTHOR]

The number of laws criminalizing homelessness is increasing in the United States. Simultaneously, more individuals are experiencing homelessness, and of those, an increasing number are experiencing vehicle homelessness. Safe parking lots, where people can legally shelter in their vehicles overnight, are one response to the criminalization of vehicle homelessness. Using a mixed-methods research design (n = 37), this study explores barriers to emergency shelters for people experiencing vehicle homelessness and the potential impact of safe parking lots. Our findings indicate that safety is a major concern for this population and safe parking lots have the potential to improve perceptions of safety. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is rapidly becoming one of the most talked-about and essential components of any digitization process. The IoT is comprised of several key necessary components, the most important of which are sensors, communication (the internet), and user interfaces for data processing. IoTs are currently finding applications in virtually every industry, including healthcare, where they are known as the internet of medical things (IoMT), industry, where they are known as the industrial internet of things (IIoT), and interconnection between people, where they are known as the internet of everything (IoE). The challenge is to leverage the Internet of Things (IoT), technology, and data to create smarter and more sustainable cities that enhance the quality of life for residents. Therefore, in this article; we have demonstrated the use of the IoT in a variety of applications for smart communities. These applications include smart transportation, smart water management, smart garbage management, smart house illumination, smart parking, smart infrastructure, etc. This research also includes an explanation of the flow process of implementing the IoT in different applications of smart communities, as well as their characteristics and particular applications. Along with their flow illustration, the stages involved in the implementation of smart city applications and the components they consist of are also displayed here. We have also taken into consideration the instances of particular cases and their implementation utilizing IoT. Some of these cases include the automated water collection methods of smart water management systems as well as the condition of the water. Based on the findings of the research, we came to the conclusion that IoT devices play an essential role in each and every one of the smart city project implementations. [ABSTRACT FROM AUTHOR]

The major problem in Thailand related to parking is time violation. Vehicles are not allowed to park for more than a specified amount of time. Implementation of closed-circuit television (CCTV) surveillance cameras along with human labor is the present remedy. However, this paper presents an approach that can introduce a low-cost time violation tracking system using CCTV, Deep Learning models, and object tracking algorithms. This approach is fairly new because of its appliance of the SOTA detection technique, object tracking approach, and time boundary implementations. YOLOv8, along with the DeepSORT/OC-SORT algorithm, is utilized for the detection and tracking that allows us to set a timer and track the time violation. Using the same apparatus along with Deep Learning models and algorithms has produced a better system with better performance. The performance of both tracking algorithms was well depicted in the results, obtaining MOTA scores of (1.0, 1.0, 0.96, 0.90) and (1, 0.76, 0.90, 0.83) in four different surveillance data for DeepSORT and OC-SORT, respectively. [ABSTRACT FROM AUTHOR]

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The last decade has seen further dynamic dissemination of access to the Internet and mobile technologies. In many areas, there is an increasing use of technology, not with the use of personal computers, even in the form of laptops, but with smartphones. The increasing technological possibilities of these devices, faster and faster communication and, what is important, the affordability of devices and services for the majority of society have made them one of the practically indispensable accessories of every human being. The aim of the article is to verify how significantly mobile payment technologies influenced consumer behavior in the area related to meeting transport needs on the example of a selected city by purchasing public transport tickets or parking fees. [ABSTRACT FROM AUTHOR]

Copyright of Techno.com is the property of Universitas Dian Nuswantoro, Fakultas Ilmu Komputer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Over the past few years, there has been a noticeable growth in the use of video surveillance systems, frequently functioning as integrated systems that remotely monitor key locations. In order to prevent terrorism, theft, accidents, illegal parking, vandalism, fighting, chain snatching, and crime, human activities can be observed through visual surveillance in sensitive and public places like buses, trains, airports, banks, shopping centers, schools, and colleges. In this paper, a review of the state-of-the-art is provided, showing the overall development of identifying suspicious behavior from surveillance recordings over the past few years. We give a quick overview of the issues and difficulties associated with recognizing suspicious human activity. The purpose of this publication is to give this field's scholars a literature evaluation of several suspicious activity recognition systems along with their general structure. [ABSTRACT FROM AUTHOR]

