Origin-destination Matrix Research Articles

Commuter welfare-based probabilistic seismic risk assessment of regional road networks

This study integrates welfare, a measure of the impact of road network disruption on individual commuters’ well-being, with a probabilistic seismic risk assessment framework in a computationally tractable way. Welfare is a network performance measure that reflects the differential impacts of changes in commute time on various groups. For a case study of the San Francisco Bay Area, welfare loss is computed by augmenting an origin–destination matrix with publicly available information about commuters’ income levels, residences, and workplaces. While commuters from all income groups have similar risk of drivers’ delay due to road network disruption, commuters with low incomes have a substantially higher risk of welfare loss than those with high incomes. A comparison of bridge retrofit policies shows that disaggregation of welfare loss by income group is necessary to examine whether such policies reduce risk equitably. While a retrofit policy determined using drivers’ delay reduces the expected drivers’ delay, it increases the disparity in the per-capita welfare loss of commuters with low and high incomes relative to the network’s baseline state. In contrast, a retrofit policy that prioritizes low-income commuters reduces the difference in welfare loss of commuters with low and high incomes compared to the baseline network state.

JP-DAP: An Intelligent Data Analytics Platform for Metro Rail Transport Systems

This paper deals with an intelligent data analytics platform - Jaison-Paul Data Analytics Platform (JP-DAP) - for metro rail transport systems. JP-DAP is intended to ensure smooth functioning, improved customer experience, ridership forecasting, and efficient administration of metro rail transportation systems by integrating and analysing its many data sources. It consists of a middleware which is built on the top of a Hadoop Distributed File System (HDFS) and Spark framework, along with a set of open-source software tools like Apache Hive, Pandas, Google TensorFlow and Spark ML-lib for real-time and legacy data processing. The benchmarking of JP-DAP was conducted using TestDFSIO and have found that it performs well according to industry standards. The specific use case for this project is Kochi Metro Rail Limited (KMRL). The analysis of Automated Fare Collection data from KMRL on JP-DAP framework have produced descriptive statistics visualisation of inflow and outflow analysis, travel patterns during weekdays and weekends, origin-destination matrix, etc.. Moreover JP-DAP framework is capable of producing short term passenger flow predictions using SVR machine learning algorithm with linear, radial basis function and polynomial kernels. Our experiments have shown that SVR linear kernel gives the most accurate results with the least errors in predicting the next day’s passenger count using the previous five weekdays data. The station usage (one-to-all) prediction using Long Short-Term Memory (LSTM) is also integrated to this framework. The visualisation as well as analytical outcomes of JP-DAP framework have also been made available to the external world using a rich set of REST APIs and are projected on to a web-dashboard.

On Heterogenous Sampling Rates in Origin–Destination Matrix Estimation Based on Trajectory Data and Link Counts

Being a fundamental metric of the transportation network, the origin–destination (O-D) flow matrix is a critical input for various transportation models and studies. This paper deals with the estimation of an O-D matrix of trip flows based on two kinds of data: probe trajectory data and local traffic counts. A Bayesian assignment framework is developed for demonstrating the relationship between the link probe sampling rates and the fractional contributions from the sampling rates on different O-D pairs. The unknown O-D matrix is estimated by applying cross entropy minimization using a prior matrix from the probe trajectories, along with the Bayesian assignment rules on link sample rates as the constraints. The methodology was applied using floating car data and camera link flow counts for a numerical experiment. The results show that the method can achieve a robust estimation of O-D matrices, even using different prior matrices. The issue of the heterogeneous sampling rates can be well addressed with link count constraints, effectively correcting the unknown bias in the probe sampling. The case study using real data also proves the feasibility of mining observed trajectory data to obtain the assignment fractions and estimate the O-D matrix inversely, avoiding the conventional sophisticated process of traffic assignment modeling.

O–D matrix estimation based on data-driven network assignment

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem. Abbreviations APR: average penetration rate; DDNA: data driven network assignment; DDNL: data driven network loading; DODME: dynamic OD matrix estimation; DTA:dynamic traffic assignment; FCD: floating-car data; ITS: intelligent transportationsystems; NNLSQ: non-negative least squares; OD: origin destination; ODME: ODmatrix estimation; PV: probe vehicle; RUM: random utility model

The Impact of Mass Exodus on the Resurgence of COVID-19 Cases: Case Study of Regions in Indonesia

