Origin-destination Matrix Research Articles

Short-Term Origin-Destination Forecasting in Urban Rail Transit Based on Attraction Degree

Origin-destination (OD) forecasting is a difficult task in urban rail transit because many random factors can influence outcomes such as numerous OD pairs with few or even no flows. Therefore, treating all OD pairs equally, which is the method adopted by all existing studies, may not only increase model complexity and computation, but also negatively influence forecasting results. Therefore, in this study, we propose an indicator called OD attraction degree (ODAD) to address this problem in the field of OD forecasting. First, we introduce the ODAD indicator and five ODAD levels to describe the attraction between OD pairs. Based on the ODAD, an OD matrix pre-processing method is presented to prepare data for the LSTM model. Second, we use the mature long short-term memory (LSTM) network model to examine the effects of the introduction of ODAD. The LSTM model's advantage of dealing with variable-length sequences by leveraging the masking layer is creatively utilized. Finally, nine cases under different time granularities and different ODAD levels are thoroughly studied to explore their optimal combination. Based on this analysis, we recommend a time granularity of 30 min and an ODAD level of “Low” for actual subway operation. In this case, the root mean squared error, mean absolute error, and weighted mean absolute percentage error are 2.31%, 0.66%, and 27.28%, respectively, for a network of nearly 300 subway stations. The introduction of ODAD can provide critical insights for subway operators to conduct short-term OD forecasting.

Sustainable approach to land development opportunities based on both origin-destination matrix and transportation system constraints, case study: Central business district of Isfahan, Iran

Given that there are restrictions on expanding the infrastructure for the purpose of fulfilling future transportation demand, it is critical to consider the capacity of the existing transportation system and the sustainable extent of development, in order to avoid problems such as traffic congestion and air pollution. In this study, by applying the inverse of conventional transportation planning, which is different from the land-use transportation interaction modeling, land-use limits for each Traffic Analysis Zone (TAZ) are determined as a function of the transportation system capacity. Indeed, this method is applied to examine the trip generation capacity of each TAZ with respect to its trip end values. Comparing the indicated capacity with the current demand for each zone can act as a key tool for urban policy makers and planners to define the spatial distribution of new activities based on the present capacity of the transportation system in the studied area. For this purpose, after selecting the transportation network of the central business district in the City of Isfahan as a case of study, the artificial neural networks solution is used to solve the inverse transportation planning problem.

Dynamic origin-destination matrix calibration for large-scale network simulators

This paper considers offline dynamic OD (origin-destination) calibration problems for large-scale simulation-based network models. We formulate the problem as a simulation-based optimization (SO) problem and propose a scalable and efficient metamodel SO algorithm. For a network with n links and a problem with T time intervals and Z OD pairs per time interval, the corresponding SO problem is a high-dimensional problem of dimension T·Z. At each iteration of the SO algorithm, we solve a set of T independent analytical and differentiable metamodel optimization problems, each of dimension Z. The T analytical problems are constrained by n nonlinear equality constraints. Hence, they scale linearly with the number of links in the network and independently of other network attributes, such as the dimension of the route choice set or the link lengths. The temporal correlation, of the link performance metrics across time intervals, is approximately captured through the parameters of the metamodel. Since the T analytical optimization problems are decoupled and can be solved independently, the proposed approach scales independently of the number of calibration time intervals, making it suitable for the calibration of demand over numerous time periods.The approach is efficient and scalable. It is suitable to address high-dimensional calibration problems for large-scale network models. It is benchmarked on both a toy network and a Singapore network versus two general-purpose algorithms: SPSA and a derivative-free pattern search algorithm. The validation experiments indicate that the proposed method identifies points with objective function estimates that outperform the benchmark methods by 1 to 2 orders of magnitude. As the problem dimension and the temporal correlation, across time intervals, increase, so does the magnitude by which the proposed method outperforms the benchmark methods. The Singapore case study considers a problem of dimension 16,200. This is 2 orders of magnitude higher than what is currently considered high-dimensional for continuous SO problems. The proposed method identifies solutions with an estimated objective function that is 1 order of magnitude better than those of the benchmark methods. It yields solutions that provide an average, across time intervals, improvement to link counts of 77%, compared to the benchmark methods. The case study also illustrates the robustness of the method to the quality of the initial points.

