Origin Destination Research Articles

Incorporating policy mixes to multimodal network capacity considering second-best constraints

In the pursuit of capacity-oriented urban sustainable development (SD), this study aims to enhance the multimodal transport network by making it more accessible, efficient, and environmentally friendly. A sustainable framework combining policy mixes and second-best constraints is developed as a multimodal transportation network capacity (TNC) model with the bi-level programming (BLP) formulation. The upper-level model is to maximise the total origin-destination (OD) demand with considering second-best constraints. The lower-level model is a combined mode split and traffic assignment with elastic demand (CMSTA-ED) model which considers policy mixes. Numerical experiments are conducted to demonstrate the effectiveness of the hybrid PSO–GWO algorithm, the synergy of the method for selecting and mixing policy instruments based on the proposed assessment system, and a methodology to establish a policy mix. The findings of this research offer practical guidance for formulating well-informed and no-redundant policy mixes, contributing to the improvement of sustainable multimodal transportation network performance.

Optimisation models for estimating public transport OD matrices using different data types

Efficient public transport systems rely on origin–destination matrices (ODMs) estimation to accurately capture passenger travel patterns, enabling adjustments to frequencies and lines as needed. In this study, we address the ODM estimation problem by employing multiple bi-level programs that consider an outdated ODM and observed passenger flows on specific transit line arcs. Additionally, we consider various optional data types, including boarding and alighting data, as well as the structure of the outdated ODM and passenger flows, either all, separately, in combination, or none. In our study, we reformulate these bi-level programs into single-level models, and we use a commercial solver to address the problem in benchmark instances. In our analysis, we focus on the impact of incorporating different types of information into the estimation process, leading to valuable insights. We find that considering all the data types leads to a higher accuracy than only a subset of these data types. In particular, focussing only on boarding and alighting data leads to improvements in the estimation process, whereas considering only the structure of the outdated ODM and passenger flows leads to reduced accuracy compared to not incorporating either. The latter highlights the significance of data selection in ODM estimations.

The Propagation of Congestion on Transportation Networks Analyzed by the Percolation Process

Percolation theory has been widely employed in network systems as an effective tool to analyze phase transitions from functional to nonfunctional states. In this paper, we analyze the propagation of congestion on transportation networks and its influence on origin–destination (OD) pairs using the percolation process. This approach allows us to identify the most critical links within the network that, when disrupted due to congestion, significantly impact overall network performance. Understanding the role of these critical links is essential for developing strategies to mitigate congestion effects and enhance network resilience. Building on this analysis, we propose two methods to adjust the capacities of these critical links. First, we introduce a greedy method that incrementally adjusts the capacities based on their individual impact on network connectivity and traffic flow. Second, we employ a Particle Swarm Optimization (PSO) method to strategically increase the capacities of certain critical links, considering the network as a whole. These capacity adjustments are designed to enhance the network’s resilience by ensuring it remains functional even under conditions of high demand and congestion. By preventing the propagation of congestion through strategic capacity enhancements, the transportation network can maintain connectivity between OD pairs, reduce travel times, and improve overall efficiency. Our approach provides a systematic method for improving the robustness of transportation networks against congestion propagation. The results demonstrate that both the greedy method and the PSO method effectively enhance network performance, with the PSO method showing superior results in optimizing capacity allocations. This research is crucial for maintaining efficient and reliable mobility in urban areas, where congestion is a persistent challenge, and offers valuable insights for transportation planners and policymakers aiming to design more resilient transportation infrastructures.

