Estimation Of Origin-destination Matrices Research Articles

Origin–destination matrices from smartphone apps for bus networks

The knowledge of passenger flows between each origin–destination (OD) pair is a main requirement in public transport for service planning, design, operation, and monitoring, and is represented by OD matrices. Although they can be determined by traditional approaches (e.g., surveys, ride-check counts, and/or smartcard-based methods), the availability of new technologies and the proliferation of portable devices triggers an emerging interest in building OD matrices from the apps of bus operators. This research proposes the first framework for the estimation of OD matrices on transit networks by processing smartphone app call detail records (SACDRs). The framework is experimentally tested on a sample of 30 workdays of an Italian bus operator. The results are represented by easy-to-read control dashboards based on maps, which help quantify and visualise the OD matrices in the metropolitan area of Cagliari (Italy). The experimentation shows that the framework can properly estimate the number of trips for both origin and destination w.r.t. OD matrices built from household surveys: the mean absolute error is on average lower than five movements for 90% of the origins and 85% of the destinations.

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.

The impact of public transport on the diffusion of COVID-19 pandemic in Lombardy during 2020

In 2020, the COVID-19 pandemic has impacted the world, affecting health, economy, education, and social behavior. Much concern was raised about the role of mobility in the diffusion of the disease, with particular attention to public transport. Indeed, understanding the relationship between mobility and the pandemic is key for developing effective public health interventions and policy decisions. In this work, we aim to understand how mobility, and more specifically mobility by public transport, has affected the diffusion of the pandemic at the regional scale. We focus our attention on Lombardy, the most populated Italian region severely hit by the pandemic in 2020. We explore static mobility data provided by Regione Lombardia, the regional service district, and dynamic mobility data provided by Trenord, a railway operator which serves Lombardy and neighboring areas. We develop an inventive pipeline for the dynamic estimation of Origin-Destination matrices obtained from tickets and passenger counts. This allows us to spot potential triggers in pandemic diffusion enhanced by the concept of proximity induced by mobility. We also develop a novel perspective for assessing the relationship between mobility and overall mortality based upon a functional approach combined with a spatial correlation analysis aimed at identifying the diversified effects on mortality in small geographical areas as a result of the restrictions on mobility introduced to contrast the pandemic.

An end-to-end statistical process with mobile network data for official statistics

Mobile network data has been proven to provide a rich source of information in multiple statistical domains such as demography, tourism, urban planning, etc. However, the incorporation of this data source to the routinely production of official statistics is taking many efforts since a diversity of highly entangled issues (access, methodology, IT tools, quality, skills) must be solved beforehand. To do this, one-off studies with concrete data sets are not enough and a standard statistical production process must be put in place. We propose a concrete modular process structured into evolvable modules detaching the strongly technological layer underlying this data source from the necessary statistical analysis producing outputs of interest. This architecture follows the principles of the so-called ESS Reference Methodological Framework for Mobile Network Data. Each of these modules deals with a different aspect of this data source. We apply hidden Markov models for the geolocation of mobile devices, use a Bayesian approach on this model to disambiguate devices belonging to the same individual, compute aggregate numbers of individuals detected by a telecommunication network using probability theory, and model hierarchically the integration of auxiliary information from the telco market and official data to produce final estimates of the number of individuals across different territorial regions in the target population. A first simple illustrative proposal has been applied to synthetic data providing preliminary software tools and accuracy indicators monitoring the performance of the process. Currently, this exercise has been applied to the estimation of present population and origin-destination matrices. We present an illustrative example of the execution of these production modules comparing results with the simulated ground truth, thus assessing the performance of each production module.

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.

