Passengers’ choices in multimodal public transport systems : A study of revealed behaviour and measurement methods

The concept of individual choice is a fundamental aspect when explaining and anticipating behavioural interactions with, and responses to, static and dynamic travel conditions in public transport (PT) systems. However, the empirical rounding of existing models used for forecasting travel demand, which itself is a result of a multitude of individual choices, is often insufficient in terms of detail and accuracy. This thesis explores three aspects, or themes, of PT trips – waiting times, general door-to-door path preferences, with a special emphasis on access and egress trip legs, and service reliability – in order to increase knowledge about how PT passengers interact with PT systems. Using detailed spatiotemporal empirical data from a dedicated survey app and PT fare card transactions, possible cross-sectional relationships between travel conditions and waiting times are analysed, where degrees of mental effort are gauged by an information acquisition proxy. Preferences for complete door-todoorpaths are examined by estimation of full path choice models. Finally, longitudinal effects of changing service reliability are analysed using a biennial panel data approach. The constituent studies conclude that there are otherexplanatory factors than headway that explain waiting times on first boarding stops of PT trips and that possession of knowledge of exact departure times reduces mean waiting times. However, this information factor is not evidentin full path choice, where time and effort-related preferences dominate with a consistent individual preference factor. Finally, a statistically significant on-average adaption to changing service reliability is individual-specific andnon-symmetrical depending on the direction of reliability change, where a relatively large portion of the affected individuals do not appear to respond to small-scale perturbations of reliability while others do, all other thingsbeing equal

Modelling route choice behaviour with incomplete data: an application to the London Underground

This thesis develops a modelling framework for learning route choice behaviour of travellers on an underground railway system, with a major emphasis on the use of smart-card data. The motivation for this topic comes from two respects. On the one hand, in a metropolis, particularly those furnished with massive underground services (e.g. London, Beijing and Paris), severe passenger-traffic congestion may often occur, especially during rush hours. In order to support the public transport managers in taking actions that are more effective in smoothening the passenger flows, there is bound to be a need for better understanding of the passengers’ routing behaviour when they are travelling on such public transport networks. On the other hand, a wealth of travel data is nowadays readily obtainable, largely owing to the widespread implementation of automatic fare collection systems (AFC) as well as popularity of smart cards on the public transport. Nevertheless, a core limitation of such data is that the actual route-choice decisions taken by the passengers might not be available, especially when their journeys involve alternative routes and/or within-station interchanges. Mostly, the AFC systems (e.g. the Oyster system in London) record only data of passengers’ entry and exit, rather than their route choices. We are thus interested in whether it is possible to analytically infer the route-choice information based on the ‘incomplete’ data. Within the scope of this thesis, passengers’ single journeys are investigated on a station basis, where sufficiently large samples of the smart-card users’ travel records can be gained. With their journey time data being modelled by simple finite mixture distributions, Bayesian inference is applied to estimate posterior probabilities for each route that a given passenger might have chosen from all possible alternatives. We learn the route-choice probabilities of every individual passenger in any given sample, conditional on an observation of the passenger’s journey time. Further to this, the estimated posterior probabilities are also updated for each passenger, by taking into account additional information including their entry times as well as the timetables. To understand passengers’ actual route choice behaviour, we then make use of adapted discrete choice model, replacing the conventional dependent variable of actual route choices by the posterior choice probabilities for different possible outcomes. This proposed methodology is illustrated with seven case studies based in the area of central zone of the London Underground network, by using the Oyster smart-card data. Two standard mixture models, i.e. the probability distributions of Gaussian and log-normal mixtures, are tested, respectively. The outcome demonstrates a good performance of the mixture models. Moreover, relying on the updated choice probabilities in the estimation of a multinomial logit latent choice model, we show that we could estimate meaningful relative sensitivities to the travel times of different journey segments. This approach thus allows us to gain an insight into passengers’ route choice preferences even in the absence of observations of their actual chosen routes

