improving parking availability prediction in smart cities with iot and ensemble based model

Abstract Smart cities are part of the ongoing advances in technology to provide a better life quality to its inhabitants. Urban mobility is one of the most important components of smart cities. Due to the growing number of vehicles in these cities, urban traffic congestion is becoming more common. In addition, finding places to park even in car parks is not easy for drivers who run in circles. Studies have shown that drivers looking for parking spaces contribute up to 30% to traffic congestion. In this context, it is necessary to predict the spaces available to drivers in parking lots where they want to park. We propose in this paper a new system that integrates the IoT and a predictive model based on ensemble methods to optimize the prediction of the availability of parking spaces in smart parking. The tests that we carried out on the Birmingham parking data set allowed to reach a Mean Absolute Error (MAE) of 0.06% on average with the algorithm of Bagging Regression (BR). This results have thus improved the best existing performance by over 6.6% while dramatically reducing system complexity

Towards the development of a cost-effective Image-Sensing-Smart-Parking Systems (ISenSmaP)

Finding parking in a busy city has been a major daily problem in today’s busy life. Researchers have proposed various parking spot detection systems to overcome the problem of spending a long time searching for a parking spot. These works include a wide variety of sensors to detect the presence of a vehicle in a parking spot. These approaches are expensive to implement and ineffective in extreme weather conditions in an outdoor parking environment. As a result, a cost-effective, dependable, and time-saving parking solution is much more desirable. In this thesis, we proposed and developed an image processing-based real-time parking-spot detection system using deep-learning algorithms. In this regard, we annotated the images using the Visual Geometry Group (VGG) annotator and preprocessed the dataset using the image contrast enhancement technique that attempts to solve the illumination changes in pictures captured in an open space, followed by training the model using the Mask-R-CNN (Region-Based Convolutional Neural Network) and Faster-RCNN algorithms. ROIs (Regions of interest) are used later to determine the vacancy status of each parking spot. Our experimental results demonstrate the effectiveness of our developed parking systems as we achieved a mean Average Precision (mAP) of 0.999 for the PKLot dataset and a mAP of 0.9758 for custom datasets. Furthermore, as part of the smart parking application, we developed an Android App that can be used by the end users. Our proposed intelligent parking system is scalable, cost-effective, and to the best of our knowledge, it offers higher parking spot detection accuracy than any other solutions in this domain

A Microscopic Simulation Laboratory for Evaluation of Off-street Parking Systems

The parking industry produces an enormous amount of data every day that, properly analyzed, will change the way the industry operates. The collected data form patterns that, in most cases, would allow parking operators and property owners to better understand how to maximize revenue and decrease operating expenses and support the decisions such as how to set specific parking policies (e.g. electrical charging only parking space) to achieve the sustainable and eco-friendly parking. However, there lacks an intelligent tool to assess the layout design and operational performance of parking lots to reduce the externalities and increase the revenue. To address this issue, this research presents a comprehensive agent-based framework for microscopic off-street parking system simulation. A rule-based parking simulation logic programming model is formulated. The proposed simulation model can effectively capture the behaviors of drivers and pedestrians as well as spatial and temporal interactions of traffic dynamics in the parking system. A methodology for data collection, processing, and extraction of user behaviors in the parking system is also developed. A Long-Short Term Memory (LSTM) neural network is used to predict the arrival and departure of the vehicles. The proposed simulator is implemented in Java and a Software as a Service (SaaS) graphic user interface is designed to analyze and visualize the simulation results. This study finds the active capacity of the parking system, which is defined as the largest number of actively moving vehicles in the parking system under the facility layout. In the system application of the real world testbed, the numerical tests show (a) the smart check-in device has marginal benefits in vehicle waiting time; (b) the flexible pricing policy may increase the average daily revenue if the elasticity of the price is not involved; (c) the number of electrical charging only spots has a negative impact on the performance of the parking facility; and (d) the rear-in only policy may increase the duration of parking maneuvers and reduce the efficiency during the arrival rush hour. Application of the developed simulation system using a real-world case demonstrates its capability of providing informative quantitative measures to support decisions in designing, maintaining, and operating smart parking facilities

Self-Reliance for the Internet of Things: Blockchains and Deep Learning on Low-Power IoT Devices

The rise of the Internet of Things (IoT) has transformed common embedded devices from isolated objects to interconnected devices, allowing multiple applications for smart cities, smart logistics, and digital health, to name but a few. These Internet-enabled embedded devices have sensors and actuators interacting in the real world. The IoT interactions produce an enormous amount of data typically stored on cloud services due to the resource limitations of IoT devices. These limitations have made IoT applications highly dependent on cloud services. However, cloud services face several challenges, especially in terms of communication, energy, scalability, and transparency regarding their information storage. In this thesis, we study how to enable the next generation of IoT systems with transaction automation and machine learning capabilities with a reduced reliance on cloud communication. To achieve this, we look into architectures and algorithms for data provenance, automation, and machine learning that are conventionally running on powerful high-end devices. We redesign and tailor these architectures and algorithms to low-power IoT, balancing the computational, energy, and memory requirements.The thesis is divided into three parts:Part I presents an overview of the thesis and states four research questions addressed in later chapters.Part II investigates and demonstrates the feasibility of data provenance and transaction automation with blockchains and smart contracts on IoT devices.Part III investigates and demonstrates the feasibility of deep learning on low-power IoT devices.We provide experimental results for all high-level proposed architectures and methods. Our results show that algorithms of high-end cloud nodes can be tailored to IoT devices, and we quantify the main trade-offs in terms of memory, computation, and energy consumption

