Origin Destination Research Articles

Connectivity, Reliability and Approachability in Public Transport: Some Indicators for Improving Sustainability

The reliability of public transport connectivity is influenced by factors such as route design, frequency, availability and infrastructure. Using a shortest path algorithm, we identify up to “k” space–time paths for each origin–destination pair (OD), considering schedules and maximum tolerable waiting times. We propose four reliability indicators and an approachability indicator to assess transport supply. These indicators are calculated at path, OD and network levels using two sets of equations. This framework enables fleet managers to evaluate and compare strategies to improve connectivity reliability and equity, such as increasing route frequency, optimizing lengths or expanding the network. Enhancing connectivity reliability encourages modal shifts to public transport, while improving approachability minimizes resource usage, contributing to sustainability. An application to the bus network of a city in Brittany, France, demonstrates the practical use of these indicators in evaluating and optimizing transport strategies.

Methodology for Measuring Mobility Emissions with High Spatial Resolution: Case Study in Valencia, Spain

Climate change is a major global issue because transportation is a major source of pollutants and greenhouse gases that affect human health and air quality. However, to effectively prioritize and fund mitigating actions, decision-makers lack scientific rigor and diagnoses with sufficient spatial resolution. Based on the Origin-Destination Matrix (ODM), this study suggests a methodology to measure and identify mobility emissions (CO2, Nox, PM) at the neighborhood level with high spatial resolution. Testing of the methodology was performed in Valencia, Spain. Even though many studies calculate carbon footprint, few make use of precise geographic information and openly accessible data, and they frequently concentrate on entire cities rather than smaller areas. To determine all potential routes for each Origin-Destination (OD) trip, the process uses geostatistics to estimate daily trip activity data (kilometers traveled). The COPERT calculator methodology from the European Union is used to analyze these routes to calculate the total emissions and the distance traveled per neighborhood. Based on road infrastructure, the methodology determines which neighborhoods receive emissions and creates measures of equitable environmental responsibility. It also identifies short trips that might be replaced by cycling or walking, as well as possible improvements to public transportation.

Solving the challenges of interpolating NO<sub>2</sub> from SPRINT data and modelling population movements in agent-based modelling

This study addresses two critical challenges in urban air quality exposure simulation and offers solutions. The first challenge is to generate nitrogen dioxide (NO2) fields across the city of London from available stations that provide Spatially Poor but Rich In Time (SPRINT) data. We first used Inverse Distance Weighting (IDW) to spatially interpolate NO2 at each half-a-day step. Each station had a list of hourly NO2 values for each time step, from which one NO2 value was selected by a stochastic process to generate the field. We also added weightings of up to a factor of 3 to London's NOx emissions to account for emissions from sources other than vehicles. We cross-validated the modelled data with the station data and found beta parameter of 1.5 to be the most appropriate 'power' parameter. The second challenge investigated the use of a fractional origin-destination (OD) matrix to see how to overcome errors when assigning destinations to a small set of population. We tested that ‘the nested bin strategy’ worked well for our 6,078 London resident agents. To enrich the dynamics to represent people’s non-work mobility patterns, we included visits to recreational areas during weekends and festive periods. This, in turn, provides a more comprehensive representation of urban mobility. Solutions to each challenge can provide more accurate assessments of pollution exposure, leading to better informed public health interventions.

Modeling and Analyzing the Spatiotemporal Travel Patterns of Bike Sharing: A Case Study of Citi Bike in New York