Incorporating weather conditions and travel history in estimating the alighting bus stops from smart card data

Origin-destination flow of passengers in bus networks is a crucial input to the public transport planning and operational decisions. Smart card systems in many cities, however, record only the bus boarding information (namely an open system), which makes it challenging to use smart card data for origin-destination estimations and subsequent analyses. This study addresses this research gap by proposing a machine learning approach and applying the gradient boosting decision tree (GBDT) algorithm to estimate the alighting stops of bus trips from open smart card data. It advances the state-of-the-art by including, for the first time, weather variables and travel history of individuals in the GBDT algorithm alongside the network characteristics. The method is applied to six-month smart card data from the City of Changsha, China, with more than 17 million trip-records from 700 thousand card users. The model prediction results show that, compared to classic machine learning methods, GBDT not only yields higher prediction accuracy but more importantly is also able to rank the influencing factors on bus ridership. The results demonstrate that incorporation of weather variables and travel history further improves the prediction capability of the models. The proposed GBDT-based framework is flexible and scalable: it can be readily trained with smart card data from other cities to be used for predicting bus origin-destination flow. The results can contribute to improved transport sustainability of a city by enabling smart bus planning and operational decisions

Integration of Automated Vehicle Location, Fare Control, and Schedule Data for Improved Public Transport Trip Definition

his paper proposes a flexible methodology to improve the definition of each distinct trip carried out in a transport system, integrating the information provided by stop-level events from its automated vehicle location and fare collection systems, and scheduling subsystem information at the initial stop of planned trips. The data are structured; and then corrected and completed utilizing several criteria, including a probabilistic approach based on the distributions of travel and dwell times, aiming to minimize the distortions that appear due to the nature of the available sources. The case study data encompass one year of records from the automated vehicle location, fare collection, and scheduling subsystems in Santander City, Spain. The results are discussed with captures from an interactive web visualization tool that has been developed for this work.This work was supported in part by the Ministerio de Ciencia Innovación y Universidades through the European Regional Development Fund under Project TRA2015-69903-R, in part by the EU Horizon 2020 Projec

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

Multimodal pricing and the optimal design of bus services: new elements and extensions

This thesis analyses the pricing and design of urban transport systems; in particular the optimal design and efficient operation of bus services and the pricing of urban transport. Five main topics are addressed: (i) the influence of considering non-motorised travel alternatives (walking and cycling) in the estimation of optimal bus fares, (ii) the choice of a fare collection system and bus boarding policy, (iii) the influence of passengers’ crowding on bus operations and optimal supply levels, (iv) the optimal investment in road infrastructure for buses, which is attached to a target bus running speed and (v) the characterisation of bus congestion and its impact on bus operation and service design. Total cost minimisation and social welfare maximisation models are developed, which are complemented by the empirical estimation of bus travel times. As bus patronage increases, it is efficient to invest money in speeding up boarding and alighting times. Once on-board cash payment has been ruled out, allowing boarding at all doors is more important as a tool to reduce both users and operator costs than technological improvements on fare collection. The consideration of crowding externalities (in respect of both seating and standing) imposes a higher optimal bus fare, and consequently, a reduction of the optimal bus subsidy. Optimal bus frequency is quite sensitive to the assumptions regarding crowding costs, impact of buses on traffic congestion and congestion level in mixed-traffic roads. The existence of a crowding externality implies that buses should have as many seats as possible, up to a minimum area that must be left free of seats. Bus congestion in the form of queuing delays behind bus stops is estimated using simulation. The delay function depends on the bus frequency, bus size, number of berths and dwell time. Therefore, models that use flow measures (including frequency only or frequency plus traffic flow) as the only explanatory variables for bus congestion are incomplete. Disregarding bus congestion in the design of the service would yield greater frequencies than optimal when congestion is noticeable, i.e. for high demand. Finally, the optimal investment in road infrastructure for buses grows with the logarithm of demand; this result depends on the existence of a positive and linear relationship between investment in infrastructure and desired running speed