Origin Destination Research Articles

Estimation of dynamic Origin–Destination matrices in a railway transportation network integrating ticket sales and passenger count data

Accurately estimating Origin–Destination matrices is a pressing challenge in transportation management and urban planning. However, traditional methods like travel surveys have limitations in availability and comprehensiveness, which have been further exacerbated by the recent changes in mobility patterns induced by the COVID-19 pandemic. To address this issue, we focused on the Trenord railway network in Lombardy, Italy, and developed an innovative pipeline to integrate ticket and subscription sales and Automated Passenger Counting data using the Iterative Proportional Fitting algorithm. By effectively navigating the complexities of diverse and incomplete data sources, our approach showcases adaptability across various transportation contexts. Our research offers a valuable tool for operators, policymakers, and researchers, bridging the gap between data availability and the need for precise OD matrices. Additionally, we emphasise the potential of dynamic OD matrices and showcase methods for detecting anomalies in mobility trends, interpreting them in the context of events from the last months of 2022.

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.

Exploring the association between multi-mode transport and the built environment: A comparative study of metro, bus, taxi, and shared bike use

Urban transportation plays a pivotal role in sustainable city planning, with multiple transportation modes coexisting to achieve sustainable goals. Despite the extensive use of various mobility datasets to analyze mobility behaviors, comparative studies focusing on multiple transportation modes for origin-destination (OD) trips remain scarce. This study, conducted in Shenzhen, presents three in-depth comparative analyses: (1) variations in the utilization of metros, buses, taxis, and shared bikes; (2) the clustering patterns of OD flows across different modes; and (3) the influence of the built environment at origins and destinations on OD flows. The findings reveal that the use of public transportation and taxis mirrors the city's polycentric layout, with each transportation mode fulfilling distinct roles in linking disparate urban areas. The nonlinear effect of the built environment on OD flows exhibits mode-specific variations, particularly in relation to thresholds. Additionally, network topology characteristics are identified as significant factors in explaining OD flows for all modes. Despite observed differences in weekday and weekend OD flow clustering, the built environment consistently correlates with daily OD flows. These findings provide valuable insights to inform mode-specific strategies that enhance sustainable urban development.

Estimating traffic demand of different transportation modes using floating smartphone data

Traffic demand is crucial for traffic planning, helping to understand traffic volume, identify congestion points, and develop strategies for efficient and sustainable transportation. This paper introduces a method to estimate origin-destination (OD) matrices for drivers, cyclists, and pedestrians using floating smartphone data (FSD) and the information minimisation (IM) model. The FSD is used in the gravity model (GM) to provide more accurate inputs for the IM model by estimating an a priori matrix and traffic demand at intersections. Additionally, FSD allowed a direct route choice proportions deriviation without needing a traffic assignment model. The proposed estimation model was tested with real data and analyzed in a simulation environment, examining the impact of increasing the penetration rate (PR) of FSD. Results indicate that traffic demand in the study area was accurately estimated with an average Rxy value of 92%, aligning well with simulation results.

Generating sparse origin–destination flows on shared mobility networks using probabilistic graph neural networks

Shared mobility services, such as bike sharing, have gained immense popularity and emerged as an integral part of sustainable urban mobility solutions. The planning of such systems requires forecasting the potential origin–destination (OD) flows between mobility sites (e.g., bike sharing stations) within the proposed network. Existing methods primarily focus on mobility flows between large regions, and do not generalize well to detailed planning applications due to the high spatial resolution required, with increased uncertainty and data sparsity. This study proposes a zero-inflated negative binomial graph neural network (ZINB-GNN) to generate sparse OD flows while capturing complex spatial dependencies. To reflect sparsity, OD flows are modeled as following ZINB distributions parameterized via feed-forward networks. To capture spatial dependencies, localized graphs are constructed to represent proximity between OD pairs, with spatial features encoded using GNNs. ZINB-GNN is validated through a case study of the bike sharing system in New York City. The results verify its prowess in both prediction accuracy and uncertainty quantification under real-world network expansion scenarios. We also demonstrate its interpretability by revealing important factors affecting OD flows. These findings can directly inform the planning of bike sharing systems, and the methodology may be adapted for other shared mobility systems.

