Origin-destination Trip Matrices Research Articles

Computational Architecture of an Integrated Urban Model Considering Physical-Virtual Activity Spaces

This study presents the computational architecture of the integrated transport, land-use, and emission (iTLE) modeling system. It incorporates the effects of physical-virtual activity-space interactions within the microsimulation framework, making it a comprehensive tool for predicting travel demand and evaluating the impacts of evolving activity-travel behavior on transport and land-use systems. The model systematically integrates households’ and individuals’ long-term, medium-term, and short-term travel related decisions, and traffic assignment allowing for more reliable estimates of travel demand. Application of the iTLE prototype in the Halifax Regional Municipality (HRM), Canada reveals notable trends, including a reduction in travel time for work activities, highlighting the potential of teleworking as a pragmatic strategy for traffic management. The model’s flexibility is demonstrated through its ability to capture the trade-offs between physical and virtual activity environments, aligning with the intricacies of modern digitalized societies. Integration of iTLE with the traffic assignment system enhances its applicability across diverse urban contexts, streamlining the process of setting origin-destination trip matrices and significantly improving computational efficiency. By systematically coupling the micro-behavioral decision processes, iTLE generates robust estimates of traffic flow, emissions, and energy use, providing reliable insights for sustainable and efficient transportation planning. This study contributes to advancing integrated urban modeling, offering a valuable tool that can be replicable for multiple cities in Canada and beyond.

Metrics for Quantifying Shareability in Transportation Networks: The Maximum Network Flow Overlap Problem

Cities around the world vary in terms of the structure of their transportation networks and travel demand patterns. Despite these variations significantly impacting opportunities for travelers to share trips, no metrics are available to jointly characterize a region’s transportation network and travel demand in terms of the potential for travelers to share space with each other on network links, or what we call person-trip shareability. This study proposes metrics to quantify the shareability of person-trips in a city subregion, as a function of three inputs—the underlying transportation network, origin–destination (OD) travel demand, and a maximum permissible detour parameter. After defining person-trip shareability and providing principles for operationalizing this definition, we formulate a fundamental metric, called flow overlap. Flow overlap measures, for a person-trip traversing a given path, the weighted (by link distance) average number of other person-trips sharing the links along the original person-trip’s path. We then formulate the Maximum Network Flow Overlap Problem (MNFLOP), a math program that assigns person-trips to network paths that maximize network-wide flow overlap. We utilize the MNFLOP output to calculate shareability metrics at various levels of aggregation: person-trip level, OD level, origin or destination level, network level, and link level. The study applies the MNFLOP and associated shareability metrics to different OD demand scenarios in the Sioux Falls network. The computational results verify that MNFLOP (i) assigns person-trips to paths such that flow overlaps significantly increase relative to shortest path assignment, (ii) can meaningfully differentiate between different OD trip matrices in terms of flow overlap, and (iii) metrics can meaningfully quantify demand dispersion from a single network node/location considering the underlying road network. Finally, we validate MNFLOP’s ability to quantify shareability by showing that demand patterns with higher flow overlap are strongly associated with lower mileage routes for a last-mile microtransit service. The paper includes an extensive discussion of other potential future uses of the MNFLOP and its associated shareability metrics.

Frequency-based path flow estimator for transit origin-destination trip matrices incorporating automatic passenger count and automatic fare collection data

A frequency-based path flow estimator is proposed to estimate the transit passenger origin–destination (OD) matrix in an urban congested transit network. The proposed model not only considers the effect of congestion under an equilibrium framework, but also benefits from being formulated as a single-level model with the route-section-based network representation. Multiple transit data sources are incorporated, including automatic passenger count, automatic fare collection and automatic vehicle location data. A path-based diagonalization approach embedded with an iterative balancing scheme is developed to solve the model. Case studies are conducted to demonstrate the features and applicability of the proposed model and algorithm.

