The major objective of this work was to establish a structural state-space model to estimate the dynamic origin-destination(O-D) matrices for urban rail transit network, using in- and out-flows at each station from au...The major objective of this work was to establish a structural state-space model to estimate the dynamic origin-destination(O-D) matrices for urban rail transit network, using in- and out-flows at each station from automatic fare collection(AFC) system as the real time observed passenger flow counts. For lacking of measurable passenger flow information, the proposed model employs priori O-D matrices and travel time distribution from historical travel records in AFC system to establish the dynamic system equations. An arriving rate based on travel time distribution is defined to identify the dynamic interrelations between time-varying O-D flows and observed flows, which greatly decreases the computational complexity and improve the model's applicability for large-scale network. This methodology is tested in a real transit network from Beijing subway network in China through comparing the predicted matrices with the true matrices. Case study results indicate that the proposed model is effective and applicative for estimating dynamic O-D matrices for large-scale rail transit network.展开更多
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
A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions.By analyzing individuals’mobility patterns r...A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions.By analyzing individuals’mobility patterns revealed from their mobile phones,researchers and practitioners are now equipped to derive the largest trip samples for a region.Because of its ubiquitous use,extensive coverage of telecommunication services and high penetration rates,travel demand can be studied continuously in fine spatial and temporal resolutions.The derived sample or seed trip matrices are coupled with surveyed commute flow data and prevalent travel demand modeling techniques to provide estimates of the total regional travel demand in the form of origindestination(OD)matrices.The methodology is evaluated in a series of real world transportation planning studies and proved its potentials in application areas such as dynamic traffic assignment modeling,integrated corridor management and online traffic simulations.展开更多
This paper develops an optimization model that determines the optimal location for Bluetooth nodes used in the determination of the origin-destination matrix within an urban network. To analyze the effectiveness of th...This paper develops an optimization model that determines the optimal location for Bluetooth nodes used in the determination of the origin-destination matrix within an urban network. To analyze the effectiveness of the model, the city of Akron, OH was utilized as a testing location. The Average Daily Traffic (ADT) was used to determine intersections that have the greatest number of Bluetooth responses. Along with maximizing the number of Bluetooth responses, the model applies financial constraints when determining the number of nodes in the urban network. The developed model selected seven locations to deploy nodes in order to stay within the financial constraints and to maximize the possible number of responses from vehicles within the Akron urban network.展开更多
The paper analyses integrating origin-destination (O-D) survey results with stochastic user equilibrium (SUE) in traffic assignment. The two methods are widely used in transportation planning but their applications ha...The paper analyses integrating origin-destination (O-D) survey results with stochastic user equilibrium (SUE) in traffic assignment. The two methods are widely used in transportation planning but their applications have not yet fully integrated. While O-D gives a generalized trip patterns, purpose and characteristics, SUE provides optimal trip distributions using the characteristics found in O-D survey. The paper utilized O-D and SUE in route relocation study for the town of Coamo in Puerto Rico. The O-D survey was used initially in studying possible trip distribution and assignment for the new route. Initial distribution and assignment of traffic to the existing roadway networks and the proposed route were allocated utilizing the O-D survey findings. The SUE was then used to optimize the assignments considering roadway characteristics such as number of lanes, capacity limits, free flow speed, signal spacing density, travel time and gasoline cost. The travel time was optimized through the Bureau of Public Roads (BPR) equation found in 2000 HCM. The optimal trips found from the SUE were then used to propose the final alignment of the new route. Traffic assignment from the SUE was slightly different from those initially assigned using O-D, indicating there was optimization. The assignment on new route was increased by 13.8% from the one assigned using O-D while assignment on the existing link was reduced by 22%.展开更多
Origin-destination(OD)modeling facilitates effective demand-responsive public transportation planning in order to meet emergent needs.Given recent advances in transit information and personal communications technology...Origin-destination(OD)modeling facilitates effective demand-responsive public transportation planning in order to meet emergent needs.Given recent advances in transit information and personal communications technology,transit OD estimation methods have evolved from relying on limited survey sources to automated big data sources.Innovative modeling approaches have also been developed over several decades to estimate trip ODs,not only for single routes,but also for full networks,including transfers.In this paper,we synthesize a review of the state of the art in research and practice,along with descriptions of key data types and methodological approaches,indicating how they interact.We also discuss current research gaps and opportunities for further innovation.This review provides a comprehensive resource that should facilitate the application of these methods to various transit systems,thus enabling planners and policymakers to gain insights from new and improved model estimates in various transit systems.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
