Updating origin–destination matrices with aggregated data of GPS traces

Ge, Qian; Fukuda, Daisuke

doi:10.1016/j.trc.2016.06.002

Cited by 82 publications

(29 citation statements)

References 33 publications

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“…Kim et al, 2014;Camus et al, 1997;Barcelo et al, 2010;Zhou and Mahmassani, 2006) or data from mobile phone or GPS based movement traces (e.g. Ge and Fukuda, 2016;Alexander et al, 2015;Zin et al, 2018;Gadzinski, 2018;Nigro et al, 2018). Such OD samples x k obs are then typically used to construct a prior OD matrix, e.g.…”

Section: Od Estimation Assumptions: Observationsmentioning

confidence: 99%

A data driven method for OD matrix estimation

Krishnakumari

Lint

Djukic³

et al. 2020

Transportation Research Part C: Emerging Technologies

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The fundamental challenge of the origin-destination (OD) matrix estimation problem is that it is severely under-determined. In this paper we propose a new data driven OD estimation method for cases where a supply pattern in the form of speeds and flows is available. We show that with these input data, we do not require an iterative dynamic network loading procedure that results in an equilibrium assignment, nor do we need an assumption on the kind of equilibrium that emerges from this process. The minimal number of ingredients which are needed are (a) a method to estimate/predict production and attraction time series; (b) a method to compute the N shortest paths from each OD zone to the next; and (c) two-possibly OD-specific-assumptions on the magnitude of N; and on the proportionality of path flows between these origins and destinations, respectively. The latter constitutes the most important behavioral assumption in our method, which relates to how we assume travelers have chosen their routes between OD pairs. We choose a proportionality factor that is inversely proportional to realized travel time, where we incorporate a penalty for path overlap. For large networks, these ingredients may be insufficient to solve the resulting system of equations. We show how additional constraints can be derived directly from the data by using principal component analysis, with which we exploit the fact that temporal patterns of production and attraction are similar across the network. Experimental results on a toy network and a large city network (Santander, Spain) show that our OD estimation method works satisfactorily, given a reasonable choice of N, and the use of so-called 3D supply patterns, which provide a compact representation of the supply dynamics over the entire network. Inclusion of topological information makes the method scalable both in terms of network size and for different topologies. Although we use a neural network to predict production and attraction in our experiments (which implies ground-truth OD data were needed), there are straightforward paths to improve the method using additional data, such as demographic data, household survey data, social media and or movement traces, which could support estimating such ground-truth baseline production and attraction patterns. The proposed framework would fit very nicely in an online traffic modeling and control framework, and we see many paths to further refine and improve the method. ☆ This paper has been accepted for a podium presentation at the 23rd International Symposium on Transportation and Traffic Theory (ISTTT23)

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Section: Od Estimation Assumptions: Observationsmentioning

confidence: 99%

A data driven method for OD matrix estimation

Krishnakumari

Lint

Djukic³

et al. 2020

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

show abstract

“…So far, studies on mode-choice inference from mobile data are scarce [118], as researchers have underlined the complexity of such a task [116]. Indeed, travel mode identification from mobile phone records requires the use of multiple data sources in conjunction to speed estimation and trip matching algorithms [117,119]. In terms of strategic planning, data-driven transit network design has been examined in [120,121] using large-sample trajectory data to (a) identify frequent mobility patterns (ignoring mode choice) from mobile phone data and (b) generate public transport routes.…”

Section: Emerging Data Sourcesmentioning

confidence: 99%

Combining ITS and optimization in public transportation planning: state of the art and future research paths

Iliopoulou

Κεπαπτσόγλου

2019

Eur. Transp. Res. Rev.

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Intelligent Transportation Systems (ITS) applications in public transportation have allowed for automated data collection, which is particularly useful for planning and operations. While technological advancement of ITS has so far been extensive, their usage for developing relevant planning and operational tools is rather limited. Research on planning and operations of public transportation systems has not widely investigated the potential of combining optimization models with data originating from ITS. Such applications, which could benefit from such an approach include route planning, scheduling and resource allocation in real time. In this context, this paper investigates and critically discusses potential models and methodologies in public transport planning and operations, which can benefit from ITS data, highlights their potential and identifies possible research paths on that area. The overview of literature collectively points to a series of common challenges faced by transportation professionals and underlines the need for better decision support tools for ITS data.

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“…In the case when a longer period dataset or larger numbers of taxi data were available, using road network becomes a better option than using the grid network. Other advantages of an OD matrix based in grid is that it could help anonymize the data where data are sensitive, and privacy needs to be protected as well as; such an OD matrix could be applied for understanding interzonal or intercity mobility as well [35]. With the origin-destination matrix for passenger trip, OD transition probability was computed for both weekday and weekend data as shown in Equation (2), which was to be used for passenger trip simulation, as described in Section 5.…”

Section: Taxi Origin and Destinationmentioning

confidence: 99%

Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data

Ranjit

Witayangkurn

Nagai

et al. 2018

IJGI

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Abstract:Taxi behavior is a spatial-temporal dynamic process involving discrete time dependent events, such as customer pick-up, customer drop-off, cruising, and parking. Simulation models, which are a simplification of a real-world system, can help understand the effects of change of such dynamic behavior. In this paper, agent-based modeling and simulation is proposed, that describes the dynamic action of an agent, i.e., taxi, governed by behavior rules and properties, which emulate the taxi behavior. Taxi behavior simulations are fundamentally done for optimizing the service level for both taxi drivers as well as passengers. Moreover, simulation techniques, as such, could be applied to another field of application as well, where obtaining real raw data are somewhat difficult due to privacy issues, such as human mobility data or call detail record data. This paper describes the development of an agent-based simulation model which is based on multiple input parameters (taxi stay point cluster; trip information (origin and destination); taxi demand information; free taxi movement; and network travel time) that were derived from taxi probe GPS data. As such, agent's parameters were mapped into grid network, and the road network, for which the grid network was used as a base for query/search/retrieval of taxi agent's parameters, while the actual movement of taxi agents was on the road network with routing and interpolation. The results obtained from the simulated taxi agent data and real taxi data showed a significant level of similarity of different taxi behavior, such as trip generation; trip time; trip distance as well as trip occupancy, based on its distribution. As for efficient data handling, a distributed computing platform for large-scale data was used for extracting taxi agent parameter from the probe data by utilizing both spatial and non-spatial indexing technique.

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Updating origin–destination matrices with aggregated data of GPS traces

Cited by 82 publications

References 33 publications

A data driven method for OD matrix estimation

A data driven method for OD matrix estimation

Combining ITS and optimization in public transportation planning: state of the art and future research paths

Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data

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