Dynamic Origin-destination Research Articles

Estimation of dynamic Origin–Destination matrices in a railway transportation network integrating ticket sales and passenger count data

Accurately estimating Origin–Destination matrices is a pressing challenge in transportation management and urban planning. However, traditional methods like travel surveys have limitations in availability and comprehensiveness, which have been further exacerbated by the recent changes in mobility patterns induced by the COVID-19 pandemic. To address this issue, we focused on the Trenord railway network in Lombardy, Italy, and developed an innovative pipeline to integrate ticket and subscription sales and Automated Passenger Counting data using the Iterative Proportional Fitting algorithm. By effectively navigating the complexities of diverse and incomplete data sources, our approach showcases adaptability across various transportation contexts. Our research offers a valuable tool for operators, policymakers, and researchers, bridging the gap between data availability and the need for precise OD matrices. Additionally, we emphasise the potential of dynamic OD matrices and showcase methods for detecting anomalies in mobility trends, interpreting them in the context of events from the last months of 2022.

Real-Time Estimation of Origin–Destination Matrices Using a Deep Neural Network for Digital Twins

The digital twin, a real-time replica of physical systems, has garnered attention as a promising tool to strategize and evaluate solutions for complex real-world issues. However, developing digital twins in the field of transportation faces significant challenges related to the real-time estimation of dynamic origin–destination (OD) matrices constrained by computation time. To bridge this gap, microscopic traffic simulations with real-time synchronization are being researched. Nonetheless, the computational issue persists, emphasizing the need for more efficient OD estimation methods. In this regard, our objective is to reduce computation time in simulation-based methods by developing a data-driven metamodel using a deep neural network. The proposed model serves to map the correlation between the OD matrix and detector data. This model simplifies the computational process using hidden layers, rather than calculating complex interactions between vehicles in the traffic simulation. Compared to conventional methods, we evaluate the capability to estimate a reasonable OD matrix within a restricted time and validate our approach using detector data from Daejeon City, South Korea. The findings indicate that by combining our data-driven metamodel with the simultaneous perturbation stochastic approximation, it becomes feasible to estimate a reasonable OD matrix within a stipulated time frame, compared to the conventional method. Given a 1-min time frame, the proposed method outperforms the conventional simulation-based method by improving the calibration performance of traffic flow by 44.5 percentage points. This paper proposes a practical and versatile approach for real-time OD estimation, laying the foundation for extending microscopic traffic simulation into the digital twin.

Modeling Multimodal Curbside Usage in Dynamic Networks

The proliferation of emerging mobility technology has led to a significant increase in demand for ride-hailing services, on-demand deliveries, and micromobility services, transforming curb spaces into valuable public infrastructure for which multimodal transportation competes. However, the increasing utilization of curbs by different traffic modes has substantial societal impacts, further altering travelers’ choices and polluting the urban environment. Integrating the spatiotemporal characteristics of various behaviors related to curb utilization into general dynamic networks and exploring mobility patterns with multisource data remain a challenge. To address this issue, this study proposes a comprehensive framework of modeling curbside usage by multimodal transportation in a general dynamic network. The framework encapsulates route choices, curb space competition, and interactive effects among different curb users, and it embeds the dynamics of curb usage into a mesoscopic dynamic network model. Furthermore, a curb-aware dynamic origin-destination demand estimation framework is proposed to reveal the network-level spatiotemporal mobility patterns associated with curb usage through a physics-informed data-driven approach. The framework integrates emerging real-world curb use data in conjunction with other mobility data represented on computational graphs, which can be solved efficiently using the forward-backward algorithm on large-scale networks. The framework is examined on a small network as well as a large-scale real-world network. The estimation results on both networks are satisfactory and compelling, demonstrating the capability of the framework to estimate the spatiotemporal curb usage by multimodal transportation. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25. Funding: This material is based upon work supported by the Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy [Award DE-EE0009659]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0522 .

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.