How comparable are origin-destination matrices estimated from automatic fare collection, origin-destination surveys and household travel survey? An empirical investigation in Lyon

Abstract Origin–destination (OD) matrices are one of the key elements in travel behaviour analysis. For decades, transportation researchers have mostly used data obtained by active solicitation such as surveys to construct these matrices but new data sources like automatic fare collection (AFC) are now available and can be used to measure OD flows. As a result, a more heterogeneous corpus of data sources is now available to estimate travel demand. However, little research examines how comparable the estimated demands may be. In this paper, three data sources namely a household travel survey, a large scale origin–destination survey and entry only automated fare collection are processed to derive typical weekday public transit OD trip matrices. Various elements of the resulting matrices are then compared. While all the matrices share some common characteristics, there are also substantial differences that must be addressed. AFC data is not error-free and needs to be supplemented with data from other sources to construct a representative OD trip matrix. This is because not all destinations can be inferred, the smart card penetration rate is far less than 100% and fare evasion cannot be ignored. Our empirical results suggest that scaling an AFC matrix with automated passenger counts may be a viable solution. The results also indicate that the household travel survey significantly underestimates the volume of public transit trips compared to the other sources. The findings of this research contribute to a better understanding of the available data sources for public transit demand estimation. They can help practitioners to improve the quality and accuracy of OD matrices.

Effects of the COVID-19 Lockdown on Urban Mobility: Empirical Evidence from the City of Santander (Spain)

This article analyses the impact that the confinement measures or quarantine imposed in Spain on 15 March 2020 had on urban mobility in the northern city of Santander. Data have been collected from traffic counters, public transport ITS, and recordings from traffic control cameras and environmental sensors to make comparisons between journey flows and times before and during the confinement. This data has been used to re-estimate Origin-Destination trip matrices to obtain an initial diagnostic of how daily mobility has been reduced and how the modal distribution and journey purposes have changed. The impact on externalities such as NO2 emissions and traffic accidents have also been quantified. The analysis revealed an overall mobility fall of 76%, being less important in the case of the private car. Public transport users dropped by up to 93%, NO2 emissions were reduced by up to 60%, and traffic accidents were reduced by up to 67% in relative terms.

An Excess-Demand Dynamic Traffic Assignment Approach for Inferring Origin-Destination Trip Matrices

The focus of this paper is on the development of an origin–destination (O-D) demand estimation method for dynamic equilibrium traffic networks. It is hypothesized that the underlying equilibrium conditions in such networks are a compromise result of minimization of individual routing costs, minimization of traffic count matching errors, and maximization of O-D demand entropies. By adding an upper bound of travel demand and a dummy path with constant travel cost to each O-D pair, we formulated the dynamic O-D demand estimation problem as an excess-demand dynamic traffic assignment (DTA) problem defined for an expanded network with dummy paths. Such a formulation enables us to apply existing DTA solution methods and software tools for deriving the path flow pattern in the expanded network and thus simultaneously obtaining the O-D demand pattern in the original network. Following this problem transformation and network expansion strategy, an iterative solution procedure is accordingly proposed, which resorts to repeatedly solving the excess-demand DTA problem and adjusting the dummy path costs. An application of the proposed modeling and solution methods for an example cell-based network problem favorably illustrates great promise of the methodological advance and solution performance.

A Robust Framework for the Estimation of Dynamic OD Trip Matrices for Reliable Traffic Management

Origin-Destination (OD) trip matrices describe the patterns of traffic behavior across the network and play a key role as primary data input to many traffic models. OD matrices are a critical requirement, either in static or dynamic models for traffic assignment. However, OD matrices are not yet directly observable; thus, the current practice consists of adjusting an initial or a priori matrix from link flow counts, speeds, travel times and other aggregate demand data. This information is provided by an existing layout of traffic counting stations, as the traditional loop detectors. The availability of new traffic measurements provided by ICT applications offers the possibility to formulate and develop more efficient algorithms, especially suited for real-time applications. However, the efficiently strongly depends, among other factor, on the quality of the seed matrix. This paper proposes an integrated computational framework in which an off-line procedure generates the time-sliced OD matrices, which are the input to an on-line estimator. The paper also analyzes the sensitivity of the on-line estimator with respect to the available traffic measurements.

