Metro systems : Construction, operation and impacts

Peer reviewedPublisher PD

Exploring human mobility for multi-pattern passenger prediction : a graph learning framework

Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To address this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN). Firstly, we construct a novel sharing-stop network to model relationships between passengers based on bus record data. Then, we employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. Furthermore, to fully utilize spatio-temporal information, we propose GCN2Flow to predict passenger flow based on various mobility patterns. To the best of our knowledge, this paper is the first work to adopt a multi-pattern approach to predict the bus passenger flow by taking advantage of graph learning. We design a case study for optimizing routes. Extensive experiments upon a real-world bus dataset demonstrate that MPGCN has potential efficacy in passenger flow prediction and route optimization. © 2000-2011 IEEE

Transit Assignment Modeling Approaches based on Interval Uncertainty of Urban Public Transit Net Impedance

The data of the regular bus in Shenzhen during October 2019 was taken as an example. The improved model for the public transportation assignment was established based on considering the interval uncertainty theory and the basic algorithm of interval value, and the interval value acquisition method of bus impedance is established, the Method of Successive Averages ( MSA) algorithm is used to solve the problem. Finally, the error analysis of bus passenger flow assignment before and after the improvement of the model is carried out. It is found that the average absolute percentage error of the improved assignment model is 8.7% compared with the real value, while the average absolute percentage error is 10.9% when the impedance is invariant value, The result of passenger flow assignment under interval impedance is obviously better than that under certain impedance. On non-working days, when the bus passenger flow changes greatly, the bus passenger flow assignment result under interval impedance is better

TBD(exp 3)

When asked by the Aeronautical Engineering staff to design a viable supersonic commercial transport, most of the students were well aware that Boeing, McDonnell Douglas, and other aircraft companies had been studying a cadre of transports for more than 30 years and had yet to present a viable aircraft. In the spirit of aviation progress and with much creative license, the TBD design team spearheaded the problem with the full intention of presenting a marketable high speed civil transport in spring of 1992. The project commenced with various studies of future market demands. With the market expansion of American business overseas, the airline industry projects a boom of over 200 million passengers by the year 2000. This will create a much higher demand for time efficient and cost effective inter-continental travel; this is the challenge of the high speed civil transport. The TBD(exp 3), a 269 passenger, long-range civil transport was designed to cruise at Mach 3.0 utilizing technology predicted to be available in 2005. Unlike other contemporary commercial airplane designs, the TBD(exp 3) incorporates a variable geometry wing for optimum performance. This design characteristic enabled the TBD(exp 3) to be efficient in both subsonic and supersonic flight. The TBD(exp 3) was designed to be economically viable for commercial airline purchase, be comfortable for passengers, meet FAR Part 25, and the current FAR 36 Stage 3 noise requirements. The TBD(exp 3) was designed to exhibit a long service life, maximize safety, ease of maintenance, as well as be fully compatible with all current high-traffic density airport facilities

Estimating Uncertainty of Bus Arrival Times and Passenger Occupancies

Travel time reliability and the availability of seating and boarding space are important indicators of bus service quality and strongly influence users’ satisfaction and attitudes towards bus transit systems. With Automated Vehicle Location (AVL) and Automated Passenger Counter (APC) units becoming common on buses, some agencies have begun to provide real-time bus location and passenger occupancy information as a means to improve perceived transit reliability. Travel time prediction models have also been established based on AVL and APC data. However, existing travel time prediction models fail to provide an indication of the uncertainty associated with these estimates. This can cause a false sense of precision, which can lead to experiences associated with unreliable service. Furthermore, no existing models are available to predict individual bus occupancies at downstream stops to help travelers understand if there will be space available to board. The purpose of this project was to develop modeling frameworks to predict travel times (and associated uncertainties) as well as individual bus passenger occupancies. For travel times, accelerated failure-time survival models were used to predict the entire distribution of travel times expected. The survival models were found to be just as accurate as models developed using traditional linear regression techniques. However, the survival models were found to have smaller variances associated with predictions. For passenger occupancies, linear and count regression models were compared. The linear regression models were found to outperform count regression models, perhaps due to the additive nature of the passenger boarding process. Various modeling frameworks were tested and the best frameworks were identified for predictions at near stops (within five stops downstream) and far stops (further than eight stops). Overall, these results can be integrated into existing real-time transit information systems to improve the quality of information provided to passengers

LSTM encoder-predictor for short-term train load forecasting

ECML/PKDD - The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Würtzburg, ALLEMAGNE, 16-/09/2019 - 20/09/2019The increase in the amount of data collected in the transport domain can greatly benefit mobility studies and help to create high value-added mobility services for passengers as well as regulation tools for operators. The research detailed in this paper is related to the development of an advanced machine learning approach with the aim of forecasting the passenger load of trains in public transport. Predicting the crowding level on public transport can indeed be useful for enriching the information available to passengers to enable them to better plan their daily trips. Moreover, operators will increasingly need to assess and predict network passenger load to improve train regulation processes and service quality levels. The main issues to address in this forecasting task are the variability in the train load series induced by the train schedule and the influence of several contextual factors, such as calendar information. We propose a neural network LSTM encoder-predictor combined with a contextual representation learning to address this problem. Experiments are conducted on a real dataset provided by the French railway company SNCF and collected over a period of one and a half years. The prediction performance provided by the proposed model are compared to those given by historical models and by traditional machine learning models. The obtained results have demonstrated the potential of the proposed LSTM encoder-predictor to address both one-step-ahead and multi-step forecasting and to outperform other models by maintaining robustness in the quality of the forecasts throughout the time horizon

Disruption analytics in urban metro systems with large-scale automated data

Urban metro systems are frequently affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. Such disruptions give rise to delays, congestion and inconvenience for public transport users, which in turn, lead to a wider range of negative impacts on the social economy and wellbeing. This PhD thesis aims to improve our understanding of disruption impacts and improve the ability of metro operators to detect and manage disruptions by using large-scale automated data. The crucial precondition of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. In pursuit of this goal, the thesis develops statistical models to detect disruptions via deviations in trains’ headways relative to their regular services. Our method is a unique contribution in the sense that it is based on automated vehicle location data (data-driven) and the probabilistic framework is effective to detect any type of service interruptions, including minor delays that last just a few minutes. As an important research outcome, the thesis delivers novel analyses of the propagation progress of disruptions along metro lines, thus enabling us to distinguish primary and secondary disruptions as well as recovery interventions performed by operators. The other part of the thesis provides new insights for quantifying disruption impacts and measuring metro vulnerability. One of our key messages is that in metro systems there are factors influencing both the occurrence of disruptions and their outcomes. With such confounding factors, we show that causal inference is a powerful tool to estimate unbiased impacts on passenger demand and journey time, which is also capable of quantifying the spatial-temporal propagation of disruption impacts within metro networks. The causal inference approaches are applied to empirical studies based on the Hong Kong Mass Transit Railway (MTR). Our conclusions can assist researchers and practitioners in two applications: (i) the evaluation of metro performance such as service reliability, system vulnerability and resilience, and (ii) the management of future disruptions.Open Acces

A Study On Saturation Flow Rates Of Through Vehicles At Signalised Intersections Based On Malaysian Road Conditions. [HE336.T7 L583 2004 f rb] [Microfiche 7668]

Aliran tepu merupakan satu parameter yang penting dalam analisis kapasiti persimpangan berlampu isyarat. Saturation flow is an important parameter in the capacity analysis of signalised intersections