Consideration of human mobility is essential for understanding the behavior of COVID-19 spread, especially when millions of people travel across borders around Eid al-Fitr. This study aims to grasp the effect of mass exodus between regions on active cases of COVID-19 through a mathematical perspective. We constructed a multiregional SIQRD (susceptible–infected–quarantined–recovered–death) model that accommodates the direct transfer of people from one region to others. The mobility rate was estimated using the proposed Dawson-like function, which requires data from an origin–destination matrix. Assuming that only susceptible, inapparently infected, and recovered individuals travel around Eid al-Fitr, the rendered model well-depicted the actual data at that time, giving either a significant spike or decline in the number of active cases due to the mass exodus. Most agglomerated regions such as Jakarta and Depok City experienced a fall in active case numbers, both in actual data and in the simulated model. However, most rural areas experienced the opposite, such as Bandung District and Cimahi City. This study confirmed that most travelers journeyed from big cities to the rural regions, and it scientifically demonstrated that mass mobility affects COVID-19 transmission between areas.

Development of Land Used Intensity-Traffic Interaction Model for Bandar Lampung City, Indonesia

Abstract Social history has shown that a city plays an important role in meeting human needs such as housing, service facilities, entertainment areas, and transportation networks. Therefore, the interaction between demand and infrastructure creates a traffic flow. Previous studies show that traffic flow is strongly correlated with land use types’ scale and operational characteristics. In addition, traffic volume can represent logistics distribution, which is one of the parameters to assess regional economic conditions. This condition prompted researchers to research to develop an interaction model between land-use intensity and traffic. The study was conducted in Bandar Lampung City, Indonesia, the economic center of Lampung Province, Indonesia. The methodology used in this study is the multiple linear regression analysis for origin-destination matrix. This analysis was performed using 23 parameters that could influence the model, but we try to find the most significant influence variables in this study. The process of evaluating some statistical parameters is considered as decision parameters. Finally, the analysis results show a strong correlation between land use and traffic volume in the city of Bandar Lampung, Indonesia the best model y = 62,3 + 2,275 x16 + 1,348 x20 with x16 is store building area, and x20 is the number of schools.

Solar photovoltaic generation for charging shared electric scooters

Scooter-sharing has been introduced as a new transportation mode. However, e-scooters have a limited battery capacity and require frequent charging, which causes the operational cost significantly high and hinders the viability of the service. To tackle this problem, this study proposes a solar charging solution with the creation of a real-time shareability network that maximizes the scooter-sharing capability and minimizes the total trip distance, constrained by e-scooters with real-time battery levels. Specifically, hourly solar potential is simulated based on a three-dimensional solar irradiation model so that photovoltaic (PV) electricity generation can be estimated when PV modules are installed at the parking stations, which enables solar charging when the origin–destination matrix of scooter-sharing trips is clustered and associated to the charging stations. As a case study in Singapore, the proposed solar charging system only needs 1–3 m2 PV modules at each station and 24%–67% of the total number of e-scooters to support almost all the real trips with a 98% reduction of trips used for charging. The system also supports 90% of on-demand mobility for at least three consecutive days without solar charging, which suggests the resilience of the system and inspires us to promote the proposed solar charging in the other global cities.

A study on universal round trip time analysis for passenger demand beyond elevator contract capacity by Monte Carlo simulation

The traditional elevator system design practice is to calculate the round trip time (RTT) and associated parameters of pure incoming traffic during up-peak, followed by real-time computer simulation. Recent studies indicated that the normal traffic is much more complicated, consisting of a mixture of incoming, outgoing and interfloor patterns. A major breakthrough to analytically calculate the Universal RTT, under such complicated traffic patterns, emerged 6 years ago based on an appropriate origin-destination matrix describing the passenger transit probability. That genesis model played safe by assuming that the total number of passengers demanding service within one round trip is limited elevator contract capacity, which is in line with the traditional up-peak incoming RTT formulae. In this article, such assumption is removed and the study is based on Monte Carlo simulation. It is found that there is room for enhancing the handling capacity, up to two times the contract capacity, by not sacrificing the RTT and average passenger transit time by too much. This phenomenon, that is, total passenger demand beyond contract capacity, is only valid under the existence of multiple entrance floors and/or mixed traffic conditions. This approach may prevent oversizing the design which could be more realistic. Practical applications: Elevator system designers, according to ISO 8100:32:2020 and CIBSE Guide D: 2020, are recommended to carry out calculation of the RTT and related parameters before any real-time computer simulation. This practice has been adopted by the elevator industry for decades. However, conventional RTT evaluation is mainly on pure incoming traffic during up-peak. The Universal RTT calculation method developed in 2014–15 extended RTT evaluation to cover dominant and complicated traffic patterns of modern buildings, but the assumed number of passengers to be handled within one round trip was limited to the contract capacity of the elevator. This article further removes this limitation to evaluate the limit of handling capacity with reasonable RTT and average passenger transit time. Then, the Universal RTT method could be more realistic and rolled out, and prevent oversizing the system design.

