Origin-destination Matrices Research Articles

Twitter mobility dynamics during the COVID-19 pandemic: A case study of London.

The current COVID-19 pandemic has profoundly impacted people's lifestyles and travel behaviours, which may persist post-pandemic. An effective monitoring tool that allows us to track the level of change is vital for controlling viral transmission, predicting travel and activity demand and, in the long term, for economic recovery. In this paper, we propose a set of Twitter mobility indices to explore and visualise changes in people's travel and activity patterns, demonstrated through a case study of London. We collected over 2.3 million geotagged tweets in the Great London Area (GLA) from Jan 2019 -Feb 2021. From these, we extracted daily trips, origin-destination matrices, and spatial networks. Mobility indices were computed based on these, with the year 2019 as a pre-Covid baseline. We found that in London, (1) People are making fewer but longer trips since March 2020. (2) In 2020, travellers showed comparatively reduced interest in central and sub-central activity locations compared to those in outer areas, whereas, in 2021, there is a sign of a return to the old norm. (3) Contrary to some relevant literature on mobility and virus transmission, we found a poor spatial relationship at the Middle Layer Super Output Area (MSOA) level between reported COVID-19 cases and Twitter mobility. It indicated that daily trips detected from geotweets and their most likely associated social, exercise and commercial activities are not critical causes for disease transmission in London. Aware of the data limitations, we also discuss the representativeness of Twitter mobility by comparing our proposed measures to more established mobility indices. Overall, we conclude that mobility patterns obtained from geo-tweets are valuable for continuously monitoring urban changes at a fine spatiotemporal scale.

Effects of Periodic Location Update Polling Interval on the Reconstructed Origin–Destination Matrix: A Dutch Case Study Using a Data-Driven Method

Global System for Mobile Communications (GSM) data provides valuable insights into travel demand patterns by capturing people's consecutive locations. A major challenge, however, is how the polling interval (PI; the time between consecutive location updates) affects the accuracy in reconstructing the spatio-temporal travel patterns. Longer PIs will lead to lower accuracy and may even miss shorter activities or trips when not properly accounted for. In this paper, we analyze the effects of the PI on the ability to reconstruct an origin–destination (OD) matrix. We also propose and validate a new data-driven method that improves accuracy in case of longer PIs. The new method first learns temporal patterns in activities and trips, based on travel diaries, that are then used to infer activity-travel patterns from the (sparse) GSM traces. Both steps are data-driven thus avoiding any a priori (behavioral, temporal) assumptions. To validate the method we use synthetic data generated from a calibrated agent-based transport model. This gives us ground-truth OD patterns and full experimental control. The analysis results show that with our method it is possible to reliably reconstruct OD matrices even from very small data samples (i.e., travel diaries from a small segment of the population) that contain as little as 1% of the population’s movements. This is promising for real-life applications where the amount of empirical data is also limited.

The potential of Wi-Fi data to estimate bus passenger mobility

Last decades have been marked by deep socio-economic transformations, an uneven evolution of transport demand in main urban areas and the emergence of new and more sustainable modes of transportation (carpooling, self-services bicycles). These changes have strongly impacted the interaction between service supply and demand in the transport industry. In this context, passive data as Wi-Fi and Bluetooth become a key source of information to understand individual mobility behaviors and ensure the sustainable development of transport infrastructures. In this paper, we present a framework that uses disruptive technology to collect passive data in buses, continuously and at a lower cost than traditional mobility surveys. Previous research, conducted over a more limited spatial and temporal framework, uses filtering methods, which do not allow the results to be replicated. This study uses artificial intelligence to sort transmitted signals, get transit ridership and build Origin–Destination matrices. Its originality consists in providing a concrete, automatic and replicable method to transport operators. The comparison of the results with other data sources confirms the relevance of the presented algorithms in demand forecasting. Therefore, our findings provide interesting insights for data-driven decision making and service quality management in urban public transport.

