Based on the framework of method of successive averages(MSA), a modified stochastic user-equilibrium assignment algorithm was proposed, which can be used to calculate the passenger flow distribution of urban rail tran...Based on the framework of method of successive averages(MSA), a modified stochastic user-equilibrium assignment algorithm was proposed, which can be used to calculate the passenger flow distribution of urban rail transit(URT) under network operation. In order to describe the congestion's impact to passengers' route choices, a generalized cost function with in-vehicle congestion was set up. Building on the k-th shortest path algorithm, a method for generating choice set with time constraint was embedded, considering the characteristics of network operation. A simple but efficient route choice model, which was derived from travel surveys for URT passengers in China, was introduced to perform the stochastic network loading at each iteration in the algorithm. Initial tests on the URT network in Shanghai City show that the methodology, with rational calculation time, promises to compute more precisely the passenger flow distribution of URT under network operation, compared with those practical algorithms used in today's China.展开更多
科学的轨道交通出行模式分析是运营决策优化的重要依据。为挖掘城市轨道交通时空流动特征及其影响机理,提出一种基于非负张量分解的OD客流强度时空分布计算方法,采用融合SHAP归因分析的极端梯度提升树(eXtreme Gradient Boosting,XGBoo...科学的轨道交通出行模式分析是运营决策优化的重要依据。为挖掘城市轨道交通时空流动特征及其影响机理,提出一种基于非负张量分解的OD客流强度时空分布计算方法,采用融合SHAP归因分析的极端梯度提升树(eXtreme Gradient Boosting,XGBoost)对各模式OD客流强度进行拟合预测。使用城市轨道交通AFC(automatic fare collection system,AFC)系统数据,从空间、时段以及出行日3个维度构建3阶客流OD张量,采用交替非负最小二乘法(alternating non negative least squares,ANLS)实现非负CP张量分解。基于张量分解结果,从北京轨道交通344个站点连续1周16266966条出行数据中,提取出晨高峰长距离通勤、早高峰中短通勤、平峰休闲中转出行、晚归出行4种出行模式的时、空分布特征。基于可解释性机器学习模型,对各模式OD客流进行预测。结果表明XGBoost与CatBoost、LightGBM、OLS相比更具优势。根据OD起终点站域环境特征,考虑起终点缓冲区内各类兴趣点(point of interest,POI)数量、小区住户数、房价、人口数量、站点偏离距离以及出行距离等指标,构建OD强度关联指标体系,解释各指标对OD客流强度的正负反馈效应。SHAP归因分析说明,居民更倾向于14站以内的中短途出行,并分别得到了就业类POI数目对晨、早通勤客流正向影响,以及餐饮类POI数目对休闲中转出行客流正向影响的临界阈值。该方法可为轨道交通精细化出行引导和客流组织提供数据支撑,优化城市轨道交通供需平衡及服务水平。展开更多
基金Project(2007AA11Z236) supported by the National High Technology Research and Development Program of ChinaProject(2012M5209O1) supported by China Postdoctoral Science Foundation
文摘Based on the framework of method of successive averages(MSA), a modified stochastic user-equilibrium assignment algorithm was proposed, which can be used to calculate the passenger flow distribution of urban rail transit(URT) under network operation. In order to describe the congestion's impact to passengers' route choices, a generalized cost function with in-vehicle congestion was set up. Building on the k-th shortest path algorithm, a method for generating choice set with time constraint was embedded, considering the characteristics of network operation. A simple but efficient route choice model, which was derived from travel surveys for URT passengers in China, was introduced to perform the stochastic network loading at each iteration in the algorithm. Initial tests on the URT network in Shanghai City show that the methodology, with rational calculation time, promises to compute more precisely the passenger flow distribution of URT under network operation, compared with those practical algorithms used in today's China.
文摘科学的轨道交通出行模式分析是运营决策优化的重要依据。为挖掘城市轨道交通时空流动特征及其影响机理,提出一种基于非负张量分解的OD客流强度时空分布计算方法,采用融合SHAP归因分析的极端梯度提升树(eXtreme Gradient Boosting,XGBoost)对各模式OD客流强度进行拟合预测。使用城市轨道交通AFC(automatic fare collection system,AFC)系统数据,从空间、时段以及出行日3个维度构建3阶客流OD张量,采用交替非负最小二乘法(alternating non negative least squares,ANLS)实现非负CP张量分解。基于张量分解结果,从北京轨道交通344个站点连续1周16266966条出行数据中,提取出晨高峰长距离通勤、早高峰中短通勤、平峰休闲中转出行、晚归出行4种出行模式的时、空分布特征。基于可解释性机器学习模型,对各模式OD客流进行预测。结果表明XGBoost与CatBoost、LightGBM、OLS相比更具优势。根据OD起终点站域环境特征,考虑起终点缓冲区内各类兴趣点(point of interest,POI)数量、小区住户数、房价、人口数量、站点偏离距离以及出行距离等指标,构建OD强度关联指标体系,解释各指标对OD客流强度的正负反馈效应。SHAP归因分析说明,居民更倾向于14站以内的中短途出行,并分别得到了就业类POI数目对晨、早通勤客流正向影响,以及餐饮类POI数目对休闲中转出行客流正向影响的临界阈值。该方法可为轨道交通精细化出行引导和客流组织提供数据支撑,优化城市轨道交通供需平衡及服务水平。
