Origin-destination Matrix Research Articles

An Evolutionary View on Equilibrium Models of Transport Flows

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

Travel Matrix Decomposition for Understanding Spatial Long-Distance Travel Structure

Mobile phone location data enable us to obtain accurate and temporally detailed long-distance travel distribution. However, the traditional long-distance travel distribution model cannot normally handle this detailed temporal information. This study proposes an approach for handling temporally detailed information of long-distance travel distribution. Considering this approach, the origin-destination matrix decomposes into two variables (indicators): destination amenity and travel cost. They can be interpreted as composite indicators of several variables that are treated in the travel-destination choice multinomial logit model. Because they are calculated only from the origin destination, we can discuss their detailed temporal variations. In this study, time changes in destination amenities and travel costs of interprefectural travel in Japan are calculated to confirm the value of this approach. These indicators have succeeded in describing the pattern of domestic long-distance travel in Japan. These quantified indicators have facilitated the understanding of the national land structure. They are useful as outcome measures for policy-making. Moreover, these indicators explain the temporal applicability of the destination choice model. Specifically, the results of destination amenities have a large seasonal variation. This indicates that the parameters of the destination amenity model (i.e., the coefficients of the destination variables) are not seasonally stable. Therefore, this must be considered when dealing with destination choice for long-distance travel.

Traffic impact analysis PT. Eastern Pearl Flour Mills – City Side Jl. Nusantara, Makassar

<span>PT. Eastern Pearl Flour Mills – City Side Makassar affects land use changes that have an impact on changes in traffic movement, so traffic impacts analysis is needed. The purpose of this study is to identify the performance of affected roads and intersections in the present and in the next 5 years, to handle traffic problems and conflicts by simulating models without handling (Do Nothing) and with handling (Do Something), analyzing time delays on the U-turns and modeling the origin-destination matrix using the Furness Method. This study uses qualitative and quantitative analysis methods that refer to the Manual Kapasitas Jalan Indonesia (MKJI, 1997) and The Furness method. The results of the analysis of the level of service on the Jl. The archipelago is in fairly good condition with LoS values A to B and DS 0.11 – 0.40 with a speed of 26.60 – 50.00 km/hour, the road performance that has the worst impact occurs at the Tol Reformasi – Jl. Nusantara with a projection for the next 5 years in 2027, where road performance has a LoS E and DS value of 0.88 with a delay time of 19 seconds/pcu. U-turn facility on Jl. Nusantara has an average delay time of 6.16 – 7.82 seconds for one rotating vehicle with a value of < 1 so that there is no queue. The results of the trip distribution analysis using The Furness model obtained the same value of generation and attraction in the second iteration, which means that it is still close to ideal conditions.</span>

Estimation of the origin-destination matrix from national road traffic data in Central Java Province using the least squares method

Central Java Province has experienced increased movement due to economic, social, and cultural developments. This increased activity has caused transportation problems, highlighting the need for effective transportation planning. A commonly used method for transportation planning is the four-step technique. Therefore, this study aimed to estimate the origin-destination (O-D) matrix for Central Java Province in 2022 and 2040. The traffic count method was utilized for calculating the O-D matrix in 2022, while the gravity method was employed for 2040. The results showed that the total movement for 2022 was 50,295 PCU/hour, while the projected amount for 2040 was 139,674 PCU/hour. In 2022, the action was primarily dominated by internal-internal trips, which account for 75% or 37,528 PCU/hour. In conclusion, the proposed approach has good validity, as indicated by a coefficient of determination (R2) of 0.677.

