Exploiting Trip Patterns in Passenger Trajectory Streams for Bus Scheduling Optimization in Real Time

Wang, Zhaoyang; Jin, Beihong; Zhang, Fusang; Yang, Ruiyang; Ji, Qiang

doi:10.1109/mdm.2017.43

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Their model could be used to describe and reason about situations such as “the object is currently to my left moving toward the right” or “at this instant of time it is crossing just in front of me, moving toward the right.” Baryannis et al (2018) introduce new qualitative frameworks called Trajectory Calculus‐6 (TC‐6) and Trajectory Calculus‐10 (TC‐10) based on answer set programming. As they claim, the suggested methods are particularly suited for applications based on trajectory databases to explain the relationships between a large number of trajectories such as origin/destination (O/D) analysis (Andrienko, Andrienko, Fuchs, & Wood, 2017; Hu, Peeta, & Liou, 2016), the discovery of transport demand (Wang, Jin, Zhang, Yang, & Ji, 2017), and population monitoring streams (Ma, Lu, Liu, Wang, & Xiong, 2017).…”

Section: Introductionmentioning

confidence: 99%

Automatic extraction of sophisticated qualitative trajectory relationships between two moving objects in space‐time

Vahidnia

2020

Transactions in GIS

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A comparison of moving objects’ trajectories in space‐time based on descriptive and qualitative interpretations is useful in many GIS‐related studies. Qualitative relationships help to answer questions such as “Do the objects meet each other?” or “Do the objects move towards or away from each other?” Questions about moving objects can be efficiently addressed using quantitative and computational techniques such as data mining of trajectory datasets but expressed in terms of qualitative relationships. It is, however, difficult to recognize these qualitative relationships when the trajectories are large and consist of many parts, where a sophisticated qualitative relationship can be formed from the aggregation of simple ones. For this reason, this study introduces two classes of trajectory relationships: initial qualitative trajectory relationships (IQTR) and combinatory qualitative trajectory relationships (CQTR). It also suggests effective algorithmic approaches to automatically retrieve them. The proposed algorithm for IQTR using min‐heap and linked‐list data structure could effectively reduce the time complexity from O(n2) to O(n log n). For CQTRs, a divide‐and‐conquer and recursive technique and a linked‐path structure again managed to reduce the time complexity from O(n2) to O(n log n). Finally, experiments on large datasets suggest the capability of the proposed methods in terms of performance and stability.

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Section: Introductionmentioning

confidence: 99%

Automatic extraction of sophisticated qualitative trajectory relationships between two moving objects in space‐time

Vahidnia

2020

Transactions in GIS

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“…TC-6 and TC-10 are not focused on real-time and predictive applications that require reasoning on specific characteristics of moving objects at any given point in time (e.g velocity and acceleration), such as collision monitoring and prevention. The proposed calculi are especially suitable for any application that relies on trajectory databases to reason about the relations among large numbers of trajectories such as Origin/Destination (O/D) analysis (Hu et al 2016;Andrienko et al 2017), transportation demand discovery (Wang et al 2017;Moreira-Matias et al 2013) and stream of population monitoring (Ma et al 2017).…”

Section: Introductionmentioning

confidence: 99%

A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming

Baryannis¹,

Tachmazidis²,

Batsakis³

et al. 2018

Theory and Practice of Logic Programming

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Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their paths. In this work, we propose two versions of a trajectory calculus based on the allowed properties over trajectories, where trajectories are defined as a sequence of non-overlapping regions of a partitioned map. More specifically, if a given trajectory is allowed to start and finish at the same region, 6 base relations are defined (TC-6). If a given trajectory should have different start and finish regions but cycles are allowed within, 10 base relations are defined (TC-10). Both versions of the calculus are implemented as ASP programs; we propose several different encodings, including a generalised program capable of encoding any qualitative calculus in ASP. All proposed encodings are experimentally evaluated using a real-world dataset. Experiment results show that the best performing implementation can scale up to an input of 250 trajectories for TC-6 and 150 trajectories for TC-10 for the problem of discovering a consistent configuration, a significant improvement compared to previous ASP implementations for similar qualitative spatial and temporal calculi.

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Mining smart card data to estimate transfer passenger flow in a metro network

Wu,

Liu,

Gong

et al. 2024

IET Intelligent Trans Sys

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Metro systems play an important role in reducing urban traffic congestion and promoting the sustainable development of urban transport in megacities. With the expansion of a metro network, transfer stations are necessary for increasing the service connectivity of a metro network. An accurate estimation of transfer passenger flow can help improve the operations management of a metro system. This study proposes a data‐driven methodology for estimating the transfer passenger flow volume of each transfer station in a metro network by mining smart card data. The estimated transfer passenger flow data are visualized to show the spatial‐temporal distribution characteristics of metro transfer passenger flow. The case study results of the Shenzhen Metro network demonstrate that the proposed data‐driven methodological framework is very effective in estimating different types of transfer passenger flows, such as total transfer passenger flow, hourly transfer passenger flow, and inbound and outbound transfer flows at each transfer station. The spatial‐temporal distribution characteristics of transfer passenger flow can be very useful for designing effective and efficient passenger flow management measures to ensure the safe and efficient operation of a metro system.

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Exploiting Trip Patterns in Passenger Trajectory Streams for Bus Scheduling Optimization in Real Time

Cited by 4 publications

References 11 publications

Automatic extraction of sophisticated qualitative trajectory relationships between two moving objects in space‐time

Automatic extraction of sophisticated qualitative trajectory relationships between two moving objects in space‐time

A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming

Mining smart card data to estimate transfer passenger flow in a metro network

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