Origin-destination Matrix Research Articles

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.

Analysis of Passenger Flows Served by Bus Routes in the city of Khujand

The study considered the role of passenger road transport as one of the most important sectors of the national economy, which influences the entire economy of a country and its regions, as well as the quality of life of the population.The analysis of the transport system in the city of Khujand, which is mainly dominated by road transport, has been carried out based on the results of the conducted survey on passenger flows on bus routes of the city, particularly, regarding origin-destination matrix, fare collectability.The conclusions argue in favour of practicability of conducting regular systematic monitoring of passenger flows to optimize suggestions in the field of urban public transport development. In fundamental terms, main problems of passenger transportation management were identified including lack of a regular timetable on certain routes, non-compliance with the traffic schedule, resulting in an increase in travel time, changes in routing due to the absence of sufficient number of passengers, systematic violations of traffic rules, excessively long stops at the hub stopping points, etc. The suggestions comprise approaches intended to reduce influence of organisational and managerial factors on efficiency of passenger transportation, on the correct choice of rolling stock, that should be selected considering design features, possibility of serving all segments of the population (categories of passengers), traffic intensity, manoeuvrability, compliance with road conditions, etc. Conclusions were also made regarding techniques to justify the volume of passenger transportation, to identify average distance of passenger’s trip, to calculate technical, operational, and quality indicators of operation of public transport, distribution of passengers per routes.The analysis of the structure of passenger flows since it influences the indicators of the effectiveness of passenger transportation, plays the most significant role in the framework of development of new approaches to the solution of transport issues in the city of Khujand.Implementation of full-scale monitoring of passenger flows will make it possible to develop appropriate timetables facilitating movement of passenger flows on routes, to select a rational type of rolling stock in terms of capacity, will contribute to time-saving passenger travelling, socialisation of transport tariffs, and improved quality of transportation.

A horizontal partitioning‐based method for frequent pattern mining in transport timetable

AbstractAnalysing transport timetables is an important task, as it brings the opportunity to discover which routes commonly lead to delays. Frequent pattern mining is a technique used to support such type of discovery. However, functional dependencies are intrinsic properties present in timetables, particularly related to attributes derived from the origin–destination matrix. Such functional dependencies compromise the search for patterns in timetables in both the number of association rules (ARs) generated and the computational cost. Several of these ARs refer to the same information. Redundancy removal techniques can reduce the number of ARs. However, these techniques are designed to be used after mining finishes, which increases the computational cost of finding useful ARs. This work presents timetable pattern mining (T‐mine), a novel method for frequent pattern mining that improves knowledge discovery in timetables. We evaluated T‐mine using Brazilian Flight Data and compared T‐mine with the direct application of frequent pattern mining approaches with and without functional dependencies. Our experiments indicate that T‐mine is about one order magnitude faster than other methods with functional dependencies.

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.

MODEL PERGERAKAN KOTA BANDAR LAMPUNG BERDASARKAN INTENSITAS GUNA LAHAN

The city functions as a service system for diverse community needs implemented through the provision of housing, trade and services, recreational areas and transportation networks. Activities between these activities cause movement, the magnitude of the movement value is influenced by the size and scale of activity (intensity) on each type of land use. The economic condition of the city depends on the ease of the people in their activities and the smooth distribution of production goods. Bandar Lampung as the economic center of Lampung Province has problems that affect movement such as land use patterns, low road accessibility. In anticipation of this movement must be in good condition that is effective and easy to do, because movement is influenced by land use intensity. Therefore, it is necessary to know the magnitude of the influence of the intensity of land use in the movement in the city of Bandar Lampung by using the Movement Model can be known the size of the influence of Land Intensity on the movement in the city of Bandar Lampung. The analytical method used in this study is the analysis of the origin destination matrix and multiple linear regression analysis. the results of the analysis show that land use related and influencing the movement of the city of Bandar Lampung are activities on land use trade and services as well as education affect movement. The resulting movement model is as follows: y = 62,3 + 2,275 + 1,348 .

Hourly Origin-Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning.