Maximizing modal shift in bicycle network design

We propose a mathematical programming model aimed to design a bicycle network which maximizes modal shift from a given potential demand. Urban planners face the problem of promoting sustainable mobility practices, including the use of bicycle for daily trips. One of the main factors which influence modal shift toward the bicycle is the existence of specialized infrastructure, which includes the network, i.e., a set of dedicated and interconnected lanes which can be used in combination with the existing street network for traveling from different origins to different destinations. The bicycle network can be built over different street sections using different infrastructure technologies, e.g., quality of pavement, which impact on the perceived travel cost and have different construction cost. In this context, the better is the network built from the point of view of travel cost, the more people currently using a different transport mode are willing to shift toward the bicycle mode. Our proposed model takes a street network, both travel and construction costs on every segment, an origin-destination matrix of potential bicycle trips and a given construction budget, and determines the segments where bicycle lanes should be built and the technology of infrastructure on each segment, so as to maximize the modal shift from the potential demand to the bicycle mode. The methodology is applied to a real case related to the city of Montevideo, Uruguay, for which we conduct sensitivity analysis and comparison with the current system.

Evacuation Scenario Simulator with Location-Based Social Network Data Support

Disaster management systems apply different evacuation models to provide transportation responses to disaster situations. The main goal of the evacuation models is to reduce evacuation times to save lives.The significant increase in the use of location-based social networks (LBSN) as a major media factor can provide data for inferring the Origin-Destination matrix that represents the demands for trips during routine times. To achieve the goal of reducing overall evacuation travel times, an innovative methodology was developed that supports LBSN data for both demand prediction and decentralized personal destination recommendation.A modular multi-dimensional tool for evacuation scenario simulation (MMDT-ESS) was developed for supporting this methodology. This tool can handle multiple scenarios and compute performance evaluation values of VhT (Vehicle-Hours Traveled). MMDT-ESS can be applied using both conventional evacuation models (i.e., evacuation to predefined shelters) or decentralized personalized evacuation models and can provide different recommendations: either self-evacuation to nearest family or best friend that is situated outside the affected area or to a predefined shelter area.A test case using data from a U.S. metropolitan agency showed significant reduction in VhT when choosing evacuation scenarios with LBSN data for decentralized evacuation. Sensitivity analysis on main model parameters illustrate the robustness of the results.

Demand Estimation for Non-Working Trips: Forecasting Passengers regarding the New Open of Railway Line Maros-Barru

This study aims to identify the demand estimation of passengers regarding the operational railway of Maros-Barru line. The new railway is expected to attract passengers using public transport from origin to destination. The existing condition of Maros-Barru majority used private vehicle as the choice and minibus with fragmented line. The new construction railway provided by the government services the passengers through Maros-Barru. The demand for the railway plays an important role in describing the potential passengers of each region. The non-working trips with flexible time for the trip are the main focus of this study. The potential passengers using the origin-destination (O/D) matrix are used. The analysis uses the growth factor of the population and forecasts demand according to OD data. The high trip based on OD estimation result showed that Pangkep was at the first position followed by Maros and ended with Barru. This result indicates that Pangkep has a high demand according to the forecasting passenger which is located in the middle regency instead of Maros and Barru that close to the Capital area. The high trip occurred for the total not only for the Origin trip but also Destination trip. Hence, the population growth of the district on Pangkep is the most compared to other districts in other regions.

Human mobility in the airport transportation network of the United States

In this research, we analyze the air transportation network of the United States of America using databases with passenger activity containing detailed records of domestic flights between airports. Different properties obtained from the origin-destination matrix in the air transport network are studied from 2011 to 2020. The results are compared with a model based on the gravity law for human mobility defined in terms of transition probabilities and travel distances. The predictions of this model are confirmed through Monte Carlo simulations. Our findings reproduce the dynamics of passengers in the airport transportation network.