Identification of Aggregate Urban Mobility Patterns of Nonregular Travellers from Mobile Phone Data

Over the last two decades, mobile phone data have appeared to be a promising data source for mobility analysis. The structure, abundance, and accessibility of call detail records (CDRs) make them particularly suitable for such use. However, their exploitation is often limited to estimating origin–destination matrices of a restricted part of the population: regular travellers. Although these studies provide valuable information for policymakers, their scope remains limited to this subpopulation analysis. In the present work, we develop a collective mobility reconstruction method adapted to nonregular travellers. The method relies on the notion of the detour ratio, which makes it robust to the lack of mobile phone data as well as its application to large instances (large and dense telecommunication networks). It is used to conduct a longitudinal analysis of the macroscopic mobility patterns in Santiago de Cali, Colombia, thanks to call detail data shared by communication provider CLARO as part of a research project conducted by Citepa, Paris, the Green City Big Data Project.

Estimating charging demand by modelling EV drivers' parking patterns and habits

The diffusion of battery electric vehicles (BEVs) requires a proper charging infrastructure to supply users the chance to charge their vehicles according to energy, time, and space needs. Thus, city planners and stakeholders need decision support tools to estimate the impacts of potential charging activities and compare alternative scenarios. The paper proposes a modelling approach to represent parking activities in urban areas and obtain key indicators of the electric energy required. The agent-based model reproduces the dynamics of user parking and assesses the impacts on the electricity grid during the day. Since the focus is on parking activities, no detailed data on vehicle trips are required to apply the standard demand modelling approach, which would require Origin-Destination matrices to simulate traffic flows on the road network. Preliminary results concerning the city of Turin are presented for simulated scenarios to identify zones where charging demand can be critical and peak events in electric power over the day. The model is designed to be scalable for all European cities because, as the case study shows, it uses available data. The results obtained can be used for the design of charging infrastructure (power and type) by zones.

Influence of the Spatial Distribution of Jobs in Intervening Opportunities Models

This article analyzes the influence of spatial distribution of jobs, which represents intervening opportunities, in aggregated and disaggregated trip distribution models that generated eight origin–destination matrices. The observed trip matrix was obtained from the Urban Transport Master Plan (PDTU) of the Rio de Janeiro Metropolitan Region in Brazil. The estimated matrices were calculated using the gravity, intervening opportunities and Naïve Bayes models, organized at two aggregation levels. These models’ performance was evaluated considering the aggregation level, the model approach adopted (aggregated or disaggregated), and the spatial distribution of the intervening opportunities. The results indicated that the intervening opportunities exert a positive influence on all models, under any level of zone size (subdistricts or neighborhoods). In the aggregate models, the variable “jobs” insertion improved the models’ performance when applied at a higher aggregation level (subdistricts). The best-case scenario showed a coefficient of determination (R2) equal to 0.8705, obtained in a disaggregated model with intervening opportunities applied in subdistricts.

Accessibility Measures in Modal Choice Models: A Proof of Concept Applied to Freight Transport

Modal choice models applied to interregional or international freight transportation network models are often based on rather coarse origin-destination matrices, containing annual transported tonnages between (sub)regions, for instance. Generally, only basic (sometimes constructed) independent variables (transportation costs or transit times) are used because other variables such as shipment sizes, service frequencies, etc. are not available. Using origin-destination matrices and an assignment model, it is also possible to compute spatial accessibility measures that can further be used as additional explanatory variables. Indeed, several published studies have identified network accessibility as an important element in the mode-choice decision. This paper also shows that the inclusion of an accessibility measure in the utility functions of a logit model substantially improves the performance of a transportation network model, both in the modal choice and the assignment levels of the classical four-step model. Consequently, the assignment of the estimated modal demands results in more accurate estimated traffic on the networks. The model presented in this paper is to be considered as a proof of concept because its workflow should further be streamlined to make it easily useable by modelers.

