Identity Privacy Protection in Smart Parking Navigation

With the increasing population in urban areas, finding a vacant parking space has always been the biggest problem for drivers, which causes a series of issues such as energy waste and environmental pollution. Most outdoor parking lots cannot provide details of vacant parking spaces due to limited sensor technology in outdoor environments (i.e., poor resistance to external interference due to heat, light or signal noise). Although some studies are based on images captured by cameras, the existing vacancy detection methods using fixed-position surveillance cameras are not flexible and accurate enough for wide use. We propose an Inclined Bounding Box (IBB) method for detecting vacant parking spaces using aerial images. The IBB method takes advantage of the feature-description ability of deep Convolutional Neural Network (CNN) to identify the status of parking spaces and develops an inclined bounding boxes calibration algorithm on the top layer of CNN architecture. The proposed IBB method can automatically identify parking spaces, locate vacant parking spaces with any direction and detect multiple parking spaces simultaneously. The experimental results show that the proposed IBB method is more accurate than the three counterpart methods.

Finding a vacant parking space in outdoor parking lots is a daily concern of most vehicle drivers during rush hours, especially in the urban context. In this paper, an outdoor parking space vacancy detection system is proposed, using mobile devices to improve parking space searching experience for vehicle drivers by providing them with the location and occupancy information of parking spaces. The system uses state-of-the-art image recognition algorithm, namely Convolutional Neural Network with a Raspberry Pi to identify vacant parking spaces from a parking lot image retrieved in real time via an IP camera. A university parking lot has been chosen as the test bed to deploy the proposed system for real time parking space vacancy detection. An Android smartphone application called Driver App is developed to enable ubiquitous visualization of real time outdoor parking spaces occupancy information for vehicle drivers. Evaluation outcomes based on the responses to System Usability Scale (SUS) questionnaire revealed high usability of the Driver App as a tool that provides smart parking service to assist vehicle drivers in searching for a vacant parking space.

In large parking area attached to shopping malls and so on, it is difficult to find a vacant parking space. In addition, searching for parking space during long time leads to drivers stress and wasteful energy loss. In order to solve these problems, the navigation system in parking area by using ZigBee networks is proposed in this paper. The ZigBee is expected to realize low power consumption wireless system with low cost. Moreover, the ZigBee can form ad-hoc network easily and more than 65000 nodes can connect at the same time. Therefore, it is suitable for usage in the large parking area. In proposed system, the shortest route to the vacant parking space is transmitted to the own vehicle by the ZigBee ad-hoc network. Thus, the efficient guide is provided to the drivers. To show the effectiveness of the proposed parking system, the average time for arrival in the parking area is evaluated, and the performance of the vehicles that equips the ZigBee terminals is compared with the ordinary vehicles that do not equip the ZigBee terminals.

In order to reduce the negative impact of urban traffic air pollution, this paper firstly analyzes the relationship between urban traffic air pollution and vehicle speed, after providing the relationship model, the paper establishes a comprehensive pricing model of urban traffic air pollution based on bi-level programming, the model considers the traffic air pollution pricing, and includes the factors of congestion pricing, bus fee, pricing revenue redistribution on improvement of public transport services and the expansion of road capacity. The case study shows that the implementation of comprehensive pricing of urban traffic air pollution can reduce traffic pollution and unreasonable traffic flow, which is conducive to the sustainable development of the city.

Rising global car ownership levels generate a variety of problems, including traffic congestion, oil depletion, air pollution and traffic accidents. These problems are usually most severe in urban areas. This paper examines the potential for large reductions in maximum speed, size and mass of urban cars to ameliorate these problems. It is found that cutting maximum speeds would significantly lower the frequency and severity of traffic accidents, especially in the third world, where fatality rates per vehicle are very high. If cars carried a maximum of two to four persons, car length and width could be reduced. Such cars would require smaller lane widths and less parking space, thus helping to ease traffic congestion. The combination of smaller and slower vehicles means that car mass can also be greatly reduced, which in turn reduces fuel use and, with it, urban air pollution and carbon dioxide emissions.

