Origin-destination Matrix Estimation Research Articles

Evaluating the Effect of Multiple Data Types on a Large Scale Static Origin–Destination Matrix Estimation, Case Study of Tehran, Iran

Evaluating the Effect of Multiple Data Types on a Large Scale Static Origin–Destination Matrix Estimation, Case Study of Tehran, Iran

Estimation of Origin-Destination Matrix Using Open-Source Applications Based on Traffic Counts for Mansoura City in Egypt

Estimation of Origin-Destination Matrix Using Open-Source Applications Based on Traffic Counts for Mansoura City in Egypt

Origin-destination matrix estimation in Solo agglomeration with the maximum likelihood method

The movement of vehicles is one form of fulfilling community needs. High vehicle movement can cause transportation problems, one of which is congestion. To overcome this problem, planning a transportation system is essential. Using the EMME/4 program, we analyzed the distribution of movements in the Origin Destination Matrix (MAT) using a gravity model. Using the User’s Equilibrium method, the study aims to determine the parameter β, data validity value (R2), and movement patterns. The research study area was carried out in Solo Agglomeration with 19 zones. The results indicate a β value of 0.0796 (R2 = 0.817), with an estimated Solo Agglomeration movement of 34428 passenger-car-units per hour in 2023. The most extensive distribution of movements occurred within internal movements, making up 62.85%, while the smallest were external-external movements, accounting only for 4.01%.

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.

An Evolutionary View on Equilibrium Models of Transport Flows

In this short paper, we describe natural logit population games dynamics that explain equilibrium models of origin-destination matrix estimation and (stochastic) traffic assignment models (Beckmann, Nesterov–de Palma). Composition of the proposed dynamics allows to explain two-stages traffic assignment models.

On Heterogenous Sampling Rates in Origin–Destination Matrix Estimation Based on Trajectory Data and Link Counts

Being a fundamental metric of the transportation network, the origin–destination (O-D) flow matrix is a critical input for various transportation models and studies. This paper deals with the estimation of an O-D matrix of trip flows based on two kinds of data: probe trajectory data and local traffic counts. A Bayesian assignment framework is developed for demonstrating the relationship between the link probe sampling rates and the fractional contributions from the sampling rates on different O-D pairs. The unknown O-D matrix is estimated by applying cross entropy minimization using a prior matrix from the probe trajectories, along with the Bayesian assignment rules on link sample rates as the constraints. The methodology was applied using floating car data and camera link flow counts for a numerical experiment. The results show that the method can achieve a robust estimation of O-D matrices, even using different prior matrices. The issue of the heterogeneous sampling rates can be well addressed with link count constraints, effectively correcting the unknown bias in the probe sampling. The case study using real data also proves the feasibility of mining observed trajectory data to obtain the assignment fractions and estimate the O-D matrix inversely, avoiding the conventional sophisticated process of traffic assignment modeling.

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

Simultaneous identification of sensor faults and origin-destination matrix estimation

Efficient estimation of the origin-destination (OD) matrix is a crucial requirement for traffic monitoring and control. The OD matrix estimation problem has received significant attention over the past decades and various approaches using traffic counts from fixed location sensors have been developed and tested. A significant challenge when using information obtained from traffic sensors, is that such sensors are subject to considerable disruptions due to system errors, that affect the quality and reliability of the information provided. This paper presents a novel approach for OD matrix estimation in the presence of faulty sensors. For the purposes of this work we assume that the network under study operates under free-flow conditions and utilise a variation of the cell transmission model to capture the traffic flow dynamics in a pre-specified time window. The problem is formulated in an optimisation framework and solved by taking into account the presence of faulty measurements. We test and validate the proposed methodology on a sample network and investigate the advantage of OD matrix estimation in the presence of faulty measurements compared to OD matrix estimation when faulty sensors are not considered.

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.

The value of additional data for public transport origin–destination matrix estimation

Passenger origin–destination data is an important input for public transport planning. In recent years, new data sources have become increasingly common through the use of the automatic collection of entry counts, exit counts and link flows. However, collecting such data can be sometimes costly. The value of additional data collection hence has to be weighed against its costs. We study the value of additional data for estimating time-dependent origin–destination matrices, using a case study from the London Piccadilly underground line. Our focus is on how the precision of the estimated matrix increases when additional data on link flow, destination count and/or average travel distance is added, starting from origin counts only. We concentrate on the precision of the most policy-relevant estimation outputs, namely, link flows and station exit flows. Our results suggest that link flows are harder to estimate than exit flows, and only using entry and exit data is far from enough to estimate link flows with any precision. Information about the average trip distance adds greatly to the estimation precision. The marginal value of additional destination counts decreases only slowly, so a relatively large number of exit station measurement points seem warranted. Link flow data for a subset of links hardly add to the precision, especially if other data have already been added.