Are Expensive Expressways in China Attractive to Businesses? Evidence From the Location Selections of Manufacturing Enterprises

Most expressways in China are tolled, making expressway transportation expensive for enterprises. Traditional location theory focuses only on transportation distances and transit costs, making it difficult to explain how expressways influence location decisions of enterprises. This research starts from the perspective of expressway travel times to explain how tolled expressways affect the location choices of new manufacturing enterprises. A geographic information system digital map of expressways for the years 2000–2013 was developed. The origin–destination matrix module was used to calculate the expressway travel times of 293 cities. Count data regression models, including probit, Poisson, zero-inflated Poisson, and negative binomial regression, were applied to empirically study the influence of expressway construction on the location selections of new manufacturing enterprises. The results show that reduced expressway travel time significantly attracts new enterprises and positively affects the location selections of manufacturing enterprises in China. Further, this finding reveals significant heterogeneity across different manufacturing industries.

Estimation and prediction of the OD matrix in uncongested urban road network based on traffic flows using deep learning

In this article, we propose a new method for OD (Origin–Destination)​ matrix prediction based on traffic data using deep learning. The input values of the developed model were determined based on data on the structure of the road network, origin and destination points of trips, as well as data on traffic intensity on road network sections recorded by video-sensing devices. The advantage of the method is that the complex process of data acquisition and processing is not required for the estimation and prediction of the matrix. Historical data and the iterative method of estimating a prior OD matrix were used only to generate training sequences for the neural network. The proposed method using deep learning neural networks with the long short-term memory (LSTM) or autoencoders layers (DLNa — deep learning networks with autoencoders) is characterized by relatively high accuracy and resistance to temporary missing data from several measurement points located in the urban road network. The case study was conducted for a network of a medium-sized city in Poland. The results show (average MAPE = 7.18% (LSTM), 6.80% (DLNa)) that the proposed method can have a practical implementation in real-time dynamic traffic assignment (DTA) systems for ITS applications. The proposed method of short-term forecasting the OD matrix does not require questionnaire research or detailed information on spatial development. Therefore, it is not as expensive and time-consuming as the methods based on these data.

Optimization model for cost estimation of decentralized wastewater treatment units

In this study, an optimization model, which is a combination of Genetic algorithm (GA) and geographical information system (GIS) was used to find minimum construction, operation, and flow reuse cost ( Fmin) . This Fmin model will be used for the estimation of the constructional and operational costs of decentralized wastewater treatment units [DTUs] and can be used for the estimation of reusing treated municipal wastewater flow for irrigation purposes in Sulaimani city, Iraq as well. In the optimization processes, an objective function technique was used based on data from 31 small-sized DTU and the costs of reusing their effluents in 827 deferent sizes green areas [GRs] across the city. A transportation matrix model of size [31x827] was built to find the Fmin for conveying the required flow from the DTUs to the GRs destinations. Also, optimum required pipe lengths and routes for the treated flow conveying were found using GIS Network Analysis – Origin-Destination (OD) Matrix Tools. The results showed that the proposed model could successfully obtain an optimal solution. The algorithm is flexible enough to consider a variety of scenarios and compare the best options.

Transportation Origin-Destination Demand Estimation with Quasi-Sparsity

Origin-destination (OD) demands for a city or a region are essential input to many transportation applications. For a real-world transportation network, the OD demand matrix may present certain quasi-sparsity property, that is, most OD pairs have small demands, whereas only a small portion of OD pairs have large demands. In this paper, we formally define quasi-sparsity and propose a quasi-sparsity–based OD (QSOD) estimation framework to explore such a property for OD demand estimation. We study two QSOD models, that is, the fixed-mapping QSOD model and the bilevel QSOD model, by applying the compressed sensing technique. We theoretically and numerically show that under certain conditions the estimated OD matrix shares the same quasi-sparsity feature with the prior OD matrix, and the estimated demands of most OD pairs (of a large-size network) will be equal to either their prior values or zeros (or a very small value). Results show that the QSOD framework has the capability in keeping OD quasi-sparsity consistency and is computationally less demanding compared with existing methods. The practical implications of the QSOD framework are also discussed. Funding: This work was supported by the National Science Foundation [Grants CMMI-1825053 and DMS-1814894]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.1178 .

