Estimation of people flow in public transportation network through the origin-destination problem for the South-Eastern corridor of Quito city in the smart cities context

Zapata, Lina Patrica; Flores, Marco; Larios, Victor M.; Maciel, Rocío; Arias, Enrique

doi:10.1109/isc246665.2019.9071778

Cited by 4 publications

(6 citation statements)

References 10 publications

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“…𝑃 (𝑠, 𝑎𝑓𝑡) = 𝑃 (𝑠, 𝑚𝑛𝑔) = 𝑒 ( ) (13) It is noted that 𝛼 (𝑠) is the service provider level of stop 𝑠 which indicates the normalized weighted sum of service providers in circle 𝑐(𝑠, 𝑟) and calculated as in (14).…”

Section: Beginning and Final Stops' Analysismentioning

confidence: 99%

“…; transportation modalities, the schedule of trips); it might not be always provided by the mobility operators. The mobility demand data can be formalized in terms of origin-destination matrices (ODM) (e.g., [11][12][13]), which in turn can be obtained from different resources (e.g., fare collection using ticket validation [10] or smart cards [11], boarding/alighting passengers [8,13], census data, on board units of insurances, mobile phone data from the telecom operators, mobile phone data from mobile app operators providing navigators, census data, etc.) that are difficult or at least very expensive to have [13].…”

Section: Introductionmentioning

confidence: 99%

“…The mobility demand data can be formalized in terms of origin-destination matrices (ODM) (e.g., [11][12][13]), which in turn can be obtained from different resources (e.g., fare collection using ticket validation [10] or smart cards [11], boarding/alighting passengers [8,13], census data, on board units of insurances, mobile phone data from the telecom operators, mobile phone data from mobile app operators providing navigators, census data, etc.) that are difficult or at least very expensive to have [13]. A certain amount of demand may be satisfied and oriented to exploit the public transportation offer, while others are oriented in using several kinds of private travel means: cars, bikes, motorbikes, etc.…”

Section: Introductionmentioning

confidence: 99%

“…A certain amount of demand may be satisfied and oriented to exploit the public transportation offer, while others are oriented in using several kinds of private travel means: cars, bikes, motorbikes, etc. To this end, it is necessary to evaluate plans and strategies, including soft (e.g., incentives, providing information and feedback [14]) and hard ones (e.g., changing frequency of trips, ticket price, and line path [15]), designed to improve the efficiency of the offered services [13]. For instance, when complaints are received by a mobility officer about periodic crowding conditions on a bus line or on a (set of) segment(s) which connect(s) specific stops, the necessary actions to resolve the issue could include performing some on-site analysis, and then implementing possible solutions.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing Demand with Respect to Offer of Mobility

et al. 2022

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A main key success for public transportation networks is their tuning by the analysis of mobility demand with respect to the offer in terms of public transportation means. Most of the solutions at the state of the art have strong limitations in taking into account: multiple contextual information as attractors/motivations for people movements, modalities of travel means, multiple operators, and a range of key performance indicators. For these reasons, a model for analyzing the demand with respect to the offer of mobility has been studied, and the corresponding tool DORAM developed. DORAM allows to perform the analysis of alternative scenarios, as what-if analyses, when the transport service offer and the mobility demand changed in the scenario, adopting a fast-computation strategy to compare scenarios with the aim of detecting/identifying motivations of crowded conditions on stops and on the vehicles. The analysis can exploit a wide range of data sources when computing a set of key performance indicators. The DORAM solution has been defined and developed in the MOSAIC research and development project with ALSTOM and other companies. The DORAM solution is validated by using real data and conditions in the Tuscany region.

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Section: Beginning and Final Stops' Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analyzing Demand with Respect to Offer of Mobility

et al. 2022

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“…OLS-DT can learn the steady change trend of the human travel flow and show better performance in drastic changes in the OD flow. LP Zapata et al [14] use Bayesian inference to build a mathematical model; the Monte Carlo algorithm generates a large number of random samples; these samples will be accepted or rejected according to the Metropolis-Hasting criteria, the arithmetic average of all accepted samples as the final results. A model experiment was carried out using the transportation network in the southeastern district of Quito.…”

Section: Related Workmentioning

confidence: 99%

Human Origin-Destination Flow Prediction Based on Large Scale Mobile Signal Data

Huang

Yang

et al. 2021

Wireless Communications and Mobile Computing

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The human origin-destination (OD) flow prediction is of great significance for urban safety control, stampede prevention, disease transmission control, urban planning, and many other aspects. Most of the existing methods generally divide the urban area into grids and use vehicle GPS trajectories and metrocard check-in data, combined with machine learning or deep learning models to predict human OD flow. However, these kinds of methods are challenging to capture fine-grained human mobility patterns. Moreover, these methods usually deviate from the actual human OD transfer patterns on a citywide scale due to the particularity of different datasets. To this end, in this paper, we use large-scale mobile phone signal data to achieve human OD flow prediction between the coverage of varying signal base stations. Many signal base stations are distributed in urban geographical space, collecting all the mobile phone user’s location information to obtain large-scale fine-grained unbiased human OD flow data. Due to the lack of natural topology structure between base stations, this paper adopts a TGCN model combined with a graph fusion module to pretrain the dynamic population distribution prediction task. The parameters of the graph fusion module are employed to capture the different semantic information in the proposed hybrid machine learning method and finally achieve citywide human OD flow prediction. Extensive experiments on the real-world signal datasets in Changchun, China, demonstrate the effectiveness of our model.

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