As the urban transportation demand continues to grow, the effective management and optimization of bike-sharing systems are of significant importance for urban planning and transportation engineering. This study aims to identify the spatiotemporal distribution of the peak-period departures and arrivals of bike sharing within Manhattan, New York, and to analyze the community clustering patterns and their underlying rules. Additionally, a comparative analysis across multiple time periods was conducted to enhance the research’s practical value. This study utilized GPS trajectory data from the New York City bike-sharing system for 2023. After analyzing the travel patterns throughout the year, we selected August, the month with the highest usage, to study the origin-destination (OD) travel aggregation patterns using flow models and the theoretical constructs of travel networks, measuring and analyzing travel characteristics. Subsequently, community detection algorithms were applied to analyze the clustering patterns and relationships among various neighborhoods. The findings revealed that the use of bike sharing in New York exhibits an overall trend of increasing and then decreasing throughout the year, with significantly higher usage in the spring and summer compared to the fall and winter. Notably, August saw the highest usage levels, with hotspots primarily concentrated in the southwestern part of Manhattan, which is also the economic center of New York City. The OD aggregation patterns across the upper, middle, and lower parts of August show distinct variations. Through community analysis, several strongly associated neighborhood clusters were identified, which exhibited both aggregation and dispersion trends over time. In southern Manhattan, a community with high modularity emerged, showcasing strong interconnections among neighborhoods. These findings provide valuable insights into the usage patterns of bike sharing in New York and the factors influencing them, offering significant implications for the optimization of bike-sharing system operations and planning.

Model Identification and Transferability Analysis for Vehicle-to-Grid Aggregate Available Capacity Prediction Based on Origin–Destination Mobility Data

Vehicle-to-grid (V2G) technology is emerging as an innovative paradigm for improving the electricity grid in terms of stabilization and demand response, through the integration of electric vehicles (EVs). A cornerstone in this field is the estimation of the aggregated available capacity (AAC) of EVs based on available data such as origin–destination mobility data, traffic and time of day. This paper considers a real case study, consisting of two aggregation points, identified in the city of Padua (Italy). As a result, this study presents a new method to identify potential applications of V2G by analyzing floating car data (FCD), which allows planners to infer the available AAC obtained from private vehicles. Specifically, the proposed method takes advantage of the opportunity provided by FCD to find private car users who may be interested in participating in V2G schemes, as telematics and location-based applications allow vehicles to be continuously tracked in time and space. Linear and nonlinear dynamic models with different input variables were developed to analyze their relevance for the estimation in one-step- and multiple-step-ahead prediction. The best results were obtained by using traffic data as exogenous input and nonlinear dynamic models implemented by multilayer perceptrons and long short-term memory (LSTM) networks. Both structures achieved an R2 of 0.95 and 0.87 for the three-step-ahead AAC prediction in the two hubs considered, compared to the values of 0.88 and 0.72 obtained with the linear autoregressive model. In addition, the transferability of the obtained models from one aggregation point to another was analyzed to address the problem of data scarcity in these applications. In this case, the LSTM showed the best performance when the fine-tuning strategy was considered, achieving an R2 of 0.80 and 0.89 for the two hubs considered.

A study on identifying representative trips for mobility service design

AbstractRecently, with growing interest in urban mobility patterns, the demand for collecting and analysing origin‐destination (OD) data is increasing. Due to the large scale and dimensionality of OD data, there are two issues in analysing the data: big‐data storage and major pattern extraction. To deal with two issues at the same time, this study suggests a principal control analysis‐based major demand identification method to improve the usability of microscopic OD data. Especially, this study focuses on finding principal components that preserve major patterns from OD data with small random noise so that the data can be effectively used for mobility service design. The proposed method is applied to smart card data of Seoul and Sejong and extracted major demand patterns from peak‐ and non‐peak hour data of these cities. The degree of daily regularity, reconstruction accuracy, and compression rate of the reconstructed data is analysed varying sets of principal components. The obtained results show that the major demands contain a low volume and a large volume of demand and with lower‐order principal components, major demands can be efficiently extracted by removing randomly appearing small‐volume demand. In addition, the trade‐off behaviour is observed between the degree of daily regularity and reconstruction accuracy depending on the compression rate. Based on the observations, it can be found that the loss of major demand patterns could be prevented when targeting a reconstruction accuracy of 90–95% and the proposed method can reduce the data size while preserving major mobility patterns.