Dynamic origin-destination matrix estimation for networks operating under free-flow conditions using macroscopic flow dynamics

The origin-destination (OD) matrix is a crucial requirement for efficient transportation planning, however its estimation still remains one of the most challenging tasks in transportation. This paper proposes a novel methodology for the estimation of dynamic OD matrices. The proposed methodology integrates disaggregated measurements from stationary sensors, e.g. loop detectors, with a macroscopic model that associates OD demands with Traffic counts on specific network links. We investigate networks that operate under free-flow conditions, hence the dynamic OD matrix estimation problem is formulated as a linear mathematical program. Finally, we reformulate the problem to obtain a computationally cheap quadratic programming problem that allows real-time estimation of OD matrices within the time-window under study. We demonstrate the effectiveness of our methodology through a literature network, showcasing its ability to accurately capture travel patterns in a computationally efficient manner, allowing informed decision-making processes in transportation planning and management.

A two-stage stochastic programming approach for dynamic OD estimation using LBSN data

Estimating origin–destination (OD) demand is essential for urban transport management and traffic control systems. With the ubiquity of smartphones, location based social networks (LBSN) data has emerged as a new rich data source with broad urban spatial and temporal coverage highly suitable for OD estimation. Its nature of confirmed trip purpose (activity) and activity chain host makes it more advantageous than other data (e.g., household travel surveys and traffic network detection). On the other hand, LBSN data is a more direct and accurate representation of demand patterns and can remove the significant burden of developing traffic models and estimating simulation-based objective functions. However, thus far, most LBSN-based estimation models only focus on static (day-level) OD estimation, making less use of those characteristics. To this end, this paper establishes a two-stage stochastic programming (TSSP) framework integrating the activity chains to model activity-level dynamic mobility flows using LBSN data. The first-stage model aims to minimize the errors introduced by the inter-zone OD flows alongside the expected errors of the check-in patterns. The second-stage model attempts to minimize the errors produced by the considered check-in pattern scenarios. Markov chain Monte Carlo (MCMC) sampling is used to generate plausible check-in scenarios. A generalized Benders decomposition (GBD) algorithm is presented to solve the two-stage stochastic programming model. We conduct the experiments on the case study of Tokyo, Japan, under the employment of the generalized least squares (GLS) estimator. The results show that algorithm convergence can be guaranteed within several iterations. The approach can provide satisfactory estimations of check-in patterns, zonal production and attraction, and OD flows. Furthermore, multiple objective function states are tested for evaluating the completeness of the proposed framework and exploring its potential for simplification and extension. Incorporating specific penalty terms into the objective function also provides a way to verify the reliability of the two-stage structure and validate the effectiveness of the model. Finally, we discuss the model enhancement from the perspectives of online OD estimation by integrating with LBSN simulations, network-wide OD extrapolation using appropriate scaling methods, and removing the user-side data requirement by leveraging activity chain modeling. The proposed framework provides a novel and effective approach to OD demand estimation using LBSN data.

A hybrid modelling framework for the estimation of dynamic origin–destination flows

The dynamic origin–destination flow estimation (DODE) problem requires scalable methods for large scale traffic networks and consistent techniques for capturing both uncongested and congested traffic conditions. Despite numerous efforts on incorporating multifold data sources and developing manifold mathematical models, the DODE problem remains a challenging problem in terms of both scalability and consistency. To fill this gap, we propose a novel hybrid DODE framework that integrates region-level (macro) and centroid-level (micro) traffic dynamics. The region-level traffic flows are described by the macroscopic fundamental diagram, while the centroid-level traffic flows are represented by the linear mapping of origin–destination flows onto link counts. This hybrid approach enables us to (i) incorporate region-level traffic measures into the problem, addressing scalability issues arising in large scale traffic networks, and (ii) capture non-linear behaviour of traffic in the regional context, enhancing consistency of the estimation results with respect to traffic conditions. The proposed methodology is experimented in a large-scale traffic network, which is benchmarked for DODE problems. The results indicate an outstanding performance of the hybrid DODE particularly in congested traffic conditions and highlight the effectiveness of aggregated (regional) traffic models in enhancing DODE methods with minimal computational burden.

