Research On The Passenger Flow State Estimation And Operation Organization Optimization In An Urban Rail Transit System

With the development of data collection technology,the multi-source observation data are available in urban rail transit systems.To offer effective traffic optimization solutions in large-scale and demand-oversaturated urban rail transit networks in the era of large quantities and varying detailed data,the transportation system managers need to fully utilize all available observations from the underlying multi-source sensor networks,to estimate the network-wide passenger flow states,and proactively design control strategies to mitigate estimated or forecasted traffic congestion.In the first part of this study,we propose the concept of the discretized passenger flow state that composes a triple of flow,density,and speed,further construct a spacetime-state(STS)hyper network,so that we can utilize a better defined three-dimensional solution space to integrate structurally heterogeneous data sources.To describe the complex urban-rail passenger flow evolution,passenger traveling and fixed sensor state transition processes can be unified within a STS path representation.To estimate the consistent system internal states between two different types of observations,we formulate a hyper network-based flow assignment model in a generalized least squares estimation framework.For applications in large-scale transportation networks,we decompose the proposed model into three easy-to-solve sub-problems.The proposed model is applied to a real-world case based on the Beijing subway network with complete smart card data for each passenger at his/her origin and destination and time-dependent passenger counts in several key transfer corridors,while the specific space-time trajectories of all passengers and high-resolution time-dependent congestion levels at platforms,in trains,and in transfer corridors are estimated.In the second part of this study,we focus on the dynamic passenger demand-driven schedule optimization of an urban rail transit line.We propose two types of space-time networks,including the space-time network for passenger assignment and the space-time network for train scheduling.Firstly,to improve the efficiency performance of the urban rail transit line in non-peak hours when there are buffer times to adjustment the timetable,we propose an optimization model to coordinately design the train timetable and circulation plan in response to passenger demand dynamics.Secondly,to improve the equity performance of the urban rail transit line in peak hours when passenger demands at some key stations are oversaturated,we proposed an optimization model to optimize the train stopping pattern to achieve the equitable distribution of passengers and the optimal train skip-stopping pattern.Thirdly,to improve the safety performance of the urban rail transit line in peak hours when passenger demands on the line are oversaturated,we proposed an optimization model to optimize the time-dependent controlled passenger inflow rate for each station to reduce the maximum number of passengers gathering at platforms.Finally,we propose an integrated optimization framework for train timetable,train stopping pattern,and passenger flow control strategy,which dynamically updates the weights of the multi-objectives adapted to the passenger demands.The proposed optimization models are validated based on the real data from Beijing Batong and Yizhuang lines.