Bus frequency optimisation considering user behaviour based on mobile bus applications

In this study, a model for bus frequency optimisation was presented, which determined the optimal bus frequencies aiming to minimise the users' total travel cost. Unlike past researches, where the impacts of real-time information were ignored, the user behaviours affected by mobile applications were considered in this study. The effects of bus real-time information on user behaviours had been proved by some other researches. By assigning a user's trip to three stages (pre-trip, on-board and end-trip), four different user behaviours were taken into account in the model building process: shortest total travel time, shortest pre-trip time, shortest walking time and shortest riding time. In addition to the object bus line and stops on it, other bus lines and stops serving the same traffic zones were also considered. The demand was given in the form of an origin-destination (OD) matrix. Each value in the OD matrix represented the passenger amount from one zone to another. Finally, the illustrated model was tested by four cases and the results showed a good performance.

Estimating Elasticities for Freight Transport Using a Network Model: An Applied Methodological Framework

This paper presents a general framework that can be used to estimate direct and cross elasticities for freight transport using a network model. This methodology combines operational research (network assignments in a geographical information system) with more classical econometrics (multinomial logit choice models). The application of the method to a real-world case is illustrated by a simple model that relies on the generalized cost of transport as the only explanatory variable in the utility function. The methodological framework allows, however, for the implementation of more complex functions. Beside the generalized cost functions for road, rail and inland waterways transport, the network model needs origin-destination matrixes and digitized networks. They are imported from ETIS Plus, a European transport policy information system. A set of direct and cross elasticities is presented. The estimated values are obtained using two methods: the first computes standard elasticities, while the second estimates arc elasticities. Figures are presented for Europe and for a large region around the Benelux countries, where more competition exists between the three modes of interest.

Research on the construction of carbon emission evaluation system of low-carbon-oriented urban planning scheme: taking the West New District of Jinan city as example

ABSTRACTCarbon emissions caused by human activities are closely linked to the process of urbanization, urban land use and traffic system are two important factors that may influence it. On the basis of clarifying the space structure of low-carbon eco-city, this paper constructing an evaluation system of urban carbon emission from four dimensions: construction land compact, traffic carbon emission potential, road network accessibility, and public transportation accessibility. Based on the evaluation system, the author took the West New District of Jinan city as an example, evaluated urban planning scheme by using several spatial models such as landscape pattern index, Spatial syntax, POI (Point of Interest) kernel density analysis, and OD (Origin Destination) matrix in support of GIS (Geographic Information System) and RS (Remote Sensing) techniques.

Improved algorithms for trip‐chain estimation using massive student behaviour data from urban transit systems

With bus global positioning system data and smart-card data, this study puts forward improved algorithms to further increase the success rate of alighting stop identification for the student group, so that trip chains and origin-destination (OD) matrixes of students can be obtained with the high estimated rate. Aimed at the student group, after using and generalising conventional alighting algorithms, this study innovatively utilises resident and non-resident students' typical trip patterns to further increase the success estimated rate. As a result, the algorithms are verified with a correct estimation of 74.9%, where the success rate steeply increases by 8.6% through the method based on students' typical trip patterns. The empirical analysis and application have shown that the methodology can observe the trip chains of students and optimise the bus service for students. In the future, OD matrixes obtained should be validated with bus OD surveys, and traffic system of metro and shared bikes as well as other datasets such as land use data will be taken into consideration.

Daily commuting in the Bratislava metropolitan area: case study with mobile positioning data

Suburban development in the metropolitan area of Bratislava (Slovakia) led to an unprecedented redistribution of population. As the majority of suburbanized families are car-dependent, the influx of new residents is a major source of growing traffic problems. However, due to the unwillingness of suburban in-migrants to change their registration (approximately 100,000 unregistered residents), the commuting based on the census data is insufficient to capture the real pattern of daily mobility in the hinterland of Bratislava. Yet, mobile positioning data represent a powerful source of information with high spatial accuracy and endless options for use. The objective of this study is to analyze mobility patterns of commuting in the Bratislava metropolitan area using mobile positioning data. The experiment was carried out in September 2017 over a fourteen-day period with a sample of 191,404 mobile phone users. The key task was to find a suitable methodological approach to extract useful information of human mobility patterns. We used the concept of anchor points, which represent main nodes of human activity and space–time trajectories. Extracting the localization of “work” and “sleep” we modeled the origin-destination matrix of daily commuting with a spatial accuracy not available until now.

