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Public Transit Planning and Operation in the Era of Automation, Electrification, and Personalization

Xiaolei Ma, Xiaoyue Liu, Xiaobo Qu
2021 IEEE transactions on intelligent transportation systems (Print)  
The experimental results of one real bus route indicate that the proposed method could quickly provide high-quality and reasonable timetable schemes for the administrator in urban transit system.  ...  The second feature is to propose a trip time estimation method in the bus scheduling process, considering real-time micro driving conditions.  ... 
doi:10.1109/tits.2021.3064090 fatcat:iebb42nofzf3xksevsnpzzhag4

A Comprehensive Comparative Analysis of the Basic Theory of the Short Term Bus Passenger Flow Prediction

Huawei Zhai, Licheng Cui, Yu Nie, Xiaowei Xu, Weishi Zhang
2018 Symmetry  
Under the help of the advanced public transportation systems, a large amount of real-time data about passenger flow is collected from the automatic passenger counters, automatic fare collection systems  ...  In order to meet the real-time public travel demands, the bus operators need to adjust the timetables in time. Therefore, it is necessary to predict the variations of the short-term passenger flow.  ...  Urban bus transit system [2] , which consists of a comprehensive route network and a reasonable departure frequency, is the main component of an urban public transportation system.  ... 
doi:10.3390/sym10090369 fatcat:wvzynb3hz5drzhlmgsbntkxwpu

Travel time prediction of urban public transportation based on detection of single routes

Xinhuan Zhang, Les Lauber, Hongjie Liu, Junqing Shi, Meili Xie, Yuran Pan, Jian Wang
2022 PLoS ONE  
This paper adopts the Kalman filter as a travel time prediction model for a single bus based on single-line detection: including the travel time prediction model of route (RTM) and the stop dwell time  ...  Effectively extracting and mining real-time, accurate, reliable, and low-cost multi-source data such as GPS, AFC, and IC can provide data support for travel time prediction.  ...  Thanks to anonymous reviewers for their constructive comments and suggestions regarding the earlier version of this paper.  ... 
doi:10.1371/journal.pone.0262535 pmid:35030209 pmcid:PMC8759653 fatcat:zeca2i6hmfebla4qacz2jylhjm

Transit Bus Travel Time Prediction using AVL Data

Dr. Stephen Arhin, Regis Z. Stinson
2016 International Journal of Engineering Research and  
The prediction of transit bus travel times along corridors is critical in the planning and operation of buses, especially in urban areas.  ...  Bus patrons tend to have more confidence in a transit system if travel times can be adequately predicted, within a certain margin of error.  ...  Each agent in the model represented a domain in a decisionmaking system that predicts travel time for each time interval based on a historical database and real-time data.  ... 
doi:10.17577/ijertv5is120019 fatcat:7g5mltskhjbrxmur4zsddciaii

Framework for Onboard Bus Comfort Level Predictions Using the Markov Chain Concept

Paweł Więcek, Daniel Kubek, Jan Hipolit Aleksandrowicz, Aleksandra Stróżek
2019 Symmetry  
The efficiency and accuracy of the obtained prediction were presented based on a real-life example, where the measurements of passengers boarding and alighting at bus stops were made in a selected Cracow  ...  To the best of the authors' knowledge, the article deals for the first time with the problem of prediction of onboard bus comfort levels based on in-vehicle occupancy.  ...  Gao [23] Wavelet analysis, Neural networks Flow Transit system L. Liu, R. C. Chen [24] Deep learning method Flow Bus rapid transit Y. Li, X. Wang, S. Sun, X. Ma, G.  ... 
doi:10.3390/sym11060755 fatcat:7lrkratv2baw7p2r2nnoq5mmyq

A fuzzy clustering approach to real-time demand-responsive bus dispatching control

Jiuh-Biing Sheu
2005 Fuzzy sets and systems (Print)  
This paper presents a real-time control methodology for demand-responsive bus operations that respond quickly to passenger needs.  ...  Quick response (QR) to passenger needs is a key objective for advanced public transportation systems (APTS), and it has become increasingly important for contemporary metropolitan bus operations to gain  ...  Acknowledgements The author would like to thank the referees for their constructive comments. Any errors or omissions remain the sole responsibility of the author.  ... 
doi:10.1016/j.fss.2004.05.006 fatcat:2ywz4qig3jerbo6tqf3b4ndpxq

Bus Dwell Time Estimation and Prediction: A Study Case in Shanghai-China

Cen Zhang, Jing Teng
2013 Procedia - Social and Behavioral Sciences  
On the other hand, Automatic Vehicle Location (AVL) and Automatic Passengers Counters (APC) systems are increasingly implemented for transit operation, which yield a vast amount of real time data.  ...  Since the effectiveness of estimation models is verified by statistical analysis methods, it will help in obtaining a reliable algorithm which can be adopted for bus arrival time/travel time prediction  ...  Shalaby, A., and A.Farhan (2004). Prediction Model of Bus Arrival and Departure Times Using AVL and APC Data. Journal of Public Transportation, 7, 41-61. Guenthner,R.P.,and K.C. Sinha (1983).  ... 
doi:10.1016/j.sbspro.2013.08.151 fatcat:b4cs2nbxpnh5hmyudwfoasbfrq

System Dynamics Model of Shanghai Passenger Transportation Structure Evolution

Yang Chao, Miao Zishan
2013 Procedia - Social and Behavioral Sciences  
, establishing flow diagram, parameter estimation and model validation.  ...  Based on the data from a comprehensive transportation survey of Shanghai in 2004 and 2009, this paper analyzed the evolution of urban passenger transportation structure using the system dynamics approach  ...  Acknowledgements This research was supported by National Natural Science Foundation of China (71171147) and Fundamental Research Funds for the Central Universities.  ... 
doi:10.1016/j.sbspro.2013.08.127 fatcat:vhchx4tjcvhglcacjabd3m2l7i

