Origin-destination Matrix Research Articles

Student mobility in Southern Italy: An empirical analysis of preferential patterns

This paper presents an empirical analysis aimed at identifying chain migration patterns among university students from Sicily, Sardinia, and Apulia. Utilizing data from the national archive of university students enrolled in an Italian university from 2008 to 2017, we construct origin–destination matrixes and calculate residuals from a log-linear model to detect key student routes. Positive residuals between specific origin–destination pairs that persist over time can suggest significant attraction and potential chain migration. The results support a “large to large” and “small to small” city mobility model, particularly for Sicily and Sardinia. Students from Palermo, Catania, Messina, and Cagliari show strong connections with large cities such as Rome and Milan. Furthermore, results show historical links between Sardinia and Tuscany date back to the 1950s, and the enduring connections between Apulian students and universities in Emilia-Romagna, Veneto, and Lombardy have been evident since the 1970s. In Sicily, mobility patterns towards the Polytechnic of Turin began in the 1970s and persist today. These patterns underscore the influence of prior migrants on the mobility decisions of university students in Southern Italy, highlighting a potential chain migration effect.

Towards more equitable cooling services of urban parks: Linking cooling effect, accessibility and attractiveness

Global warming and rapid urbanization have caused frequent occurrences of heat waves and urban heat island effect, presenting a significant threat to health of urban residents. Researches have indicated that cooling services provided by parks are essential in alleviating impact of heat wave events and urban heat island effect. However, previous researches on park cooling services center around cooling effect, with a lack of exploration regarding the fairness of such services. To fill this gap, this study quantifies the level of equity in cooling services in 18 parks in the core area of Hangzhou. Through this study, we hope to clarify the current situation of fairness of cooling services in urban parks and provide fairer park cooling services through scientific and reasonable park layouts. This will alleviate the threat of rapid urbanization and climate change to urban residents, and make the urban environment develop in a more livable direction. We assessed the cooling effect using remote sensing and the ArcGIS platform to screen parks with cooling effect and to quantify their cooling service efficiency. We utilized spatial network analysis to quantify the accessibility and origin-destination matrix data to quantify the attractiveness to reflect the level of park cooling services. The results reveal that 18 parks exhibit a noticeable cooling effect, albeit with variations observed among parks. The percentage of urban parks with low accessibility is 77.80%, indicating that the distribution of accessible space presents an uneven status quo. In addition, 72.20% of parks have low attractiveness of cooling services, indicating that some parks have insufficient attractiveness of cooling services. Based on each indicator of cooling services, we categorize urban parks into four types based on supply and demand, and propose adaptive planning measures and intervention strategies to provide a reference for equitable distribution of cooling services in urban parks.

Data driven origin–destination matrix estimation on large networks—A joint origin–destination-path-choice formulation

This paper presents a novel approach to data-driven time-dependent origin–destination (OD) estimation using a joint origin–destination-path choice formulation, inspired by the well-known equivalence of doubly constraint gravity models and multinomial logit models for joint O–D choice. This new formulation provides a theoretical basis and generalizes an earlier contribution. Although including path choice increases the dimensionality of the problem, it also dramatically improves the quality of the data one can directly use to solve it (e.g. measured path travel times versus coarse centroid-to-centroid travel times); and opens up possibilities to combine different assimilation techniques in a single framework: (1) fast shortest path set computation using static (e.g. road type) and dynamic (speed, travel time) link properties; (2) predicting a “prior OD matrix” using the resulting path-shares and (estimated or measured) production and attraction totals; and (3) scaling/constraining this prior using link flows (informative of demand). If the resulting system of equations has insufficient rank, we use principal component analysis to reduce the dimensionality, solve this reduced problem, and transform that solution back to a full OD matrix. Comprehensive tests and sensitivity analysis on 7 networks with different sizes and characteristics give an empirical underpinning of the extended equivalence principle; demonstrate good accuracy and reliability of the OD estimation method overall; and suggest that the method is robust with respect to major assumptions and contributing factors.

