Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

A Framework on Fast Mapping of Urban Flood Based on a Multi-Objective Random Forest Model

Fast and accurate prediction of urban flood is of considerable practical importance to mitigate the effects of frequent flood disasters in advance.To improve urban flood prediction efficiency and accuracy,we proposed a framework for fast mapping of urban flood:a coupled model based on physical mechanisms was first constructed,a rainfall-inundation database was generated,and a hybrid flood mapping model was finally proposed using the multi-objective random forest(MORF)method.The results show that the coupled model had good reliability in modelling urban flood,and 48 rainfall-inundation scenarios were then specified.The proposed hybrid MORF model in the framework also demonstrated good performance in predicting inundated depth under the observed and scenario rainfall events.The spatial inundated depths predicted by the MORF model were close to those of the coupled model,with differences typically less than 0.1 m and an average correlation coefficient reaching 0.951.The MORF model,however,achieved a computational speed of 200 times faster than the coupled model.The overall prediction performance of the MORF model was also better than that of the k-nearest neighbor model.Our research provides a novel approach to rapid urban flood mapping and flood early warning.

Urban Ecosystem Prediction and Comprehensive Evaluation Ideology

In the light of the study of domestic and foreign ur-ban ecosystem,this article puts forward a set of ideological systemsfor the forecast,evaluation and tactic,and conducts initial primaryexploration of its forecast and evaluation methods.

Evaluation of model fidelity for solar analysis in the context of distributed PV integration at urban scale

This paper examines the level of model fidelity required to support design phases in the urban solar planning process.The two modelling features crucial to the fidelity of the photovoltaic(PV)yield prediction on urban surfaces are(1)a level of fidelity for modelling urban shading and solar reflection and(2)a level of fidelity for modelling PV system operation.The paper compares three different models for predicting urban shading and reflection and two different PV models for predicting PV system operation.The relevance of the model fidelities is investigated through a case study of an urban area in Wuhan,China under three decision-making contexts:setting a solar target,place-making,and economic assessment for urban-scale distributed PV integration.Predictions for the decision-makings are generated using the selected models through computational simulation under the same annual weather profile.The results show that the relatively less accurate canyon-based method tends to overpredict with 57 buildings identified as suitable for PV installation for walls in the studied urban area;the more accurate vector-based model predicts only 14 suitable buildings.The results predicted with additional consideration of dynamic PV system operation exhibit differences from those predicted by the static PV system model,with a difference of roughly 13 buildings on average within each payback-time category.The differences are noticeable but can be regarded as incremental for urban-scale economic assessment compared with the significant difference due to the fidelity level of modelling urban shading and reflection.