Subnetwork Origin-Destination Matrix Estimation Under Travel Demand Constraints

Sun, Chao; Chang, Yunlong; Shi, Yuji; Cheng, Lin; Ma, Jie

doi:10.1007/s11067-019-09449-6

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…All these studies consider the treatment of congestion effects in the network. The optimization model applying EM for origin-destination trip matrix estimation with fuzzy entropic [10], the estimation of freight tour flows using fuzzy entropic [28], estimating OD matrixes under travel demand constraints [29], modeling interregional transportation [30], modeling taxi trip distributions [31], input-output analysis [32], and modeling highway traffic flows [33] are some other works developed using EM.…”

Section: Em In Transportation Modelingmentioning

confidence: 99%

“…where R: number of possible routes (tours) in the bus system; r: Node sequence (or tour), an ordered set of the nodes visited by a bus, from the start node until the end node; N: total number of nodes in the system; Q: total number of links with traffic counts in the system; t r : number of bus journeys (tour flow) following node sequence (or tour) r (a listing of the nodes visited), i.e., the number of buses that travel along the same tour; Or 1 i , Or 2 i , Or 3 i : triangular parameters for bus tour production; Cr 1 , Cr 2 , Cr 3 : triangular parameters for total cost in the transit system; Vr 1 a , Vr 2 a , Vr 3 a : triangular parameters for bus traffic counts (volume); C r : Cost of tour r, associated with travel on the tour; δ ri : a binary parameter equal to 1 if node i is in tour r, equal to 0 otherwise; δ ra : a binary parameter equal to 1 if the tour r uses link a, equal to 0 otherwise. The model above, presented in Equation (29) to Equation (32), has been rewritten in Equation (33) to Equation (40). The constraints are represented in the Equation (30) to Equation (32).…”

Section: Tts Formulation Using the Fuzzy Logic Formulationmentioning

confidence: 99%

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Entropy-Based Transit Tour Synthesis Using Fuzzy Logic

et al. 2022

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This paper presents an entropy-based transit tour synthesis (TTS) using fuzzy logic (FL) based on entropy maximization (EM). The objective is to obtain the most probable transit (bus) tour flow distribution in the network based on traffic counts. These models consider fixed parameters and constraints. The costs, traffic counts, and demand for buses vary depending on different aspects (e.g., congestion), which are not captured in detail in the models. Then, as the FL can be included in modeling that variability, it allows obtaining solutions where some or all the constraints do not entirely satisfy their expected value, but are close to it, due to the flexibility this method provides to the model. This optimization problem was transformed into a bi-objective problem when the optimization variables were the membership and entropy. The performance of the proposed formulation was assessed in the Sioux Falls Network. We created an indicator (Δ) that measures the distance between the model’s obtained solution and the requested value or target value. It was calculated for both production and volume constraints. The indicator allowed us to observe that the flexible problem (FL Mode) had smaller Δ values than the ones obtained in the No FL models. These results prove that the inclusion of the FL and EM approaches to estimate bus tour flow, applying the synthesis method (traffic counts), improves the quality of the tour estimation.

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Section: Em In Transportation Modelingmentioning

confidence: 99%

Section: Tts Formulation Using the Fuzzy Logic Formulationmentioning

confidence: 99%

Entropy-Based Transit Tour Synthesis Using Fuzzy Logic

et al. 2022

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“…Informasi mengenai kedua hal tersebut dapat diperoleh melalui matriks origin-destination. M atriks origin-destination adalah matriks yang setiap selnya merupakan jumlah perjalanan dari titik asal (baris) ke tujuan (kolom) [1] [2].…”

Section: Pendahuluanunclassified

Estimasi Tujuan Penumpang Menggunakan Predictive Model dengan Data Smart Card

Alam

Hardini

Panjaitan

et al. 2019

JSC

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Bus Rapid Transit (BRT) is one of the main choices of public transportation that supports mobility of Jakarta community. As one of the main choices of public transportation, BRT should provide good service and always improve its performance. Needs for moving or mobility will cause a problem if the moving itself is heading at the same area and at the same time. That will cause some problems which are often faced in urban areas such as traffic and delay. To overcome those problems there needs to be a strategy to build good public transportation planning, besides need to know individual travel patterns to overcome problems and improve BRT service. In case to realize those plans needs to be built origin-destination (O-D) matrix. O-D matrix is a matrix that each cell is an amount of trip from the source(row) to the destination (column). O-D matrix is beneficial for analysis, design and public transportation management. O-D matrix also provides useful information like amount of trip between 2 different locations, that can be utilized as fundamental information for decision making for three levels of strategic management (long term planning), tactic (service adjustment and network development), and operational (scheduling, passenger statistic, and performance indicator). To build O-D matrix is required a predictive model that can be measured to predict passenger destination. The predictive model will be build using classification algorithms such as Decision Tree and K-Nearest Neighbor (KNN).

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