From roads to roofs: How urban and rural mobility influence building energy consumption

Understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

Short-Term Prediction of Origin–Destination Passenger Flow in Urban Rail Transit Systems with Multi-Source Data: A Deep Learning Method Fusing High-Dimensional Features

Short-term origin–destination (OD) passenger flow forecasting is crucial for urban rail transit enterprises aiming to optimise transportation products and increase operating income. As there are large-scale OD pairs in an urban rail transit system, OD passenger flow cannot be obtained in real time (temporal hysteresis). Additionally, the distribution characteristics are also complex. Previous studies mainly focus on passenger flow prediction at metro stations, while few methods solve the OD passenger flow prediction problems of an urban rail transit system. In view of this, we propose a novel deep learning method fusing high-dimensional features (HDF-DL) with multi-source data. The HDF-DL method is combined with three modules. The temporal module incorporates the time-varying, trend, and cyclic characteristics of OD passenger flow, while the latest OD passenger flow time sequence (within 1 h) is excluded from the time-varying characteristics. In the spatial module, the K-means and K-shape algorithms are used to classify OD pairs from multiple perspectives and capture the spatial features, reducing the difficulty of OD passenger flow predictions with large-scale and complex characteristics. Weather factors are considered in the external feature module. The HDF-DL method is tested on a large-scale metro system in China, in which eight baseline models are designed. The results show that the HDF-DL method achieves high prediction accuracy across multiple time granularities, with a mean absolute percentage error of about 10%. OD passenger flow in every departure time interval can be predicted with high and stable accuracy, effectively capturing temporal characteristics. The modular design of HDF-DL, which fuses high-dimensional features and employs appropriate neural networks for different data types, significantly reduces prediction errors and outperforms baseline models.

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.

Passenger Origin-Destination Estimation in Public Transit Using Boarding Count Data

Passenger boarding and alighting data is critical for understanding travel patterns in transit systems, yet many systems only record boarding, making it difficult to track alighting. This study introduces a method to estimate alighting counts using boarding data using transit symmetry at the cluster level. Bus stops are grouped into clusters, assuming equal inflow and outflow, and a gravity model is calibrated to estimate an Origin-Destination (OD) matrix. Using data from three bus routes in Regina, Canada (2017), the method achieved a symmetry ratio of 60% across clusters and a Mean Structural Similarity Measure score of 0.84, validating the accuracy of the OD estimates. This approach provides a practical solution to the challenge of missing alighting data. While effective for single-mode transit systems, future research will expand its application to multi-modal systems and address factors like seasonal and land-use variations, offering a cost-effective alternative for OD estimation.

An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques

Origin-destination (OD) flow modeling is an extensively researched subject across multiple disciplines, such as the investigation of travel demand in transportation and spatial interaction modeling in geography. However, researchers from different fields tend to employ their own unique research paradigms and lack interdisciplinary communication, preventing the cross-fertilization of knowledge and the development of novel solutions to challenges. This article presents a systematic interdisciplinary survey that comprehensively and holistically scrutinizes OD flows from utilizing fundamental theory to studying the mechanism of population mobility and solving practical problems with engineering techniques, such as computational models. Specifically, regional economics, urban geography, and sociophysics are adept at employing theoretical research methods to explore the underlying mechanisms of OD flows. They have developed three influential theoretical models: the gravity model, the intervening opportunities model, and the radiation model. These models specifically focus on examining the fundamental influences of distance, opportunities, and population on OD flows, respectively. In the meantime, fields such as transportation, urban planning, and computer science primarily focus on addressing four practical problems: OD prediction, OD construction, OD estimation, and OD forecasting. Advanced computational models, such as deep learning models, have gradually been introduced to address these problems more effectively. We have constructed the benchmarks for these four problems at https://github.com/tsinghua-fib-lab/OD_benckmark. Finally, based on the existing research, this survey summarizes current challenges and outlines future directions for this topic. Through this survey, we aim to break down the barriers between disciplines in OD flow related research, fostering interdisciplinary perspectives and modes of thinking.