Implementation of Big Data to Develop Origin-Destination Matrix Estimation Model

Big data is a huge collection of electronic data which is currently popular to be developed in various scientific fields, including the transportation sector. Several data categories that can be used for transportation infrastructure planning are real-time location/position and timestamp of road users. This technological sophistication addresses a very crucial transportation planning problem that has been faced for many years, that is the inaccurate and unrepresentative prediction of the Origin-Destination Matrix. Up to this point, the Origin-Destination Matrix has been obtained using conventional and unconventional methods. Data was collected through manual traffic counts survey and direct interview in the study area. This results in unreliable data and inadequate sampling rates, which are only able to cover 20% of the population. In addition, the application of these techniques is expensive, time-consuming, and require a lot of human resources. Therefore, this study tries to develop a model capable of predicting the origin and destination of mobility, which in this case is represented by the sub-district boundary as a zone. As a result, an Origin-Destination Matrix can be obtained with a high degree of accuracy. The case study used is Bandung city. In the future, this research is expected to become a milestone in elaborating a validated Origin-Destination Matrix estimation model that can be used in various areas.

PERFORMANCE MATTERS ON IDENTIFICATION OF ORIGIN-DESTINATION MATRIX ON BIG GEOSPATIAL DATA

Abstract. One of the common problems at the intersection of geographical information science and transportation science is the estimation of origin-destination (OD) matrices. The emergence of sensor technologies offers unprecedented opportunities in this regard since massive amounts of traffic data can be collected in an easy way. Researchers and practitioners need to choose a suitable DataBase Management System (DBMS) among alternatives, such that storing and analysing traffic data to estimate the OD matrix is feasible. The aim of this paper is to compare the performance of two such notable DBMSs, PostgreSQL and MongoDB, in the context of OD matrix estimation. The experiments are carried out on New York City’s openly available taxi data on two different polygon sets: taxi zones and census blocks. These polygon layers consist of 263 and 38794 features respectively. The results suggest that Postgres outperforms MongoDB by generating the OD matrix instantly. The run time of MongoDB varies depending on the analysed time interval and follows a trip demand curve. As there are more trips involved in the generation of the OD matrix, so does the execution time increases in MongoDB. On the other hand, the query results are the same. Finally, the origin points of the taxi trips are visualised in QGIS using the ‘TimeManager’ plugin, and results are presented through a web-interface.

Multiple Uses of Big Data for Model Validation and Express Lanes Traffic Forecasts

This paper highlights a variety of uses for Big Data when developing project forecasts and model validations. In most travel models, validation often refers to estimating model volumes close to the observed highway counts. While this is an established practice for producing reasonable confidence in the model, these statistics are often not sufficient to build confidence in the project forecast. This is especially true for investment-grade level traffic and revenue forecasts for projects involving congestion pricing. This paper explores the application of Big Data to validate subarea models in multiple dimensions: subarea district-to-district origin-destination (O-D) flows; corridor segment-to-segment O-D flows; and trip length distribution by O-D types for the I-4 Ultimate Express Lanes Study. Additionally, the paper reviews historical O-D flows to determine the peak seasonal flow and appropriate O-D data to use in model validation and seed tables for Origin-Destination Matrix Estimation (ODME). In addition to model validation, the expanded Big Data O-D trips were assigned to multiple paths for each O-D pair built via Google Directions API to study the eligible corridor trips and the alternative corridors competing with the project. Furthermore, spatiotemporal distributions from Big Data were used to develop time-dependent trip tables for the Dynamic Traffic Assignment (DTA) model. Several international tourist attractions located along the I-4 Ultimate Corridor serve high visitor and weekend traffic, and Big Data was used to analyze and develop a weekend distribution model. Each of these modules involves some form of observed data, all coming from one source, “Big Data.”

Temporal origin–destination matrix estimation of passenger car trips. Case study: Medellin, Colombia

This paper develops a passenger car demand synthesis model based on entropy maximization to estimate origin–destination matrices for passenger cars trips using traffic counts for different years in an urban area. The model discussed in this paper improve current Origin-Destination matrix estimation techniques incorporating the total number of passenger car trips in the transportation network as a constraint, and secondary data sources for different years, giving a point of comparison for the results.The resulting model reveals that the trips are an exponential function of the Lagrange multipliers related to the trip cost, the observed traffic counts in the links, and the total number of passenger cars trips in the transportation network.Based on the results of this research, the authors recommend to the transportation authorities to collect traffic counts in strategic locations in the urban road network for different years to estimate OD matrices in an easier and cheaper way than the traditional OD surveys.The performance of the formulation for different years (2005–2008) is tested in the Medellín (Colombia) network.

Origin-destination matrix estimation with a conditionally binomial model

A doubly stochastic, conditionally binomial model is proposed to describe volumes of vehicular origin-destination flows in regular vehicular traffic, such as morning rush hours. The statistical properties of this model are motivated by the data obtained from inductive loop traffic counts. The model parameters can be expressed as rational functions of the first and second order moments of the observed link counts. Challenges arising from the inaccuracy of moment estimates are studied. A real origin-destination traffic problem of Tampere city is solved by optimisation methods and the accuracy of the solution is examined.