Dynamic origin–destination flow estimation for urban road network solely using probe vehicle trajectory data

Dynamic origin–destination (OD) flow is a fundamental input for dynamic network models and simulators. Numerous studies have conducted dynamic OD estimations based on fixed detectors, where a high device coverage rate and data quality are often required to accomplish the desired results. Several existing methods have used probe vehicle trajectories as an additional data source, and generalized least squares (GLS) is commonly recognized as an effective framework. However, the prior matrices used in these models either came from historical data or data obtained by uniform scaling that neglected the variation in penetration rates and suffer from sparsity issues. Moreover, the microscopic information contained in the high-resolution probe vehicle trajectories has not been fully utilized. The possibility of estimating OD flows using only vehicle trajectories without external information is rarely discussed in current literature. Therefore, this paper introduces a dynamic OD flow estimation model solely using probe vehicle trajectories. In the proposed model, two methods based on probe OD pair distribution are proposed to infer prior OD flows. Then the GLS framework is extended by including link travel times as another objective term, and the solution algorithm is adapted to deal with uncertain priors. To validate the proposed model, extensive experiments were conducted on a simulation network. The results show that the proposed model could reliably estimate dynamic OD flows and showed superiority to two existing models. In sensitivity analysis concerning the penetration rate and degree of saturation, the proposed model presented satisfactory performance and could adapt to various conditions.

Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach

This study focuses on dynamic origin–destination demand estimation problem on freeway networks. Existing studies on this problem rely on high-coverage of traffic measurements and assumptions on travel times, exhibiting limitations in real-world applications. We formulate the problem as a bi-level programming model, where micro-simulations are incorporated to precisely model traffic flows/travel times on freeways. The bi-level programming model cannot provide explicit closed-form expressions for the objective function and its derivatives, and also intrinsically high-dimensional. Thus, it is highly challenging to find efficient solution algorithms. In this regard, a problem-specific and computationally efficient Bayesian optimization approach is designed. Herein, a novel surrogate model is proposed by embedding a physical surrogate model (it characterizes underlying physical mechanisms and provides global yet less precise approximations) into a functional surrogate model (it provides precise local approximations). The embedding provides problem-specific knowledge for the surrogate model. More importantly, it also restricts the feasible region, enabling the surrogate model to efficiently deal with high-dimensional problems. Gaussian process can be served as the functional surrogate model. Two linear physical surrogate models are proposed to capture interactions between travel demand and traffic measurements. To deal with constraints in the surrogate model, a projection-distance based acquisition function is designed. In searching for new points, the proposed acquisition function is capable of assigning unique weight of exploration to each feasible solution. The proposed approach is validated based on a freeway corridor example, which indicates its outperformance over existing dynamic origin–destination estimation methods in terms of computational efficiency and solution accuracy.

Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System

Intelligent Transportation Systems (ITS) have the potential to improve traffic conditions and reduce travel delays. As a decision support software system for ITS, DynasTIM is based on the principle of dynamic traffic assignment and developed for real-time online simulation, prediction and optimization of dynamic traffic flows in urban or expressway networks. This paper introduces the models, algorithms and some typical applications of DynasTIM. The main contents include: the functional architecture; the application architecture of the system; dynamic OD (Origin-Destination) flows estimation method with novel formula for assignment matrix computation; mesoscopic traffic model using variable-length speed influence region and calibrating speed online based on connected vehicles data; and parallel SPSA algorithm based urban area signal optimization method. The functions of DynasTIM are implemented basically through three main modules: state estimation (ES), state prediction and control strategy optimization (PS&CSO), and guidance strategy optimization (GSO). The case study is aimed at the populated Futian Central Business District (CBD) road network in Shenzhen, China, which has an area of about 7 square kilometers. Based on the archived turning counts collected from 359 video traffic detection locations, DynasTIM was calibrated offline for this network, in order to validate the capability of simulating actual traffic conditions, and to set up basic conditions for testing signal optimization methods. The results show that the simulation output flows of DynasTIM have fairly good matching accuracy with the real surveillance flows in the field. Furthermore, for the CBD network with 38 signalized intersections, the signal optimization method is evaluated and better signal timing plans are found which can reduce about 13% average travel delay, compared with the signal plans currently implemented in the field.