Deep multi-task learning for individuals origin–destination matrices estimation from census data

Rapid urbanization has made the estimation of the human mobility flows a substantial task for transportation and urban planners. Worker and student mobility flows are among the most weekly regular displacements and consequently generate road congestion issues. With urge of demands on efficient transport planning policies, estimating their commuting facilitates the decision-making processes for local authorities. Worker and student censuses often contain home location, work places and educational institutions. This paper proposes a novel approach to estimate individuals origin–destination matrices from census datasets. We use a multi-task neural network to learn a generic model providing the spatio-temporal estimations of commuters dynamic mobility flows on daily basis from static censuses. Multi-task learning aims at leveraging functional information incorporated in multiple tasks, which allows ameliorating the generalization performance within all the tasks. We first aggregate individuals household travel surveys and census databases with working and studying trips. The model learns the temporal distribution of displacements from these static sources and then it is applied on scholar and worker mobility sources to predict the temporal characteristics of commuters’ displacements (i.e. origin–destination matrices). Our method yields substantially more stable predictions in terms of accuracy and results in a significant error rate control in comparison to single task learning.

Estimation of origin-destination matrices by fusing detector data and Floating Car Data

Abstract Neither traffic detector counts nor Floating Car Data are able to determine the absolute traffic demand with sufficient accuracy. Therefore, traffic management depends on simple and often inaccurate estimations. In recent years, various research projects have shown that plausible results can be achieved from mere road detector data by applying the Information Minimization model and eliminating redundant information. However, a problem that has not yet been solved in this approach is the lack of information about the structure of traffic demand. This can be extracted from Floating Car Data. To merge both pieces of information into one consistent model the present paper introduces a methodology based on the work of Pohlmann utilizing the Information Minimization Model by Van Zuylen.

Estimation of Origin-Destination matrices under Automatic Fare Collection: the case study of Porto transportation system

Abstract Entry-only Automatic Fare Collection (AFC) systems are widely used in urban transports. Its main advantages include easy usability by passengers, improvement of the efficiency of revenue management, adequacy to integrate inter-modality approaches, easy cooperation between operators, systematic data collection and gathering tools, contributing to improve the planning process. This work starts with the literature review on applications of the Trip-Chaining Method (TCM) to the estimation of Origin-Destination (OD) matrices using entry-only AFC data. The main contribution of this study is to provide an OD matrix for the city of Porto, allowing to improve the quality of its public transport system. The paper reports the implementation of the TCM to estimate the alighting locations at the disaggregated level in the case study of Porto. The main assumptions adopted are: passengers start the next journey stage at or near the alighting location of their previous trip, passengers end the last trip of the day at the boarding location of the first trip of the day, passengers can only alight in the sequence of stops not yet traveled by the route / direction they boarded, passengers have a maximum interchange distance, above which the destination of that journey stage is not inferred.

Obtaining the Intercity Bus Travel Matrix in Turkey and Analysing it in GIS Environment

Decision makers need appropriate information systems to carry out spatial analyses related to transportation. Hence, Geographical Information Systems (GIS) are one of the effective tools used for realizing spatial analyses. Therefore, GIS were also used in this study for evaluating the information about intercity bus travels in Turkey. For this purpose, the intercity bus travel matrix obtained is transferred to GIS environment in order to evaluate this matrix in a visual environment. Estimation of Origin-Destination (O-D) matrices is very important in transportation planning, and several methods are used to obtain them. However, since it is generally expensive and time-consuming to use classical methods based on home surveys or roadside interviews, other methods have been developed. The model developed by Bell is also one of them, and estimates travel matrices from traffic counts. For finding the daily intercity bus travel matrix for the year of 2015 in Turkey, this model was used.

Discussão conceitual sobre métodos de reconstrução de matrizes origem-destino estáticas em redes de transportes

Matrizes origem-destino (OD) quantificam a demanda por transporte em uma região geográfica e constituem peças-chave em estudos de planejamento, projeto e gerenciamento de sistemas de transportes urbanos e regionais. Tradicionalmente, matrizes OD são obtidas por meio da realização de levantamentos amostrais diretos, tais como: pesquisas domiciliares, contagem de placas de veículos e entrevistas em terminais de transporte público. Uma alternativa de menor custo consiste em sintetizar uma matriz OD por meio de métodos matemáticos com o uso de dados provenientes de contagens de tráfego feitas na rede de transportes em estudo, os quais podem ser classificados em métodos de reconstrução e de estimação. Este artigo apresenta uma discussão conceitual com foco nas premissas e limitações dos principais métodos de reconstrução baseados em maximização de entropia, mínimos quadrados generalizados e inferência bayesiana. Descreve-se detalhadamente a fundamentação matemática dos métodos e são feitas recomendações para o avanço e aplicação efetiva dos mesmos.