Smart Card Data Mining to Analyze Mobility Patterns in Suburban Areas

This paper aims to define an algorithm capable of building the origin-destination matrix from check-in data collected in the extra-urban area of Torino, Italy, where thousands of people commute every day, using smart cards to validate their travel documents while boarding. To this end, the methodological approach relied on a survey over three months to record smart-card validations. Peak and off-peak periods have been defined according to validation frequency. Then, the origin-destination matrix has been estimated using the time interval between two validations to outline the different legs of the journey. Finally, transport demand has been matched with existing bus services, showing which areas were not adequately covered by public transport. The results of this research could assist public transport operators and local authorities in the design of a more suitable transport supply and mobility services in accordance with user needs. Indeed, tailoring public transport to user needs attracts both more customers and latent demand, reducing reliance on cars and making transport more sustainable.

Extended Methodology for the Estimation of a Zonal Origin-Destination Matrix: A Planning Software Application Based on Smartcard Trip Data

A key planning consideration when designing transit services is the projected demand on each route, as this is a necessary input to determine frequencies and vehicle capacities. Predicting demand changes in complex networks requires a thorough modelling of the public transport system and its users’ decisions. Aiming to help in this process, a new transit planning tool (STEPTRANS) is being developed, seeking to predict the reactions of public transport users to network changes, allowing better design and operation of the transport system. This tool needs a reliable origin-destination (O-D) matrix for the city of interest, which is assigned to the transit network, using state of practice route choice models. This work presents an extension of the former methodology implemented in STEPTRANS, which allows a zonal O-D matrix for a city to be estimated, given a stop-to-stop matrix obtained from smartcard trip data. The methodology and extension were applied to estimate models and O-D matrices for Santiago de Chile’s integrated transit system, Transantiago. The extension focuses on three improvements: (1) addressing data limitations by using the latest large scale O-D survey available for Santiago; (2) updating the zonal system, including a new methodology for access links construction; and (3) enhancing the model formulation, incorporating trip pattern corrections and reformulating cost measures using route choice models. The inclusion of the resulting matrices in STEPTRANS will allow the evaluation of major changes to the transport system, as a zonal O-D matrix allows modelled users to choose their initial and final stops.

Theoretical substantiation of trip length distribution for home-based work trips in urban transit systems

Modern approaches to the modeling of transport demand imply the use of calibration procedures during the origin-destination (O-D) matrix estimation or transit assignment. These procedures lead to misrepresenting generated and attracted trips or changing the trip length distribution (TLD). It means that the methods of transport planning can be improved by means of determination, validation and implementation of the TLD to calculate the O-D matrix. The analysis of research results in the field of mass transit reveals an explicit similarity between TLD in different cities and the gamma distribution. It points to general regularities in various systems of mass transit that lead to the similarity in TLD. The regularities are determined by studying the spatial distribution of mass transit stops, which are considered trip origins and destinations. The experimental research was conducted in 10 Ukrainian cities using probability theory methods.

Interplay between urban communities and human‐crowd mobility: A study using contributed geospatial data sources

AbstractAn intense process of urbanization, witnessed particularly in the last decade, has stressed the need to comprehend human mobility behavior in urban settings. Although the emergence of contributed geospatial data (i.e., pervasive activity‐based data) has contributed to substantial progress toward understanding human activity, the relationship between human‐crowd mobility and the functional structure of a city is not yet well understood. In this context, the present research focuses on the intra‐urban origin–destination matrix modeling founded on a combination of two major crowdsourced datasets as well as the inclusion of urban communities’ structure. Specifically, the well‐known “radiation” and “PWO” models were modified through first, identifying the communities embedded in the cyberspace network then employing the identified hierarchical structure of the spatial‐interaction network for the formulation of the users’ movement network and second, imposing proper input variables including the telecommunication activity volume and check‐in frequency. The results obtained by various empirical analyses suggest that the modified community‐constrained origin–destination flow estimation models exhibit better performance levels than those of alternative conventional mobility models.

The coal origin-destination matrix analysis and multimodal transportation cost modelling in the Yangtze River region, China

ABSTRACTThe railway-road-inland waterway (IWW) transportation network in the Yangtze River basin was extracted using ArcGIS software and the triangular mesh covered the simulated region was generated using GIS software. The population density and gross domestic product were chosen as the characteristic economic variables, which were interpolated spatially into the mesh to analyse the origin-destination (OD) matrix of the coal transport network. Then, the multimodal transportation cost model was developed considering the detailed cost functions of railway, road and IWW. Finally, the coal transportation cost under different transportation allocation scenarios was calculated and compared to investigate the advantages and potential of the multimodal transportation mode in the Yangtze River basin. The cost analysis about the multimodal transportation system showed that the IWW mode has great potential to decrease the transportation cost of the coal within the OD matrix although the railway mode took at least 80% of the total transportation load.

