A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation

Origin-Destination Matrix (ODM) estimation is a classical problem in transport engineering aiming to recover flows from every Origin to every Destination from measured traffic counts and a priori model information. In addition to traffic counts, the present contribution takes advantage of probe trajectories, whose capture is made possible by new measurement technologies. It extends the concept of ODM to that of Link dependent ODM (LODM), keeping the information about the flow distribution on links and containing inherently the ODM assignment. Further, an original formulation of LODM estimation, from traffic counts and probe trajectories is presented as an optimisation problem, where the functional to be minimized consists of five convex functions, each modelling a constraint or property of the transport problem: consistency with traffic counts, consistency with sampled probe trajectories, consistency with traffic conservation (Kirchhoff's law), similarity of flows having close origins and destinations, positivity of traffic flows. A primal-dual algorithm is devised to minimize the designed functional, as the corresponding objective functions are not necessarily differentiable. A case study, on a simulated network and traffic, validates the feasibility of the procedure and details its benefits for the estimation of an LODM matching real-network constraints and observations

Travel Time in Macroscopic Traffic Models for Origin-Destination Estimation

Transportation macroscopic modeling is a tool for analyzing and prioritizing future transportation improvements. Transportation modeling techniques continue to evolve with improvements to computer processing speeds and traffic data collection. These improvements allow transportation models to be calibrated to real life traffic conditions. The transportation models rely on an origin-destination (OD) matrix, which describes the quantity and distribution of trips in a transportation network. The trips defined by the OD matrix are assigned to the network through the process of traffic assignment. Traffic assignment relies on the travel time (cost) of roadways to replicate route choice of trips between OD trip pairs. Travel time is calculated both along the roadway and from delay at the intersections. Actuated traffic signals, one form of signalized intersections, have not been explicitly modeled in macroscopic transportation models. One of the objectives of this thesis is to implement actuated signals in the macroscopic modeling framework, in order to improve traffic assignment by more accurately representing delay at intersections. An actuated traffic signal module was implemented into QRS II, a transportation macroscopic model, using a framework from the 2010 Highway Capacity Manual. Results from actuated intersections analyzed with QRS II indicate the green time for each phase was reasonably distributed and sensitive to lane group volume and input parameters. Private vendor travel time data from companies such as Navteq and INRIX, have extensive travel time coverage on freeways and arterials. Their extensive travel time coverage has the potential to be useful in estimating OD matrices. The second objective of this thesis is to use travel time in the OD estimation framework. The presented OD estimation method uses travel time to determine directional split factors for bi-directional traffic counts. These directional split factors update target volumes during the OD estimation procedure. The OD estimation technique using travel time from floating car runs was tested using a mid-sized network in Milwaukee, WI. The analysis indicates applicability of using travel time in OD estimation

Transport analytic approaches to the dynamic origin-destination estimation problem

Dynamic traffic models require dynamic inputs, and one of the main inputs are 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 hard problem since no direct full 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 bilevel optimization problem has been widely used. This formulation solves at the upper level a nonlinear optimization 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 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 on used paths could be interpreted as an empirical assignment observed de facto. This allows extracting empirically the same information provided by an assignment that is used in the analytical approaches. This research report explores how to extract such information from the recorded data, proposes a new optimization model to solve the DODME problem, and computational results on its performance.Postprint (author's final draft

An assigment free data driven approach to the dynamic origin destination matrix estimation problem

Document de recerca desenvolupat dins del Doctorat Industrial 2017-DI-041Dynamic traffic models require dynamic inputs, and one of the main inputs are 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 hard problem since no direct full 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 bilevel optimization problem has been widely used. This formulation solves at the upper level a nonlinear optimization 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 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 on used paths could be interpreted as an empirical assignment observed de facto. This allows extracting empirically the same information provided by an assignment that is used in the analytical approaches. This research report explores how to extract such information from the recorded data, proposes a new optimization model to solve the DODME problem, and computational results on its performance.Preprin

Adapting a dynamic OD matrix estimation approach for private traffic based on bluetooth data to passenger OD matrices

The primary data input used in principal traffic models comes from Origin-Destination (OD) trip matrices, which describe the patterns of commuters across the network. In this way, OD matrices become a critical requirement in Advanced Transport Control and Management and/or Information Systems that are supported by Dynamic Traffic Assignment models (DTA models). Dynamic Transit Assignment models are a research topic, but once a dynamic transit assignment be available to practitioners, the problem of estimating the time-dependent number of trips between transportation zones shall be a critical aspect for real applications. However, OD matrices are not directly observable, neither for private nor public transport, and the current practice consists on adjusting an initial or seed matrix from link/segment counts which are provided by counting stations or data gathering in the field (detection layout). The emerging Information and Communication Technologies, especially those based on the detection of the electronic signature of on-board devices provide a rich source of data that can be used in space-state models for dynamic matrix estimation. We present a linear Kalman filter approach that makes use of counts of passengers and travel times provided by Bluetooth devices to simplify an underlying space-state model. The formulation for dynamic passenger OD matrix estimation proposed was originally developed for auto trip matrices, but in this paper, we explore the possibility of adapting the approach to the estimation of OD matrices in public transport networks.Peer ReviewedPostprint (author’s final draft