Inference of dynamic origin–destination matrices with trip and transfer status from individual smart card data

BackgroundThe provision of seamless public transport supply requires a complete understanding of the real traffic dynamics, comprising origin-to-destination multimodal mobility patterns along the transport network. However, most current solutions are centred on the volumetric analysis of passengers’ flows, generally neglecting transfer, walking, and waiting needs, as well as the changes in the mobility patterns with the calendar and user profile. These challenges prevent a comprehensive assessment of the routing and scheduling vulnerabilities of (multimodal) public transport networks.Research aims/questionsThe research presented in this paper aims at addressing the above challenges by proposing a novel approach that extends dynamic Origin-Destination (OD) matrix inference to dynamic OD matrix inference with aggregated statistics, highlighting vulnerabilities and multimodal mobility patterns from individual trip record data.MethodologyGiven specific spatial and temporal criteria, the proposed methodology extends dynamic Origin-Destination (OD) matrices with aggregated statistics, using smart-card validations gathered from (multimodal) public transport networks. More specifically, three major contributions are tackled; i) the data enrichment in the OD matrices with statistical information besides trip volume (e.g., transfer and trip features); ii) the detection of vulnerabilities on the network pertaining to walking distances and trip durations in a user-centric way and iii) the decomposition of traffic flows in accordance with calendrical rules and user (passenger) profiles. The set of contributions are validated on the bus-and-metro public transport network in the city of Lisbon.ResultsThe proposed approach for inferring OD matrices yields four unique contributions. First, we allow inference to consider multimodal commuting patterns, detecting individual trips undertaken along with different operators. Second, we support dynamic matrices’ OD inference along with parameterizable time intervals and calendrical rules, and further support the decomposition of traffic flows according to the user profile. Third, we allow parameterization of the desirable spatial granularity and visualisation preferences. Fourth, our solution efficiently computes several statistics that support OD matrix analysis, helping with the detection of vulnerabilities throughout the transport network. More specifically, statistical indicators related to travellers’ functional mobility needs (commuters for working purposes, etc.), walking distances and trip durations are supported. The inferred dynamic OD matrices are the outcome of a developed software with strict guarantees of usability. Results from the case study using data gathered from the two main public transport operators (Bus and Metro) in the city of Lisbon show that 77.3% of alighting stops can be estimated with a high confidence degree from bus smart-card data. The inferred OD matrices (Bus and Metro) in the city of Lisbon reveal vulnerabilities along specific OD pairs, offering the bus public operators in Lisbon new knowledge and a means to better understand dynamics and validate OD assumptions.

Dynamic Origin-Destination Flow Prediction Using Spatial-Temporal Graph Convolution Network With Mobile Phone Data

Massive mobile phone data provide continuous and large-scale dynamic origin–destination (OD) flow information for multiple modes of transportation. In this study, we represent the dynamic OD flows obtained from mobile phone data as time-dependent graphs and propose two novel spatial-temporal graph convolutional network (STGCN)-based models to predict dynamic OD flows. Both models directly operate on the graph-structured OD flows, capture correlations among OD flows far apart in the Euclidean space, and fully explore the complex spatial-temporal features. We first formulate OD flows as explicit edges that specify the travels between two locations and propose an edge-focused STGCN. The edge-focused STGCN applies a novel three-step strategy to effectively update edge features in large-scale graphs. Second, we formulate OD flows as vertices in graph and propose a vertex-focused STGCN. The vertex-focused STGCN infers the relations among OD flows by establishing an adjacency matrix based on the temporal similarity between OD flows. The proposed models were validated using real-world mobile phone data collected in Kunshan, China. OD flows in the next hour were predicted, and the mean absolute percent errors of the edge-focused STGCN and the vertex-focused STGCN were 1.755% and 1.672%, respectively; both were significantly lower than the current baseline models.

O–D matrix estimation based on data-driven network assignment

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem. Abbreviations APR: average penetration rate; DDNA: data driven network assignment; DDNL: data driven network loading; DODME: dynamic OD matrix estimation; DTA:dynamic traffic assignment; FCD: floating-car data; ITS: intelligent transportationsystems; NNLSQ: non-negative least squares; OD: origin destination; ODME: ODmatrix estimation; PV: probe vehicle; RUM: random utility model