Accurate origin–destination(OD)demand prediction is crucial for the efficient operation and management of urban rail transit(URT)systems,particularly during a pandemic.However,this task faces several limitations,incl...Accurate origin–destination(OD)demand prediction is crucial for the efficient operation and management of urban rail transit(URT)systems,particularly during a pandemic.However,this task faces several limitations,including real-time availability,sparsity,and high-dimensionality issues,and the impact of the pandemic.Consequently,this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network(PAG-STAN)for metro OD demand prediction under pandemic conditions.Specifically,PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices.Subsequently,a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices.Thereafter,PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic.Finally,a masked physics-guided loss function(MPG-loss function)incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability.PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios,highlighting its robustness and sensitivity for metro OD demand prediction.A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.展开更多
A new method of bus-route network planning is presented by analyzing theservice characteristics of bus-route network.The following optimization objects areadopted:to minimize the total number of transfers from one rou...A new method of bus-route network planning is presented by analyzing theservice characteristics of bus-route network.The following optimization objects areadopted:to minimize the total number of transfers from one route to another,to minimizethe total number of passenger-kilometers(or passenger-hours),to even passenger distri-bution on the network,and to make the directions of bus-routes consistent with the direc-tions of passenger flows.The algorithm is very simple.This method is suitable forlarge-sized and medium-sized cities and has been adopted in the transportation planningof Nanjing and Zhengzhou.The result is satisfactory.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
In the early nineties of the last century, the transportation system in Gaza Strip was born and new infrastructure projects, especially road networks, were constructed. However, the construction lacked efficient appli...In the early nineties of the last century, the transportation system in Gaza Strip was born and new infrastructure projects, especially road networks, were constructed. However, the construction lacked efficient application of a transportation planning process. Transportation planning relies on traffic demand forecasting process. The conventional process is impeded by extensive amount of socioeconomic data. One of the most widely-used models which mitigate this problem is the TransCAD Model. This model is rarely used in Gaza Strip for traffic demand forecasting, and most of the practices depend mainly on a constant growth rate of traffic. Therefore, the main objective of this research is to apply this model in Gaza City for traffic estimation. This model estimates the origin-destination matrix based on traffic count. The traffic count was carried out at 36 intersections distributed around Gaza City. The results of traffic flow estimation obtained from TransCAD are assigned to the Gaza maps using the GIS techniques for spatial analysis. It is shown that the most congested area at present is the middle of the city especially at Aljala-Omer Almokhtar intersection. Therefore, improvement scenarios of this area should be carried out. The results of calibration of traffic flow estimation show that the differences between the estimated and the actual flows were less than 10%. In addition, network evaluation results show that the network is expected to be more congested in 2015. This work can be used by transportation planners for testing any network improvement scenarios and for studying their network performance.展开更多
The widely-existed uncertainty of origin-destination(OD)demand in transportation networks has attracted extensive attention.Most characterizations or models of stochastic OD demands in networks assume a homogenous pro...The widely-existed uncertainty of origin-destination(OD)demand in transportation networks has attracted extensive attention.Most characterizations or models of stochastic OD demands in networks assume a homogenous probability distribution,though empirical studies are lacking of large-scale networks to justify this assumption.Given that the longterm continuous automatic fare collection(AFC)data of metro networks can provide complete OD passenger demand information,this study takes the Shanghai metro network as an example to empirically examine the stochasticity characteristics of OD passenger demands of metro network.Based on the morning peak OD demand data for 250 weekdays,a local outlier factor(LOF)method is used to identify and remove outliers in the data.A clustering method is used to cluster the OD pairs,study the fluctuation and distribution characteristics of the OD passenger demands,and select the optimal distribution type through goodness-of-fit indices.The results show that 1)the coefficients of variation of morning peak OD demands in the network are mainly distributed in the range of 0.2-0.6,different OD pairs have different fluctuations,and the degree of demand fluctuation decreases as the mean increases;2)the probability distribution types of OD demands based on statistical characteristics are heterogeneous;and 3)the optimal distribution type of OD demands is Poisson,lognormal/Gamma,and normal for OD pairs characterized by a small mean and right-skewness,a small mean and skewness close to 0,and a large mean,respectively.In contrast to the simple average-based data processing of OD passenger demand in metro networks,this paper presents a new perspective of mining long-term continuous data to understand the inherent stochasticity of OD passenger demands.The results can provide more realistic and practical inputs and assumptions for theoretical research on stochastic OD demands in metro networks.展开更多