A Novel Methodology to Assimilate Sub-Path Flows in Bi-Level OD Matrix Estimation Process

Traditional bi-level origin-destination (OD) matrix estimation process adjusts the matrix (at the upper level) based on the deviation between the observed and simulated traffic counts. The problem is mathematically under-determined, and the quality of the solution can be enhanced by restricting the upper level search space with information from other sources. This paper presents a methodology that assimilates sub-path flows in the upper level objective function. The contributions of the study are two-fold: first, it proposes the idea of “structural comparison of sub-path flows” to relax the requirement of “known” penetration rate of vehicles’ trajectories; second, it proposes an innovative upper level formulation where the structural difference between the observed and assigned sub-path flows is integrated with the traditional deviations between the observed and assigned link flows. The sub-path flows can be estimated from advanced data sources such as Bluetooth MAC scanner. The proposed methodology is tested using simulation on a realistic network from Brisbane, Australia and results indicate its practical relevance for situations when the penetration rate of Bluetooth trajectories is generally unknown. The proposed method has a better ability to maintain structural consistency and showed considerable improvements in the quality of OD estimates as compared to the traditional traffic counts-based approach.

A Bicycle Origin–Destination Matrix Estimation Based on a Two-Stage Procedure

As more people choose to travel by bicycle, transportation planners are beginning to recognize the need to rethink the way they evaluate and plan transportation facilities to meet local mobility needs. A modal shift towards bicycles motivates a change in transportation planning to accommodate more bicycles. However, the current methods to estimate bicycle volumes on a transportation network are limited. The purpose of this research is to address those limitations through the development of a two-stage bicycle origin–destination (O–D) matrix estimation process that would provide a different perspective on bicycle modeling. From the first stage, a primary O–D matrix is produced by a gravity model, and the second stage refines that primary matrix generated in the first stage using a Path Flow Estimator (PFE) to build the finalized O–D demand. After a detailed description of the methodology, the paper demonstrates the capability of the proposed model for a bicycle demand matrix estimation tool with a real network case study.

A data driven method for OD matrix estimation

The fundamental challenge of the origin-destination (OD) matrix estimation problem is that it is severely under-determined. In this paper we propose a new data driven OD estimation method for cases where a supply pattern in the form of speeds and flows is available. We show that with these input data, we do not require an iterative dynamic network loading procedure that results in an equilibrium assignment, nor do we need an assumption on the kind of equilibrium that emerges from this process. The minimal number of ingredients which are needed are (a) a method to estimate/predict production and attraction time series; (b) a method to compute the N shortest paths from each OD zone to the next; and (c) two—possibly OD-specific—assumptions on the magnitude of N; and on the proportionality of path flows between these origins and destinations, respectively. The latter constitutes the most important behavioral assumption in our method, which relates to how we assume travelers have chosen their routes between OD pairs. We choose a proportionality factor that is inversely proportional to realized travel time, where we incorporate a penalty for path overlap. For large networks, these ingredients may be insufficient to solve the resulting system of equations. We show how additional constraints can be derived directly from the data by using principal component analysis, with which we exploit the fact that temporal patterns of production and attraction are similar across the network. Experimental results on a toy network and a large city network (Santander, Spain) show that our OD estimation method works satisfactorily, given a reasonable choice of N, and the use of so-called 3D supply patterns, which provide a compact representation of the supply dynamics over the entire network. Inclusion of topological information makes the method scalable both in terms of network size and for different topologies. Although we use a neural network to predict production and attraction in our experiments (which implies ground-truth OD data were needed), there are straight-forward paths to improve the method using additional data, such as demographic data, household survey data, social media and or movement traces, which could support estimating such ground-truth baseline production and attraction patterns. The proposed framework would fit very nicely in an online traffic modeling and control framework, and we see many paths to further refine and improve the method.

Estimation of origin–destination matrices using link counts and partial path data

After several decades of work by several talented researchers, estimation of the origin–destination matrix using traffic data has remained very challenging. This paper presents a set of innovative methods for estimation of the origin–destination matrix of large-scale networks, using vehicle counts on links, partial path data obtained from an automated vehicle identification system, and combinations of both data. These innovative methods are used to solve three origin–destination matrix estimation models. The first model is an extension of Spiess’s model which uses vehicle count data while the second model is an extension of Jamali’s model and it uses partial path data. The third model is a multiobjective model which utilizes combinations of vehicle counts and partial path data. The methods were tested to estimate the origin–destination matrix of a large-scale network from Mashhad City with 163 traffic zones and 2093 links, and the results were compared with the conventional gradient-based algorithm. The results show that the innovative methods performed better as compared to the gradient-based algorithm.