Estimation of the Origin-Destination Matrix for Trucks That Use Highways: A Case Study in Chile

Nowadays, freight transport is crucial in the functioning of cities worldwide. To dig further into the understanding of urban freight transport movements, in this research, we conducted a case study in which we estimated an origin-destination matrix for the trucks traveling on Autopista Central, one of Santiago de Chile’s most important urban highways. To do so, we used full real-world vehicle-by-vehicle information of freight vehicles’ movements along the highway. This data was collected from several toll collection gates equipped with free-flow and automatic vehicle identification technology. However, this data did not include any vehicle information before or after using the highway. To estimate the origins and destinations of these trips, we proposed a multisource methodology that used GPS information provided by SimpliRoute, a Chilean routing company. Nevertheless, this GPS data involved only a small subset of trucks that used the highway. In order to reduce the bias, we built a decision tree model for estimating the trips’ origin, whose input data was complemented by other public databases. Furthermore, we computed trip destinations using proportionality factors obtained from SimpliRoute data. Our results showed that most of the estimated origins belonged to outskirt municipalities, while the estimated destinations were mainly located in the downtown area. Our findings might help improve freight transport comprehension in the city, enabling the implementation of focused transport policies and investments to help mitigate negative externalities, such as congestion and pollution.

Short-term forecasting of origin-destination matrix in transit system via a deep learning approach

ABSTRACT Short-term travel demand forecasting is the critical first step to support transportation system management. Complex relevance among Origin-Destination (OD) pairs, temporal dependencies, and external factors bring challenges to it. An innovative deep learning approach, Multi-Fused Residual Network (MF-ResNet) is proposed to forecast travel demand. The complex relevance among OD pairs is converted into graphical-based spatial dependencies by treating OD matrix as the input of the model. The residual network units enable MF-ResNet to model not only near but also distant spatial correlations. Three conv-based residual network units model the temporal closeness, mid-term periodicity, as well as long-term periodicity features, and Fully-Connected (F-C) layers capture external factors. The fusion techniques coordinate all of the features. The proposed method is applied to the short-term forecasts of metro OD matrix in Shenzhen, China. The experimental results show that MF-ResNet can capture multiple complex dependencies robustly and outperforms traditional methods in terms of forecasting accuracy.

Deep Learning of Spatiotemporal Patterns for Urban Mobility Prediction Using Big Data

Timely and accurate prediction of human movement in urban areas offers instructive insights into transportation management, public safety, and location-based services, to name a few. Yet, modeling urban mobility is challenging and complex because of the spatiotemporal dynamics of movement behavior and the influence of exogenous factors such as weather, holidays, and local events. In this paper, we use bus transportation as a proxy to mine spatiotemporal travel patterns. We propose a deep-learning-based urban mobility prediction model that collectively forecasts passenger flows between pairs of city regions in an origin-destination (OD) matrix. We first process OD matrices in a convolutional neural network to capture spatial correlations. Intermediate results are reconstructed into three multivariate time series: hourly, daily, and weekly time series. Each time series is aggregated in a long short-term memory (LSTM) network with a novel attention mechanism to guide the aggregation. In addition, our model is context-aware by using contextual embeddings learned from exogenous factors. We dynamically merge results from LSTM components and context embeddings in a late fusion network to make a final prediction. The proposed model is implemented and evaluated using a large-scale transportation data set of more than 200 million bus trips with a suite of Big Data technologies developed for data processing. Through performance comparison, we show that our approach achieves sizable accuracy improvements in urban mobility prediction. Our work has major implications for efficient transportation system design and performance improvement. The proposed deep neural network structure is generally applicable for sequential graph data prediction.

On mobility trends analysis of COVID-19 dissemination in Mexico City.

This work presents a tool for forecasting the spread of the new coronavirus in Mexico City, which is based on a mathematical model with a metapopulation structure that uses Bayesian statistics and is inspired by a data-driven approach. The daily mobility of people in Mexico City is mathematically represented by an origin-destination matrix using the open mobility data from Google and the Transportation Mexican Survey. This matrix is incorporated in a compartmental model. We calibrate the model against borough-level incidence data collected between 27 February 2020 and 27 October 2020, while using Bayesian inference to estimate critical epidemiological characteristics associated with the coronavirus spread. Given that working with metapopulation models leads to rather high computational time consumption, and parameter estimation of these models may lead to high memory RAM consumption, we do a clustering analysis that is based on mobility trends to work on these clusters of borough separately instead of taken all of the boroughs together at once. This clustering analysis can be implemented in smaller or larger scales in different parts of the world. In addition, this clustering analysis is divided into the phases that the government of Mexico City has set up to restrict individual movement in the city. We also calculate the reproductive number in Mexico City using the next generation operator method and the inferred model parameters obtaining that this threshold is in the interval (1.2713, 1.3054). Our analysis of mobility trends can be helpful when making public health decisions.