Predicting the travel demand plays an indispensable role in urban transportation planning. Data collection methods for estimating the origin–destination (OD) demand matrix are being extensively shifted from traditional survey techniques to the pre-collected data from intelligent transportation systems (ITSs). This shift is partly due to the high cost of conducting traditional surveys and partly due to the diversity of scattered data produced by ITSs and the opportunity to derive extra benefits out of this big data. This study attempts to predict the OD matrix of Tehran metropolis using a set of ITS data, including the data extracted from automatic number plate recognition (ANPR) cameras, smart fare cards, loop detectors at intersections, global positioning systems (GPS) of navigation software, socio-economic and demographic characteristics as well as land-use features of zones. For this purpose, five models based on machine learning (ML) techniques are developed for training and test. In evaluating the performance of the models, the statistical methods show that the convolutional neural network (CNN) leads to the best performance in terms of accuracy in predicting the OD matrix and has the lowest error in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). Moreover, the predicted OD matrix was structurally compared with the ground truth matrix, and the CNN model also shows the highest structural similarity with the ground truth OD matrix in the presented case.

Use of 2SFCA Method to Identify and Analyze Spatial Access Disparities to Healthcare in Jeddah, Saudi Arabia

The issue of reducing spatial disparities in access to healthcare is one of the most important healthcare planning issues that policy makers and planners investigate and consider as a key focus until present time. A healthcare system that meets the requirements of availability and affordability will be useless if the spatial accessibility to healthcare is not provided to all equally. Therefore, this study aims to identify and analyze spatial disparities in access to healthcare centers in Jeddah, Saudi Arabia. The two-step floating catchment area (2SFCA) method was used to measure spatial accessibility of healthcare centers based on the travel time threshold (i.e., 30-min drive time in this study). The GIS technology was used to execute the 2SFCA method. A geodatabase, which includes the population districts, locations of healthcare centers, and road network, was created. Some procedures were performed within the road network database to set the travel time that is considered as an essential step to compute the origin–destination (OD) cost matrix. The OD matrix was later used as the source for calculating provider-to-population ratios and the spatial accessibility scores for population districts. The results of the study revealed spatial disparities in access to healthcare centers in Jeddah city. The majority of the Jeddah population (i.e., 97.51%) have accessibility to healthcare centers, but with disparate levels. The central districts have a higher access score compared to the rest of the city’s districts. Most districts that do not have accessibility to healthcare centers are concentrated in the southeast of the city. The results can help local health planners improve spatial equity in access to healthcare centers through giving the less-served districts a priority when allocating future healthcare centers in Jeddah city.

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.

A DBSCAN-based framework to mine travel patterns from origin-destination matrices: Proof-of-concept on proxy static OD from Brisbane

Limited studies exist in the literature on demand related travel patterns, the analysis of which requires a rich database of Origin Destination (OD) matrices with appropriate clustering algorithms. This paper develops a methodological framework to explore typical travel patterns from multi-density high dimensional matrices and estimate typical OD corresponding to those patterns. The contributions of the paper are multi-fold. First, to cluster high-dimensional OD matrices, we deploy geographical window-based structural similarity index (GSSI) as proximity measure in the DBSCAN algorithm that captures both OD structure and network related attributes. Second, to address the issue of multi-density data points, we propose clustering on individual subspaces. Third, we develop a simple two-level approach to identify optimum DBSCAN parameters. Finally, as proof-of-concept, the proposed framework is applied on proxy OD matrices from real Bluetooth data (B-OD) from Brisbane City Council region. The OD matrix clusters, typical travel patterns, and typical B-OD matrices are estimated for this study region. The analysis reveals nine typical travel patterns. The methodology was also found to perform better when GSSI was used instead of Euclidian distance as a proximity measure, and two-level DBSCAN instead of K-medoids, Spectral, and Hierarchical methods. The framework is generic and applicable for OD matrices developed from other data sources and any spatiotemporal context. DBSCAN is chosen for this study because it does not require a pre-determined number of clusters, and it identifies outliers as noise.

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.