Toward learning dynamic origin-destination matrices from crowd density heatmaps

Knowing the origins and destinations of pedestrians’ paths is key to the initialization of crowd simulations. Unfortunately, they are difficult to measure in the real world. This is one major challenge for live predictions during events such as festivals, soccer games, protest marches, and many others. Sensor data can be used to feed real-world observations into simulations in real-time. As input data for this study, we use density heatmaps generated from real-world trajectory data obtained from stereo sensors. Density information is compact, of constant size, and in general easier to obtain than e.g., individual trajectories. Therefore, the information limitation improves the applicability to other scenarios. We include the absolute pedestrian trip counts from origins to destinations during a brief time interval in an OD matrix, including unknown destinations due to sensor errors. Our goal is to estimate these OD matrices from a series of density heatmaps for the same interval. For this, we compute the ground truth OD matrices and density heatmaps using real-world trajectory data from a train station. We employ linear regression as a statistical learning method for estimation. We observe that the linear share of the relationship between density and OD matrix is estimated successfully. Nevertheless, a portion of the data remains that cannot be explained. We attempt to overcome this difficulty with random forest as a nonlinear model. The results indicate that both a linear and a nonlinear model can estimate some features of the OD matrices. However, there is no clear winner in terms of the chosen metric, the R 2 score. Overall, our findings are a strong indicator that OD matrices can indeed be estimated from density heatmaps extracted automatically from sensors.

A GAN framework-based dynamic multi-graph convolutional network for origin–destination-based ride-hailing demand prediction

Ride-hailing demand prediction plays an important role in ride-hailing vehicle scheduling, traffic condition control and intelligent transportation system construction. Accurate and real-time ride-hailing demand prediction is crucial for improving supply–demand imbalance, vehicle utilization and traffic conditions. However, most existing works mainly address the region-based demand prediction whereas only a few works focus on the origin–destination (OD)-based demand prediction. To address the issue, we develop several dynamic OD graphs to character the ride-hailing demand transactions between the origin and destination. We propose a novel deep learning model, referred to as the Dynamic Multi-Graph Convolutional Network with Generative Adversarial Network structure (DMGC-GAN), to investigate the challenging problem. Different from previous studies, we develop the temporal multi-graph convolutional network (TMGCN) layer with different dynamic OD graphs to capture the spatial topologies contained in the dynamic OD graphs in terms of time, and exploit GAN structure to overcome the high sparsity of OD demand. We conduct extensive numerical experiments on the real-world ride-hailing demand dataset (from Manhattan district, New York City). The results demonstrate that the model we propose performs the best against to nine baseline models.

A novel metamodel-based framework for large-scale dynamic origin–destination demand calibration

Calibrating dynamic traffic demand for stochastic traffic simulators is one of the big challenges due to computational burden. This paper proposes a novel framework to calibrate a dynamic car origin–destination matrix of large-scale networks, which has high computational efficiency. The proposed framework is relied on the metamodel optimization technique, based on the reference of aggregated traffic flow dynamics as expressed by the recent advance in traffic flow theory, namely the bi-modal macroscopic fundamental diagram (MFD).We validate our proposed approach with the Stochastic Perturbation Simultaneous Approximation (SPSA) algorithm which is widely used for the same purpose in the literature. Our result confirms that our approach can facilitate the demand calibration effectively. We also show that the traffic conditions at a link level are also reproduced realistically from our network-level calibration, and that our approach outperforms the traditional link-level OD calibration by comparing with the Aimsun OD adjustment in addition to SPSA.Furthermore, we demonstrate via different realistic network studies that the proposed approach is computationally efficient compared to the existing state-of-the-art approach. Our result shows that the proposed approach can be applied effectively regardless of the topology of the network, and that the model parameters do not have significant influence on the optimization results. Our calibration results show that just a few iterations are needed to calibrate the OD demand in the proposed approach even for the large-scale complex network underpinned by a bi-modal MFD derived from multiple real data sources.