The Impact of Fuel Cell Electric Freight Vehicles on Fuel Consumption and CO2 Emissions: The Case of Italy

The Italian Recovery and Resilience Plan promotes, among its many actions, the use of hydrogen by the deployment of refuelling stations for heavy-duty vehicles, predicting a 5–7% penetration rate of fuel cell electric vehicles (FCEVs) for long-distance freight transport. In this work, the impact of this action on the reduction of greenhouse gas emissions and consumption was estimated, assuming the plan’s objectives are met. To achieve this aim, a national simulation model of the road freight transport system was implemented, consisting of a graph of the national road network and an inter-provincial origin-destination matrix; the graph was based on data available from OpenStreetMap, while the interprovincial matrix was estimated from the interregional matrix with the use of two linear regression models, one for emitted goods and one for attracted goods. The simulation of the system made it possible to estimate the impact of this action on CO2 emissions and fuel consumption under three different scenarios. From 2025 to 2040, a reduction in CO2 emissions ranging from around 9 to around 16.5 million tonnes was estimated, and a reduction in consumption ranging from around 3 billion to around 5.6 billion litres of diesel. These results show how this action can be seen as one of the bricks contributing to the fight against global warming.

Completion and augmentation-based spatiotemporal deep learning approach for short-term metro origin-destination matrix prediction under limited observable data

Accurate prediction of short-term origin-destination (OD) matrix is crucial for operations in metro systems. Recently, some deep learning-based models have been proposed for OD matrix forecasting in ride-hailing or high-way scenarios. However, the metro OD matrix forecasting receives less attention, and it has different prior knowledge and complex spatiotemporal contextual setting; for example, the sparse destination distribution and the incomplete OD matrices collection in recent time intervals due to unfinished trips before the predicted time interval. This paper designs a deep learning approach for metro OD matrix prediction by addressing the recent destination distribution availability, augmenting the flow presentation for each station, and digging out the global spatial dependency and multiple temporal scale correlations in the mobility patterns of metro passengers. Specifically, it first proposes to complete the recent OD matrices by combining some empirical knowledge including the historical mobility pattern and arrival time distribution. Then, it learns the complementary spatiotemporal contextual features by embedding methods to enrich the station representation. Finally, it captures global mobility trend of metro passengers at each origin station through aggregating the trend of all other origin stations by self-attention mechanism since the mobility synchronizes among stations from spatial perspective. Three temporal convolutional networks are leveraged to extract three temporal trends in passenger mobility data, i.e., recent trend, daily trend, and weekly trend. Smart card data from Shenzhen and Hangzhou metro systems are utilized to demonstrate the superiority of our model over other competitors.

Spatiotemporal Virtual Graph Convolution Network for Key Origin-Destination Flow Prediction in Metro System

Short-term Origin-Destination (OD) flow prediction plays a major part in the realization of Smart Metro. It can help traffic managers implement dynamic control strategies to improve operation safety. Also, it can assist passengers in making reasonable travel plans to improve the passenger experience. However, there are problems that the dimension of OD short-term traffic prediction is much higher than the base number of metro stations and the OD matrix is sparse. To resolve the above two problems, a threshold-based method is proposed to extract key OD pairs first. OD passenger flow contains the attribute information of the Origin-Destination station and exhibits similar time evolution characteristics, so the spatial and temporal correlation needs to be considered in the prediction. Pearson correlation matrix is used to build a virtual graph and model the virtual connection between OD pairs. A spatiotemporal virtual graph convolutional network (ST-VGCN), which combines the advantages of a graph neural network and gated recurrent neural network, is proposed to identify spatial associations and temporal patterns simultaneously. The proposed method is evaluated on 39 days of real-world data from Shenzhen Metro, which outperforms other benchmarks. The research in this work can contribute to the development of short-term OD flow forecasts and help to provide ideas for the research on real-time operation and management of rail transit. Furthermore, it can help to establish passenger flow prediction and early warning mechanisms to quickly evacuate a large number of passengers in case of emergency.