Methods to enhance the quality of bi-level origin–destination matrix adjustment process

ABSTRACT The quality of Origin–Destination matrix (OD) estimation depends on number of factors including the selection of appropriate upper-level function of bi-level formulation, constraints to the OD flows, and suitable solution algorithm. Addressing these aspects, the study first explored different upper-level formulations using two types of traffic information: traffic counts and sub-path flows. Second, it investigated the effects of OD constraints on the quality of solution. Third, it proposed modified genetic algorithm (MGA) to address the computational limitations of traditional genetic algorithm (GA). The study findings were as follows: a) Using symmetric mean absolute percentage error (SMAPE) to match traffic counts showed greater improvements in the OD quality; b) The estimates improved as more number of OD pairs were known to have a-priori knowledge about their flows with higher confidence levels; c) The MGA approach outperformed GA in terms of computational efficiency, and gradient descent (GD) in terms of solution quality.

A novel metamodel-based framework for large-scale dynamic origin–destination demand calibration

Calibrating dynamic traffic demand for stochastic traffic simulators is one of the big challenges due to computational burden. This paper proposes a novel framework to calibrate a dynamic car origin–destination matrix of large-scale networks, which has high computational efficiency. The proposed framework is relied on the metamodel optimization technique, based on the reference of aggregated traffic flow dynamics as expressed by the recent advance in traffic flow theory, namely the bi-modal macroscopic fundamental diagram (MFD).We validate our proposed approach with the Stochastic Perturbation Simultaneous Approximation (SPSA) algorithm which is widely used for the same purpose in the literature. Our result confirms that our approach can facilitate the demand calibration effectively. We also show that the traffic conditions at a link level are also reproduced realistically from our network-level calibration, and that our approach outperforms the traditional link-level OD calibration by comparing with the Aimsun OD adjustment in addition to SPSA.Furthermore, we demonstrate via different realistic network studies that the proposed approach is computationally efficient compared to the existing state-of-the-art approach. Our result shows that the proposed approach can be applied effectively regardless of the topology of the network, and that the model parameters do not have significant influence on the optimization results. Our calibration results show that just a few iterations are needed to calibrate the OD demand in the proposed approach even for the large-scale complex network underpinned by a bi-modal MFD derived from multiple real data sources.

Analytical and simulation-based estimation of public transport demand

We consider the problem of public transport demand estimation. For the practical use of many models of transit assignment in any planning study, the knowledge of the transport demand systematized as an origin-destination matrix is required. Generally, this matrix is very expensive and difficult to obtain. In Bhouri et al. (2021) we proposed to estimate the OD-matrix through the numerical solution of a bilevel optimization problem. One disadvantage of this formulation is the difficulty of obtaining descent directions; therefore, we proposed a derivative-free method for the resolution of the optimization problem. Our goal in this work is to extend the study to larger networks, combining analytical modeling and simulation-optimization methodology. We use both approaches to perform the demand estimation over a section of a real network, and compare the obtained results for different scenarios and parameters.

Simultaneous identification of sensor faults and origin-destination matrix estimation

Efficient estimation of the origin-destination (OD) matrix is a crucial requirement for traffic monitoring and control. The OD matrix estimation problem has received significant attention over the past decades and various approaches using traffic counts from fixed location sensors have been developed and tested. A significant challenge when using information obtained from traffic sensors, is that such sensors are subject to considerable disruptions due to system errors, that affect the quality and reliability of the information provided. This paper presents a novel approach for OD matrix estimation in the presence of faulty sensors. For the purposes of this work we assume that the network under study operates under free-flow conditions and utilise a variation of the cell transmission model to capture the traffic flow dynamics in a pre-specified time window. The problem is formulated in an optimisation framework and solved by taking into account the presence of faulty measurements. We test and validate the proposed methodology on a sample network and investigate the advantage of OD matrix estimation in the presence of faulty measurements compared to OD matrix estimation when faulty sensors are not considered.

