Transit Data Analytics for Planning, Monitoring, Control, and Information

Koutsopoulos, Haris N.; Ma, Zhenliang; Noursalehi, Peyman; Zhu, Yiwen

doi:10.1016/b978-0-12-812970-8.00010-5

Cited by 29 publications

(18 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AFC systems provide the exact locations and times of passengers' entry and exit transactions, which can be used to extract OD demand and passengers' journey times. ey provide rich information for analyzing passenger behavior [3].…”

Section: Introductionmentioning

confidence: 99%

Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data

Koutsopoulos

et al. 2021

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

Transit network simulation models are often used for performance and retrospective analysis of urban rail systems, taking advantage of the availability of extensive automated fare collection (AFC) and automated vehicle location (AVL) data. Important inputs to such models, in addition to origin-destination flows, include passenger path choices and train capacity. Train capacity, which has often been overlooked in the literature, is an important input that exhibits a lot of variabilities. The paper proposes a simulation-based optimization (SBO) framework to simultaneously calibrate path choices and train capacity for urban rail systems using AFC and AVL data. The calibration is formulated as an optimization problem with a black-box objective function. Seven algorithms from four branches of SBO solving methods are evaluated. The algorithms are evaluated using an experimental design that includes five scenarios, representing different degrees of path choice randomness and crowding sensitivity. Data from the Hong Kong Mass Transit Railway (MTR) system is used as a case study. The data is used to generate synthetic observations used as “ground truth.” The results show that the response surface methods (particularly constrained optimization using response surfaces) have consistently good performance under all scenarios. The proposed approach drives large-scale simulation applications for monitoring and planning.

show abstract

Section: Introductionmentioning

confidence: 99%

Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data

Koutsopoulos

et al. 2021

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

show abstract

“…The use of anonymous big data, e.g., PT Smart Card Fare Collection (PT-SCFC) data, has begun to figure more predominantly in the analysis of travel behavior and mobility patterns [ 12 , 13 , 14 ]. However, the misrepresentation and mis-modelling that characterizes the above in the form of using such large data can be found in a number of recently published studies [ 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…PT smart card data benefits scholars and practitioners in understanding urban dynamics and human activities [ 12 , 33 ]. For instance, smart card data can be used to estimate the Origin-Destination (OD) of PT users, long-term network planning, demand forecasting, operational purposes like timetable and schedule adjustments as well as PT funding and investment decisions [ 13 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring Equity in Public Transportation Planning Using Smart Card Data

Ghasemlou

Ergun

Dadashzadeh

2021

Sensors

View full text Add to dashboard Cite

Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users’ share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16–21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1–6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments.

show abstract

“…SHORT-term demand prediction facilitates advanced online applications in public transport, including predictive operations control, service, and customized information provision to passengers [1].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Passenger Flow Prediction With Decomposition in Urban Railway Systems

Zhao

Yang

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Accurate prediction of short-term passenger flow is vital for real-time operations control and management. Identifying passenger demand patterns and selecting appropriate methods are promising to improve prediction accuracy. This paper proposes a hybrid prediction model with time series decomposition and explores its performance for different types of passenger flows with varied characteristics in urban railway systems. The Seasonal and Trend decomposition using Loess (STL) is used to decompose the passenger flow into the seasonal, trend, and residual time series, representing the constant, long-term fluctuant, and stochastic passenger demand patterns. The approximate entropy (ApEn) is utilized to quantify the predictability of each component. After that, the Holt-Winters (HW) method is developed to predict the seasonal and trend components that both with high predictability. The long short-term memory networks (LSTM NN) are proposed to predict the residual with low predictability. The outputs from these models are combined to predict the short-term passenger flow. We assess the model performance with entry, exit, and transfer passenger flows at the target stations, which are selected via the agglomerative hierarchical clustering (AHC) algorithm in the Shanghai Metro. Compared with three representative models, the results show that 1) the decomposition-based hybrid model performs well for both one-step and multi-step predictions in terms of accuracy and robustness; 2) ApEn is useful for choosing appropriate prediction models. INDEX TERMS Short-term metro passenger flow prediction, Seasonal and Trend decomposition using Loess (STL)-based model, approximate entropy (ApEn), agglomerative hierarchical clustering (AHC) algorithm I. Yangyang Zhao received the B.E. degree and the M.E. degree in transportation engineering from Chang'an University, Xi'an, Shaanxi, China, in 2014 and 2017. He is currently pursuing the Ph.D. degree in transportation planning and management with the school of transportation and logistics,

show abstract

Transit Data Analytics for Planning, Monitoring, Control, and Information

Cited by 29 publications

References 18 publications

Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data

Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data

Exploring Equity in Public Transportation Planning Using Smart Card Data

Short-Term Passenger Flow Prediction With Decomposition in Urban Railway Systems

Contact Info

Product

Resources

About