The role of everyday mobility in adaptation to air pollution hazard: A mixed-method approach combining big and traditional data

The empirical study aims to examine how residents perceive and respond to air pollution in their daily lives, whether they use mobility as an adaptation strategy to avoid or mitigate their exposure, and how socioeconomic and demographic factors modify such responses in mobility. To this end, this study conducts an analysis in the city of Chengdu using a mixed-method approach combining surveys and large-scale mobile phone data. It is found that most at-risk individuals take protective measures, and some choose to change mobility patterns to protect themselves from exposure to air pollution. Regression results suggest that engagement with air quality information and the perceived effectiveness of protective measures are the most important predictors of human mobility changes in response to air pollution. The use of mobility as an adaptation strategy occurs despite the availability of in-situ strategies in general, while low-cost and effective in-situ adaptation choices and high-cost mobility strategies are considered as substitutes. Using changes in origin–destination trips in Sichuan generated from 5,393,739 cellphone users in Chengdu, this study reveals that an increase in the difference of the air quality index at origin versus at destination is associated with more trips from the origin to the destination, and travelers are more sensitive to air quality at origin that drives them to escape from the polluted areas. The findings suggest the (re)production of inequality and marginalization of some population groups in hazard adaptation.

Location method for emergency rescue node on expressways based on spatio-temporal characteristics of vehicle operation

Expressway networks are continuously developing and emergency rescue demand is increasing proportionately. The location of expressway emergency rescue nodes needs refinement to meet changing requirements. In this study, the expressway was modeled as an expressway network. The differences in the origin destination (OD) distribution matrices for working days and major holidays were used as the bases for determining the need for temporary emergency rescue nodes. Overlapping and non-overlapping community detection algorithms were used to extract the distribution characteristics of OD during both day categories. These distributions were used to determine permanent and temporary emergency rescue sites. In this study, we considered the differences in traffic volume, distance, and impact of four vehicle types on traffic accidents to select the location of emergency rescue nodes, and allocate emergency resources. An emergency rescue node selection model for an expressway network was established based on spatio-temporal characteristics. The results based on a regional example determined that 22 permanent and 25 temporary emergency rescue nodes were appropriate. The average rescue time for traffic accidents during working days and major holidays compared to the P-center location model, was reduced by approximately 27.08% and 6.70%, respectively. The coefficient of variation of emergency rescue time was reduced by approximately 28.22% and 21.41%, respectively. The results indicated that the model satisfied the expressway emergency rescue demand requirements, and improved the rationality of the rescue center node layout.

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.

Tensor Decomposition for Spatiotemporal Mobility Pattern Learning with Mobile Phone Data

Detecting urban mobility patterns is crucial for policymakers in urban and transport planning. Mobile phone data have been increasingly deployed to measure the spatiotemporal variations in human mobility. This work applied non-negative Tucker decomposition (NTD) to mobile phone-based origin–destination (O-D) matrices to explore mobility patterns’ latent spatial and temporal relationships in the province of Liège, Belgium. Four [Formula: see text] traffic tensors have been built for one regular weekday, one regular weekend day, one holiday weekday, and one holiday weekend day, respectively. The proposed method inferred spatial clusters and temporal patterns while interpreting the correlation between spatial clusters and temporal patterns through geographical visualization. As a result, we found the similarity of O-D and destination–origin (D-O) patterns and the symmetry for the trips of the temporal patterns with evening peak and morning peaks on the weekday. Moreover, we investigated the attraction of different spatial clusters with two temporal patterns on a regular weekday and validated the reconstructed demand using population counts and commuting matrices. Finally, the differences in spatial and temporal interactions have been addressed in detail.

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.

Optimization of Green Multimodal Transport Schemes Considering Order Consolidation under Uncertainty Conditions

As society becomes increasingly concerned with sustainable development, the demand for high-efficiency, low-cost, and green technology makes air–land multimodal transportation one of the effective means of fast freight transportation. In the actual transportation business, some orders will have overlapping transportation routes, and transporting each order separately will result in resource waste, high costs, and carbon emissions. This paper proposes a multimodal transportation scheme optimization model considering order consolidation to improve transport efficiency and reduce costs and carbon emissions. An improved genetic algorithm incorporating the ride-sharing scheduling method is designed to solve the model. The results show that order consolidation will reduce multimodal transport costs and carbon emissions but increase transportation time slightly, and the advantages in cost and carbon emission reduction will vary with origin–destination scenarios, which are ranked in order of single-origin single-destination, single-origin multi-destinations, multi-origin single-destination, and multi-origin multi-destination. For the fourth scenario, the cost and carbon emissions decrease by 16.6% and 26.69%, respectively, and the time increases by 5.56% compared with no consolidation. For the sensibility of customer demands, it is found that order consolidation has the advantage for price-sensitive, time- and price-sensitive, and time- and carbon emission-sensitive customers; however, it is specifically beneficial for time-sensitive customers only in single-origin single-destination scenarios.