In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever b...In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever before. Al- though a great number of prediction methods have been pre- sented in the field of transportation, all of them belong to the station oriented approach, which is not well suited to the Bei- jing subway system. This paper proposes a novel metro-net oriented method, called the probability tree based passenger flow model, which is also based on historic origin-destination （OD） information. First it learns and obtains the appearance probabilities for each kind of OD pair. For the real-time origin datum, the destination datum is calculated, and then several kinds of passenger flow in the metro-net can be pre- dicted by gathering all the contributions. The results of exper- iments, using the historical data of Beijing subway, show that although the proposed method has lower performance than existing prediction approaches for forecasting exit passenger flows, it is able to predict several additional kinds of passen- ger flow in stations and throughout the subway system; and it is a more feasible, suitable, and advanced passenger flow prediction model for Beijing subway system.展开更多
Although the available traffic data from navigation systems have increased steadily in recent years,it only reflects average travel time and possibly Origin-Destination information as samples,exclusively.However,the n...Although the available traffic data from navigation systems have increased steadily in recent years,it only reflects average travel time and possibly Origin-Destination information as samples,exclusively.However,the number of vehicles participating in the traffic-in other words,the traffic flows being the basic traffic engineering information for strategic planning or even for real-time management-is still missing or only available sporadically due to the limited number of traditional traffic sensors on the network level.To tackle this gap,an efficient calibration process is introduced to exploit the Floating Car Data combined with the classical macroscopic traffic assignment procedure.By optimally scaling the Origin-Destination matrices of the sample fleet,an appropriate model can be approximated to provide traffic flow data beside average speeds.The iterative tuning method is developed using a genetic algorithm to realize a complete macroscopic traffic model.The method has been tested through two different real-world traffic networks,justifying the viability of the proposed method.Overall,the contribution of the study is a practical solution based on commonly available fleet traffic data,suggested for practitioners in traffic planning and management.展开更多
基金Project(51478036)supported by the National Natural Science Foundation of ChinaProject(20120009110016)supported by Research Fund for Doctoral Program of Higher EducationChina
文摘The major objective of this work was to establish a structural state-space model to estimate the dynamic origin-destination(O-D) matrices for urban rail transit network, using in- and out-flows at each station from automatic fare collection(AFC) system as the real time observed passenger flow counts. For lacking of measurable passenger flow information, the proposed model employs priori O-D matrices and travel time distribution from historical travel records in AFC system to establish the dynamic system equations. An arriving rate based on travel time distribution is defined to identify the dynamic interrelations between time-varying O-D flows and observed flows, which greatly decreases the computational complexity and improve the model's applicability for large-scale network. This methodology is tested in a real transit network from Beijing subway network in China through comparing the predicted matrices with the true matrices. Case study results indicate that the proposed model is effective and applicative for estimating dynamic O-D matrices for large-scale rail transit network.
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.
文摘A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions.By analyzing individuals’mobility patterns revealed from their mobile phones,researchers and practitioners are now equipped to derive the largest trip samples for a region.Because of its ubiquitous use,extensive coverage of telecommunication services and high penetration rates,travel demand can be studied continuously in fine spatial and temporal resolutions.The derived sample or seed trip matrices are coupled with surveyed commute flow data and prevalent travel demand modeling techniques to provide estimates of the total regional travel demand in the form of origindestination(OD)matrices.The methodology is evaluated in a series of real world transportation planning studies and proved its potentials in application areas such as dynamic traffic assignment modeling,integrated corridor management and online traffic simulations.
文摘This paper develops an optimization model that determines the optimal location for Bluetooth nodes used in the determination of the origin-destination matrix within an urban network. To analyze the effectiveness of the model, the city of Akron, OH was utilized as a testing location. The Average Daily Traffic (ADT) was used to determine intersections that have the greatest number of Bluetooth responses. Along with maximizing the number of Bluetooth responses, the model applies financial constraints when determining the number of nodes in the urban network. The developed model selected seven locations to deploy nodes in order to stay within the financial constraints and to maximize the possible number of responses from vehicles within the Akron urban network.