Lung function and -inflammation after short term exposures to traffic related air pollution and physical activity

Green Mobility Application (GMAP)

There is relatively little evidence of health effects of long-term exposure to traffic-related pollution in susceptible populations. We investigated whether long-term exposure to traffic air and noise pollution was associated with all-cause mortality or hospital readmission for myocardial infarction (MI) among survivors of hospital admission for MI. Patients from the Myocardial Ischaemia National Audit Project database resident in Greater London (n = 1 8,138) were followed for death or readmission for MI. High spatially-resolved annual average air pollution (11 metrics of primary traffic, regional or urban background) derived from a dispersion model (resolution 20 m × 20 m) and road traffic noise for the years 2003-2010 were used to assign exposure at residence. Hazard ratios (HR, 95% confidence interval (CI)) were estimated using Cox proportional hazards models. Most air pollutants were positively associated with all-cause mortality alone and in combination with hospital readmission. The largest associations with mortality per interquartile range (IQR) increase of pollutant were observed for non-exhaust particulate matter (PM(10)) (HR = 1.05 (95% CI 1.00, 1.10), IQR = 1.1 μg/m(3)); oxidant gases (HR = 1.05 (95% CI 1.00, 1.09), IQR = 3.2 μg/m(3)); and the coarse fraction of PM (HR = 1.05 (95% CI 1.00, 1.10), IQR = 0.9 μg/m(3)). Adjustment for traffic noise only slightly attenuated these associations. The association for a 5 dB increase in road-traffic noise with mortality was HR = 1.02 (95% CI 0.99, 1.06) independent of air pollution. These data support a relationship of primary traffic and regional/urban background air pollution with poor prognosis among MI survivors. Although imprecise, traffic noise appeared to have a modest association with prognosis independent of air pollution.

Self-reported traffic air pollution and respiratory symptoms among never-smokers and ever-smokers in a general population

SummaryIn big cities, the drivers face a major problem in identifying an optimal vacant parking space for their vehicles as the number of vehicles has rapidly grown over the recent years. This objective of determining the available parking space results in wastage of time, air pollution, and traffic congestion. Smart parking systems facilitate the driver to book parking slots and attain real‐time parking information. However, the existing smart parking solutions necessitate the disclosure of sensitive information like desired destination from the vehicle drivers. Moreover, the current smart parking solutions are highly centralized, making them vulnerable for launching privacy breaches by service providers and resulting in the issues of single‐point failure. In this paper, ring signature and improved multi‐transaction mode consortium blockchain‐based private information retrieval (RS‐IMTMCB‐PIR) scheme is proposed for privacy preserving in smart parking system. This RS‐IMTMCB‐PIR scheme initially utilizes an improved multi‐transaction mode consortium blockchain constructed by different number of parking lot owners in order to achieve maximized parking offers based on the factor of availability, transparency, and security. It incorporates an improved private information retrieval approach for protecting the location privacy of drivers and secretly retrieving the offers of parking from the improved multi‐transaction mode consortium blockchain. It further utilizes the benefits of ring signature for permitting the drivers in a potential anonymous authentication process that aids in available parking slot reservation. The simulation results of this proposed RS‐IMTMCB‐PIR scheme portrayed a predominant performance in ensuring maximized degree of drivers' privacy with reduced computation and communication overheads.

Community is a basic social unit taking care of groups of families. Since the day community becomes operational, it has a routine cycle of maintenance. And in turn take services as input from multiple vendors and hence generate improved ambience and fully functional services. And for this it require that maintenance issues are registered timely. But the major concern is recording and registration of the complaints for every community issues. These targeted issues are street lights control, speedy vehicle movements, tracking hijacked or vacant parking space, over grown green belt growth, electricity issues, water management control and growth through IOT. Also it is not easy for residents to register complaints via different portals or apps. This paper intends to provide a smart solution using IOT sensors. These sensors would automatically detect the issues and inform the concerned authorities by using an automated complained recording backend. This backend also would manage the complaint register status for further necessary reviews. Problem statement and its criticality are discussed at the beginning of the paper. The paper also intends to detail how to make the sensor system, low maintenance and self-healing. Then the proposed solutions are discussed case by case. The paper also provide a detailed insight of the iot sensors.