Connectivity reliability evaluation and most reliable shipping route choice in a seaborne crude oil network

The connectivity reliability of strait and canal nodes in seaborne crude oil networks is uncertain because of various risk factors. Existing studies have mainly focused on road networks and often ignored real-world factors by assuming fixed and identical values for the connectivity reliability of each node, leading to inaccurate estimations. Few studies have considered node reliability when identifying the most reliable routes for oil shipments in response to various external and changing risks. To address these limitations, we first establish new connectivity reliability evaluation methods for both nodes and networks. Then, we develop the α-most reliable shipping route and the very most reliable shipping route models using uncertain programming to dynamically identify the most reliable routes for crude oil, ensuring timely and safe transportation. We apply these models to China's seaborne network of imported crude oil. The results show a network connectivity reliability of 0.6228, which is impacted by unreliable origin–destination pairs in the Middle East. The risk values of the most reliable oil shipping routes vary regionally, with higher values in Africa and the Middle East than in Asia and Latin America. As node risk increases, regional disparities also increase. These findings will aid in the development of energy transportation and import strategies to enhance transportation reliability.

A stochastic ridesharing user equilibrium model with origin-destination-based ride-matching strategy

Ridesharing, as an emerging mode of modern urban transportation, has gained widespread popularity due to its ability to improve convenience and reduce the expenses in daily travel. To capture its impact on the network-wide traffic flow pattern, this paper develops a new variational inequality (VI) formulation for modeling the stochastic ridesharing user equilibrium (SRUE) problem considering an origin–destination (O-D) -based ride-matching strategy. The proposed SRUE problem is formulated based on a hierarchical logit choice model to depict the multi-stage decision-making (i.e., role choice, ride-matching, and route choice) of the ridesharing travelers. The SRUE model also reflects a two-sided market equilibrium considering the interaction effect between the ridesharing market and the road traffic equilibrium market. The logit choice model captures the ride-matching problem between the drivers and the passengers traveling on the same/different O-D pairs. Moreover, the lower- and upper-bound constraints for a ridesharing driver picking up passengers are established at the O-D level. For solving the SRUE model with O-D-based ride-matching constraints, the Lagrangian dual method (LDM) is adopted by incorporating the Barzilai-Borwein (BB) step size to update the dual variables, which accelerates the convergence of the LDM. Three numerical examples are conducted to illustrate the features of the proposed SRUE model and demonstrate the performance of the solution algorithm.

Flowers and Bees: Spatial Network Effects in the Adoption of a Sharing-Economy Platform

This article empirically analyzes the spatiotemporal diffusion of a car-sharing platform that connects consumers with individual car owners. The authors develop a model of adoption by both consumers and providers that accommodates network effects across locations, as consumers travel to pick up cars, and information asymmetry, as only the providers’ locations are shown on a map. They apply it to data at the earliest stage of the platform and find that proximity and consumer mobility play a significant role in the spatial network effects of existing providers on consumers across locations. Consequently, the impact of additional supply at a destination on consumer adoptions at an origin varies by origin–destination pair and is asymmetric, as certain destinations draw more visitors than others and given differences in local characteristics. In contrast, existing consumers have a limited impact on provider adoptions. Through seeding experiments, the authors investigate how the geographic distribution of initial participants impacts the platform diffusion and find that targeting the supply side in big cities leads to the highest platform growth. They also use their parameter estimates to measure the long-term impact of a local promotional campaign and show that it is mostly local despite spatial network effects.

Short-Term Origin-Destination Demand Prediction Based on Spatiotemporal Encoder-Decoder Network with a Residual Feature Extractor

Online ride-hailing services play a crucial role in daily transportation, However, challenges persist in certain regions with limited access, and drivers encounter difficulties in receiving orders. Accurate prediction of short-term origin-destination (OD) demand is crucial for addressing these issues. This study leverages recent advancements in artificial intelligence and big data to introduce a spatiotemporal encoder-decoder network with a residual feature extractor (RF-STED) for short-term OD demand prediction in online ride-hailing services. The RF-STED model, built on deep learning models such as graph convolutional networks and convolutional long short-term memory (Conv-LSTM), includes spatiotemporal networks, encoding layers, and a residual feature extractor. The spatiotemporal network has two branches: branch one processes multi-pattern OD data using a multi-pattern temporal feature extraction module, utilizing a multi-channel Conv-LSTM to capture temporal correlations. Branch two utilizes a multi-spatial feature extraction module to convert OD pair associations into a spatial topology, extracting multi-spatial correlations. The encoding layer captures spatiotemporal dependencies, while the residual feature extractor decodes compressed vectors back into an OD graph for forecasting future demand. Experiments with a Manhattan taxi dataset in the U.S. show the RF-STED model outperforms 10 baseline models and four ablation models. The results emphasize the model’s strength and robustness in short-term OD flow prediction.