Urban Greenway Planning and Designing Based on MGWR and the Entropy Weight Method

Travelers’ attention to high-quality human habitats is increasing, and the role of urban greenways in improving the quality of travelling spaces has also been appreciated. This research aims at making the weight calculation of suitability more scientific and reasonable, clustering the shared bicycle travelling OD points according to suitability, and analyzing the distribution of OD points. Taking Xiamen as an example, multiscale geographically weighted regression and entropy weight methods were used to calculate the weights of variables using multi-source big data. The clustering of origin-destination (OD) points for shared bicycle travel are identified using the DBSCAN clustering algorithm, which can provide accurate support for greenway planning and shared bicycle placement. The results show that the density of tourist attractions, POI entropy index, road density, and intermediate are four important factors affecting the suitability of greenways. The clustering results of the shared bicycle OD points show that the high-aggregation areas of origin and destination points are located in the northeast and southwest directions as well as west and east directions. This study provides a theoretical and modelling analysis reference for greenway planning and design.

A time-series-based model to detect homogeneous regions of residents’ dynamic living habits

ABSTRACT The identification of homogeneous regions representing the dynamic living habits of residents has long been a central focus in human activity research. Although extensive studies have been conducted in this area, the majority of these efforts have concentrated on static residential behaviors, neglecting the temporal fluctuations in urban residents’ activities throughout the day. This study introduces a novel model that integrates facility operation data with a time-series-based regionalization approach to detect residents’ dynamic living habits at the block scale and identify homogeneous regions. The model first transforms textual data into temporal Origin-Destination (OD) flows and introduces the concept of an “urban schedule” to represent the Temporal OD of various resident activities within a specific unit. Building on this foundation, the urban schedule is subsequently converted into operating probability curves over time, revealing the spatial structure of residents’ dynamic living habits. Our findings demonstrate that the homogeneous regions of residents’ dynamic living habits not only reflect the intensity of human activity but also show a significant correlation with distinct urban functional areas, highlighting their importance for urban planning and development.

Can rail reduce British aviation emissions?

We analyse UK airport origin–destination data from 1990 to 2021 to understand the extent to which a modal shift to rail may reduce aviation emissions. We find that 41 % of UK aviation passengers travel on routes that can be done by rail in less than 24 h. However, these passengers account for only 14 % of UK aviation emissions because long-haul flights contribute the majority of emissions. Some popular destinations (e.g. Spanish Islands) are inaccessible by rail and may be suitable for destination switching. We also find rapid growth in flights to international hub airports used for connecting journeys. This has implications for carbon accounting, suggesting that a significant and growing proportion of UK residents’ aviation emissions are being excluded from national accounts. Finally, the paper recommends some interventions that might encourage a modal shift to rail.

Predicting origin-destination flows by considering heterogeneous mobility patterns

The accurate prediction of origin-destination (OD) flows is essential for advancing sustainable urban mobility and supporting resilient urban planning. However, the inherent heterogeneity of mobility patterns results in complex geographic unit relations, diverse spatial organizational structures, and the long-tailed effect on OD flow distribution. This study proposes a novel OD flow prediction method based on graph-based deep learning (named as HMCG-LGBM). Specifically, 1) a modularity-based graph reconstruction strategy is presented for geographic unit relation augmentation by eliminating weak connections; 2) the heterogeneous spatial organization of OD flows is captured by combining the community detection approach and graph attention mechanism with the introduction of socio-economic and spatial features; and 3) a weighted loss function with distribution smoothing paradigm is developed to enhance the prediction for low-probability mobility events, addressing the challenges posed by long-tailed distributions. Extensive experiments conducted on real-world datasets show that the predictive performance of the proposed method is significantly improved, with the RMSE and MAE reduced from the baselines by 11.1%–33.3% and 14.1%–22.2%, respectively. The results also demonstrate the robustness of the proposed method for dealing with imbalanced OD flow distributions, providing valuable insights for spatial interaction predictive modeling in the context of sustainable urban systems.