Shared e-scooters were introduced in urban public spaces as a way to promote a modal shift from cars in short-distance trips, as well as to improve sustainability, resilience, and equity in urban transport. However, the expansion of shared e-scooter services in 2019 proved that this mode of transport, without integrated planning strategies, can bring some problems to cities, which are related to the illegal parking of e-scooters, an increase in head injuries, and the lack of population diversity among users. Regarding the latest, this research work aims at conducting a case study in the city of Braga, Portugal to reveal who the actual and potential users of shared e-scooters are and how their socioeconomic profile (e.g., gender, age, income range, literacy, occupation) and usage patterns related to the ones found in other cities. For this, a revealed preference survey was deployed on the case study site, and the respondents' profiles were statistically correlated with the socioeconomic characteristics of the city's general population in order to assess if this mode of transport provides an equitable service. Results show that shared e-scooters are not equally used by people of different genders, ages, and income ranges. Information on e-scooter usage inequalities across the population is useful for the city to proceed with more socially equitable mobility policies. [ABSTRACT FROM AUTHOR]

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A lightweight parking wedge was developed from the three aspects, including structure, materials, and technology based on the requirements of laws and regulations as well as the actual demand for automobile lightweight, and the resulting product was tested and analyzed from its performance to the whole vehicle according to the technical requirements. The results indicated that the developed parking wedge met the requirement of all the tests specified through adopting a cylindrical hollow structure and using the PP-matrix composite containing 30 wt % glass fiber. Its weight was only 0.98 kg, indicating a significant weight reduction effect compared to the original rubber and metal parking wedge, together with good economy and good environmental protection. [ABSTRACT FROM AUTHOR]

The study presents spatial analysis of traffic violations in Luzhou city, China. Three metrics of spatial point pattern, namely intensity, spatial correlation, and spacing, are evaluated using (i) kernel density and quadrant count; (ii) K and L functions; and (iii) nearest neighbor and empty-space distance methods, respectively. The results show that: (i) three specific places have a high rate of illegal turning movements, disobeying prohibited signs, and illegal parking violations; (ii) all the violation categories have hot spots, among which 53.49% of the total appear at nine different places; (iii) the spatial correlation discloses the existence of spatial dependence and closeness of similar violations; and (iv) the spacing between traffic violations also signifies clustering of violations. Generally, the spatial patterns of traffic violations are nonuniform, have identifiable hot spots, and are clustered. The findings are beneficial for conceiving safety treatment strategies against traffic violations and additional relevant clustering studies. [ABSTRACT FROM AUTHOR]

On-street parking could be considered one of the most well-known kinds of parking in urban areas. This study investigated the role of factors affecting the parking maneuver time. These factors included driver characteristics (gender, age, weight, and clothing type), parking space-related factors (parking space length and parking permit), maneuver type, lighting condition, and vehicle length. The effect of these factors was measured utilizing several statistical methods as well as Poisson and negative binomial regressions. Analysis results showed that illegal parking significantly reduced the parking maneuver time. This is in terms of the larger parking spaces available in illegal places. Moreover, front-in parking took less time than reverse parking. While front-in parking takes just one movement, reverse parking requires at least two, making it a time-consuming process. Because the length of the parking space was inversely linked with the time required to navigate, increasing the length of the parking space resulted in a reduction in maneuver time. The result showed that maneuver time at night was longer compared to the daytime. Result of inadequate lighting, cars do the parking maneuver more carefully and add more time to the process. Furthermore, the average maneuver time was 12.44 s, significantly different from 18 s indicated in the highway capacity manual. [ABSTRACT FROM AUTHOR]