Analysis of cross-border traffic for large cities using multiple data sources and new technologies

Within metropolitan regions planning agencies show vital interest to receive information on commuting traffic between city centres and suburbs. To collect this data traditionally expensive surveys had to be carried out which are quickly outdated. We developed an analysis framework which uses multiple, passively created and continuously available data sources to receive travel information with less effort to allow a cost-effective annual monitoring of trips passing the city boundaries. Therefore, we synthesized floating phone data (FPD) and origin-destination (OD) matrices with traffic and passenger counts. The method presented in this paper shows reasonable results for mobility indicators at the city border. The analysis also indicates some shortcomings of the method but the total effort of data collection and data analysis can be reduced receiving valuable mobility information.

The impact of public transport on the diffusion of COVID-19 pandemic in Lombardy during 2020

In 2020, the COVID-19 pandemic has impacted the world, affecting health, economy, education, and social behavior. Much concern was raised about the role of mobility in the diffusion of the disease, with particular attention to public transport. Indeed, understanding the relationship between mobility and the pandemic is key for developing effective public health interventions and policy decisions. In this work, we aim to understand how mobility, and more specifically mobility by public transport, has affected the diffusion of the pandemic at the regional scale. We focus our attention on Lombardy, the most populated Italian region severely hit by the pandemic in 2020. We explore static mobility data provided by Regione Lombardia, the regional service district, and dynamic mobility data provided by Trenord, a railway operator which serves Lombardy and neighboring areas. We develop an inventive pipeline for the dynamic estimation of Origin-Destination matrices obtained from tickets and passenger counts. This allows us to spot potential triggers in pandemic diffusion enhanced by the concept of proximity induced by mobility. We also develop a novel perspective for assessing the relationship between mobility and overall mortality based upon a functional approach combined with a spatial correlation analysis aimed at identifying the diversified effects on mortality in small geographical areas as a result of the restrictions on mobility introduced to contrast the pandemic.

Assessing pandemic era stadium events and infections using mobile phone based population mobility data: An exploratory study from Ireland, 2021

Mass gathering events (MGEs) attracting local, national, or international crowds presented particular challenges in the context of the coronavirus disease 2019 (COVID-19) pandemic. Sporting, religious, music and other cultural events held during the early months of the pandemic, without social distancing or other safeguards, have been regarded as so-called ‘super spreader’ events. By the summer of 2020, MGEs were generally banned or subject to severe restrictions. Regular European sporting fixtures such as England’s Football Association and Germany’s Bundesliga matches began to return in the autumn with protective measures in place, such as matches initially held behind closed doors, and later with sub-capacity crowd limits and mandatory social distancing [1, 2, 3, 4, 5]. With protective measures in place, and proof of COVID-19 vaccination or recovery required for entry, a series of six sporting MGEs, ‘the All-Ireland Finals’ were held in the Republic of Ireland’s largest stadium, Croke Park in Dublin, during August-September 2021. This study draws on a high-resolution human population mobility dataset to quantify journeys to/from the stadium area on MGE days by destination. The anonymised, aggregated, data used is based on mobile phone usage, and consists of a series of fine-grained geographical origin-destination matrices presenting daily estimates of area to area journey numbers. With mobility from the stadium area serving as a proxy for MGE attendance, this study explores associations between MGE attendance numbers and local COVID-19 infections over subsequent five week periods. No evidence was found of association between attendance at any of the six 2021 All-Ireland MGEs and COVID-19 infections over subsequent five week periods. This finding contrasts with studies of comparable MGEs in 2020, such as English Association Football matches held during spring 2020, and German Bundesliga football matches held during autumn 2020. These differing outcomes may point to the effectiveness of transmission mitigation policies and behaviours.

Accessibility and Public Transport Mobility for a Smart(er) Island: Evidence from Sardinia (Italy)