Exploring the Impact of Built Environment on Elderly Metro Ridership at Station-to-Station Level

Understanding the relationship between the built environment and metro ridership has become essential for advancing sustainable transportation development. Limited research has been given to how built environment factors influence metro ridership at a station-to-station level. Moreover, most studies focus on the general population, overlooking the special groups. This study examines the influence of the built environment on metro origin–destination (OD) ridership for older adults. Specially, we employ the CatBoost model, along with SHAP interpretation, to assess feature importance and capture nonlinear effects. Taking Xi’an as a case study, the results show that: (1) The CatBoost model demonstrates superior fitting and predictive performance, outperforming both the XGBoost and Logistic Regression models. (2) There are distinct variations in the influence of built environment factors at origin and destination stations. Traffic-related variables have a stronger effect at origin stations, while land-use variables exert a more significant influence at destination stations. (3) The built environment’s impact on older adults’ metro ridership exhibits a clear nonlinear relationship. Notably, an optimal land-use mix of 1.8–1.9 and a job density of 5000–7000 significantly enhance ridership. These findings provide valuable insights into how the built environment shapes older adults’ metro travel, contributing to the sustainable urban development.

Evaluating Google Maps’ Eco-Routes: A Metaheuristic-Driven Microsimulation Approach

Eco-routing, as a key strategy for mitigating urban pollution, is gaining prominence due to the fact that minimizing travel time alone does not necessarily result in the lowest fuel consumption. This research focuses on the challenge of selecting environmentally friendly routes within an urban street network. Employing microsimulation modelling and a computer-generated mirror of a small traffic network, the study integrates real-world traffic patterns to enhance accuracy. The route selection process is informed by fuel consumption and emissions data from trajectory parameters obtained during simulation, utilizing the Comprehensive Modal Emission Model (CMEM) for emission estimation. A comprehensive analysis of specific origin–destination pairs was conducted to assess the methodology, with all vehicles adhering to routes recommended by Google Maps. The findings reveal a noteworthy disparity between microsimulation results and Google Maps recommendations for eco-friendly routes within the University of Pittsburgh Campus street network. This incongruence underscores the necessity for further investigations to validate the accuracy of Google Maps’ eco-route suggestions in urban settings. As urban areas increasingly grapple with pollution challenges, such research becomes pivotal for refining and optimizing eco-routing strategies to effectively contribute to sustainable urban mobility.

Enhancing origin–destination flow prediction via bi-directional spatio-temporal inference and interconnected feature evolution

Origin–destination (OD) flow prediction is crucial for predicting inter-station passenger flows in intelligent transport systems. However, previous OD prediction methods have ignored the delay of OD flows and failed to focus on the supplementary effect of the arrival OD flow (Out-OD) flow data on OD flows. We innovatively propose an OD flow prediction method based on Bidirectional Attention and Interconnected Feature Evolution (BiST-IF) to address these challenges. Firstly, we propose a correction method based on period delay probability and real-time flow features for OD flow information. Furthermore, for the flow information of different periods, we design a bi-directional attention module to achieve the preliminary prediction by deconstructing and analyzing the temporal and flow features of OD flow and fusing the temporal and spatial features. After that, we design the mutual information module of Out-OD and OD flow, combining it with the new gating algorithm to enhance the periodic features. Finally, the prediction results from the periodic pattern and the temporary fluctuation of OD flow are fused to obtain the OD flow prediction results. Extensive experiments on two real-world datasets show that the prediction performance of BiST-IF significantly outperforms other state-of-the-art models. Specifically, BiST-IF improves the MAE and RMSE by an average of 7.55% and 7.31% on the HZMetro dataset, and 5.77% and 2.33% on the NYC-TOD2018 dataset, respectively, compared to the best baseline approach.