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.

Estimation of Origin-Destination Demand Matrix for the Urban Multimodal Traffic Network

In this paper we consider a procedure for the estimation of origin-destination (O-D) matrices for a multimodal transit network. The structure of urban multimodal transport system is fully analyzed and then a bi-level programming model is established for O-D demand matrices estimation, where the upper-level problem seeks to minimize the sum of error measurements in traffic counts and O-D matrices, while the lower-level problem is to assign a target O-D matrix onto the network according to the user equilibrium principle. And a heuristic algorithm is proposed, finally a numeral example is given which indicates that the solution approach can be applied in practice.

A Bayesian Network Model for Origin-Destination Matrices Estimation Using Prior and Some Observed Link Flows

This paper presents a Bayesian network model for estimating origin-destination matrices. Most existing Bayesian methods adopt prior OD matrixes, which are always troublesome to be obtained. Since transportation systems normally have stored large amounts of historical link flows, a Bayesian network model using these prior link flows is proposed. Based on some observed link flows, the estimation results are updated. Under normal distribution assumption, the proposed Bayesian network model considers the level of total traffic flow, the variability of link flows, and the violation of the traffic flow conservation law. Both the point estimation and the corresponding probability intervals can be provided by this model. To solve the Bayesian network model, a specific procedure which can avoid matrix inversion is proposed. Finally, a numerical example is given to illustrate the proposed Bayesian network method. The results show that the proposed method has a high accuracy and practical applicability.

AN AGGREGATE TRANSPORT DEMAND MODEL FOR IMPORT AND EXPORT FLOW SIMULATION

This paper presents a system of models for the estimation of international (import/export) freight flows through a partial share approach. It allows us to simulate attraction, production, distribution and modal split for the estimation of modal Origin-Destination matrices in quantities. Aiming at predicting long term effects for strategic planning, the modelling system has been specified through easy-to-capture variables represented by level-of-service attributes and aggregate socio-economic variables. The calibration was carried out by using a set of available data in Italy that allowed us to consider import/export flows for different freight types given by the aggregation of classes provided by European NST/R classifications.

Estimation of Dynamic Link Flows and Origin-Destination Matrices from Lower Polling Frequency Probe Vehicle Data

The authors present a methodology of estimating dynamic link flows and origin-destination matrices using lower polling frequency probe vehicle data (e.g. one point every 30-60s). Link travel time is first obtained from map-matched probe points using a method of proportional allocation. A derived speed-density function is then fitted for different types of roads. A Bayesian method that carefully incorporates prior information is used to estimate dynamic link flows from link travel speed. A bi-level generalized least-square (GLS) estimator is formulated so as to estimate dynamic OD matrices from estimated link flows. A traffic simulator in VISSIM is developed for a median size urban network using an open data set. The results validate the advantages of the proposed method for lower polling frequency probe vehicle data.

Sensitivity Analysis of an Evolutionary-Based Time-Dependent Origin/Destination Estimation Framework

This paper presents sensitivity analysis results for a distributed evolutionary implementation of the estimation of time-dependent origin-destination (TDOD) matrices. The system uses a noniterative origin-destination (OD) estimation process, which attempts to minimize the discrepancy between observed link flow counts and assigned counts. At the same time, the system anchors the search process to the vicinity of an a priori OD matrix to maintain travel patterns and OD structure. The sensitivity analysis examines various factors that are expected to affect the performance of the evolutionary-based TDOD estimation method. The factors examined are congestion levels, network size, size of the search space, and degree of precision of the a priori matrix. Simulation results for the waterfront network in Toronto, ON, Canada, show that the algorithm is robust in terms of replicating observed vehicle counts and the closeness to the real demand. The use of distributed evolutionary algorithm is also shown to provide good results for a large network and within fast computing speeds. However, the quality of the estimated OD relative to the true OD is shown to deteriorate if a totally random a priori matrix is used as a starting point, which has no structural resemblance to the prevailing OD patterns in the network. In addition, the quality of the estimated OD matrix was found to deteriorate as congestion levels and the network size increased. It is notable that, in all cases, the estimated OD matrix resulted in better matching flows.