Analysis of Decarbonization Potential in Mobility Sector with High Spatial Resolution: Study Case of the Metropolitan Area of Valencia (Spain)

This study addresses the urgent need for sustainable urban mobility through a comprehensive analysis of decarbonization potential in the metropolitan area of Valencia, Spain. The research is motivated by the imperative to mitigate climate change amidst high traffic and congestion levels. Utilizing the Origin–Destination matrix from the Valencian community’s mobility plan, the study prioritizes displacements for substitution by sustainable alternatives. Through a detailed case study analysis, critical areas for decarbonization are identified, and practical strategies to reduce carbon emissions are proposed. The methodology encompasses a thorough literature review on decarbonization measures, examination of existing plans, and analysis of sustainable trips with high spatial resolution using geographic information systems (GISs). The study identifies trips with significant decarbonization potential and assesses the current public transportation network. Spatial analyses illustrate demand patterns for sustainable transport options, emphasizing the need for tailored strategies. Key findings highlight the role of private and commercial transport in CO2 emissions, advocating for targeted interventions to enhance active transport infrastructure, promote carpooling, adopt low-emission vehicles, and improve public transport accessibility and efficiency.

Real-Time Estimation of Origin–Destination Matrices Using a Deep Neural Network for Digital Twins

The digital twin, a real-time replica of physical systems, has garnered attention as a promising tool to strategize and evaluate solutions for complex real-world issues. However, developing digital twins in the field of transportation faces significant challenges related to the real-time estimation of dynamic origin–destination (OD) matrices constrained by computation time. To bridge this gap, microscopic traffic simulations with real-time synchronization are being researched. Nonetheless, the computational issue persists, emphasizing the need for more efficient OD estimation methods. In this regard, our objective is to reduce computation time in simulation-based methods by developing a data-driven metamodel using a deep neural network. The proposed model serves to map the correlation between the OD matrix and detector data. This model simplifies the computational process using hidden layers, rather than calculating complex interactions between vehicles in the traffic simulation. Compared to conventional methods, we evaluate the capability to estimate a reasonable OD matrix within a restricted time and validate our approach using detector data from Daejeon City, South Korea. The findings indicate that by combining our data-driven metamodel with the simultaneous perturbation stochastic approximation, it becomes feasible to estimate a reasonable OD matrix within a stipulated time frame, compared to the conventional method. Given a 1-min time frame, the proposed method outperforms the conventional simulation-based method by improving the calibration performance of traffic flow by 44.5 percentage points. This paper proposes a practical and versatile approach for real-time OD estimation, laying the foundation for extending microscopic traffic simulation into the digital twin.

A study of size threshold for cooling effect in urban parks and their cooling accessibility and equity

Rapid urbanization has led to increasingly prominent urban heat island phenomena and social inequality. It is urgent to quantify the threshold area of urban parks from multiple perspectives to maximize the cooling effect and improve the equity of park cooling services. Using 33 urban parks in Harbin City as research objects, four indices, i.e., park cooling intensity (PCI), park cooling distance (PCD), park cooling area (PCA), and park cooling efficiency (PCE), were used to explore the park cooling effect and the threshold value of efficiency (TVoE) of the size. The OD (origin–destination) matrix model was constructed to assess the spatial accessibility from the community to the cooling range. The Gini coefficient was used to assess the equity of cooling range accessibility. The relative contribution of each influencing factor to the cooling indicator was quantified through regression modeling. The results showed that the average PCI was 3.27 ℃, the average PCD was 277 m, the average PCA was 115.35 ha, and the average PCE was 5.74. Gray space area was the dominant factor for PCI, PCD, and PCA (relative contributions of 100%, 31%, and 19%, respectively). Park area was the dominant factor for PCE (relative contribution of 28%). The TVoE of park sizes based on PCA and PCE were calculated as 82.37 ha and 2.56 ha, respectively. 39.2% and 94.01% of communities can reach cooling ranges within 15 min in walk mode and transit mode, respectively. Approximately 18% of neighborhood residents are experiencing severe inequities in cooling range accessibility. This study can guide park design that maximizes cooling effects, as well as inform city planners on more equitable allocation of urban park resources.

Origin–destination matrix estimation for public transport: A multi-modal weighted graph approach

Estimating the large-scale Origin–Destination (OD) matrices for multi-modal public transport (PT) in different cities can vary largely based on the network itself, what modes exist, and what traffic data is available. In this study, to overcome the issue of traffic data unavailability and effectively estimate the demand matrix, we employ several data sets like the total boarding and alighting, smart card as well as the General Transit Feed Specification (GTFS) in order to capture the PT dynamic patronage patterns.First, we propose a new method to model the dynamic large-scale stop-by-stop OD matrix for PT networks by developing a new enhancement of the Gravity Model via graph theory and Shannon’s entropy. Second, we introduce a method entitled “Entropy-weighted Ensemble Cost Features” that incorporates diverse sources of costs extracted from traffic states and the topological information in the network, scaled appropriately. Last, we compare the efficiency of a single travel cost versus various combinations of travel costs when using traditional methods like the Traverse Searching and the Hyman’s method, alongside our proposed “Entropy-weighted” method; we demonstrate the advantages of using topological features as travel costs and prove that our method, coupled with multi-modal PT OD matrix modelling, is superior to traditional methods in improving estimation accuracy, as evidenced by lower MAE, MAPE and RMSE, and reducing computing time.

