MetaCity: An Edge Emulator with the Feature of Realistic Geospatial Support for Urban Computing

The edge computing paradigm is an important supplement to the traditional cloud computing paradigm in current IoT application scenarios.However,edge computing is highly related to a specific application scenario,in which the mobility of edge devices and the geographical distribution of edge infrastructure are strongly correlated.However,it is expensive to deploy the solution in the real world and most current edge computing emulators lack realistic scenario support and mobility support.Therefore,it is challenging to evaluate whether an edge infrastructure deployment solution can satisfy the QoS(Quality-of-Service)requirement of an edge application in a cost-effective manner.In this paper,we propose and implement an edge emulator,MetaCity,which is able to effectively enforce edge computing policies and construct realistic application scenarios.MetaCity can leverage geographical data to establish an emulation environment according to the realistic infrastructure deployment strategy and emulate the mobility process of edge devices based on the actual urban road network.MetaCity can also provide an extensible network QoS monitoring module that supports the concurrent execution of various QoS monitoring in an emulated environment.In addition,MetaCity provides a user-friendly web-based graphical user interface instead of text-based configuration files.For evaluation,three smart transportation-based experiments are conducted to validate the functionality,scalability,and emulation accuracy of MetaCity.

Relationship Between Urban Road Traffic Characteristics and Road Grade Based on a Time Series Clustering Model： A Case Study in Nanjing, China

With the increasing number of vehicles in large-and medium-sized cities challenges in urban traffic management, control, and road planning are being faced. Taxi GPS trajectory data is a novel data source that can be used to study the potential dynamic traffic characteristics of urban roads, and thus identify locations that show a notable lack of road planning. Considering that road traffic characteristics on their own are insufficient for a comprehensive understanding of urban traffic, we develop a road traffic characteristic time series clustering model to analyze the relationship between urban road traffic characteristics and road grade based on existing taxi trajectory data. We select the main urban area of Nanjing as our study area and use the taxi trajectory data of a single month for evaluating our method. The experiments show that the clustering model exhibit good performance and can be successfully used for road traffic characteristic classification. Moreover, we analyze the correlation between traffic characteristics and road grade to identify road segments with planning designs that do not match the actual traffic demands.

Artificial Intelligence Based Clustering with Routing Protocol for Internet of Vehicles

With recent advances made in Internet of Vehicles(IoV)and Cloud Computing(CC),the Intelligent Transportation Systems(ITS)find it advantageous in terms of improvement in quality and interactivity of urban transportation service,mitigation of costs incurred,reduction in resource utilization,and improvement in traffic management capabilities.Many trafficrelated problems in future smart cities can be sorted out with the incorporation of IoV in transportation.IoV communication enables the collection and distribution of real-time essential data regarding road network condition.In this scenario,energy-efficient and reliable intercommunication routes are essential among vehicular nodes in sustainable urban computing.With this motivation,the current research article presents a new Artificial Intelligence-based Energy Efficient Clustering with Routing(AI-EECR)Protocol for IoV in urban computing.The proposed AI-EECR protocol operates under three stages namely,network initialization,Cluster Head(CH)selection,and routing protocol.The presented AI-EECR protocol determines the CHs from vehicles with the help of Quantum Chemical Reaction Optimization(QCRO)algorithm.QCROalgorithmderives a fitness function with the help of vehicle speed,trust level,and energy level of the vehicle.In order to make appropriate routing decisions,a set of relay nodeswas selected usingGroup Teaching Optimization Algorithm(GTOA).The performance of the presented AI-EECR model,in terms of energy efficiency,was validated against different aspects and a brief comparative analysis was conducted.The experimental outcomes established that AI-EECR model outperformed the existing methods under different measures.

Understanding urban structures and crowd dynamics leveraging large-scale vehicle mobility data

A comprehensive understanding of city structures and urban dynamics can greatly improve the efficiency and quality of urban planning and management,while the traditional approaches of which,such as manual surveys,usually incur substantial labor and time.In this paper,we propose a data-driven framework to sense urban structures and dynamics from large-scale vehicle mobility data.First,we divide the city into fine-grained grids,and cluster the grids with similar mobility features into structured urban areas with a proposed distance-constrained clustering algorithm(DCCA).Second,we detect irregular mobility traffic patterns in each area leveraging an ARIMA-based anomaly detection algorithm(ADAM),and correlate them to the urban social and emergency events.Finally,we build a visualization system to demonstrate the urban structures and crowd dynamics.We evaluate our framework using real-world datasets collected from Xiamen city,China,and the results show that the proposed framework can sense urban structures and crowd comprehensively and effectively.