Application of the Conditional Inverse Matrix Theory for Estimation of Origin-Destination Trip Matrices from Link Traffic Flows

Application of the Conditional Inverse Matrix Theory for Estimation of Origin-Destination Trip Matrices from Link Traffic Flows

Macroscopic attraction-based simulation of pedestrian mobility: A dynamic individual route-choice approach

This paper presents a dynamic distribution and assignment simulation model based on discrete time simulation techniques and dynamic route assignment for planning, engineering design, and operation analysis of big exhibition events from a pedestrian circulation perspective. Both, the distribution and assignment stages are incorporated in an interlaced way with a dynamic behavior along a specific time horizon. In the proposed model, the individual route choice is dynamically determined as consequence of facilities attractiveness and network congestion. Therefore, in contrast with other simulation approaches, it does not require the usual origin–destination trip matrices to describe the transportation demand or the specification of different paths to be followed by visitors. This modeling approach turns out to be very appropriate for the simulation of these big exhibition events where each visitor usually has multiple and a priori unordered destination choices after entering the scenario.The main aim of the proposed model is to obtain measures of pavilions occupation, queue sizes and links density at the various facilities rather than the exhaustive tracking of individual movements or the precise description of the interaction between different entities. So, the use of pure microscopic continuous models is not needed. This approach can be viewed as a macroscopic simulation based on individual route-choice behavior of entities (microscopic level).As illustration, here we show how the model works when dealing with the pedestrian mobility in the International Exhibition of Zaragoza held in 2008, where it was used as a planning and operation analysis tool in the preliminary stages.

Visualization of Origin-Destination Trip Matrices through Applications of Neighborhood Graph

Visualization of Origin-Destination Trip Matrices through Applications of Neighborhood Graph

Inferring origin-destination trip matrices from aggregate volumes on groups of links: a case study using volumes inferred from mobile phone data

SUMMARY The origin–destination matrix is an important source of information describing transport demand in a region. Most commonly used methods for matrix estimation use link volumes collected on a subset of links in order to update an existing matrix. Traditional volume data collection methods have significant shortcomings because of the high costs involved and the fact that detectors only provide status information at specified locations in the network. Better matrix estimates can be obtained when information is available about the overall distribution of traffic through time and space. Other existing technologies are not used in matrix estimation methods because they collect volume data aggregated on groups of links, rather than on single links. That is the case of mobile systems. Mobile phones sometimes cannot provide location accuracy for estimating flows on single links but do so on groups of links; in contrast, data can be acquired over a wider coverage without additional costs. This paper presents a methodology adapted to the concept of volume aggregated on groups of links in order to use any available volume data source in traditional matrix estimation methodologies. To calculate volume data, we have used a model that has had promising results in transforming phone call data into traffic movement data. The proposed methodology using vehicle volumes obtained by such a model is applied over a large real network as a case study. The experimental results reveal the efficiency and consistency of the solution proposed, making the alternative attractive for practical applications. Copyright © 2011 John Wiley & Sons, Ltd.

Time-Dependent Origin–Destination Demand Estimation

This paper proposes a modified bi-level optimization algorithm to estimate the time-dependent origin–destination trip matrices for large-scale networks with multiple vehicle classes. Methodologies are presented to overcome the challenges caused by the scale of the problem. The upper-level problem, a bound-constrained quadratic problem, had many variables and parameters for a network with around 68,000 links, 28,000 nodes, and 3,700 zones. Techniques to reduce the number of variables and parameters are described in this study, along with an approach to reduce the time and memory requirement of the lower-level problem. Furthermore, the basic approach, which had been applied only to a single vehicle class, was extended and adapted in this study to estimate matrices for single-occupancy and high-occupancy vehicles jointly. Two solution packages, MINOS and KNITRO, were tested for the upper-level problem. The solution package KNITRO was run with an option to use an interior point–conjugate gradient algorithm, which was well suited to large-scale nonlinear problems. The modified bi-level algorithm was applied to estimate the time-dependent demand patterns for the New York City regional network.