A practical approach to assignment-free Dynamic Origin–Destination Matrix Estimation problem

Dynamic traffic models require dynamic inputs, one of the main ones being the Dynamic Origin–Destinations (OD) matrices describing the variability over time of the trip patterns across the network. The Dynamic OD Matrix Estimation (DODME) is a challenging problem since no direct observations are available, and therefore one should resort to indirect estimation approaches. Among the most efficient approaches, the one that formulates the problem in terms of a bi-level optimization problem has been widely used. This formulation solves at the upper level a nonlinear optimization problem that minimizes some distance measures between observed and estimated link flow counts at certain counting stations located in a subset of links in the network, and at the lower level a traffic assignment that estimates these link flow counts assigning the current estimated matrix. The variants of this formulation differ in the analytical approaches that estimate the link flows in terms of the traffic assignment and their time dependencies. Since these estimations are based on a traffic assignment at the lower level, these analytical approaches, although numerically efficient, imply a high computational cost. The advent of ICT applications has made available new sets of traffic-related measurements enabling new approaches; under certain conditions, the data collected allows to estimate the most likely used paths, from which a de facto assignment matrix can be computed. This allows extracting empirically similar information to that provided by the dynamic traffic assignment that is used in the analytical approaches. This paper explores how to extract such information from the recorded commercial data, proposes a new constrained non-linear optimization model to solve the DODME problem, with a reduced number of variables linearly depending on network size instead of quadratically. Moreover, the bilevel iterative process and the traffic assignment need are avoided. Validation and computational results on its performance are presented.

Exploring the Direct and Indirect Use of ICT Measurements in DODME (Dynamic OD Matrix Estimation)

The estimation of the network traffic state, its likely short-term evolution, the prediction of the expected travel times in a network, and the role that mobility patterns play in transport modeling is usually based on dynamic traffic models, whose main input is a dynamic origin–destination (OD) matrix that describes the time dependencies of travel patterns; this is one of the reasons that have fostered large amounts of research on the topic of estimating OD matrices from the available traffic information. The complexity of the problem, its underdetermination, and the many alternatives that it offers are other reasons that make it an appealing research topic. The availability of new traffic data measurements that were prompted by the pervasive penetration of information and communications technology (ICT) applications offers new research opportunities. This study focused on GPS tracking data and explored two alternative modeling approaches regarding how to account for this new information to solve the dynamic origin–destination matrix estimation (DODME) problem, either including it as an additional term in the formulation model or using it in a data-driven modeling method to propose new model formulations. Complementarily, independently of the approach used, a key aspect is the quality of the estimated OD, which, as recent research has made evident, is not well measured by the conventional indicators. This study also explored this problem for the proposed approaches by conducting synthetic computational experiments to control and understand the process.

A Bayesian Method for Dynamic Origin-Destination Demand Estimation Synthesizing Multiple Sources of Data.

In this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O–D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O–D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O–D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O–D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen–Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O–D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O–D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and “true” O–D demands is relatively small, and the O–D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O–D demands with fine accuracy.

Day-to-day dynamic origin–destination flow estimation using connected vehicle trajectories and automatic vehicle identification data

Dynamic vehicular origin–destination (OD) flow is a fundamental component of traffic network modeling and its estimation has long been studied. Although ideal observing conditions and behavioral assumptions are often indispensable for estimation, day-to-day traffic recurrences and variations are seldom utilized to improve the estimation performance. In this paper, we propose a new method to recover day-to-day dynamic OD flows using both connected vehicle (CV) trajectories and automatic vehicle identification (AVI) observations. The method involves two modules: the first module provides reliable prior OD flows given limited observations, while the second module seeks the optimal estimates based on the prior OD flows. In the first module, linear projection is extended to consider temporal and spatial variation of the CV penetration rate, and non-negative Tucker decomposition (NTD) is adopted to address the data sparsity issue caused by the low CV penetration rate. In the second module, a self-supervised learning model called the latency-constrained autoencoder (LCAE) is established to search for the optimal OD flows according to the priors with given robust latent features. To avoid local minima and ensure consistency between estimates, a novel algorithm called adaptive sub-sample correction (ASC) is proposed and integrated into the optimization process of LCAE, which can iteratively correct the most inconsistent samples based on the day-to-day traffic flow characteristics. The proposed method is examined on an empirical urban arterial network, a calibrated simulation network, and a synthetic large-scale grid network. Our results indicated that the proposed method requires very few AVI detectors and CV trajectories to achieve competitive estimation performance against two benchmark models. Furthermore, general robustness to several factors with respect to observing conditions and data quality was investigated, and satisfactory scalability was also demonstrated in terms of both estimation accuracy and computational cost.