Real-time origin-destination matrices estimation for urban rail transit network based on structural state-space model

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.

Semantics Analytics of Origin-Destination Flows from Crowd Sensed Big Data

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.

Deriving Operational Origin-Destination Matrices From Large Scale Mobile Phone Data

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.

An Optimization of Bluetooth Sensor Locations for Origin-Destination in an Urban Network

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.

Integrating Origin-Destination Survey and Stochastic User Equilibrium: A Case Study for Route Relocation

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%.

Ride-hailing origin-destination demand prediction with spatiotemporal information fusion

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.

Physics Guided Deep Learning-Based Model for Short-Term Origin–Destination Demand Prediction in Urban Rail Transit Systems Under Pandemic

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.

基于IC卡信息的公交客流起止点反推方法

公交客流起止点(origin-destination,OD)矩阵是公交线路调整和优化的重要依据,通过公交IC(integrated-circuit)卡信息获得的数据研究了公交出行的特征.以公交站点上下客人数为约束条件提出了综合考虑了公交乘客上下车概率与公交站点产生吸引因素的反推模型,实例分析表明该方法能显著提高反推的精度,反推结果更具可靠性.

网络层析成像研究综述

网络层析成像技术作为通信网络、层析成像技术和统计学理论相结合的产物,是一种全新的网络链路级参数推理、拓扑结构识别和OD(Origin-Destination)流估计的技术。在简要介绍网络层析成像的基本概念和数学模型的基础上,本文从网络链路级参数的推理、网络拓扑结构识别和OD流的估计3方面介绍了网络层析成像的研究现状,分析和展望了一些有价值的新发展,并指出了下一步的研究方向。

RESEARCH ON BUS-ROUTE NETWORK PLANNING

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.

Determining capacity of multimodal multi-commodities freight transportation network from critical link volumes

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.

TransCAD and GIS Technique for Estimating Traffic Demand and Its Application in Gaza City

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.

随机OD需求下城市道路交通网络设计问题的BP神经网络算法研究

本文应用反向传播(back-propagation,BP)神经网络方法研究随机Origin-Destination(OD)需求的城市道路交通网络设计问题.假设OD交通需求量服从某一分布,首先通过蒙特卡罗模拟的方法得到训练样本对神经网络进行训练并用测试样本测试所得到的神经网络,然后运用训练好的神经网络预测了系统总出行时间.将Nine-node网络作为测试网络,比较分析了不同样本规模下BP神经网络与蒙特卡罗模拟方法得出的结果和OD交通需求服从不同分布时系统总出行时间的分布情况.结果表明,BP神经网络的方法能够较好预测系统总出行时间的分布,且与蒙特卡罗模拟计算相比,能够产生一个合理且较窄的预测区间;当OD交通需求服从均匀分布、正态分布和对数正态分布时,系统总出行时间分布较为相似,接近正态分布.

Probability tree based passenger flow prediction and its application to the Beijing subway system

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.