Penggunaan Model Gravity (Gr) Dalam Estimasi Matriks Asal-tujuan (Mat) Menggunakan Data Arus Lalulintas

Many problems in transport planning and management tasks require an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of this is the gravity model (GR); its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung\u27s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment

Pengembangan ”Real TIME Traffic Information System” Bagi Pengguna Jalan

Traffic information condition is a very useful information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung's Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment

Link ağırlık matrislerinin belirlenmesi için çekim modeli ve logit model yaklaşımı

Link weight matrices have been used in the origin-destination estimation models. Both origin-destination matrix and traffic flows on network links are important information for transportation planning, traffic operation and control. Suggested approaches for estimation of origin-destination matrix are examined under two categories. First category is based on transportation models and second is statistical inference approaches. Link weight matrices have been determined using proportional or equilibrium assignment for suggested models in recent years. The treatment of congestion effects is an important property distinguishing various models for origin-destination matrix estimation. The models assume either that congestion can be treated exogenously (by proportional assignment) or endogenously (by equilibrium assignment). In proportional assignment case, it has been assumed that link counts and link weight matrices were independent from each other. The proportion of travelers choosing a route will not depend on congestion in the network but only on traveler and route characteristics. On the other hand, link weight matrices have been estimated under congestion effect in the equilibrium assignment. Link weight matrices have been determined using proportional assignment methods for inter-city traffic and using equilibrium assignment methods for urban traffic. In this work, gravity and logit model approach has been suggested to estimate link weight matrices for inert-city roads. Suggested models are based on proportional stochastic assignment methods and utility functions have been used instead of cost function. Keywords: Link weight matrices, estimation of origin-destination matrix, link counts.Link ağırlık matrisleri, ulaştırma planlamasında çok önemli bir yere sahip olan, başlangıç-son matrisi tahmin modellerinde kullanılmaktadır. Trafik sayımlarını kullanarak başlangıç-son matrisi tahmini için önerilen yaklaşımlar iki grup altında toplanmaktadır. Birinci grup ulaştırma modellerini temel almaktadır. İkinci grup ise, istatistiksel sonuç çıkarma yaklaşımlarıdır. Her iki grup için günümüze dek önerilen modellerde link ağırlık matrisleri, orantılı atama veya denge ataması kullanılarak belirlenmektedir. Şehirler arasındaki  trafiği belirlemek amacıyla başlangıç-son matrisi tahmin edilmek istendiğinde, sıkışma etkisi olmadığı için orantılı atama kullanılarak link ağırlık matrisleri belirlenebilmektedir. Bu çalışmada, başlangıç-son matrisi tahmininde ihtiyaç duyulan link ağırlık matrislerinin tahmini için “çekim modeli” ve “logit model”  yaklaşımı önerilmektedir. Anahtar Kelimeler: Link ağırlık matrisleri, başlangıç-son matrisi tahmini, trafik sayımları. &nbsp

Dynamic urban origin-destination matrix estimation methodology

The aim of this thesis is to develop a new methodology to determine dynamic Origin-Destination (OD) matrices for urban networks characterized by a high number of traffic hubs, complex route choice possibilities and a high level of traffic controls. By reviewing existing methods, from static to dynamic OD matrix evaluation, deficiencies in the approaches are identified: mainly, the level of detail of the traffic assignment for complex urban networks and the lack in dynamic approaches. The proposed methodology is comprised of a heuristic bi-level approach. Assignment of the initial demand is performed by mesoscopic simulation based on the Dynamic User Equilibrium to model detailed dynamic traffic patterns without numerous calibration parameters. OD flow adjustment is executed by an efficient least square solution which takes into account dynamic aspects of the flow propagation and traffic counts. For this task, a LSQR algorithm has been selected for its capacities to deal with a large matrix and its ability to constrain outputs. Parallel comparison with the most common approach for OD estimation (sequential static approach) has shown: first, the ability of the method to generate OD flows close to the actual demand, compared to the common practice; second, the utilization of the obtained demand by a dynamic traffic model has established its aptitude to reproduce realistic assignment patterns. Finally, applicability and example of utilization of the proposed method has been presented by solving realistic problems using the simulation software AIMSUN in which the proposed methodology is implemented as a plug-in. This research has shown the importance of input data for the OD estimation process and mainly the detection layout configuration used for traffic count data. Sensitivity analysis has shown that a small number of detectors is usually sufficient for efficient OD estimation in short computation time, if the traffic detectors intercept the most critical flows