文摘The paper analyses integrating origin-destination (O-D) survey results with stochastic user equilibrium (SUE) in traffic assignment. The two methods are widely used in transportation planning but their applications have not yet fully integrated. While O-D gives a generalized trip patterns, purpose and characteristics, SUE provides optimal trip distributions using the characteristics found in O-D survey. The paper utilized O-D and SUE in route relocation study for the town of Coamo in Puerto Rico. The O-D survey was used initially in studying possible trip distribution and assignment for the new route. Initial distribution and assignment of traffic to the existing roadway networks and the proposed route were allocated utilizing the O-D survey findings. The SUE was then used to optimize the assignments considering roadway characteristics such as number of lanes, capacity limits, free flow speed, signal spacing density, travel time and gasoline cost. The travel time was optimized through the Bureau of Public Roads (BPR) equation found in 2000 HCM. The optimal trips found from the SUE were then used to propose the final alignment of the new route. Traffic assignment from the SUE was slightly different from those initially assigned using O-D, indicating there was optimization. The assignment on new route was increased by 13.8% from the one assigned using O-D while assignment on the existing link was reduced by 22%.
文摘Origin-destination(OD)modeling facilitates effective demand-responsive public transportation planning in order to meet emergent needs.Given recent advances in transit information and personal communications technology,transit OD estimation methods have evolved from relying on limited survey sources to automated big data sources.Innovative modeling approaches have also been developed over several decades to estimate trip ODs,not only for single routes,but also for full networks,including transfers.In this paper,we synthesize a review of the state of the art in research and practice,along with descriptions of key data types and methodological approaches,indicating how they interact.We also discuss current research gaps and opportunities for further innovation.This review provides a comprehensive resource that should facilitate the application of these methods to various transit systems,thus enabling planners and policymakers to gain insights from new and improved model estimates in various transit systems.
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.
基金supported by the National Natural Science Foundation of China(72288101,72201029,and 72322022).
文摘Accurate origin–destination(OD)demand prediction is crucial for the efficient operation and management of urban rail transit(URT)systems,particularly during a pandemic.However,this task faces several limitations,including real-time availability,sparsity,and high-dimensionality issues,and the impact of the pandemic.Consequently,this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network(PAG-STAN)for metro OD demand prediction under pandemic conditions.Specifically,PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices.Subsequently,a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices.Thereafter,PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic.Finally,a masked physics-guided loss function(MPG-loss function)incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability.PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios,highlighting its robustness and sensitivity for metro OD demand prediction.A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.
基金The Project Supported by National Natural Science Foundation of China.
文摘A new method of bus-route network planning is presented by analyzing theservice characteristics of bus-route network.The following optimization objects areadopted:to minimize the total number of transfers from one route to another,to minimizethe total number of passenger-kilometers(or passenger-hours),to even passenger distri-bution on the network,and to make the directions of bus-routes consistent with the direc-tions of passenger flows.The algorithm is very simple.This method is suitable forlarge-sized and medium-sized cities and has been adopted in the transportation planningof Nanjing and Zhengzhou.The result is satisfactory.
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.
文摘In the early nineties of the last century, the transportation system in Gaza Strip was born and new infrastructure projects, especially road networks, were constructed. However, the construction lacked efficient application of a transportation planning process. Transportation planning relies on traffic demand forecasting process. The conventional process is impeded by extensive amount of socioeconomic data. One of the most widely-used models which mitigate this problem is the TransCAD Model. This model is rarely used in Gaza Strip for traffic demand forecasting, and most of the practices depend mainly on a constant growth rate of traffic. Therefore, the main objective of this research is to apply this model in Gaza City for traffic estimation. This model estimates the origin-destination matrix based on traffic count. The traffic count was carried out at 36 intersections distributed around Gaza City. The results of traffic flow estimation obtained from TransCAD are assigned to the Gaza maps using the GIS techniques for spatial analysis. It is shown that the most congested area at present is the middle of the city especially at Aljala-Omer Almokhtar intersection. Therefore, improvement scenarios of this area should be carried out. The results of calibration of traffic flow estimation show that the differences between the estimated and the actual flows were less than 10%. In addition, network evaluation results show that the network is expected to be more congested in 2015. This work can be used by transportation planners for testing any network improvement scenarios and for studying their network performance.