Clustering and Analysis of GPS Trajectory Data Using Distance-Based Features

The proliferation of smartphones has accelerated mobility studies by largely increasing the type and volume of mobility data available. One such source of mobility data is from GPS technology, which is becoming increasingly common and helps the research community understand mobility patterns of people. However, there lacks a standardized framework for studying the different mobility patterns created by the non-Work, non-Home locations of Working and Nonworking users on Workdays and Offdays using machine learning methods. We propose a new mobility metric, Daily Characteristic Distance, and use it to generate features for each user together with Origin-Destination matrix features. We then use those features with an unsupervised machine learning method, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering, and obtain three clusters of users for each type of day (Workday and Offday). Finally, we propose two new metrics for the analysis of the clustering results, namely User Commonality and Average Frequency. By using the proposed metrics, interesting user behaviors can be discerned and it helps us to better understand the mobility patterns of the users.

Using location based social media data to explain COVID-19 spread in Italy

During crisis, social network data is valuable information for authorities and researchers. Since the COVID-19 outbreak, a significant amount of data, including mobility-related data, has been released from various agencies to support studies. This paper aims to check the suitability of mobility-related datasets in describing and predicting new cases, about the spatial dimension during the initial phase of the outbreak. We focus on rich anonymized datasets through Facebook – Data for Good program: colocation matrices, movement matrices and stay-at-home data. However, we also compare their usability with a traditional Origin-Destination matrix. Our test case is Italy, the second country hit after China, where the infection spread irregularly from a few northern provinces to rest of Italy and abroad. Our regression models explain the number of actual new cases at the provincial level (corresponding to NUTS-3) by three groups of variables: active cases proxying local infections, interprovincial mobility proxying the arrival of cases from outside, and the degree of people staying at home proxying infections from cohabitants. The variants among the models consist of different measures of interprovincial mobility, thus allowing us to confront them. The result is the inclusion of time-dependent mobility data improving the significance of model and is effective in explaining irregular rise of cases in different parts of the country. Moreover, colocation results as the best measure. From a policy perspective, results show that mobility restrictions help reduce the geographical spread of infection at the very beginning, but once the outbreak, the interprovincial mobility becomes less relevant.

Cost-effectiveness analysis of Origin-Destination matrices estimation using Floating Car Data. Experimental results from two real cases

The aim of this paper is to estimate static Origin-Destination (OD) matrices combining traditional data sources and Floating Car Data (FCD), testing several scenarios with different penetration rates and representativeness of probe vehicles. For each scenario, the accuracy of results is related to the cost of the data acquisition. To the best of the authors’ knowledge, this paper represents the first attempt to define a relationship among FCD penetration rate and representativeness, traditional data sources, OD matrices accuracy and cost of estimation, thus contributing to the literature on the subject and providing useful considerations for practical applications. The procedure was applied to two real cases for which ground-truth matrices were available, in order to calculate goodness of fit indicators for the estimated matrices. In this way, it was possible to define optimal solutions, which can maximize the reliability of results and minimize their cost. Results highlighted that the representativeness of probe vehicles is the main factor affecting the difference between estimated and real matrices. Moreover, when heterogeneity of penetration rates among OD pairs occurs, estimation errors can be reduced by the availability of traffic counts from on-site observations, although increasing costs. For this reason, optimal solutions were identified among scenarios balancing economic effort and estimation performance. In addition, the use of FCD was found to be competitive with procedures exclusively based on traditional on-field activities, both in terms of obtained OD matrix accuracy and costs.

N-219 AIRCRAFT TRAFFIC DISTRIBUTION MODEL BASED ON THE MOVEMENT POTENTIAL OF THE COVERAGE ZONE AT SULTAN SYARIF KASIM II AIRPORT PEKANBARU

Pekanbaru Sultan Syarif Kasim II Airport has a limited domestic flight schedule. Based on data from Angkasa Pura II, Pekanbaru Sultan Syarif Kasim II Airport serves flights to 14 domestic destinations. By using a small aircraft type N-129 with a capacity of 19 passengers, it will be able to produce several new routes that are possible to be formed and the aircraft can enter on short runways. The volume of passenger traffic at Sultan Syarif Kasim II Airport Pekanbaru is projected in 2029 to reach 8749856 passenger departures per year and 7515557 passenger arrivals per year. With the N-219 plan aircraft, one main zone and five coverage zones are obtained to distribute passenger movements. In the Origin-Destination Matrix analysis using the Furness and Fratar method, a more constant calculation is the Fratar Method. The number of aircraft needed per day with the Furness Method is at most 8 N-219 aircraft with the Pekanbaru - Palembang route. The maximum number of aircraft needed per day using the Fratar Method is 7 N-219 aircraft with the Pekanbaru - Palembang route and vice versa.