Exploring the effectiveness of demand management policy in reducing traffic congestion and environmental pollution: Car-free day and odd-even plate measures for Bandung city in Indonesia

Traffic congestion has been a major problem in big cities around the world, not to mention several large cities in Indonesia. Bandung is the second largest metropolitan area after Jakarta in Indonesia which suffers from extreme levels of congestion. With a high number of motorcycles and large private car population, congestion in this city is ever growing worsening the environment. While the local authorities struggle to find resources to fund capital intensive capacity expansion projects, this research explores the use of cost effective demand management policy measures to reduce the congestion and pollution. This study aims at assessing two relatively under-researched demand management policy measures that restrict vehicle flows viz., car-free day and odd-even plate schemes to investigate the effect on traffic congestion and the environment. SATURN traffic network modelling software has been used to predict the route choices of vehicles. Bandung city road network and origin destination matrix have been adapted to simulate the two measures during the peak hour. As well as providing the necessary inputs to a pollutant emission estimation model, traffic network modelling output forms the basis for assessing the congestion levels. Results show that both car-free day and odd-even plate measures have unintended consequences that undermine their effectiveness which if addressed could make them highly beneficial solutions. Car-free day scheme reduces the traffic flow levels in the vicinity of scheme but diverts the vehicle flow elsewhere to other routes which may adversely affect the congestion/pollution. Odd-even plate scheme is very effective at the beginning of its implementation but the performance gradually diminishes as drivers start to adapt by buying a second vehicle or even using fake number plates.

An Algorithm for Determining Optimum Link Traffic Volume Counts for Estimation of Origin-Destination Matrix

Travel demand information is one of the most important inputs in transportation planning. Today, the access to origin-destination (OD) matrix using traffic volume count information has caught the researchers’ attention because these methods can estimate OD matrices based on the flow volume in the links of network with a high accuracy at a much lower cost over a short time. In such algorithms, the number and location of links are one of the main parameters for traffic volume count; hence a better OD matrix can be achieved by choosing the optimum links. In this paper, an algorithm is presented to determine the number and location of optimum links for traffic volume count. The method specifies the minimum links to cover the maximum elements of OD matrix. This algorithm is especially useful for the estimation of ODM through gradient method, because only the O-D pairs covered by link traffic counts are adjusted and estimated in the gradient method. The algorithm is then scripted via EMME/2 and FoxPro and implemented for a large-scale real network (Mashhad). The results show that about 95% of the ODM can be covered and then adjusted by counting only 8% of the links in the network of Mashhad.

Inferencing Human Spatiotemporal Mobility in Greater Maputo via Mobile Phone Big Data Mining

The mobility patterns and trip behavior of people are usually extracted from data collected by traditional survey methods. However, these methods are generally costly and difficult to implement, especially in developing cities with limited resources. The massive amounts of call detail record (CDR) data passively generated by ubiquitous mobile phone usage provide researchers with the opportunity to innovate alternative methods that are inexpensive and easier and faster to implement than traditional methods. This paper proposes a method based on proven techniques to extract the origin–destination (OD) trips from the raw CDR data of mobile phone users and process the data to capture the mobility of those users. The proposed method was applied to 3.4 million mobile phone users over a 12-day period in Mozambique, and the data processed to capture the mobility of people living in the Greater Maputo metropolitan area in different time frames (weekdays and weekends). Subsequently, trip generation maps, attraction maps, and the OD matrix of the study area, which are all practically usable for urban and transportation planning, were generated. Furthermore, spatiotemporal interpolation was applied to all OD trips to reconstruct the population distribution in the study area on an average weekday and weekend. Comparison of the results obtained with actual survey results from the Japan International Cooperation Agency (JICA) indicate that the proposed method achieves acceptable accuracy. The proposed method and study demonstrate the efficacy of mining big data sources, particularly mobile phone CDR data, to infer the spatiotemporal human mobility of people in a city and understand their flow pattern, which is valuable information for city planning.