Estimation of Arterial Path Flow Considering Flow Distribution Consistency: A Data-Driven Semi-Supervised Method

To implement fine-grained progression signal control on arterial, it is essential to have access to the time-varying distribution of the origin–destination (OD) flow of the arterial. However, due to the sparsity of automatic vehicle identification (AVI) devices and the low penetration of connected vehicles (CVs), it is difficult to directly obtain the distribution pattern of arterial OD flow (i.e., path flow). To solve this problem, this paper develops a semi-supervised arterial path flow estimation method considering the consistency of path flow distribution by combining the sparse AVI data and the low permeability CV data. Firstly, this paper proposes a semi-supervised arterial path flow estimation model based on multi-knowledge graphs. It utilizes graph neural networks to combine some arterial AVI OD flow observation information with CV trajectory information to infer the path flow of AVI unobserved OD pairs. Further, to ensure that the estimation results of the multi-knowledge graph path flow estimation model are consistent with the distribution of path flow in real situations, we introduce a generative adversarial network (GAN) architecture to correct the estimation results. The proposed model is extensively tested based on a real signalized arterial. The results show that the proposed model is still able to achieve reliable estimation results under low connected vehicle penetration and with less observed label data.

DistOD: A Hybrid Privacy-Preserving and Distributed Framework for Origin–Destination Matrix Computation

The origin–destination (OD) matrix is a critical tool in understanding human mobility, with diverse applications. However, constructing OD matrices can pose significant privacy challenges, as sensitive information about individual mobility patterns may be exposed. In this paper, we propose DistOD, a hybrid privacy-preserving and distributed framework for the aggregation and computation of OD matrices without relying on a trusted central server. The proposed framework makes several key contributions. First, we propose a distributed method that enables multiple participating parties to collaboratively identify hotspot areas, which are regions frequently traveled between by individuals across these parties. To optimize the data utility and minimize the computational overhead, we introduce a hybrid privacy-preserving mechanism. This mechanism applies distributed differential privacy in hotspot areas to ensure high data utility, while using localized differential privacy in non-hotspot regions to reduce the computational costs. By combining these approaches, our method achieves an effective balance between computational efficiency and the accuracy of the OD matrix. Extensive experiments on real-world datasets show that DistOD consistently provides higher data utility than methods based solely on localized differential privacy, as well as greater efficiency than approaches based solely on distributed differential privacy.

Multi-Objective Routing and Categorization of Urban Network Segments for Cyclists

This study develops a progressive navigation and guidance model for the route selection of cyclists executed in a designated area. The route selection of cyclists is modeled as a Pareto multi-objective optimization problem which is solved with the NSGA-II algorithm. The study aims to contribute to the ongoing efforts to create efficient and cyclist-friendly navigation tools to promote sustainable urban mobility. Data collection methods include GPS tracking, field measurements, and qualitative approaches to understand cyclists’ behavior and preferences. Nine objective functions are constructed based on criteria related to safety and comfort, incorporating decision variables related to cyclists riding on sidewalks, capturing the complexity of urban cycling infrastructure. Tests are performed in a defined area in the center of Athens, Greece. The NSGA-II algorithm is executed with modifications and the Pareto front is constructed, which consists of 28 alternative routes between two origin–destination points. The four routes that optimize the nine criteria of the objective functions are presented, with most routes passing through the Zappeion Gardens. The NSGA-II algorithm is proven to be a suitable approach for applications in networks with complex characteristics and for capturing cyclists’ choices when they face conflicting options. The study presents how a novel approach for the multi-objective optimization of cyclists’ route choice, which considers a wide range of cyclists’ needs and preferences, can be implemented in an urban environment with a lack of cycle infrastructure.