Traffic Control Scheme for Social Optimum Traffic Assignment with Dynamic Route Pricing for Automated Vehicles

In transportation modeling, after defining a road network and its origin-destination (OD) matrix, the next important question is how to assign traffic among OD-pairs. Nowadays, advanced traveler information systems (ATIS) make it possible to realize the user equilibrium solution. Simultaneously, with the advent of the Cooperative Intelligent Transport Systems (C-ITS), it is possible to solve the traffic assignment problem in a system optimum way. As a potential traffic assignment method in the future transportation system for automated cars, the deterministic system optimum (DSO) is modeled and simulated to investigate the potential changes it may bring to the existing traditional traffic system. In this paper, stochastic user equilibrium (SUE) is used to simulate the conventional traffic assignment method. This work concluded that DSO has considerable advantages in reducing trip duration, time loss, waiting time, and departure delay under the same travel demand. What is more, the SUE traffic assignment has a more dispersed vehicle density distribution. Moreover, DSO traffic assignment helps the maximum vehicle density of each alternative path arrive almost simultaneously. Furthermore, DSO can significantly reduce or avoid the occurrence of excessive congestion.

A Data-Efficient Approach for Evacuation Demand Generation and Dissipation Prediction in Urban Rail Transit System

Predicting evacuation demand, including its generation and dissipation process, for urban rail transit systems under disruptions, such as line and station closure, often requires comprehensive historical data recorded under homogeneous situations. However, data under disruptions are hard to collect due to various reasons, which makes traditional methods impractical in evacuation demand prediction. To address this problem from the modeling perspective, we develop a data-efficient approach to predict evacuation demand for urban rail transit systems under disruptions. Our model-based approach mainly uses historical data obtained from the natural state, when no shocks take place. We first formulate the mathematical representation of the evacuation demand for every type of urban rail transit station. Input variables in this step are location features related to the station under the disruption, as well as an origin–destination matrix under the natural state. Then, based on these mathematical expressions, we develop a simulation system to imitate the spatio-temporal evolution of evacuation demand within the whole network under disruptions. The transport capacity drop under disruptions is used to describe the disruption situation. Several typical scenarios from the Shanghai metro network are used as examples to implement the proposed method. The results show that our method is able to predict the generation and dissipation processes of evacuation demand, as well model how severely stations will be affected by given disruptions. One general observation we draw from the results is that the most vulnerable stations under disruption, where the locations peak evacuation demand occurs, are mainly turn-back stations, closed stations, and the transfer stations near closed stations. This paper provides new insight into evacuation demand prediction under disruptions. It could be used by transport authorities to better respond to the urban rail transit system disruption.

Optimal Excess Commuting Evaluation Based on Local Minimal Costs

Excess commuting refers to the value of unnecessary commuting or distance costs. Traditional commuting distance models adapt the most efficient scenario with people working in the nearest workplace geographically. Even though there have been some attempts to include constraints with commuter attributes and neighborhood features, problems arise with traditional geographical space and the subjectivity of these predefined characteristics. In this paper, we propose a method to calculate theoretical local minimal costs, which considers preferences that are inherently behavioral based on current work–home trips in the process of reassigning the work–home configuration. Our method is based on a feature space with a higher dimension and with the enlargement of attributes and relations of and between commuters and neighborhoods. Additionally, our solution is arrived at innovatively by improved Fuzzy C-Means clustering and linear programming. Unlike traditional clustering algorithms, our improved method adapts entropy information and selects the initial parameters based on the actual data rather than on prior knowledge. Using the real origin–destination matrix, theoretical minimal costs are calculated within each cluster, referred to as local minimal costs, and the average sum of local minimal costs is our theoretical minimal cost. The difference between the expected minimal cost and the actual cost is the excess commuting. Using our method, experimental results show that only 13% of the daily commuting distance in Wuhan could be avoided, and the theoretical distance is approximately 1.06 km shorter than the actual commuting distance.