Estimation of origin–destination matrices from link counts and sporadic routing data

Estimation of origin–destination (OD) matrices from link count data is a challenging problem because of the highly indeterminate relationship between the observations and the latent route flows. Conversely, estimation is straightforward if we observe the path taken by each vehicle. We consider an intermediate problem of increasing practical importance, in which link count data is supplemented by routing information for a fraction of vehicles on the network. We develop a statistical model for these combined data sources and derive some tractable normal approximations thereof. We examine likelihood-based inference for these normal models under the assumption that the probability of vehicle tracking is known. We show that the likelihood theory can be non-standard because of boundary effects, and provide conditions under which such irregular behaviour will be observed in practice. For regular cases we outline connections with existing generalised least squares methods. We then consider estimation of OD matrices under estimated and/or misspecified models for the probability of vehicle tracking. Theoretical developments are complemented by simulation experiments and an illustrative example using a section of road network from the English city of Leicester.

A fixed point approach to origin–destination matrices estimation using uncertain data and fuzzy programming on congested networks

Origin–destination (O–D) matrix estimation methods based on traffic counts have been largely discussed and investigated. The most used methods are based on Generalised Least Square estimators (GLS) that use as input data a starting O–D matrix and a set of traffic counts. In addition to traffic counts, the analysts could know other general information about travel demand or link flows, based on their experience, or spot data, but few works deal with the matter of including effectively these sources of information.This paper proposes a Fuzzy-GLS estimation method that allows to improve the estimation performances of classic GLS estimator by including, in addition to traffic counts, uncertain information about starting O–D demand (i.e. outdated estimates, spot data, expert knowledge, etc.). The methods explicitly take into account the relevant level of uncertainty by taking as much advantage as possible from the few vague available data. The method is developed using fuzzy sets theory and fuzzy programming that seems to be a convenient theoretical framework to represent uncertainty in the available data. A solution algorithm for the proposed problem is also presented. The method has been tested by numerical applications and then compared to the classical GLS method under different sets of constraints to the problem.

Estimation of origin-destination matrices for a multimodal public transit network

This paper presents a procedure for the estimation of origin-destination (O-D) matrices for a multimodal public transit network. The system consists of a number of favored public transit modes that are obtained from a modal split process in a traditional four-step transportation model. The demand of each favored mode is assigned to the multimodal network, which is comprised of a set of connected links of different public transit modes. An entropy maximization procedure is proposed to simultaneously estimate the O-D demand matrices of all favored modes, which are consistent with target data sets such as the boarding counts and line segment flows that are observed directly in the network. A case study of the Hong Kong multimodal transit network is used to demonstrate the effectiveness of the proposed methodology.

An estimation model of time-varying origin -destination flows in expressway corridors based on unscented Kalman filter

On the basis of measurable time series of mainline and ramp flows from traffic counts and the assumption of travel time distributions, this research presents a dynamic system model and its on-line estimation algorithm for recursive estimation of time-varying origin-destination (OD) matrices in expressway corridors. The proposed model employs a macro-traffic flow model to estimate travel times of OD flows and uses parameters of the traffic model as state variables, which are added to the constrained function of the system. To improve the model efficiency, we revise the travel time distribution based on the feature of normal distribution. The research employs a newly developed filtering technique, called unscented Kalman filter. The proposed model is evaluated with simulation experiments. Numerical analyses with respect to the sensitivity of the selection of initial parameters on the estimation results indicate that the proposed model is sufficiently reasonable and stable for real-world applications.