Estimating Transit Route OD Flow Matrices from APC Data on Multiple Bus Trips Using the IPF Method with an Iteratively Improved Base: Method and Empirical Evaluation

Ji, Yuxiong; Mishalani, Rabi G.; McCord, Mark R.

doi:10.1061/(asce)te.1943-5436.0000647

Cited by 21 publications

(21 citation statements)

References 9 publications

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“…Several studies have developed algorithms to estimate origin-destination (OD) related data and structures using AFC data: Trépanier et al [38] exploited AFC data to account for similarities between trips over successive days and identify transit alighting points, while Munizaga and Palma [39] combined AFC and AVL data to describe travel patterns for metro and bus trips. Other efforts focused on using AFC/APC data for modifying the iterative proportional fitting (IPF) method [40], which has been widely applied for estimating route-level OD matrices from boarding and alighting counts [41,42]. In detail, as a seed OD matrix is required for IPF implementation, Ji et al [42,43] derived such a matrix within hybrid IPF-based methodologies, using APC data.…”

Section: Origin-destination and Transfer Inferencementioning

confidence: 99%

“…Other efforts focused on using AFC/APC data for modifying the iterative proportional fitting (IPF) method [40], which has been widely applied for estimating route-level OD matrices from boarding and alighting counts [41,42]. In detail, as a seed OD matrix is required for IPF implementation, Ji et al [42,43] derived such a matrix within hybrid IPF-based methodologies, using APC data. The simultaneous presence of AFC and AVL/farebox data has also been exploited within rigorous estimation algorithms to overcome the difficulty of distinguishing short activities from transfers when trying to identify linked trips [43][44][45][46][47].…”

Section: Origin-destination and Transfer Inferencementioning

confidence: 99%

See 1 more Smart Citation

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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Section: Origin-destination and Transfer Inferencementioning

confidence: 99%

Section: Origin-destination and Transfer Inferencementioning

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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“…APC data are mainly used to estimate total ridership, average journey length, and bus loads. They could also be used to estimate OD flow matrices [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Estimating Bus Loads and OD Flows Using Location-Stamped Farebox and Wi-Fi Signal Data

Zhao

Zhang

et al. 2017

Journal of Advanced Transportation

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Electronic fareboxes integrated with Automatic Vehicle Location (AVL) systems can provide location-stamped records to infer passenger boarding at individual stops. However, bus loads and Origin-Destination (OD) flows, which are useful for route planning, design, and real-time controls, cannot be derived directly from farebox data. Recently, Wi-Fi sensors have been used to collect passenger OD flow information. But the data are insufficient to capture the variation of passenger demand across bus trips. In this study, we propose a hierarchical Bayesian model to estimate trip-level OD flow matrices and a period-level OD flow matrix using sampled OD flow data collected by Wi-Fi sensors and boarding data provided by fareboxes. Bus loads on each bus trip are derived directly from the estimated trip-level OD flow matrices. The proposed method is evaluated empirically on an operational bus route and the results demonstrate that it provides good and detailed transit route-level passenger demand information by combining farebox and Wi-Fi signal data.

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“…Most of traditional OD estimation methods (e.g., the IPF method) estimate OD flows based on boarding and alighting counts on individual bus trips or based on aggregated boarding and alighting counts . As a result, they do not take advantage of all relevant information in the boarding and alighting data set for OD estimation.…”

Section: Introductionmentioning

confidence: 99%

Statistical inference on transit route‐level origin–destination flows using automatic passenger counter data

You

Jiang

et al. 2015

J of Advced Transportation

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SUMMARYWe consider inferring transit route-level origin-destination (OD) flows using large amounts of automatic passenger counter (APC) boarding and alighting data based on a statistical formulation. One critical problem is that we need to enumerate the OD flow matrices that are consistent with the APC data for each bus trip to evaluate the model likelihood function. The OD enumeration problem has not been addressed satisfactorily in the literature. Thus, we propose a novel sampler to avoid the need to enumerate OD flow matrices by generating them recursively from the first alighting stop to the last stop of the bus route of interest. A Markov chain Monte Carlo (MCMC) method that incorporates the proposed sampler is developed to simulate the posterior distributions of the OD flows. Numerical investigations on an operational bus route under a realistic OD structure demonstrate the superiority of the proposed MCMC method over an existing MCMC method and a state-of-the-practice method.

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Estimating Transit Route OD Flow Matrices from APC Data on Multiple Bus Trips Using the IPF Method with an Iteratively Improved Base: Method and Empirical Evaluation

Cited by 21 publications

References 9 publications

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

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

Estimating Bus Loads and OD Flows Using Location-Stamped Farebox and Wi-Fi Signal Data

Statistical inference on transit route‐level origin–destination flows using automatic passenger counter data

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