The Impact of Autonomous Vehicles on Urban Land Use Patterns

Autonomous vehicles are coming. The only questions are how quickly they will arrive, how we will manage the years when they share the road with conventional vehicles, and how the legal system will address the issues they raise. This Article examines the impact the autonomous vehicle revolution will have on urban land use patterns. Autonomous vehicles will transform the use of land and the law governing that valuable land. Automobiles will drop passengers off and then drive themselves to remote parking areas, reducing the need for downtown parking. These vehicles will create the need for substantial changes in roadway design. Driverless cars are more likely to be shared, and fleets may supplant individual ownership. At the same time, people may be willing to endure longer commutes, working while their car transports them. These dramatic changes will require corresponding adaptations in real estate and land use law. Zoning laws, building codes, and homeowners\u27association rules will have to be updated to reflect shifting needs for parking. Longer commutes may create a need for stricter environmental controls. Moreover, jurisdictions will have to address these changes while operating under considerable uncertainty, as we all wait to see which technologies catch on, which fall by the wayside, and how quickly this revolution arrives. This Article examines the legal changes that are likely to be needed in the near future. It concludes by recommending that government bodies engage in scenario planning so they can act under conditions of ambiguity while reducing the risk of poor decisions.

Shared Autonomous Vehicle (SAV) fleet operations across the Minneapolis-Saint Paul region, with emphasis on empty travel, response times, and no-idling laws over space and time of day

Many well-known enterprises are road-testing fully-automated vehicles (AVs), including General Motors, Waymo, Uber, Tesla, and Apple. Most AVs are expected to be used in shared AV (SAV) fleets initially, for daily trip-by-trip use, as an autonomous ride-hailing service. SAVs will allow savings on vehicle ownership and maintenance costs, parking search time, and parking access times. This study micro-simulates passenger travel throughout the Minneapolis-Saint Paul (MSP) region of Minnesota, when relying on a system of SAVs. The extended region includes 9.5 million person-trips per weekday, 7 counties, 2485 traffic analysis zones (TAZs), and about 42,000 roadway links (obtained using OpenStreetMap). An agent-based toolkit, MATSim, allows tracking of individual travelers throughout the day and across their activity locations. The region's metropolitan planning organization, Metropolitan Council, provided all travelers' itineraries, trip purposes, origins, and destinations, along with land use data (jobs and population counts) by TAZ. To simulate SAV assignments to each traveler requesting a trip, along with traveler wait times and arrival times at their destinations, the code form Hörl (2017) - who extended Bischoff and Maciejewski's (2016) MATSim codes - and MATSim's autonomous mobility-on-demand (AMoD) simulator were used here. The SAV fleet size and starting locations were specified before a typical weekday's simulation for 2015. All travel demands sampled from the MSP population must be met by the SAV fleet if they can be met within a pre-specified max-wait-time duration of 1 hour. Travelers are assumed to cancel their SAV request after waiting more than 1 hour. Finally, special SAV parking lots or waiting areas were created to avoid SAVs idling on busy streets in the downtown and other popular locations, between serving trips, to see how such curb-use policies affect wait times and other fleet performance metrics. Using supercomputers, this work simulated 180,000 person-trips and 450,000 person-trips (2% and 5% of the region's 9.2 million daily person-trips) and 480,000 person-trips for the Twin Cities over a 24-hour weekday. Results suggest that the average SAV in this region can serve at most 30 person-trips per day with less than 5 minutes of average wait time for travelers, thus replacing about 10 household vehicles (assuming no one needs to leave the region) but generating another 13 % vehicle-miles traveled (VMT) each day, thereby adding some congestion to the network. By enabling and encouraging active use of for dynamic ride-sharing (DRS), where strangers share rides together, the SAV fleetwide VMT fell, on average, by 17% - and empty VMT (eVMT) fell by 26%, as compared to scenarios without DRS. Interestingly, the 81% and 84% of TAZs with less than 6 minutes average wait times (in the AM and PM peak periods, respectively) are uniformly distributed over this large, 7-county region, suggesting that MSP residents will enjoy similar SAV service levels everywhere (though response times do rise during peak times of day). For the Twin Cities region, most eVMT emerges in the northern and southern subregions, rather than in the cities' CBDs. eVMT and wait times are relatively high during the AM and PM peak periods (6 am to 9 am and 3 pm to 6 pm) but fall significantly during the PM peak period if DRS is offered and actively used by travelers. When compared to idling-at-curb scenarios, the no-idling-on-busy-downtown-road-segment scenarios (using central SAV parking lots) generated 8% more VMT, while eVMT rose by 9 percentage points on average, across all 4 companion scenarios. This study also estimated various energy and emissions savings of SAVs versus the U.S. status quo. Compared to the average household passenger car (a 4-door sedan), which uses 31 miles per gallon, a fleet of 52 mi/gallon hybrid electric SAVs are estimated here to lower the energy demands by 21% and emission related health costs by roughly 30%, sulfur dioxide (SO₂) by 20%, carbon monoxide (CO) by 46%, oxides of nitrogen (NOx) by 30%, volatile organic compounds (VOC) by 48%, particulate matter that is 10 micrometers or less in effective diameter (PM10) by 20%, carbon dioxide (CO₂) by 20% and methane (CH₄) by 35%. Such fleet shifts would save roughly 30% in emissions-related health costs and 64% in energy use.Civil, Architectural, and Environmental Engineerin