Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human activity.Taxi-GPS traces,acted as one kind of typical crowd sensed data,it can be used ...Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human activity.Taxi-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD flows.In this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China.The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows.Then based on a novel complex network model,a semantics mining method of OD flows is proposed through compounding Points of Interests(POI)network and public transport network to the OD flows network.The propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.展开更多
Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand,and improving the service level of ride-hailing platforms.In contrast to previous studies,which have primarily foc...Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand,and improving the service level of ride-hailing platforms.In contrast to previous studies,which have primarily focused on the inflow or outflow demands of each zone,this study proposes a conditional generative adversarial network with a Wasserstein divergence objective(CWGAN-div)to predict ride-hailing origin-destination(OD)demand matrices.Residual blocks and refined loss functions help to enhance the stability of model training.Interpretable conditional information is employed to capture external spatiotemporal dependencies and guide the model towards generating more precise results.Empirical analysis using ride-hailing data from Manhattan,New York City,demon-strates that our proposed CWGAN-div model can effectively predict the network-wide OD matrix and exhibits strong convergence performance.Comparative experiments also show that the CWGAN-div outperforms other benchmarking methods.Consequently,the proposed model displays potential for network-wide ride-hailing OD demand prediction.展开更多
The commodity transportation capacity between all origin-destination （ OD ） pairs over the multimodal multi-commodities freight transportation network （MMFTN） is determined. A multi-ob- jectives mathematical model...The commodity transportation capacity between all origin-destination （ OD ） pairs over the multimodal multi-commodities freight transportation network （MMFTN） is determined. A multi-ob- jectives mathematical model is formulated for determining the OD capacity over the MMFTN accord- ing to a transporting capacity matrix that increased from the reference matrixes. The corresponding incremental factor for estimating the capacity matrix is obtained via the maximal likelihood estima- tion method that samples data of differences between the estimated commodity volumes and carrying capacities of the critical links. The proposed formulations are tested by an experimental highway and railroad freight transportation network in an existing literature. The relevant results of OD capacities are displayed and applicability of the algorithm is certified.展开更多
基金This work is supported by Shandong Provincial Natural Science Foundation,China under Grant No.ZR2017MG011This work is also supported by Key Research and Development Program in Shandong Provincial(2017GGX90103).
文摘Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human activity.Taxi-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD flows.In this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China.The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows.Then based on a novel complex network model,a semantics mining method of OD flows is proposed through compounding Points of Interests(POI)network and public transport network to the OD flows network.The propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.
基金supported by the National Natural Science Foundation of China(Grant No.72371251)the National Science Foundation for Distinguished Young Scholars of Hunan Province(Grant No.2024JJ2080)+1 种基金the Excellent Youth Foundation of Hunan Education Department(Grant No.21B0015)the State Key Lab-oratory of Rail Traffic Control and Safety of Beijing Jiaotong Uni-v ersity,China(Gr ant No.RCS2022K004).
文摘Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand,and improving the service level of ride-hailing platforms.In contrast to previous studies,which have primarily focused on the inflow or outflow demands of each zone,this study proposes a conditional generative adversarial network with a Wasserstein divergence objective(CWGAN-div)to predict ride-hailing origin-destination(OD)demand matrices.Residual blocks and refined loss functions help to enhance the stability of model training.Interpretable conditional information is employed to capture external spatiotemporal dependencies and guide the model towards generating more precise results.Empirical analysis using ride-hailing data from Manhattan,New York City,demon-strates that our proposed CWGAN-div model can effectively predict the network-wide OD matrix and exhibits strong convergence performance.Comparative experiments also show that the CWGAN-div outperforms other benchmarking methods.Consequently,the proposed model displays potential for network-wide ride-hailing OD demand prediction.
文摘The commodity transportation capacity between all origin-destination （ OD ） pairs over the multimodal multi-commodities freight transportation network （MMFTN） is determined. A multi-ob- jectives mathematical model is formulated for determining the OD capacity over the MMFTN accord- ing to a transporting capacity matrix that increased from the reference matrixes. The corresponding incremental factor for estimating the capacity matrix is obtained via the maximal likelihood estima- tion method that samples data of differences between the estimated commodity volumes and carrying capacities of the critical links. The proposed formulations are tested by an experimental highway and railroad freight transportation network in an existing literature. The relevant results of OD capacities are displayed and applicability of the algorithm is certified.