Rail transit OD‐matrix completion via manifold regularized tensor factorisation

Urban rail transit has become an indispensable mode in major cities worldwide regarding the advantages of large capacity, high speed, punctuality, and environmental protection. Origin-destination (OD) matrix data is crucial to the organisation of rail train operation and management. Nevertheless, rail transit OD matrices are inevitably suffered from data loss problems due to the data transmission and acquisition failures. Tensor completion is a state-of-the-art method for missing data imputation. In this paper, a novel tensor completion method for OD- matrix completion is proposed. To this end, an OD-matrix tensor is established to represent OD information, and the similarity matrix of OD-matrix tensor for each dimension is extracted as a piece of auxiliary information expressing underlying multi-mode relationships of OD data. Finally, a manifold regularised tensor factorisation is applied to impute the missing OD data, in which the Graph Laplacians inferred from similarity weight matrices are used as regularisation priors on factorisation factors. The proposed model is applied to a case study of the metro line in Xi'an, China. The experimental results indicate that the proposed method outperforms baselines. It can accurately impute missing data within the OD matrices and work well even when the missing ratio is up to 80%.

Performance Evaluation of Bus Network in Yogyakarta, Indonesia Using Macroscopic Simulation Model (VISUM)

The problem of TransJoga as an alternative mode in Yogyakarta is the shortage of resources required for the optimum operation, thus there is a need for constant revision and enhancement of the transit system through performance evaluation. Main objective of this study is to evaluate the performance of TransJogja from the government as the operator and the commuters as the passenger. Evaluation was done based on Origin-Destination matrix travel demand in Yogyakarta. The existing network was modelled with traffic macrosimulation VISUM software. Headway-based assignment procedure was used to model TransJogja passengers travel behaviours. Assignment calibration using TFlowFuzzy was done to produce model that can represent the existing condition the best. The results revealed that network of TransJogja has decent coverage and accessibility. However, it has low frequency and low passenger demand, thus effects in very low productivity and line efficiency. There is a long waiting time from and to line 8 (feeder line) with a waiting time up to 19 – 20 minutes in the morning rush hour and 16-20 minutes in the afternoon rush hour. All line routes have smaller number of average passenger volume than the required number due to low demand or low passenger turnover rate.

A Structural Comparison between the Origin-Destination Matrices Based on Local Windows with Socioeconomic, Land-Use, and Population Characteristics

The origin-destination (OD) matrices express the number and the pattern of trips distributed between OD pairs. OD matrix structural comparison can be used to identify different mobility patterns in the cities. A comparison of two OD matrices could express their difference from both numerical and structural aspects. Limited methods, such as the mean structural similarity (MSSIM) index and geographical window-based structural similarity index (GSSI), have been developed to compare the structural similarity (SSIM) of two matrices. These methods calculate the structural similarities of two OD matrices by grouping the OD pairs into local windows. The obtained results from the MSSIM entirely depend on the dimensions of the chosen windows. Meanwhile, the GSSI method only focuses on the geographical adjacency and correlation of zones while selecting local windows. Accordingly, this paper developed a novel method named Socioeconomy, Land-use, and Population Structural Similarity Index (SLPSSI) in which local windows are selected according to socioeconomic, land-use, and population properties for SSIM comparison of OD matrices. The proposed method was tested on Tehran’s OD matrix extracted from cell phone Geographic Position System (GPS) data. The advantage of this method over two previous ones was observed in determining the new pattern of trips on local windows and more precise detection of SSIM of the weekdays. The SLPSSI approach is up to 10 percent more accurate than the MSSIM method and up to 5.5 percent more accurate than the GSSI method. The proposed method also had a better performance on sparse OD matrices. It is capable of better determining the SSIM of sparse OD matrices by up to 8% compared with the GSSI method. Finally, the sensitivity analysis results indicate that the suggested method is robust and reliable since it is sensitive to applying both constant and random coefficients.