Dynamic road crime risk prediction with urban open data

Crime risk prediction is helpful for urban safety and citizens’life quality.However,existing crime studies focused on coarse-grained prediction,and usually failed to capture the dynamics of urban crimes.The key challenge is data sparsity,since that 1)not all crimes have been recorded,and 2)crimes usually occur with low frequency.In this paper,we propose an effective framework to predict fine-grained and dynamic crime risks in each road using heterogeneous urban data.First,to address the issue of unreported crimes,we propose a cross-aggregation soft-impute(CASI)method to deal with possible unreported crimes.Then,we use a novel crime risk measurement to capture the crime dynamics from the perspective of influence propagation,taking into consideration of both time-varying and location-varying risk propagation.Based on the dynamically calculated crime risks,we design contextual features(i.e.,POI distributions,taxi mobility,demographic features)from various urban data sources,and propose a zero-inflated negative binomial regression(ZINBR)model to predict future crime risks in roads.The experiments using the real-world data from New York City show that our framework can accurately predict road crime risks,and outperform other baseline methods.

A Spatiotemporal Causality Based Governance Framework for Noisy Urban Sensory Data

Urban sensing is one of the fundamental building blocks of urban computing.It uses various types of sensors deployed in different geospatial locations to continuously and cooperatively monitor the natural and cultural environment in urban areas.Nevertheless,issues such as uneven distribution,low sampling rate and high failure ratio of sensors often make their readings less reliable.This paper provides an innovative framework to detect the noise data as well as to repair them from a spatial-temporal causality perspective rather than to deal with them inclividually.This can be achieved by connecting data through monitored objects,using the Skip-gram model to estimate spatial correlation and long shortterm memory to estimate temporal correlation.The framework consists of three major modules:1)a space embedded Bidirectional Long Short-Term Memory(BiLSTM)-based sequence labeling module to detect the noise data and the latent missing data;2)a space embedded BiLSTM-based sequence predicting module calculating the value of the missing data;3)an object characteristics fusion repairing module to correct the spatial and temporal dislocation sensory data.The approach is evaluated with real-world data collected by over 3000 electronic traffic bayonet devices in a citywide scale of a medium-sized city in China,and the result is superior to those of several referenced approaches.With a 12.9%improvement,in data accuracy over the raw data,the proposed framework plays a significant,role in various real-world use cases in urban governance,such as criminal investigation,traffic violation monitoring,and equipment maintenance.

Understanding bike trip patterns leveraging bike sharing system open data

Bike sharing systems are booming globally as a green and flexible transportation mode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the spatio-temporal bike trip patterns in a bike sharing system, such as the popular trip origins and destinations during rush hours, is important for researchers to design models for bike scheduling and sta- tion management. However, due to privacy and operational concerns, bike trip data are usually not publicly available in many cities. Instead, the station feeds about real-time bike and dock number in stations are usually public, which we refer to as bike sharing system open data. In this paper, we propose an approach to infer the spatio-temporal bike trip patterns from the public station feeds. Since the number of possible trips （i.e., origin-destination station pairs） is much larger than the number of stations, we define the trip infer- ence as an ill-posed inverse problem. To solve this problem, we identify the sparsity and locality properties of bike trip patterns, and propose a sparse and weighted regularization model to impose both properties in the solution. We evaluate our method using real-world data from Washington, D.C. and New York City. Results show that our method can effectively infer the spatio-temporal bike trip patterns and outperform the baselines in both cities.

Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

The COVID-19 pandemic has severely harmed every aspect of our daily lives,resulting in a slew of social problems.Therefore,it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery.To this end,various types of social sensing tools,such as tweeting and publicly released news,have been employed to understand individuals’and communities’thoughts,behaviors,and attitudes during the COVID-19 pandemic.However,some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19.This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience.We use fact-checking organizations to classify news as real,mixed,or fake,and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience(CR).Based on the news articles and tweets collected,we quantify CR based on two key factors,community wellbeing and resource distribution,where resource distribution is assessed by the level of economic resilience and community capital.Based on the estimates of these two factors,we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets.To improve the operationalization and sociological significance of this work,we use dimension reduction techniques to integrate the dimensions.