Generalizing OD Maps to Explore Multi-dimensional Geospatial Datasets

ABSTRACT Understanding the mobility of entities in geospatial data is important to many fields, ranging from the social sciences to epidemiology, economics or air traffic control. Visualizing such entities can be challenging as it requires preserving both their explicit properties (spatial trajectories) and their implicit properties (abstract attributes of those trajectories). An existing technique called origin–destination maps preserves both explicit and implicit properties of datasets, using the spatial nesting technique. In this paper, we aim at generalizing this technique beyond an origins-and-destinations dataset (2-attribute datasets), to explore multi-dimensional datasets (N-attribute datasets) with the nesting approach. We present an abstraction framework – we call Gridify – and an interactive open-source tool implementing this framework using several levels of nested maps. We report on several case studies representative of the types of dimensions found in geospatial datasets (quantitative, temporal, discrete, boolean), showing the applicability of this approach to achieve visual exploratory analysis tasks in various application domains.

An Integrated Framework for Estimating Origins and Destinations of Multimodal Multi-Commodity Import and Export Flows Using Multisource Data

Estimating origin-destination (OD) demand is integral to urban, regional, and national freight transportation planning and modeling systems. However, in developing countries, existing studies reveal significant inconsistencies between OD estimates for domestic and import/export commodities derived from interregional input-output (IO) tables and those from regional IO tables. These discrepancies create a significant challenge for properly forecasting the freight demand of regional/interregional multimodal transportation networks. To this end, this study proposes a novel integrated framework for estimating regional and international (import/export) OD freight flows for a set of key commodities that dominate long-distance transportation. The framework leverages multisource data and follows a three-step process. First, a spatial economic model, PECAS activity allocation, is developed to estimate freight OD demand within a specific region. Second, the international (import and export) freight OD is estimated from different zones to foreign countries, including major import and export nodes such as international seaports, using a gravity model with the zone-pair friction obtained from a multimodal transportation model. Third, the OD matrices are converted from monetary value to tonnage and assigned to the multimodal transportation super network using the incremental freight assignment method. The model is calibrated using traffic counts of the highways, railways, and port throughput data. The proposed framework is tested through a case study of the Province of Jiangxi, which is crucial for forecasting freight demand before the planning, design, and operation of the Ganyue Canal. The predictive analytics of the proposed framework demonstrated high validity, where the goodness-of-fit (R2) between the observed and estimated freight flows on specific links for each of the three transport modes was higher than 0.9. This indirectly confirms the efficacy of the model in predicting freight OD demands. The proposed framework is adaptable to other regions and aids practitioners in providing a comprehensive tool for informed decision-making in freight demand modeling.

Cycle route uptake and scenario estimation (CRUSE): an approach for developing strategic cycle network planning tools

This paper describes an approach for developing strategic cycle network planning tools. Based on our experience developing and deploying the Cycle Route Uptake and Scenario Estimation (CRUSE) Tool for Ireland, we outline the underlying methods, including disaggregation of origin–destination data with the open source ‘odjitter’ software, incorporation of additional trip purposes, routing, scenario generation, and development of an intuitive user interface that is tested and used by practitioners. Commissioned by the national infrastructure agency Transport Infrastructure Ireland, CRUSE provides estimates of current and potential future cycling levels under ‘snapshot’ scenarios to inform investment decisions. The publicly available results at https://cruse.bike/ enable planners, engineers, and other stakeholders to make more evidence-based decisions. CRUSE goes beyond previous work by: modeling networks at high spatial resolution; simulating multiple trip purposes (social, shopping, personal utility, recreational, and cycle touring), supplementing official origin–destination datasets on travel for work and education; and providing estimates of ‘quietness’ (a proxy for cyclist comfort and route preference) at the route segment level. Three network types—‘Fastest’, ‘Balanced’, and ‘Quietest’—help plan both arterial and residential cycle networks. Workshops with stakeholders were used to inform the development of the tool. Feedback shows that the tool has a wide range of uses and is already being used in practice to inform urban, inter-urban, and rural cycle network designs. The approach is flexible and open source, allowing the underlying ideas and code to be adapted, supporting more evidence-based and effective cycling policies and interventions internationally.