Integrating hybrid deep learning and path allocation for real-time inbound passenger flow prediction and anomaly detection in urban rail transit

This paper study the problem of real-time prediction of inbound passenger flow and the detection and alerting of abnormal passenger flows in urban rail transit (URT) networks. We propose a fused framework that combines a hybrid deep learning model and an evaluation strategy. Specifically, the learning model incorporates Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and attention mechanisms to effectively capture spatial and temporal correlations in passenger flow data. The evaluation strategy utilizes a depth-first search algorithm to determine the optimal travel paths for each individual passenger. And based on the paths, we develop a real-time method for estimating the origin–destination (OD) matrix that utilizes both long-term and short-term historical destination trend vectors to reduce dimensions while improving predictive accuracy. Through extensive testing using data from the Shanghai rail transit system, we demonstrate that this fused framework achieves high prediction accuracy for inbound passenger flow at various stations while efficiently identifying and warning sudden large-scale events involving significant increases in passenger flow volume. This research contributes towards improving overall passenger experience as well as operational resilience within urban rail systems when dealing with large-scale influxes of passengers.

Optimization of bus stop layout considering multiple factors including passenger flow direction.

Bus stop layout typically requires consideration of urban population distribution, traffic conditions, and passenger flow demand to establish an efficient foundation for the bus system's operation. Based on the above key factors, this paper introduces a strategic method to optimize the bus stop layout from a macro perspective in order to save passengers' travel time and improve the attractiveness of the bus system. This approach accounts for the matching degree between the Origin-Destination (OD) direction of passengers and their walking direction heading to bus stops. Initially, we take into account factors such as the population and area of traffic districts, and urban road conditions. Utilizing the hypernetwork multidimensional data clustering method along with GIS technology, we construct an alternative set of bus stops based on the hypernetwork framework. This set serves as a reference for the positioning of newly built and moved bus stops. Subsequently, we develop a two-stage model for bus stop layout decision-making. The first stage focuses on determining the bus stop layout at the traffic district level, taking into account multi-factors including the passenger flow matching degree. The second stage is designed to mitigate the negative impact of bus stop optimization on the overall service level of the urban bus system. A case study conducted in XT city demonstrates the effectiveness of our approach. Post-optimization, there is a 15.83% increase in the alignment between passenger flow direction and bus stop layout. Additionally, the average travel time for passengers is reduced by 7.55 minutes.

Lane-free signal-free intersection crossing via model predictive control

The operation of signal-free intersections, where Connected Automated Vehicles (CAVs) cross simultaneously for all Origin-Destination (OD) movements, has the potential to greatly increase throughput and reduce fuel consumption. Since the intersection crossing areas naturally include no lanes, an extended crossing area, appropriately delineated, can be considered as a lane-free infrastructure so as to enable further efficiency benefits. This paper presents two Model Predictive Control (MPC) schemes to manage CAVs in signal-free and lane-free intersections. In fact, the control inputs of all vehicles are optimized over a time-horizon by online solving of a joint Optimal Control Problem (OCP) that minimizes a cost function including proper terms to ensure smooth and collision-free vehicle motion, while also considering fuel consumption and desired-speed tracking, when possible. Additionally, appropriate constraints are designed to respect the intersection boundaries and ensure smooth vehicle movements towards their respective destinations. A fast Feasible Direction Algorithm (FDA) is employed for the numerical solution of the introduced OCP. Multiple simulations are carried out to assess the efficiency and practicality of the proposed methods. A comparison with signalized intersection operation is provided.

Regional determinants in origin–destination remittance flows from the US to Mexico: evidence from the COVID-19 pandemic

ABSTRACT Despite expectations of a decline in remittances due to rising US unemployment, total remittances to Mexico increased during the COVID-19 pandemic, although with regional disparities. Using a spatial gravity econometric model with state-level origin–destination data, we analysed these variations, considering regional economic and health conditions and demand for digital remittance services. The study focused on the second quarter of 2020, marked by lockdowns and economic disruptions. Findings suggest a strong link between employment changes and digital service demand, influencing US remittance flows. Results for Mexico support the significant impact of digital services over the altruistic hypothesis, highlighting the need for a regional approach to studying remittances.