Delineating urban functional zones using mobile phone data: A case study of cross-boundary integration in Shenzhen-Dongguan-Huizhou area

As cities continuously expand and with the emergence of mega-city regions, the urban functional zones (UFZs) have spread beyond their original administrative boundaries. An accurate and updated delineation of the UFZs is crucial for assessing the functional integration between cities within a mega-city region. Mobility data provides a chance to depict the UFZs from actual human activities at a finer spatial scale. Existing studies mostly adopted network-based approaches relying on the topological relationship but ignoring spatial factors, causing the lack of sensitivity in detecting the cross-cities integration of the functional region. This research proposed a novel regionalisation algorithm that redraws non-overlap boundaries of urban functional zones based on the commuting origin-destination matrix, representing the spatial interactions within cities and cross-cities. In particular, functional zones are drawn by searching for the best partition with the best goodness of fitting in the hierarchical spatial interaction model. The algorithm was applied to a case study of a mega-city region, Shenzhen-Dongguan-Huizhou (SDH) area in China using mobile phone signalling data. By adopting two different settings, this model evaluated the current status and predict the future trend of urban integration respectively. The results show the current boundary of UFZs in the SDH area almost coincides with administrative boundaries. Meanwhile, the results of long-term predictions might be utilised by policymakers to give more attention to the areas near the Dongguan-Huizhou boundary to promote industry cooperation and avoid mismatches between the functional and administrative regions.

Inference of dynamic origin–destination matrices with trip and transfer status from individual smart card data

BackgroundThe provision of seamless public transport supply requires a complete understanding of the real traffic dynamics, comprising origin-to-destination multimodal mobility patterns along the transport network. However, most current solutions are centred on the volumetric analysis of passengers’ flows, generally neglecting transfer, walking, and waiting needs, as well as the changes in the mobility patterns with the calendar and user profile. These challenges prevent a comprehensive assessment of the routing and scheduling vulnerabilities of (multimodal) public transport networks.Research aims/questionsThe research presented in this paper aims at addressing the above challenges by proposing a novel approach that extends dynamic Origin-Destination (OD) matrix inference to dynamic OD matrix inference with aggregated statistics, highlighting vulnerabilities and multimodal mobility patterns from individual trip record data.MethodologyGiven specific spatial and temporal criteria, the proposed methodology extends dynamic Origin-Destination (OD) matrices with aggregated statistics, using smart-card validations gathered from (multimodal) public transport networks. More specifically, three major contributions are tackled; i) the data enrichment in the OD matrices with statistical information besides trip volume (e.g., transfer and trip features); ii) the detection of vulnerabilities on the network pertaining to walking distances and trip durations in a user-centric way and iii) the decomposition of traffic flows in accordance with calendrical rules and user (passenger) profiles. The set of contributions are validated on the bus-and-metro public transport network in the city of Lisbon.ResultsThe proposed approach for inferring OD matrices yields four unique contributions. First, we allow inference to consider multimodal commuting patterns, detecting individual trips undertaken along with different operators. Second, we support dynamic matrices’ OD inference along with parameterizable time intervals and calendrical rules, and further support the decomposition of traffic flows according to the user profile. Third, we allow parameterization of the desirable spatial granularity and visualisation preferences. Fourth, our solution efficiently computes several statistics that support OD matrix analysis, helping with the detection of vulnerabilities throughout the transport network. More specifically, statistical indicators related to travellers’ functional mobility needs (commuters for working purposes, etc.), walking distances and trip durations are supported. The inferred dynamic OD matrices are the outcome of a developed software with strict guarantees of usability. Results from the case study using data gathered from the two main public transport operators (Bus and Metro) in the city of Lisbon show that 77.3% of alighting stops can be estimated with a high confidence degree from bus smart-card data. The inferred OD matrices (Bus and Metro) in the city of Lisbon reveal vulnerabilities along specific OD pairs, offering the bus public operators in Lisbon new knowledge and a means to better understand dynamics and validate OD assumptions.