• Multimodal integration model for Public Transport Service (PTS) in an island scenario • Mobility-based spatial interpretation for the benefit of the population • Use of real data obtained by the Kriging method • Transport modes do not reflect the socio-physical and economic reality of the island The purpose of this paper is to highlight the critical aspects of islands context transportation accessibility by suggesting a more innovative, safer, and more sustainable framework for public transport service (PTS) and place-based organisation, as well as by integrating the latest tendencies in mobility. Specifically, this research focuses on multimodal integration models for PTS that consider the existing infrastructure system and the socioeconomic issues typical of an island environment. This topic has received inadequate consideration in the scientific literature on islands. To achieve these aims, an analytical-numerical approach is adopted. Starting from initial origin-destination matrices (O/D) surveyed by the National Institute of Statistics (ISTAT), a methodology was implemented to compute the geographical distribution of trips, and thereafter, the values distributed over the whole region were interpolated by Surfer software with the Kriging method. This methodology was applied to the Sardinia case study particularly emblematic because it has seen a tremendous transition over the previous decades, resulting in a massive socio-economic gap between inland and coastal areas, that led to an increase in private vehicles for transportation purposes, primarily for business and pleasure. The application, replicable in other islands that have highlighted the same socio-economic problems linked to poor mobility planning, shows an accessible spatial planning approach, combining PTS and rental for driver services, by considering the principles and issues of island contexts. This research gives an important scientific contribution by emphasising the quality of transport infrastructures, place-based organisation, population distribution, and physical configuration of the Insular Region, as well as by considering the most pressing issues of island contexts. Findings could help island governments in revising their policy and practice of transport, accordingly.

The imported infections among foreign travelers in China: an observational study

BackgroundIn the past few decades, globalization has rendered more frequent and intensive population movement between countries, which has changed the original disease spectrum and brought a huge health impact on the global population including China. This study aims to describe the spectrum and epidemiological characteristics of imported infections among foreign travelers travelling to China.MethodsThe data on imported infections among foreign travelers were obtained from Custom Inbound Screening System (CISS) and the National Notifiable Infectious Disease Reporting System (NNIDRS). All the infections were classified into respiratory, gastrointestinal, vector-borne, blood/sex-transmitted and mucocutaneous diseases, of which case numbers and incidences were calculated and the proportions were compared among subgroups.ResultsIn total, 17,189 travelers diagnosed with 58 imported infectious diseases were reported from 2014 to 2018, with an overall incidence of 122.59 per million. Respiratory infection (7,351 cases, mainly influenza) and blood/sex-transmitted diseases (6,114 cases mainly Hepatitis B and HIV infection) were the most frequently diagnosed diseases, followed by vector-borne infections (3,128 cases, mainly dengue fever and malaria). The highest case number was from Asia and Europe, while the highest incidence rate was from Africa (296.00 per million). When specific diagnosis was compared, both the highest absolute case number and incidence were observed for influenza. An obvious seasonal pattern was observed for vector-borne diseases, with the annual epidemic spanning from July to November. The origin–destination matrices disclosed the movement of imported infection followed specific routes.ConclusionsOur study provided a profile of infectious diseases among foreign travelers travelling to China and pinpointed the target regions, seasons and populations for prevention and control, to attain an informed control of imported infections in China.

Bus Passenger Origin-Destination Estimation and Related Analyses Using Automated Data Collection Systems

This research explores the application of archived data from Automated Data Collection Systems (ADCS) to transport planning with a focus on bus passenger travel behavior, including Origin-Destination (OD) inference, using London as a case study. It demonstrates the feasibility and ease of applying trip-chaining to infer bus passenger OD from smart card transactions and Automatic Vehicle Location (AVL) data and is the first known attempt to validate the results by comparing them with manual passenger survey data. With the inferred OD matrices, the variations of weekday and weekend bus route OD patterns are examined for planning purposes. Moreover, based on the inferred OD matrices and the AVL data, alighting times for bus passengers also can be estimated. Bus journey stages, therefore, can easily be linked. By comparing the interchange time and the connecting bus route’s headway, it provides a way to evaluate bus connections.

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

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

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

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

Extracting Bus Transit Boarding and Alighting Information Using Smart Card Transaction Data

The smart card–based automated fare collection (AFC) technologies provide new opportunities for transportation data collection since smart card data (SCD) contain a significant amount of archived information that can be gathered and leveraged to help estimate public transit origin-destination (OD) matrices. Both boarding and alighting location detection are important particularly when there is no automatic vehicle location (AVL) system or other information in the database. With the analysis of raw data using limited information in this paper, an algorithm for boarding stop identification is presented after the trip direction and boarding cluster identification. Alighting stops are then identified based on the relationship between records. Finally, the feasibility and practicality of the methodology are tested using the bus transit SCD collected in Guangzhou, China.