基金sponsored by the National Natural Science Foundation of China(No.72021002)the Fundamental Research Funds for the Central Universities of China.
文摘The widely-existed uncertainty of origin-destination(OD)demand in transportation networks has attracted extensive attention.Most characterizations or models of stochastic OD demands in networks assume a homogenous probability distribution,though empirical studies are lacking of large-scale networks to justify this assumption.Given that the longterm continuous automatic fare collection(AFC)data of metro networks can provide complete OD passenger demand information,this study takes the Shanghai metro network as an example to empirically examine the stochasticity characteristics of OD passenger demands of metro network.Based on the morning peak OD demand data for 250 weekdays,a local outlier factor(LOF)method is used to identify and remove outliers in the data.A clustering method is used to cluster the OD pairs,study the fluctuation and distribution characteristics of the OD passenger demands,and select the optimal distribution type through goodness-of-fit indices.The results show that 1)the coefficients of variation of morning peak OD demands in the network are mainly distributed in the range of 0.2-0.6,different OD pairs have different fluctuations,and the degree of demand fluctuation decreases as the mean increases;2)the probability distribution types of OD demands based on statistical characteristics are heterogeneous;and 3)the optimal distribution type of OD demands is Poisson,lognormal/Gamma,and normal for OD pairs characterized by a small mean and right-skewness,a small mean and skewness close to 0,and a large mean,respectively.In contrast to the simple average-based data processing of OD passenger demand in metro networks,this paper presents a new perspective of mining long-term continuous data to understand the inherent stochasticity of OD passenger demands.The results can provide more realistic and practical inputs and assumptions for theoretical research on stochastic OD demands in metro networks.
基金This work was supported by the National High- Tech Research and Development Plan of China （863） （2011AA010502）, the National Natural Science Foundation of China （Grant No. 61103093）, the Doctoral Fund of Ministry of Education of China （20091102110017）, the International Science ＆ Technology Cooperation Program of China （2010DFB 13350）, the Supported Project （SKLSDE-2012ZX-16） of the State Key Laboratory of Software Development Environment, and the Fundamen- tal Research Funds for the Central Universities. We are thankful to Bei- jing Municipal Committee of Transportation, Beijing Metro Network Con- trol Center, Beijing Mass Transit Railway Operation Corporation Limited, and Beijing MTR Corporation for their great help.
文摘In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever before. Al- though a great number of prediction methods have been pre- sented in the field of transportation, all of them belong to the station oriented approach, which is not well suited to the Bei- jing subway system. This paper proposes a novel metro-net oriented method, called the probability tree based passenger flow model, which is also based on historic origin-destination （OD） information. First it learns and obtains the appearance probabilities for each kind of OD pair. For the real-time origin datum, the destination datum is calculated, and then several kinds of passenger flow in the metro-net can be pre- dicted by gathering all the contributions. The results of exper- iments, using the historical data of Beijing subway, show that although the proposed method has lower performance than existing prediction approaches for forecasting exit passenger flows, it is able to predict several additional kinds of passen- ger flow in stations and throughout the subway system; and it is a more feasible, suitable, and advanced passenger flow prediction model for Beijing subway system.
基金Project No.TKP2021-NVA-02.Project No.TKP2021-NVA-02 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research,Development and Innovation Fund,financed under the TKP2021-NVA funding schemeThe research was also supported by Project No.2022-2.1.1-NL-2022-00012,which has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research,Development and Innovation Fund,financed under the National Laboratories funding scheme.
文摘Although the available traffic data from navigation systems have increased steadily in recent years,it only reflects average travel time and possibly Origin-Destination information as samples,exclusively.However,the number of vehicles participating in the traffic-in other words,the traffic flows being the basic traffic engineering information for strategic planning or even for real-time management-is still missing or only available sporadically due to the limited number of traditional traffic sensors on the network level.To tackle this gap,an efficient calibration process is introduced to exploit the Floating Car Data combined with the classical macroscopic traffic assignment procedure.By optimally scaling the Origin-Destination matrices of the sample fleet,an appropriate model can be approximated to provide traffic flow data beside average speeds.The iterative tuning method is developed using a genetic algorithm to realize a complete macroscopic traffic model.The method has been tested through two different real-world traffic networks,justifying the viability of the proposed method.Overall,the contribution of the study is a practical solution based on commonly available fleet traffic data,suggested for practitioners in traffic planning and management.