Urban Mobility Pattern Detection: Development of a Classification Algorithm Based on Machine Learning and GPS.

This study introduces an innovative algorithm for classifying transportation modes. It categorizes modes such as walking, biking, tram, bus, taxi, and private vehicles based on data collected through sensors embedded in smartphones. The data include date, time, latitude, longitude, altitude, and speed, gathered using a mobile application specifically designed for this project. These data were collected through the smartphone's GPS to enhance the accuracy of the analysis. The stopping times of each transport mode, as well as the distance traveled and average speed, are analyzed to identify patterns and distinctive features. Conducted in Cuenca, Ecuador, the study aims to develop and validate an algorithm to enhance urban planning. It extracts significant features from mobility patterns, including speed, acceleration, and over-acceleration, and applies longitudinal dynamics to train the classification model. The classification algorithm relies on a decision tree model, achieving a high accuracy of 94.6% in validation and 94.9% in testing, demonstrating the effectiveness of the proposed approach. Additionally, the precision metric of 0.8938 signifies the model's ability to make correct positive predictions, with nearly 90% of positive instances correctly identified. Furthermore, the recall metric at 0.83084 highlights the model's capability to identify real positive instances within the dataset, capturing over 80% of positive instances. The calculated F1-score of 0.86117 indicates a harmonious balance between precision and recall, showcasing the models robust and well-rounded performance in classifying transport modes effectively. The study discusses the potential applications of this method in urban planning, transport management, public transport route optimization, and urban traffic monitoring. This research represents a preliminary stage in generating an origin-destination (OD) matrix to better understand how people move within the city.

Spatial accessibility and inequality analysis of rabies-exposed patients to rabies post-exposure prophylaxis clinics in Guangzhou City, China

BackgroundThe incidence of rabies exposure is high and increasing in China, leading to an urgent demand of rabies post-exposure prophylaxis (PEP) clinics for the injured. However, the spatial accessibility and inequality of rabies-exposed patients to rabies PEP clinics is less known in China.MethodsBased on rabies exposure data, PEP clinic data, and resident travel origin-destination (OD) matrix data in Guangzhou City, China, we first described the incidence of rabies exposure in Guangzhou from 2020 to 2022. Then, the Gaussian two-step floating catchment area method (2SFCA) was used to analyze the spatial accessibility of rabies-exposed patients to rabies PEP clinics in Guangzhou, and the Gini coefficient and Moran’s I statistics were utilized to evaluate the inequality and clustering of accessibility scores.ResultsFrom 2020 to 2022, a total of 524,160 cases of rabies exposure were reported in Guangzhou, and the incidence showed a significant increasing trend, with an average annual incidence of 932.0/100,000. Spatial accessibility analysis revealed that the overall spatial accessibility scores for three scenarios (threshold of driving duration [d0] = 30 min, 45 min, and 60 min) were 0.30 (95% CI: 0.07, 0.87), 0.28 (95% CI: 0.11, 0.53) and 0.28 (95% CI: 0.14, 0.44), respectively. Conghua, Huangpu, Zengcheng and Nansha districts had the higher accessibility scores, while Haizhu, Liwan, and Yuexiu districts exhibited lower spatial accessibility scores. The Gini coefficient and Moran’s I statistics showed that there were certain inequality and clustering in the accessibility to rabies PEP clinics in Guangzhou.ConclusionsThis study clarifies the heterogeneity of spatial accessibility to rabies PEP clinics, and provide valuable insights for resource allocation to achieve the WHO target of zero human dog-mediated rabies deaths by 2030.