Disaggregated approach to urban trip distribution: a comparative analysis between artificial neural networks and discrete choice models

Discrete choice models have been used over the years in disaggregated approaches to forecast destination choices. However, there are important constraints in some of these models that pose obstacles to using them, such as the Independence of Irrelevant Alternatives (IIA) property in the Multinomial Logit model, the need to assume specific structures and high calibration times, depending on the complexity of the case being evaluated. However, some of these mentioned constraints could be mitigated using Mixed Models or Nested Logit. Therefore, this paper proposes a comparative analysis between the Artificial Neural Network (ANNs), the Multinomial and Nested Logit models for disaggregated forecasting of urban trip distribution. A case study was conducted in a medium-sized Brazilian city, Santa Maria (RS), Brazil. The data used come from a household survey, prepared for the Urban Mobility Master Plan. For the sake of comparison, hit rates and frequency of trip distribution distances were analyzed, showing that ANNs can be as efficient as the Discrete Choice models for disaggregated forecasting of urban trip destination without, however, assuming some constraints. Finally, based on the results obtained, the efficiency of ANNs is observed for predicting alternatives with a low number of observations. They are important tools for obtaining Origin-Destination matrices from incomplete sample matrices or with a low number of observations. However, it is important to mention that discrete choice models can provide important information for the analyst, such as statistical significance of parameters, elasticities, subjective value of attributes, etc.

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

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

A Matheuristic for the Integrated Disruption Management of Traffic, Passengers and Stations in Urban Railway Lines

In big cities, the metro lines usually face great pressure caused by huge passengers demand, especially during peak hours. When disruptions occur, passengers accumulate quickly at stations. It is of great importance for dispatchers to take passenger flow control into consideration for the traffic management to ensure passengers’ safety and to maintain their satisfaction. This paper proposes an integrated disruption management model, which incorporates train rescheduling and passenger flow control. In this model, the train services can be short-turned, cancelled and rerouted, while the number of passengers entering a station is managed by controlling the station gates with consideration of the capacities of platforms and trains. Moreover, the number of passengers arriving at a station is calculated according to the origin-destination matrices. The objectives are to recover the train operation to the original timetable as soon as possible and to minimize the waiting time of passengers outside the stations. With the interaction between train services, passengers and station gates, an iterative metaheuristic approach is proposed to solve the integrated disruption management problem. Based on the data of a Beijing metro line, numerical experiments are conducted to test the proposed algorithm. The results demonstrate the importance of integrated disruption management and the effectiveness of our solution method.

Estimation of Green House Gas and Contaminant Emissions from Traffic by microsimulation and refined Origin-Destination matrices: a methodological approach

The high levels of air contamination and presence of different pollutants are a large problem in most of the cities in which road transport is the primary source of emissions.The governments of more than 100 countries are adopting different policies and strategies to help reduce and mitigate their global emissions. In terms of road transport, reductions in emissions could be achieved by replacing conventional vehicle technologies or by changing the travel patterns of individuals using a private vehicle as their primary means of transportation. However, accurately quantifying the emissions related to the urban traffic from multiple possible scenarios is a very complicated task, even when appropriate tools made for this purpose are available. Here we apply a scientifically rigorous protocol to accurately estimate greenhouse and other polluting gases. We describe the methodological steps we followed to analyse the vast quantities of data available from different heterogeneous sources. This data can aid decision-makers in planning better strategies for urban transportation. We used the origin-destination matrices already available for Valencia city (Spain), as well as historical information for their street induction-loops and the phases and times of their traffic light system as our input data for the traffic model. Rather than a brute-force algorithm, we used a fast-convergence Lagrangian algorithm model which deals with that vast quantities of information. Based on the elements mentioned above together with the statistics about the types of vehicles in the city by simulations the urban mobility city's traffic was reconstructed at different times to quantify the emissions produced with a high spatial and temporal resolution.