Alejandro Tirachini

Bus dwell time: the effect of different fare collection systems, bus floor level and age of passengers

Understanding the nature of transit dwell time has potential benefits for both users and operators. In this article, multiple regression models are estimated to analyse the influence of different payment methods, the existence of steps at doors, the age of passengers and the possible friction between users boarding, alighting and standing, on explaining observed variation in dwell times. Using the estimated boarding and alighting average times, performance comparisons are simulated assuming a service with payment outside buses, a prepaid card validated inside buses and cash transactions. Results show the specific effect of all the variables involved in determining the bus dwell time at stops, in particular, that substantial time savings are accruable if payment methods are upgraded from slow techniques, such as cash transactions to the fastest one (fare paid outside buses) while intermediate technologies such as prepaid cards validated inside buses (with or without contact) fall in between. On the other hand, the existence of two steps at the front door makes the boarding process slower, and senior passengers are slower to board and alight than younger travellers. A friction effect was also found when passengers boarding form two queues through a single door, which increases the boarding time for one of the queues; and a crowding effect, as the presence of passengers standing inside the bus slightly slows both boarding and alighting.

Hybrid predictive control strategy for a public transport system with uncertain demand

In this article, a hybrid predictive control (HPC) strategy is formulated for the real-time optimisation of a public transport system operation run using buses. For this problem, the hybrid predictive controller corresponds to the bus dispatcher, who dynamically provides the optimal control actions to the bus system to minimise users’ total travel time (on-vehicle ride time plus waiting time at stops). The HPC framework includes a dynamic objective function and a predictive model of the bus system, written in discrete time, where events are triggered when a bus arrives at a bus stop. Upon these events, the HPC controller makes decisions based on two well-known real-time transit control actions, holding and expressing. Additionally, the uncertain passenger demand is included in the model as a disturbance and then predicted based on both offline and online information of passenger behaviour. The resulting optimisation problem of the HPC strategy at every event is Np-hard and needs an efficient algorithm to solve it in terms of computation time and accuracy. We chose an ad hoc implementation of a Genetic Algorithm that permits the proper management of the trade-off between these two aspects. For real-time implementation, the design of this HPC strategy considers newly available transport technology such as the availability of automatic passenger counters (APCs) and automatic vehicle location (AVL) devices. Illustrative simulations at 2, 5 and 10 steps ahead are conducted, and promising results showing the advantages of the real-time control schemes are reported and discussed.

Embedding Risk Attitudes in a Scheduling Model: Application to the Study of Commuting Departure Time

In traditional travel time reliability valuation studies, the value of travel time savings and the value of travel time reliability (or reduced time variability) are estimated within a linear utility functional form, which assumes risk-neutral attitudes for decision makers. In this paper, we develop nonlinear scheduling models to address both risk attitude and preference in the context of a stated choice experiment of car commuters facing risky choices where the risk is associated with the trip time. We also investigate unobserved between-individual heterogeneity in time-related parameters and risk attitudes using a mixed multinomial logit model. The willingness-to-pay values for reducing the mean travel time and variability (earlier/later than the preferred arrival time) are also estimated within the nonlinear scheduling framework. The model is then used to estimate preferred departure times for commuters, assuming that random link capacities are the source of travel time variability. Results show that the more variable travel times are, the earlier commuters depart and that the nonlinear scheduling model predicts earlier optimal departure times than the linear scheduling model does. The application in this paper helps to bridge the gap between theory and practice.

Multimodal Transport Pricing: First Best, Second Best and Extensions to Non-motorized Transport

In this paper, we examine the main concepts of transport pricing in an urban environment, focusing on the automobile, public transport and walking or cycling as travel alternatives. A review of the literature on the first-best and second-best pricing policies is provided, with an emphasis on public transport pricing, including the setting of frequency and vehicle capacity, the influence of bus congestion externalities and the interactions between transport pricing reforms and the broader tax system. A model is developed to analyse the impact of non-motorized transport on optimal public transport pricing policy, congestion interactions between cars and buses associated with the transfer of passengers at bus stops and the existence of a capacity constraint within the public transport mode.

Choosing Public Transport—Incorporating Richer Behavioural Elements in Modal Choice Models

The development of behaviourally richer representations of the role of well-established and increasingly important influences on modal choice, such as trip time reliability and accounting for risk attitude and process rules, has moved forward at a fast pace in the context of automobile travel. In the public transport setting, such contributions have, with rare exception, not been considered. In this paper, we discuss and empirically illustrate the merits of advanced modelling developments aimed at improving our understanding of public transport choice, namely the inclusion of reliability in extended expected utility theoretic forms, to recognize risk attitude and perceptual conditioning, the consideration of passenger crowding and its inclusion in linear additive models, and the role of multiple heuristics in representing attribute processing as a way of conditioning modal choice. We illustrate the mechanics of introducing these behaviourally appealing extensions using a modal choice data set collected in Sydney.