Multisource Data-Driven Modeling Method for Estimation of Intercity Trip Distribution

Traditional intercity trip distribution modeling methods are merely derived from household travel survey due to its limitation to partial or inaccurate information. With the development of information construction, reliable historical data can be easily collected from different sources, such as sensor and statistical data. In this study, a data-driven method based on Poisson distribution theory is proposed to estimate intercity trip distribution using sensor data and various city features. A Poisson model, which reveals the deep correlation between city feature variables and trip distribution, is initially formulated. The L1-norm approach and the coordinate descent algorithm are then adopted in selecting related features and estimating model parameters, respectively, to reduce the complexity of the model. Finally, a k-means clustering method is used to analyze the latent correlation between city features and improve the availability of the model. The methodology is tested on a realistic dataset containing the highway trips of 17 cities in Shandong Province, China. The city feature variables have 66 dimensions, including economic index and population indicator. In comparison with traditional gravity model, which regards population as the most important factor affecting city attraction, our result shows that one of the core positive factors is the economic feature, such as gross regional domestic product. Moreover, the dimension of city features in the developed model decreases from 66 to 13 dimensions. The model developed in this study performs well in replicating the observed intercity origin-destination matrix.

Quasi-Dynamic Estimation of OD Flows From Traffic Counts Without Prior OD Matrix

This paper proposes a fully specified statistical model for the quasi-dynamic estimation of origin–destination (OD) flows from traffic counts for highway stretches and networks or for urban areas where the path choice is of minor importance. Hereby, the approach (E. Cascetta et al. , Transp. Res. B, Methodol. , vol. 55, pp. 171–187, 2013) is extended by eliminating the need for supplying a historic OD matrix. This is done by a combination of least squares estimation for replicating measured link flows with maximum entropy methods to fill in the non-observable part of the distribution across paths. Additionally, it is stressed that the quasi-dynamic assumption of constant path choice proportions over time-of-day-intervals for days of the same day category can be used in order to enhance estimation by including multi-day observations. Jointly one obtains a statistical framework with an explicit estimation algorithm that can be used to test the quasi-dynamic assumption. The approach is demonstrated to provide accurate results in a small-scale simulation study as well as two real-world case studies, one dealing with a highway segment where taxi floating car data provides the true OD flows for the taxis, and the other one dealing with an urban area with a very limited number of alternative paths allowing for explicit path enumeration.

How Should Travel Demand and Supply Models Be Jointly Calibrated?

Calibration is a critical aspect of model development that has long been recognized by researchers as a challenging issue. In particular, difficulties arise when the observed data used for calibration do not match the model output, which is the case in the majority of transport planning models. In the traditional calibration process, the origin–destination (OD) matrices are the key interface between demand and supply models, which could lead to issues when observed traffic link counts are used to update the OD matrix, causing a loss of key demand characteristics in the process. Developing a unified structure for modeling both demand and supply requires a calibration process that meets the requirements of both types of models, a serious issue which has received less attention in the literature. In this paper, the existing processes of developing and integrating demand and supply models are discussed and then examined using a case study in the Melbourne area. The numerical results show that the standard OD calibration procedure causes unrealistic changes in the OD matrix. Finally, some possible solutions to address the current limitations in development of a unified structure are discussed.

Mobility Dataset Generation for Vehicular Social Networks Based on Floating Car Data

Vehicular social networks (VSNs) have attracted the research community due to its diverse applications ranging from safety to entertainment. Social vehicles standing for private cars and floating cars standing for taxis are two important components of VSN. However, the lack of social vehicles data causes some factors are neglected including social aspects and macroscopic features, which blocks researching social attributes of vehicles in VSN. Generating a realistic mobility dataset for VSN validation has been a great challenge. In this paper, we present the detailed procedure to generate social vehicular mobility dataset from the view of floating car data, which has the advantage of wide universality. First, through the deep analysis and modeling of the dataset of floating cars and combining with the official data, we predict the origin–destination (OD) matrix of social vehicles with the gravity model, and then calibrate the OD matrix with the average growth factor method. Second, we construct network description after editing the road network. Third, we use simulation of urban mobility to reproduce the scenario in view of microsimulation by generating the mobility dataset of social vehicles based on floating car data and urban functional areas. At last, we prove the effectiveness of our method by comparing with real traffic situation in Beijing. The generated mobility model may not accurately represent the mobility of social vehicles in few spots, such as train station or airport, however, exploiting figures and facts of transportation in the city have been considered in the study to calibrate the model up to maximum possible realization.