Bus arrival time prediction using mixed multi-route arrival time data at previous stop

Hua, Xuedong; Wang, Wei; Wang, Yinhai; Min, Ren

doi:10.3846/16484142.2017.1298055

Cited by 25 publications

(16 citation statements)

References 21 publications

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“…As the basis of the ITS, the provision of travel-time is an important issue (Chen 2017;Yu et al 2006;Yang et al 2016). Because of the overall dynamics of the transportation system, there is often a disparity between the actual bus travel-time and the scheduled time (Hua et al 2018). Bus travel-time prediction has attracted extensive scholarly attention.…”

Section: Literature Reviewmentioning

confidence: 99%

Study on Dynamic Influence of Passenger Flow on Intelligent Bus Travel Service Model

Liu

2021

Transport

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To improve the service quality and convenience of bus travel services, this paper proposes the Intelligent Bus Travel Service Model (IBTSM). The IBTSM makes it possible to provide a travel strategy considering every aspect of bus travel, specifically, delay in the peak period arising from limited carrying-capacities of buses. A three-step approach was executed toward implementing the IBTSM. First, the bus travel-time was predicted using Long Short-Term Memory (LSTM). Next, the crowding level in the bus was evaluated using a fuzzy expert system, based on which a reasonable start-off time was planned, and the delay caused by large passenger flow was circumvented. The k-Nearest Neighbours (k-NN) algorithm was used to provide input data of passenger flow. In this study, the correlation between passenger flow variation and bus services was investigated to extend the provisions of the travel strategy to include start-off time scheduling and target bus selection, rather than only bus running-time estimation. The proposed model was evaluated using a bus in China as a case study, and its reliability and positive impact on promoting both the quality of bus services and development of intelligent travel were demonstrated.

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Section: Literature Reviewmentioning

confidence: 99%

Study on Dynamic Influence of Passenger Flow on Intelligent Bus Travel Service Model

Liu

2021

Transport

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“…However, on segments of the route used by both public transport vehicles and private transport vehicles, the model that combined the KNN-KF techniques produced the best results. Hua et al [19] compared prediction methods based on ANN , SVM and Linear Regression, introducing three Forgetting Factor Functions; the aim was to develop a prediction model using actual multi-route bus arrival time data from previous stops as inputs.…”

Section: Related Workmentioning

confidence: 99%

Bus Travel Time Prediction Model Based on Profile Similarity

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Padrón

Quesada-Arencibia

et al. 2019

Sensors

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In road-based mass transit systems, travel time is a key factor in providing quality of service. This article proposes a method of predicting travel time for this type of transport system. This method estimates travel time by taking into account its historical behaviour, represented by historical profiles, and the current behaviour recorded on the public transport vehicle for which the prediction is to be made. The model uses the k-medoids clustering algorithm to obtain historical travel time profiles. A relevant feature of the model is that it does not require recent travel time data from other vehicles. For this reason, the proposed model may be used in intercity transport contexts in which service planning is carried out according to timetables. The proposed model has been tested with two real cases of intercity public transport routes and from the results obtained we may conclude that, in general, the average error of the predictions is around 13% compared to the observed travel time values.

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“…Time series models assume that the historical traffic patterns will remain the same in the future, and their precision is highly dependent on the correspondence between real-time and historical traffic patterns [12]. Regression models build transparent relationships between travel times and a set of independent variables that can affect travel times [13]- [15]. Patnaik et al developed a set of regression models to estimate bus travel times using data collected by automatic passenger counters installed in buses [16].…”

Section: Introductionmentioning

confidence: 99%

Estimating Travel Times of Mid-Volume Buses Considering Right-of-Way Variation

et al. 2020

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Transit agencies often provide estimates of bus travel times to downstream stops. This study aims to improve the perceived reliability of bus transit systems and enhance their competitiveness. This study considers the characteristics of low headway and high demand for mid-volume bus lanes. Considering the variation in right-of-way with respect to both time and space, a stop-based bus route is built to divide the road into sections. Available real-time data from a schedule-based mid-volume bus route are used, including bus global positioning system (GPS) data, road condition information, and weather. Based on the accelerated failure time (AFT) model, a dynamic travel time model considering right-of-way variation is established to estimate bus travel times between adjacent stops and explore the specific impact of bus right-of-way variation on the travel time. The AFT model is chosen because it can reveal the significance of different variables to estimate travel times, and simultaneously estimate expected travel times as well as travel time uncertainty. The experimental results indicate that bus right-of-way variation significantly affects travel times. In contrast to the linear model, the parameter estimated by the AFT model conforms better to expectations, especially for long-distance travel. INDEX TERMSTravel time, mid-volume bus, accelerated failure time model, GPS data, right-of-way variation. YI LI received the B.S. degree in transportation engineering from Southeast University, Nanjing, China, in 2013, where she is currently pursuing the M.S. degree in transportation planning and management. From 2017 to 2019, as the Project Leader or a Key Researcher, she has been involved in a number of transportation planning projects in China. Her research interests include transport modeling and transportation planning. Ms. Li, as a Key Researcher, she and her team had obtained the Second Prize in the National Competition of Transport Science and Technology for students in China, from 2014 to 2015.JUMEI ZHU is currently pursuing the bachelor's degree in traffic engineering with Southeast University, Nanjing, China. From 2018 to 2019, as a Key Researcher, her team's SRTP project was rated as a provincial university student innovation and entrepreneurship project.

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Bus arrival time prediction using mixed multi-route arrival time data at previous stop

Cited by 25 publications

References 21 publications

Study on Dynamic Influence of Passenger Flow on Intelligent Bus Travel Service Model

Study on Dynamic Influence of Passenger Flow on Intelligent Bus Travel Service Model

Bus Travel Time Prediction Model Based on Profile Similarity

Estimating Travel Times of Mid-Volume Buses Considering Right-of-Way Variation

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