Speeding likelihood is usually used to measure drivers'propensity of committing speeding.Albeit some studies have analyzed speeding likelihood,most of them are inadequate in considering spatial effects when analyz...Speeding likelihood is usually used to measure drivers'propensity of committing speeding.Albeit some studies have analyzed speeding likelihood,most of them are inadequate in considering spatial effects when analyzing speeding behaviors on urban road networks.This study aims to fill this knowledge gap by modeling speeding likelihood with spatial models and then evaluate the influence of contributing factors.The percent of speeding observations(PSO)is adopted to represent the speeding likelihood.The speeding behaviors and PSO of each floating car(i.e.,taxi)are extracted from the GPS trajectories in Chengdu,China.PSO is modeled by several Bayesian beta general linear models with spatial effects,namely the beta model,beta logit-normal model,beta intrinsic conditional autoregressive(ICAR)model,beta Besag-York-Molli e(BYM)model,and beta BYM2 model.Results show that the beta BYM2 model performs better than other models in terms of data-fitting.According to the estimates from the beta BYM2,spatial correlation is the main reason for the model variability.The roads with more lanes and roads linked by elevated roads are found to increase the speeding likelihood,while higher speed limits,intersection density,traffic congestion,and roadside parking are associated with lower speeding likelihood.These findings provide valuable insights for designing effective anti-speeding countermeasures on urban road networks.展开更多
Water quality restoration in rivers requires identification of the locations and discharges of pollution sources,and a reliable mathematical model to accomplish this identification is essential.In this paper,an innova...Water quality restoration in rivers requires identification of the locations and discharges of pollution sources,and a reliable mathematical model to accomplish this identification is essential.In this paper,an innovative framework is presented to inversely estimate pollution sources for both accident preparedness and normal management of the allowable pollutant discharge.The proposed model integrates the concepts of the hydrodynamic diffusion wave equation and an improved Bayesian-Markov chain Monte Carlo method(MCMC).The methodological framework is tested using a designed case of a sudden wastewater spill incident(i.e.,source location,flow rate,and starting and ending times of the discharge)and a real case of multiple sewage inputs into a river(i.e.,locations and daily flows of sewage sources).The proposed modeling based on the improved Bayesian-MCMC method can effectively solve high-dimensional search and optimization problems according to known river water levels at pre-set monitoring sites.It can adequately provide accurate source estimation parameters using only one simulation through exploration of the full parameter space.In comparison,the inverse models based on the popular random walk Metropolis(RWM)algorithm and microbial genetic algorithm(MGA)do not produce reliable estimates for the two scenarios even after multiple simulation runs,and they fall into locally optimal solutions.Since much more water level data are available than water quality data,the proposed approach also provides a cost-effective solution for identifying pollution sources in rivers with the support of high-frequency water level data,especially for rivers receiving significant sewage discharges.展开更多
基金supported by the Open Fund of Key Laboratory of Flight Techniques and Flight Safety(FZ2021KF05)the National Natural Science Foundation of China(71801182)+3 种基金the Fundamental Research Funds for the Central Universities(AUGA5710010222)the Opening Project of Intelligent Policing Key Laboratory of Sichuan Province(ZNJW2023KFZD001)the Natural Science Foundation of Heilongjiang Province of China(LH2022E074)the Smart Transportation Safety International Joint Lab Program。
文摘Speeding likelihood is usually used to measure drivers'propensity of committing speeding.Albeit some studies have analyzed speeding likelihood,most of them are inadequate in considering spatial effects when analyzing speeding behaviors on urban road networks.This study aims to fill this knowledge gap by modeling speeding likelihood with spatial models and then evaluate the influence of contributing factors.The percent of speeding observations(PSO)is adopted to represent the speeding likelihood.The speeding behaviors and PSO of each floating car(i.e.,taxi)are extracted from the GPS trajectories in Chengdu,China.PSO is modeled by several Bayesian beta general linear models with spatial effects,namely the beta model,beta logit-normal model,beta intrinsic conditional autoregressive(ICAR)model,beta Besag-York-Molli e(BYM)model,and beta BYM2 model.Results show that the beta BYM2 model performs better than other models in terms of data-fitting.According to the estimates from the beta BYM2,spatial correlation is the main reason for the model variability.The roads with more lanes and roads linked by elevated roads are found to increase the speeding likelihood,while higher speed limits,intersection density,traffic congestion,and roadside parking are associated with lower speeding likelihood.These findings provide valuable insights for designing effective anti-speeding countermeasures on urban road networks.
基金the National Natural Science Foundation of China(Grant No.51979195)the National Key R&D Program of China(No.2021YFC3200703).
文摘Water quality restoration in rivers requires identification of the locations and discharges of pollution sources,and a reliable mathematical model to accomplish this identification is essential.In this paper,an innovative framework is presented to inversely estimate pollution sources for both accident preparedness and normal management of the allowable pollutant discharge.The proposed model integrates the concepts of the hydrodynamic diffusion wave equation and an improved Bayesian-Markov chain Monte Carlo method(MCMC).The methodological framework is tested using a designed case of a sudden wastewater spill incident(i.e.,source location,flow rate,and starting and ending times of the discharge)and a real case of multiple sewage inputs into a river(i.e.,locations and daily flows of sewage sources).The proposed modeling based on the improved Bayesian-MCMC method can effectively solve high-dimensional search and optimization problems according to known river water levels at pre-set monitoring sites.It can adequately provide accurate source estimation parameters using only one simulation through exploration of the full parameter space.In comparison,the inverse models based on the popular random walk Metropolis(RWM)algorithm and microbial genetic algorithm(MGA)do not produce reliable estimates for the two scenarios even after multiple simulation runs,and they fall into locally optimal solutions.Since much more water level data are available than water quality data,the proposed approach also provides a cost-effective solution for identifying pollution sources in rivers with the support of high-frequency water level data,especially for rivers receiving significant sewage discharges.