Analysis of urban agglomeration structure through spatial network and mobile phone data

AbstractUrban agglomeration is an important strategy used to promote economic development and urbanization in China. Understanding the structure of urban agglomeration is therefore essential for policy‐makers and planners. In this study, the Beijing–Tianjin–Hebei urban agglomeration (BTHUG) is explored through a proposed spatial network analytical framework and a large mobile phone data set (over 20 million users). We first construct a weight‐directed spatial interaction network based on an origin–destination matrix derived from the data set. Several network metrics (i.e., degree, strength, the rich‐club coefficient, and the assortativity coefficient) and three selected community detection algorithms (i.e., Infomap, Louvain, and Regionalization) are applied and compared to reveal the structure of the BTHUG. A four‐level hierarchical structure is defined and observed: one global center, two local centers, major cities that have low mobility flow but strong linkages with the three centers, and peripheral cities that have low mobility flow and weak linkages with the three centers. In particular, the results imply that the spatial structure of the BTHUG is over‐dependent on the global center (i.e., Beijing and northern Langfang). Further, ignoring spatial interaction patterns in top‐down administrative planning for urban agglomeration may lead to ineffective integrated development. The implications for BTHUG planning are discussed.

Improving Air Transportation by Using the Fuzzy Origin–Destination Matrix

The work is devoted to the development of new methods and algorithms to support decision making when planning air travel using uncertainties in the form of fuzzy numbers. The proposed approach makes it possible to define rational methods of choice: how to change the transport graph to better meet the needs of the population. This is particularly relevant in the context of the reduced demand for air travel caused by the pandemic and the need to switch from large to smaller aircraft types. The problem is solved by restoring the fuzzy origin–destination matrix of current statistics on air traffic between airports. The problem is that we do not know what proportion of passengers moving between the specified points are forced to use large transport hubs as intermediate destinations. To determine the validity of the origin–destination matrix, we build a number of optimization models to determine fuzzy intervals and search for correspondence with the maximum value of the membership function. Algorithmic and software search for the fuzzy origin–destination matrix and fuzzy ranking of potentially promising routes are developed. The perspective of the given approach is shown by an example of a task concerning a choice of new routes between regional airports.

Single-level approach to estimate origin-destination matrix: exploiting turning proportions and partial OD flows

ABSTRACT This research proposes a novel single-level approach to traffic counts-based OD estimation using observed turning proportions and partial OD flows. The key idea is to relax the need for explicit assignment-based bi-level models and exploit the observations from big traffic data. The observed turning proportions are used to estimate traffic counts; and the structural knowledge of partial OD flows is used to improve the quality of OD estimates. Numerical experiments considering different spatial coverage of partial OD from Bluetooth are conducted on two networks. The study findings indicated that the quality of estimates improve with higher percentage of partial OD information. The proposed approach is computationally faster and yielded slightly better results than the traditional bi-level. The methodology is not limited to data from Bluetooth but adaptive with other sources such as GPS, mobile phone, and travel surveys that can provide turning proportions and/or partial OD flows.

Generating Trips and Assigning Route to a SUMO Network Through the Origin–Destination Matrix: A Case Study of Mobility Routing Model for VANETs

Vehicular Ad-hoc NETworks (VANETs) is an important research domain as it deals with the safety of vehicles moving on roads. The selection of the most prominent routing protocol in this field is a challenging job due to the high mobility rate of VANETs. The foremost motive for writing this paper is to recognize the most suitable protocol that can be used for a high-density traffic region. Therefore, the evaluation of six popular routing protocols (AODV, DSDV, DSR, GPSR, OLSR, and ZRP) of VANETs is examined in the congested city of Delhi. For this, a methodology is being presented that explains the creation of a network using the SUMO tool and assigning a trip and route to the obtained network through the Origin-Destination matrix. Then, by adopting the created network file, the TCL File and NAM File are generated using the NS-2. Finally, selected protocols' performance is examined and evaluated based on the metrics such as Throughput, Packet Delivery Ratio (PDR), Delay, Packet Loss Ratio (PLR), and Routing Overhead. The results showed that the AODV protocol acts as an appropriate routing protocol and could perform better with our presented approach. Also, the comparison is conducted with the results obtained for the AODV routing protocol using the presented approach and the existing approaches. The comparison results depict that the AODV routing protocol presents much better results by using the presented methodology than by using the existing methods in terms of throughput, delay, and PDR.