Origin-destination inference in public transportation systems: A comprehensive review

Mohammed, Mohammed; Oke, Jimi

doi:10.1016/j.ijtst.2022.03.002

Cited by 20 publications

(6 citation statements)

References 56 publications

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“…Cycleway networks, rather than simply isolated routes or other geographically sparse interventions, are vital for successful active travel investment (Buehler and Dill, 2016). Our results are therefore highly policy relevant, adding value to established methods of adding value to OD data to support sustainable transport planning (Larsen et al, 2013;Lovelace et al, 2017;Mohammed and Oke, 2022).…”

Section: Discussionmentioning

confidence: 78%

Exploring Jittering and Routing Options for Converting Origin-Destination Data Into Route Networks: Towards Accurate Estimates of Movement at the Street Level

Lovelace

Félix

Carlino

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Origin-Destination (OD) datasets provide vital information on how people travel between areas in many cities, regions and countries worldwide. OD datasets are usually represented geographically with straight lines or routes between zone centroids. For active travel, this geographic representation has substantial limitations, especially when zone origins and centroids are large: only using a single centroid origin/destination for each large zone results in sparse route networks covering only a small fraction of likely walking and cycling routes. This paper implements and explores the use of jittering and different routing options to overcome this limitation, thereby adding value to aggregate OD data to support investment in sustainable transport infrastructure. The route network results — generated from on an open dataset representing cycling trips in Lisbon, Portugal — were compared with a ground-truth dataset from 67 count locations distributed throughout the city. This approach enabled exploration of which jittering parameters and routing options lead to the most accurate route network results approximating the real geographic distribution of cycling trips in the study area. We found that jittering and disaggregating OD data, combined with routing using low level of traffic stress (quieter) preferences resulted in the most accurate route networks. We conclude that a combined approach involving 1) jittering with intermediate levels of disaggregation and 2) careful selection of routing options can lead to much more realistic route networks than using established OD processing techniques. The methods can be deployed to support evidence-based investment in strategic cycling and other sustainable transport networks in cities worldwide.

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Section: Discussionmentioning

confidence: 78%

Exploring Jittering and Routing Options for Converting Origin-Destination Data Into Route Networks: Towards Accurate Estimates of Movement at the Street Level

Lovelace

Félix

Carlino

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…We provide a summary of all the key characteristics of the typologies in Table 5. Trip chaining uses consecutive AFC records to predict boarding/alighting stops and infer complete journeys made by each passenger (Mohammed and Oke, 2022). However, it requires several parameters to correctly estimate the end nodes in passenger trips.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal trip pattern analysis in a regional bus network with limited automated fare collection data: a case study of the Pioneer Valley

Mohammed

Oke

2022

Preprint

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Understanding passenger trip patterns is crucial to enabling transit agencies plan effectively and equitably, particularly in response to disruptive events. However, current data collection systems either do not collect detailed passenger behaviour or are expensive to implement. In this paper, we exploit the relatively cheap big data collected by a mobile ticketing system to efficiently extract distinctive travel patterns. We employed our methods on a sample of anonymized data from the Pioneer Valley Transit Authority in Massachusetts. First, we applied a greedy approach to infer trip boarding stop locations. Then we computed the multi-dimensional dissimilarity of the passenger activation time series using the AWarp alignment algorithm, which works well with sparse data. Finally, we clustered these spatiotemporal patterns using hierarchical clustering. Our novel method has resulted in four spatiotemporal trip pattern typologies which we analysed based on demographics, hourly and daily distributions, faretypes, trip length and duration, among other metrics. Three of the typologies were associated with regular commuters, differentiated by either boarding time or transfer propensity. The fourth typology was mostly associated with leisure or other activities. Beyond yielding insights and facilitating demand estimation for planners in the study area, we expect that our framework can be readily applied to aid future decision-making efforts in similar study areas with minimal data availability.

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“…The demand for PT is changing over time. A reliable OD matrix is essential for transportation analysis and planning, as it provides spatio-temporal trip estimates that enable planners to understand which modes people use, how long they travel, and where they go [38]. An OD matrix can be categorized as either static or dynamic.…”

Section: Trip Distributionmentioning

confidence: 99%

Specifics of Creating a Public Transport Demand Model for Low-Density Regions: Lithuanian Case

Ranceva,

Ušpalytė-Vitkūnienė

2024

Sustainability

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A transport model usually consists of a demand model and an available transport network model. The purpose of this article is to identify the key specifics for the development of a regional public transport (PT) demand model and to point out the differences from the urban PT demand model. The traditional four-step transport planning demand model consists of trip generation, trip distribution, modal split, and assignment. This article consists of PT model development, calibration, and validation. A PTV VISUM macroscopic modeling program is used for this research. As a result, this article presents basic suggestions for how a PT demand model should be developed in regions. The presented suggestions for developing a PT demand model can be applied to any low-density region. The rest of the article is structured as follows: (1) Background: presents a literature analysis of the four-step model, modal splits, and the features of the PTV VISUM program; (2) Methods: describes the considered region of Lithuania and the data of the developed model; describes the four-step model, which is adapted to the Lithuanian region; (3) Results: presents the results and main suggestions for creating a PT demand model; and (4) Conclusions: presents the main conclusions of the study.

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Origin-destination inference in public transportation systems: A comprehensive review

Cited by 20 publications

References 56 publications

Exploring Jittering and Routing Options for Converting Origin-Destination Data Into Route Networks: Towards Accurate Estimates of Movement at the Street Level

Exploring Jittering and Routing Options for Converting Origin-Destination Data Into Route Networks: Towards Accurate Estimates of Movement at the Street Level

Spatiotemporal trip pattern analysis in a regional bus network with limited automated fare collection data: a case study of the Pioneer Valley

Specifics of Creating a Public Transport Demand Model for Low-Density Regions: Lithuanian Case

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