A common way citizens interact with the legal system is through parking tickets. Existing literature suggests that there is a relationship between parking tickets and neighborhood characteristics, particularly higher rates in economically vulnerable and historically marginalized neighborhoods. The current research is limited to a select number of cities. We seek to examine if these relationships exist at a larger geographical scale. Specifically, we investigate the spatial distribution of parking tickets across 16 U.S. cities. Employing multivariate regression models, we examine the relationship between neighborhood parking ticket rates and racial/ethnic and socioeconomic composition at the block group level. We found that percent Hispanic and percent poor are positively associated with total parking tickets, and parking tickets for violations, days of the week, and times of the day that may be more likely to be issued to neighborhood residents. While percent Black exhibits no association with total parking tickets, it is positively associated with tickets issued by police officers, revealing the complicated relationship between infrastructure, policing, and race. • Studies a common way citizens interact with the legal system • Examines the spatial distribution of parking tickets across 16 U.S cities • Identifies relationships between neighborhood composition and parking tickets • Higher ticketing rates in Hispanic and poor neighborhoods • Positive association between police-issued tickets and Black neighborhoods [ABSTRACT FROM AUTHOR]

• Deep learning-based on-street parking violation prediction system is proposed. • The proposed method works in an indirect way, i.e., without using sensors. • A data augmentation and smoothing method is used to deal with noisy data. • Evaluated using a dataset with 3.9 million scans for illegally parked cars. The lack of available parking spaces can be among the most significant issues that can affect the quality of life of citizens in large cities. This has led to the development of on-street parking systems that typically ensure that parking spaces will be available for the local population, as well as provide easy access to parking for visitors, e.g., by providing directions for finding sectors where parking slots are available. Unfortunately, such systems are affected by illegal parking, i.e., parking without paying the parking fee, since in this case, the number of registered parked cars does not match the number of cars that are actually parked, leading to providing incorrect suggestions to drivers. This can discourage drivers from using such systems, potentially further increasing the parking violation rate. Such phenomena can be addressed by using smart sensors that can detect the presence of cars in various areas. However, installing and maintaining such systems is costly, which usually discourage cities from implementing such solutions. The main contribution of this paper is a Deep Learning (DL)-based pipeline that works in an indirect way (i.e., without using sensors) and allows for developing an accurate fine-grained parking violation prediction system, increasing in this way the accuracy of the information provided to on-street parking systems with minimal cost. To deal with missing and noisy data we also propose a data augmentation and smoothing technique that can further improve the accuracy of DL models, when used in such scenarios. The effectiveness of the developed system is validated using experiments on a large-scale dataset, which contains more than 3.9 million scans for illegally parked cars collected by the municipal police in Thessaloniki, Greece. [ABSTRACT FROM AUTHOR]

In recent years, many progressive U.S. cities have witnessed the rapid popularization of dockless small vehicles as a car-free travel alternative to meet the short distance travel demand. The research gap exists in revealing the social outcome of the massive influx of shared small vehicles on public space. To that end, this study analyzed 4,100 parking violation reports in Austin, Texas, crowdsourced from the Austin 311 non-emergency service request system. The results showed that sidewalk and other public space intrusions were the two most frequently reported violations. Additionally, it found that improperly parked vehicles in parks required the longest time to be cleaned. Among the three reporting methods included in this study, 91% were submitted through smartphone applications, compared to 5% by phone calls and 2% through the web interface. The response time of smartphone reports was significantly greater than that of phone call reports (17.4 hours vs. 2.5 hours). Finally, the GIS hotspot analysis showed that university campus and downtown were both violation clusters, yet campus violations were solved more quickly. This study proposed a shared responsibility framework of key players in shared micro-mobility management and suggested using crowdsourcing 311 system data to facilitate communications between stakeholders. [ABSTRACT FROM AUTHOR]