Evaluating the Effect of Multiple Data Types on a Large Scale Static Origin–Destination Matrix Estimation, Case Study of Tehran, Iran

Evaluating the Effect of Multiple Data Types on a Large Scale Static Origin–Destination Matrix Estimation, Case Study of Tehran, Iran

Hybrid gradient-swarm intelligence to improve quality of solutions for origin–destination matrix adjustment problem

Hybrid gradient-swarm intelligence to improve quality of solutions for origin–destination matrix adjustment problem

Real-time prediction of transit origin–destination flows during underground incidents

Efficient transportation planning and management are critical for ensuring the smooth operation of rail transit systems, particularly in urban areas with high passenger demand. Real-time prediction of transit flow during underground incidents is vital for ensuring passenger safety, minimizing disruptions, and optimizing resource allocation. This study presents a comprehensive framework to address the considerable challenges associated with short-term origin–destination (OD) flow prediction, particularly during incidents. We propose integrating convolutional long short-term memory cells with self-attention cells to learn the long-range spatiotemporal information of historical OD flows. To overcome the absence of a real-time OD matrix, we introduce an improved graph convolution operation, considering the local connection of ridership, to capture vital spatiotemporal interaction patterns. Quantitative and qualitative analyses were conducted to evaluate the performance of the framework, providing insights into the role of each module. Experimental results suggest that incorporating incident matrix theory enhances the accuracy of capturing declining OD flows during incidents by up to approximately 25%. Furthermore, integrating external features like weather and time comprehensively assesses the impact of erratic change factors, especially during off-peak periods. Additionally, introducing more than one cycle of historical OD flow as an input may introduce noise, and increased attention is needed for OD flows with average daily flows exceeding 30 passengers. Designed with a high degree of modularity and adaptability, the proposed framework serves as a valid tool for transportation professionals and researchers to address evolving challenges in the management and operation of urban transportation systems.

Assessment of the Impact of Intercity Bus Station Relocation on Passenger Flows in Urban Public Transport

Problem. The analysis of existing studies on transport hub operation in a city as well as on the methods for modelling the transport demand they generate showed that there are no specific recommendations for assessing the impact of the intercity bus station location on the trip patterns in urban transport system. This caused the need to search for a tool suitable to assess the influence of intercity bus station relocation on passenger service and passenger flows in the urban transport system. This paper shows the results of addressing this issue respective to urban public transport operation. Goal. The goal of the paper is to model the passenger flows to and from intercity bus stations considered transport anchors in urban public transport system as well as estimate the impact of station relocation on passenger flows and quality of service. Methodology. To achieve the stated goal, a survey of intercity passengers at the bus stations in the city of Rivne was made. This allowed for collecting the distances of the trips to and from existing bus stations and fitting the theoretical distribution to describe them. This distribution was used as the deterrence function during trip matrix calculation, which was required for the development of the public transport model. The model made it possible to quantify the potential impact of the Bus Station “Rivne” relocation on the urban transport system. Results. The considered bus station relocation can cause a decrease in the quality of service for passengers travelling to and from the station by public transport due to a probable significant increase in the average travel time and distance, the number of transfers, the passenger hours spent on travelling, and the passenger-kilometres covered to ensure the trips under study. Originality For the first time, the quantitative estimation of the impact of bus station relocation based on the regularities in the lengths of the trips to and from the stations was obtained. Practical value. The obtained trip length distribution can be used to calculate the origin-destination matrix, develop the public transport model, and quantify the probable consequences of the transport hub relocation.

Impact of Traffic Flow Rate on the Accuracy of Short-Term Prediction of Origin-Destination Matrix in Urban Transportation Networks

Information about spatial distribution (OD flows) is a key element in traffic management systems in urban transport networks that enables efficient traffic control and decisions to redirect traffic to less congested sections of the network in emergencies. With the development of modern techniques of remote sensing, more and more advanced methods are used to measure traffic and determine OD flows. However, they may produce results with different levels of errors caused by various factors. The article examines the impact of traffic volume and its variability on the error values of short-term prediction of the OD matrix in the urban network. The OD flows were determined using a deep learning network based on data obtained from video remote sensing devices. These data were recorded at earlier intervals concerning the forecasting time. The extent to which there is a correlation between the size of OD flows and the prediction error was examined. The most frequently used measure of prediction accuracy, i.e., MAPE (mean absolute percentage error), was considered. The analysis carried out made it possible to determine the ranges of traffic flow rate for which the MAPE stabilizes at the level of approximately 6%. A set of video remote sensing devices was used to collect spatiotemporal data. They were located at the entrances and exits from the study area on important roads of a medium-sized city in Poland. The conclusions obtained may be helpful in further research on improving methods to determine OD matrices and estimate their reliability. This, in turn, involves the development of more precise methods that allow for reliable traffic forecasting and improve the efficiency of traffic management in urban areas.