Carbon emission allocation policy making in liner shipping: A novel approach toward equitable and efficient maritime sustainability

Maritime shipping, an integral component of global trade, accounts for nearly 3% of global carbon dioxide emissions and faces significant challenges in mitigating its carbon footprints. This study addresses the shortcomings of current carbon emission surcharges levied by shipping companies and the deficiencies of allocation methods utilized by the government, such as the inequity of uniform carbon emission charges across diverse origin–destination (od) pairs and the reliance on potentially inaccurate self-reported emissions data to assign carbon emission allowances. We propose a novel approach to carbon emission allocation per twenty-foot equivalent unit (TEU) container per od pair in liner shipping from the perspective of the government, aiming to enhance government oversight and minimize social costs. Leveraging a bi-level programming model, our research navigates the complex interplay between government regulatory objectives and shipping companies’ operational decisions, identifying a socially optimal carbon allocation policy. Our approach introduces three innovative carbon allocation methods that advance beyond the traditional metric that typically focuses solely on emissions and cargo volume. Our first two innovative methods integrate shipping distances into the carbon allocation process, specifically considering both the actual traveling distance and the shortest sailing distance. Furthermore, we propose the third innovative “harmonic distance,” which is a convex combination of the actual traveling distance and the shortest sailing distance. This measure effectively reflects both the operational realities and equity concerns associated with shipping services. Our findings reveal the effectiveness of utilizing the shortest sailing distance for carbon allocation per TEU per od pair, offering a straightforward, route-independent, and equitable solution. This approach diverges from current industrial practices, favoring a fairer emission allocation basis and balancing the interests of governments, shipping companies, and consumers. Through comprehensive experiments, we demonstrate the superiority of this method in minimizing total social costs and providing a viable path toward environmental sustainability and economic efficiency in liner shipping.

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.

Exploring usage pattern variation of free-floating bike-sharing from a night travel perspective

Free-floating bike sharing (FFBS) attracts increasing research focusing on usage patterns, determining factors, and integrated transportation. However, existing researchers tend to overlook the variation in usage characteristics over various time ranges, particularly the usage pattern at night. This paper is conducted to fill the gap through a series of analysis approaches on FFSB in Beijing. The characteristics of the usage pattern, including time-varying usage and traveling distance distributions, are initially illustrated. Subsequently, the spatial patterns of FFBS are visualized and thoroughly analyzed in different time ranges and origin-destination (O-D) flows. A statistical model evaluating the environmental effects of FFBS trips revealed the source of FFBS usage. In addition to focusing on the nighttime, the usage patterns varying day and night are compared through the analysis. The findings explain the usage pattern variation and the unique pattern at night, providing valuable insight for improving the management of the FFBS system.

Linear regression coupled Wasserstein generative adversarial network for direct demand modeling of ride-hailing trips in Chicago and Austin

ABSTRACT Accurate estimation of ride-hailing demand and understanding of its influencing factors are necessary for modern-day transportation planning. Although modern machine learning techniques improve the predictive accuracy of zone-to-zone direct demand models, they lack inference ability due to their complex model structure. Furthermore, zone-to-zone direct demand models suffer from limited sample size for machine learning due to fixed number of origin-destination (OD) pairs. To overcome these limitations, we propose a linear regression coupled Wasserstein generative adversarial network (LR-WGAN) for direct demand modeling of zone-to-zone ride-hailing trips that captures both linear explanatory components and non-linear patterns and improves the predictive accuracy by generating data to expand the OD sample size. The proposed LR-WGAN is found to significantly improve the predictive accuracy of OD ride-hailing demand in Chicago and Austin. Furthermore, the linear explanatory component of the model is utilized to draw inferences regarding the relationship between OD ride-hailing demand and predictor variables.

Distributionally robust origin–destination demand estimation

Gaining a good understanding of the travel demands of a city or region is extremely important for many transportation applications. For stochastic origin–destination (OD) estimation problems, an accurate distribution assumption or observation of OD estimates or data is usually desired but not always available. In this paper, we establish a novel two-stage OD estimation framework based on distributionally robust optimization (DRO) and quasi-sparsity property of large-scale OD demand matrices. The proposed two-stage Distributionally Robust Quasi-Sparsity OD estimation (DR-QSOD) model does not require an accurate or complete distribution assumption of estimates/data. Numerical results demonstrate that DR-QSOD model outperforms stochastic QSOD model in estimating OD demands when the distribution assumption of data is biased. This paper also discusses two different approaches to solve the DR-QSOD model as well as compares their computational efficiency. In addition, DR-QSOD model is shown to keep relatively high quasi-sparsity consistency, which also brings lots of meaningful practical insights.