Estimating Origin-Destination Trip Matrices from Roadside Survey Data with Consideration of Passing Traffic Data

Estimating Origin-Destination Trip Matrices from Roadside Survey Data with Consideration of Passing Traffic Data

Robust Transportation Network Design Under Demand Uncertainty

Abstract: This article addresses the problem of a traffic network design problem (NDP) under demand uncertainty. The origin–destination trip matrices are taken as random variables with known probability distributions. Instead of finding optimal network design solutions for a given future scenario, we are concerned with solutions that are in some sense “good” for a variety of demand realizations. We introduce a definition of robustness accounting for the planner's required degree of robustness. We propose a formulation of the robust network design problem (RNDP) and develop a methodology based on genetic algorithm (GA) to solve the RNDP. The proposed model generates globally near‐optimal network design solutions, f, based on the planner's input for robustness. The study makes two important contributions to the network design literature. First, robust network design solutions are significantly different from the deterministic NDPs and not accounting for them could potentially underestimate the network‐wide impacts. Second, systematic evaluation of the performance of the model and solution algorithm is conducted on different test networks and budget levels to explore the efficacy of this approach. The results highlight the importance of accounting for robustness in transportation planning and the proposed approach is capable of producing high‐quality solutions.

Traffic Counting Location and Error Bound in Origin-Destination Matrix Estimation Problems

Estimating origin-destination trip matrices from link traffic counts has been a subject of substantial research. It is well known that the accuracy of the resulting estimated origin-destination (O-D) matrix largely depends on the employed estimation approach itself, errors of the input data, and an appropriate set of links from which flow information should be collected. Previous studies have overwhelmingly focused on the development of various estimation models, while paying very limited attention to the traffic counting location and error bound issues. Recognizing their interdependence, this study makes a joint investigation of the traffic counting location, estimation method, and error bound in an integrated manner, while taking into account the effects of various route choice assumptions made in the traffic assignment models and the levels of traffic congestion on the network. A few useful properties of the counting location rules and error bound measures for the O-D matrix estimation problem are demonstrated theoretically and numerically.

Inferring origin–destination trip matrices with a decoupled GLS path flow estimator

Recently, path flow estimators (PFE) have been used for the estimation of origin–destination (O–D) matrices. This paper develops a formulation that incorporates a decoupled path flow estimator in a generalized least squares (GLS) framework. The approach seeks to solve a GLS problem that minimizes the sum of errors in traffic counts and O–D matrices based on an equilibrium assignment mapping derived exogenously from a K-shortest path ranking procedure. Solving the GLS–PFE inevitably involves non-invertible linear systems and non-negative constraints. A solution algorithm is designed to iteratively identify active constraints and solve linear systems by computing the pseudoinverse. A simplified version of this algorithm is further developed to improve its computational efficiency. The solution properties and computational efficiency of the two methods are tested and compared for small to mid-size networks. It is concluded that the simplified algorithm is efficient in solving the decoupled GLS–PFE problem for realistic size networks.

A philosophical context for methods to estimate origin – destination trip matrices using link counts

A philosophical context for methods to estimate origin – destination trip matrices using link counts

Overcoming the (Apparent) Problem of Inconsistency in Origin-Destination Matrix Estimations

Recently a number of models for the estimation of origin-destination trip matrices from traffic counts have been presented. One class of models assumes knowledge of the proportional usage of each link by the interzonal traffic. These models result in an underspecified, and often inconsistent, system of linear equations. Several authors have addressed the problem of inconsistency by, in different ways, changing the traffic count data so as to achieve consistency. We argue that, modeling-wise, this is not a sound approach because inconsistent data is a natural part of any origin-destination matrix estimation problem. By using a stochastic programming approach the inconsistent input data becomes a natural part of the estimation process. This is done by viewing the traffic counts as realizations of some unknown underlying distribution of traffic flows.