Real Entrance Capacity of a Single-Lane Roundabout Owing to Congestion on the Circular Lane

In contrast to conventional methods, this study took a holistic approach to investigating roundabout capacity. Conventional methods aim to identify the capacities of each single entrance, without any consideration of mutual interactions between all the entrances and exits of the roundabout. This study investigated the interrelationships between traffic operations at the elements of a single-lane roundabout based on probabilistic considerations obtained from queueing theory. Here the spread of potential congestion on the circle plays a decisive role. As a starting point, the elements of the roundabout were defined as points of conflict. Equations for the capacity of these points were then derived. These equations were combined into an iterative procedure that took into account the potential congestion on the circle. As a result, based on a given origin–destination matrix of traffic volumes, the entry capacities of the roundabout were calculated. These were then applied to calculate estimates for delays and queue lengths in the conventional way. Application of the model for several examples—two of which are described in this paper—showed rather drastically that conventional evaluations of traffic performance at roundabouts may be misleading, with overestimated capacity and underestimated delays. This happens preferably in a range with volume/capacity values above 0.7 and under significant pedestrian traffic. The study underlines that the current assessment methods of analytical performance at roundabouts fall short.

Spatio-Temporal Self-Attention Network for Origin–Destination Matrix Prediction in Urban Rail Transit

Short-term origin–destination (OD) prediction in urban rail transit (URT) is vital for improving URT operation. However, due to the problems such as the unavailability of the OD matrix of the current day, high dimension and long-range spatio-temporal dependencies, it is difficult to further improve the prediction accuracy of an OD matrix. In this paper, a novel spatio-temporal self-attention network (SSNet) for OD matrix prediction in URT is proposed to further improve the prediction accuracy. In the proposed SSNet, a lightweight yet effective spatio-temporal self-attention module (STSM) is proposed to capture complex long-range spatio-temporal dependencies, thus helping improve the prediction accuracy of the proposed SSNet. Additionally, the finished OD matrices on previous days are used as the only data source without the passenger flow data on the current day in the proposed SSNet, which makes it possible to predict the OD matrices of all time intervals on the current day before the operation of the current day. It is demonstrated by experiments that the proposed SSNet outperforms three advanced deep learning methods for short-term OD prediction in URT, and the proposed STSM plays an important role in improving the prediction accuracy.

Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands

Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users’ dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified.

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.

Origin-destination matrix estimation in Solo agglomeration with the maximum likelihood method

The movement of vehicles is one form of fulfilling community needs. High vehicle movement can cause transportation problems, one of which is congestion. To overcome this problem, planning a transportation system is essential. Using the EMME/4 program, we analyzed the distribution of movements in the Origin Destination Matrix (MAT) using a gravity model. Using the User’s Equilibrium method, the study aims to determine the parameter β, data validity value (R2), and movement patterns. The research study area was carried out in Solo Agglomeration with 19 zones. The results indicate a β value of 0.0796 (R2 = 0.817), with an estimated Solo Agglomeration movement of 34428 passenger-car-units per hour in 2023. The most extensive distribution of movements occurred within internal movements, making up 62.85%, while the smallest were external-external movements, accounting only for 4.01%.

The model validation for trip assignment using all or nothing and user’s equilibrium method

In transportation planning, an accurate transportation model is crucial. The trip-based approach is widely used, which relies on the four-step model (FSM) for transportation modeling. The model generates an origin-destination (O-D) Matrix as the starting point, which is then used to determine traffic flow on the road network through a trip assignment. This approach is superior because the (O-D) Matrix is a result of the interaction between land use and accessibility. In Boyolali Regency, research was conducted using traffic volume data that reflected the conditions at the beginning of 2023. The gravity model was used to determine trip distribution for this study, and the Least Squares method and negative exponential function calibrated the model. The study aim is to compare the performance of All or Nothing and User’s Equilibrium assignment methods using the EMME/3 software. The results showed that All or Nothing had a higher R2 value of 0.913 compared to User’s Equilibrium’s 0.911, suggesting that the All or Nothing assignment method is more suitable for Boyolali Regency. The research predicted that the total movement of Boyolali Regency in 2023 would be 12,354 pcu/hour, with most of the movement being external-external. As Boyolali Regency is a transit area, vehicles only pass through it.