Multi-layer perceptron based transfer passenger flow prediction in Istanbul transportation system

Anıl Utku, Munzur University, Department of Computer Engineering, Tunceli, Turkey, Sema Kayapinar Kaya, Munzur University, Department of Computer Engineering, Tunceli, Turkey
2022 Decision Making: Applications in Management and Engineering  
Estimating passenger movement in transportation networks is a critical aspect of public transportation systems.  ...  It allows for a greater understanding of traffic patterns, as well as efficient system evaluation and monitoring.  ...  Acknowledgement: The authors would like to express their gratitude to the editors and anonymous referees for their informative, helpful remarks and suggestions to improve this paper as well as the important  ... 
doi:10.31181/dmame0315052022u fatcat:7lravsnqj5e5zmohlhkwv2msea

An Ensemble Learning Model for Short-Term Passenger Flow Prediction

Xiangping Wang, Lei Huang, Haifeng Huang, Baoyu Li, Ziyang Xia, Jing Li, Abd E.I.-Baset Hassanien
2020 Complexity  
This paper builds a short-term passenger flow prediction model for urban public transportation based on the idea of integrated learning.  ...  The research results of this paper can enrich the short-term passenger flow forecasting system of urban public transportation and provide effective data support and scientific basis for the passenger flow  ...  Based on the historical passenger flow data collected by the urban rail transit automatic ticketing system, Cai et al. [4] used the ARIMA model to predict the passenger flow of Guangzhou metro.  ... 
doi:10.1155/2020/6694186 fatcat:zydd3uaiajb2loj6pv6ig76wba

Short-Term Passenger Flow Prediction in Urban Public Transport: Kalman Filtering Combined K-Nearest Neighbor Approach

Shidong Liang, Minghui Ma, Shengxue He, Hu Zhang
2019 IEEE Access  
Short-term prediction of passengers' flow is one of the essential elements of the operation and real time control for public transit.  ...  The proposed methodology was found as one of the effective approaches based on the historical data and current data in the area of passengers' flow forecasting for urban public transit.  ...  A. DATA FOUNDATION USED IN THE PROPOSED METHOD Generally, the characteristics of passengers' flow in the urban public transit in weekday and weekend are different.  ... 
doi:10.1109/access.2019.2937114 fatcat:5kcmwz7el5b6bp4hnywu65ccoq

Analyzing Rail Traffic Diversion Based on Machine Learning Technique considering Transportation Security

Jing Luo, Dai Zhou, Wenjun Ma, Guohua Zhao, Xinqiang Chen
2022 Journal of Advanced Transportation  
Results show that the travel time most notably affects the passenger flow transfer, followed by the vehicle comfort.  ...  Based on a stated preference survey, we comprehensively analyze the travel psychology of residents and the advantages and disadvantages of rail transit and conventional buses, travel time, travel cost,  ...  Acknowledgments is work was supported by the National Natural Science Foundation of China (71803110), "Chen Guang" project of the Shanghai Municipal Education Commission, and Shanghai Education Development  ... 
doi:10.1155/2022/8349173 fatcat:urxxmn3f4zbbjfeuchk6jrs4me

Model for Estimation Urban Transportation Supply-Demand Ratio

Chaoqun Wu, Yulong Pei, Jingpeng Gao
2015 Mathematical Problems in Engineering  
The paper establishes an estimation model of urban transportation supply-demand ratio (TSDR) to quantitatively describe the conditions of an urban transport system and to support a theoretical basis for  ...  This TSDR estimation model is supported by the system dynamic principle and the VENSIM (an application that simulates the real system).  ...  Figure 2 : 2 Urban TSDR estimating framework. Figure 4 : 4 Estimated module for rail transit passengers-carrying supply.  ... 
doi:10.1155/2015/502739 fatcat:o45sktans5dubbbiredched3vy

CPT Model-Based Prediction of the Temporal and Spatial Distributions of Passenger Flow for Urban Rail Transit under Emergency Conditions

Wei Li, Min Zhou, Hairong Dong, Wen LIU
2020 Journal of Advanced Transportation  
Accordingly, this paper proposes a passenger flow-based temporal and spatial distribution model for urban rail transit emergencies based on the CPT.  ...  Emergencies have a significant impact on the passenger flow of urban rail transit.  ...  BX20190029), and the State Key Laboratory of Rail Traffic Control and Safety (Contract no. RCS2020ZZ002).  ... 
doi:10.1155/2020/8850541 fatcat:6o6zljqjejcs7ifsf2it7fysv4

Emerging Technologies for Smart Cities' Transportation: Geo-Information, Data Analytics and Machine Learning Approaches

Kenneth Li-Minn Ang, Jasmine Kah Phooi Seng, Ericmoore Ngharamike, Gerald K. Ijemaru
2022 ISPRS International Journal of Geo-Information  
With the recent increase in urban drift, which has led to an unprecedented surge in urban population, the smart city (SC) transportation industry faces a myriad of challenges, including the development  ...  The paper gives a comprehensive review and discussion with a focus on emerging technologies from several information and data-driven perspectives including (1) geoinformation approaches; (2) data analytics  ...  The authors in [178] combined deep learning (DL) and support vector machines (SVM) and proposed a DL-SVM model for urban rail transit (URT) passenger flow prediction.  ... 
doi:10.3390/ijgi11020085 fatcat:bjkv6cu7zbfqbl7q7ezfhai5ya
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