Modeling Parking

The development of the COVID-19 pandemic and the related lockdown had a major impact on vehicle traffic in cities. Based on available data from the selected city of Rzeszow, Poland, it was decided to assess changes in vehicle traffic and the impact of these changes on roadside environmental pollution. As part of the research, data from the first half of 2020 regarding vehicle traffic on selected streets of the city and on the city’s inlet routes were analyzed. For the selected road sections, changes in hourly traffic volume in 2020, compared with 2019, were also determined. With data on traffic volume, an attempt was made to estimate the impact of changes in traffic volume on air pollution in the city. Research on air pollution from motor vehicles was focused on a selected section of a city road that was equipped with an automatic air pollution measurement station located very close to the road. Additionally, at the road intersection and in the vicinity of the measuring station, a sensor was installed in the roadway to count passing vehicles. A preliminary analysis of air pollution data revealed that factors such as wind speed and direction and outside temperature had a large impact on measurement results. To eliminate the influence of these factors and to obtain data concerning only contamination originating from motor vehicles traveling along the road, an appropriate mathematical model of the traffic flow–roadside environment system was built. This model was designed to determine the air pollution in the vicinity of the road generated by traffic flow. The constructed model was verified, and the conditions for its use were determined in order to study the impact of traffic and roadside environment on the level of air pollution from harmful exhaust substances. It was shown that at certain times of the day, especially at low temperatures, other sources of harmful emissions related to home heating play a major role in air pollution in the city.

Correlation of nitrogen dioxide with other traffic pollutants near a major expressway

BackgroundAir pollution is a suspected developmental neurotoxicant. Many schools are located in close proximity to busy roads, and traffic air pollution peaks when children are at school. We aimed to assess whether exposure of children in primary school to traffic-related air pollutants is associated with impaired cognitive development.Methods and FindingsWe conducted a prospective study of children (n = 2,715, aged 7 to 10 y) from 39 schools in Barcelona (Catalonia, Spain) exposed to high and low traffic-related air pollution, paired by school socioeconomic index; children were tested four times (i.e., to assess the 12-mo developmental trajectories) via computerized tests (n = 10,112). Chronic traffic air pollution (elemental carbon [EC], nitrogen dioxide [NO2], and ultrafine particle number [UFP; 10–700 nm]) was measured twice during 1-wk campaigns both in the courtyard (outdoor) and inside the classroom (indoor) simultaneously in each school pair. Cognitive development was assessed with the n-back and the attentional network tests, in particular, working memory (two-back detectability), superior working memory (three-back detectability), and inattentiveness (hit reaction time standard error). Linear mixed effects models were adjusted for age, sex, maternal education, socioeconomic status, and air pollution exposure at home.Children from highly polluted schools had a smaller growth in cognitive development than children from the paired lowly polluted schools, both in crude and adjusted models (e.g., 7.4% [95% CI 5.6%–8.8%] versus 11.5% [95% CI 8.9%–12.5%] improvement in working memory, p = 0.0024). Cogently, children attending schools with higher levels of EC, NO2, and UFP both indoors and outdoors experienced substantially smaller growth in all the cognitive measurements; for example, a change from the first to the fourth quartile in indoor EC reduced the gain in working memory by 13.0% (95% CI 4.2%–23.1%). Residual confounding for social class could not be discarded completely; however, the associations remained in stratified analyses (e.g., for type of school or high-/low-polluted area) and after additional adjustments (e.g., for commuting, educational quality, or smoking at home), contradicting a potential residual confounding explanation.ConclusionsChildren attending schools with higher traffic-related air pollution had a smaller improvement in cognitive development.