The studies of Unmanned Air/Ground Vehicle (UAV/UGV) cooperative detection systems have received much attention due to their wide applications in the disaster rescue, target tracking, intelligent surveillance, and automatical package delivery missions. UAVs provide a broad view and have a fast speed in the air, while UGVs have sufficient load capacity and can serve as repeater stations on the ground. The path planning of a UAV/UGV cooperative system is an important but difficult issue, which aims to plan paths for both the UAVs and the UGVs in the system to cooperatively complete a mission. In this article, we consider the path planning problem of the UAV/UGV cooperative system for illegal urban building detection, by taking the limits of UGV speed, UAV load power, and UAV/UGV communication restriction into consideration. To solve this problem, we first model the path planning problem as a constraint optimization problem which tries to minimize an overall execution time for completing the illegal urban building detection tasks, and then propose a two-level memetic algorithm (called Two-MA) to solve the path planning problems of both the UAV and the UGV. Experiments on both synthetic and real-world data sets show the superiority of the proposed Two-MA over several states-of-the-art algorithms in solving the path planning problems of the UAV and UGV for illegal urban building detection tasks. Note to Practitioners—This article was motivated by the task of detecting illegal buildings in cities by unmanned vehicles. Previous works mainly focus on path planning of either UAVs or UGVs in this task. This article proposes a new approach using an Unmanned Air/Ground Vehicle (UAV/UGV) cooperative system for detecting illegal buildings in parks, by taking the limits of UGV speed, UAV load power, and UAV/UGV communication restriction into consideration. This cooperative system consists of a UAV, UGV, and control center. The UAV equipped with cameras takes aerial photography in the air, and can transmit collected photos to the control center. The UGV executes loading and transportation on the ground, and can serve as takeoff and landing platforms for the UAV. The control center executes computationally intensive tasks such as data transmission and processing, task scheduling, and vehicle coordination. To quickly complete all detection tasks, a memetic algorithm is proposed for path planning of both the UAV and the UGV. The simulated results show that the proposed algorithm enables the UAV/UGV cooperative system to visit all buildings in cities with a minimum task execution time. [ABSTRACT FROM AUTHOR]

Commercial vehicles are more likely to park close to their destinations than passenger vehicles even though sometimes parking violations are inevitable for their freight or service activities. Although clear distinctions between commercial and passenger vehicles lead to different illegal parking behaviors, little is known about the difference in double parking behaviors between the two types. To better utilize limited curb spaces, a better understanding of differences and similarities is required so that corresponding policies can be established accordingly. Therefore, this research aims to quantify the difference in double parking behavior between commercial and passenger vehicles using count models from two different perspectives: (a) an individual vehicle level count model to investigate vehicle attributes influencing the number of double parking violations per vehicle and (b) a ZIP Code level count model to investigate economic and built environment factors affecting double parking behaviors. The results showed that double parking is a 'one-time event' for a passenger vehicle and a 'recurring event' for a commercial vehicle. With respect to economic factors, different types of establishments had different levels of impact on double parking for commercial and passenger vehicles in an area. This research discusses how parking policies and strategies can be established based on the similarities and differences between commercial and passenger vehicles' parking violation behavior. [ABSTRACT FROM AUTHOR]

Combating illegal parking and drinking in public is the raison d'être of Russia's best-known law-and-order youth initiatives, StopKham and Lev Protiv. These initiatives enforce and promote neotraditional morals amongst young people by challenging alleged offenders on camera and uploading the entertaining, humorous and often violent video clips to YouTube. I argue that their practices encapsulate flexible authoritarianism, in which the regime incentivises citizens to take initiative while expanding repressive measures against dissenters. Not only do these enterprises reflect the regime's goals back at itself, they also popularise a new ideal of heroic masculinity that fuses patriotism with entrepreneurialism. [ABSTRACT FROM AUTHOR]

"Paved Paradise: How Parking Explains the World" by Henry Grabar is a comprehensive book that explores the multifaceted impact of parking on urban living, the environment, and the economy. The author discusses various topics, including the effect of parking on affordable housing, the corrupting influence of parking on individuals and government bodies, and the negative impact of parking on the climate crisis. The book also highlights extreme behaviors and conflicts that arise over parking spaces, as well as the historical development of parking policies. It emphasizes the need for parking reform and showcases the efforts of individuals and organizations working towards change. This book is recommended for planners, transportation engineers, citizen advocates, and anyone interested in understanding the profound effects of parking on our cities and lives. [Extracted from the article]