Network-wide ride-sourcing passenger demand origin-destination matrix prediction with a generative adversarial network

Accurate short-term passenger demand origin-destination (OD) matrix prediction contributes to the coordination of traffic supply and demand. This study proposes a novel generative adversarial network (GAN) named Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) to predict the network-wide ride-sourcing passenger demand OD matrix. The proposed CWGAN-GP model can not only capture internal spatiotemporal features of OD matrices, but also characterise external dependencies of OD matrices on conditional information, such as the traffic zone-based average traffic speeds, the traffic zone area, and time variables. Based on the ride-sourcing GPS trajectories from Didi Chuxing, Chengdu, China, and ride-sourcing data from the New York City, numerical results illustrate that the predicted OD matrices are in good agreement with the actual ones, and CWGAN-GP has good convergence performance by analysing the discriminator loss and the Wasserstein distance with respect to training epochs. Comparison results also validate the outperformance of CWGAN-GP compared with the other counterpart prediction methods and the reasonability of specific structures of CWGAN-GP. Thus, CWGAN-GP is concluded to be promising to predict network-wide ride-sourcing passenger demand OD matrices.

Spatial Analysis of Flow Patterns to Healthcare Facilities in Kano Metropolis, Nigeria

Healthcare facilities in developing countries are most times inadequate and access to the available ones is made difficult by some uncongenial urban environmental conditions. These among other factors results in the poor health indicators normally reported for cities in developing countries. Hence, this study examined the accessibility patterns of people to healthcare facilities and factors responsible for such patterns in Kano Metropolis, Nigeria. The locations of healthcare facilities and respondents in the study area were captured through handheld GPS receivers. Also, a set of 591 questionnaires were administered to elicit information about people’s perception of healthcare facilities in the city. Spatial analyses of phenomena of interest were carried out using GIS analytical tools. Specifically, the road network was digitized with a connectivity rule from Ikonos Satellite Image and was converted to network data set. Also, the analysis of travel patterns to each healthcare facility was done using origin-destination matrix in the network analysis within the GIS environment. Results showed that the travel distance of most patients to tertiary healthcare facilities is above the WHO standard; while the travel distance to primary and secondary healthcare facilities is within the WHO standard. Furthermore, result showed disparities in the preference of people to healthcare facilities occasioned by distance, transport cost, travel time, quality of service and socio-economic factors. Approximately 62% of the household patronized hospital for treatment, 23% made use of herb and 15% engaged in self-medication. The result of this study also showed distance covered to healthcare facilities and flow patterns of healthcare users in which the distance are geometrically measured in numerical unit which has been rarely employed and these were documented in the study. The study concluded that spatial restructuring and reorganization of healthcare facilities is necessary in order to enhance access to healthcare facilities in Kano city.

PROYEKSI POLA PERGERAKAN PADA SIMPANG BERSINYAL (MAKASSAR - SELAWESI SELATAN)

MAT (Origin Destination Matrix) is a two-dimensional matrix that contains information about the magnitude of movement between locations (zones) within a specific area. If a MAT is charged to a transportation network system, it will produce a movement flow that describes the movement pattern in the area. This research is quantitative descriptive research. The purpose of this research is to project the future traffic movement pattern. Four methods have been developed to date that generally tries to overcome the shortcomings of the previous method, namely the Analog method (average, Detroit, Fratar, and Furness). These four methods guarantee that the magnitude of the pull generation in the future is the same as expected. Data collection techniques in research are in the form of direct data collection in the field using video cameras placed at several observation points. Hourly flow patterns calculate traffic volume. Based on the results of the analysis, it was found that there were differences in repetition in each analog method. The average method reached the 75th repetition, the Detroit method reached the 8th repetition, the Fratar method reached the 41st repetition, and the Furness method reached the third repetition.