Flow prediction of mountain cities arterial road network for real-time regulation

This study aims to enhance the understanding of vehicle path selection behavior within arterial road networks by investigating the influencing factors and analyzing spatial and temporal traffic flow distributions. Using radio frequency identification (RFID) travel data, key factors such as travel duration, route familiarity, route length, expressway ratio, arterial road ratio, and ramp ratio were identified. We then proposed an origin–destination path acquisition method and developed a route-selection prediction model based on a multinomial logit model with sample weights. Additionally, the study linked the traffic control scheme with travel time using the Bureau of Public Roads function—a model that illustrates the relationship between network-wide travel time and traffic demand—and developed an arterial road network traffic forecasting model. Verification showed that the prediction accuracy of the improved multinomial logit model increased from 92.55 % to 97.87 %. Furthermore, reducing the green time ratio for multilane merging from 0.75 to 0.5 significantly decreased the likelihood of vehicles choosing this route and reduced the number of vehicles passing through the ramp. The flow prediction model achieved a 97.9 % accuracy, accurately reflecting actual volume changes and ensuring smooth operation of the main airport road. This provides a strong foundation for developing effective traffic control plans.

On the calibration and improvement of human mobility models in intercity transportation system

As one of the research highlights of physics-level modelling, human mobility has generated numerous universal laws over the past decades. However, the majority of research has concentrated on the intracity networks, leaving the intercity mobility systems with insufficient attention despite its increasingly crucial role in the development of urban agglomerations. Related research gaps further extend to the limited understanding of spatiotemporal heterogeneity in intercity human mobility. To bridge these gaps, our study systematically validated and improved the modelling framework of intercity mobility flows utilizing real-world data sources. Specifically, building upon the nationwide Location-based Services (LBS) datasets in China, the applicability of classic human mobility models, including gravity model and intervening opportunities-class models, was extensively explored in the intercity domain by developing fitting models that incorporated multi-class urban attributes. Then, we contributed to proposing improved models that consider the diverse attraction effect of the origin and potential destinations. Moreover, our research scope was expanded to incorporate spatiotemporal heterogeneity through model comparisons among various city sets during both regular period and holiday. The findings suggested that our improved models effectively enhance the modelling accuracy while strengthening the explanatory power. They especially demonstrate a balanced performance even when handling datasets with spatiotemporal heterogeneity. Consequently, this study provides valuable insights into understanding intercity human mobility from the intrinsic mechanism of opportunity attraction. Our models hold practical significance in accurately modelling intercity mobility flows utilizing observable urban attributes and spatial layouts, further providing effective tools for preemptive traffic management.

Travel Time Variability in Urban Mobility: Exploring Transportation System Reliability Performance Using Ridesharing Data

Travel time variability (TTV) is a crucial indicator of transportation network performance, assessing travel time reliability and delays. This study investigates TTV metrics within the context of shared mobility using probe data from transportation network companies (TNCs) in Chicago, Los Angeles, and Dallas–Fort Worth. Eight reliability metrics are analyzed and compared for each origin–destination (OD) pair in the network, including standard deviation (SD), the Planning Time Index (PTI), the Travel Time Index (TTI), the Buffer Index (BI), On-time Measures PR (alpha), and the Misery Index (MI), to evaluate their effectiveness in clustering OD pairs using K-means clustering. The findings confirm that SD, PTI, and MI are particularly effective in measuring travel time reliability and clustering within urban systems. This study identifies the most unbalanced supply–demand OD pairs/regions in each city, noting that low/medium-SD clusters around metropolitan airports indicate stable travel times even in high-demand zones, while high-SD clusters in downtown areas reveal significant traffic demands and unreliability. These patterns become more pronounced in study areas with multiple city centers. This study highlights the need for targeted strategies to enhance travel time reliability, particularly in regions like Dallas–Fort Worth, where public transportation alternatives are limited.

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.