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

Abstract

Artículo de publicación ISIIn 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.ACT-32 Project 'Real-Time Intelligent Control for Integrated Transit Systems' ACT-32 Project Fondecyt Chile 1061261 Millennium Institute ICM: P-05-004-F CONICYT: FBO1

Crowding in public transport systems: Effects on users, operation and implications for the estimation of demand

“…When a bus is full and does not stop to pick up passengers at a bus stop (or if it stops but it is unable to load all passengers waiting), a larger number of passengers than is expected are left to wait for the next bus, which will need to stop for a longer period of time to board the increased number of passengers, presuming it too has capacity to accept the additional passenger load. As such, this second bus will likely be delayed and run late, decreasing its headway relative to the next bus behind, and increasing its headway with respect to the next bus ahead, a phenomenon that is amplified as buses advance along the route if control measures like bus holding are not applied (Sun and Hickman, 2008;Daganzo, 2009;Delgado et al, 2009;Sáez et al, 2012). In short, bus bunching leads to variability in headways, which increases average waiting time (Welding, 1957).…”

Real-Time Integrated Limited-Stop and Short-Turning Bus Control with Stochastic Travel Time

“…Sidi et al proposed a multiobjective optimization approach to determine which stations should be skipped, as well as the departure time of the controlled buses. Cortés et al [13] and Sáez et al [14] proposed an integrated stop-skipping and holding strategy, which was intended to minimize waiting time costs and the passengers' invehicle time. In these strategies, GAs were used to solve the formulation.…”

Reconciling transfer synchronization and service regularity: real-time control strategies using passenger data

“…Prior to the rapid deployment of intelligent transport systems that can track the position of vehicles (automatic vehicle location systems, AVL) and the number of passengers (automatic passenger counting systems, APC), the application of real-time control strategies Sáez et al (2012) x x x Sánchez-Martínez, Koutsopoulos, and Wilson (2016) x X x Zhao, Bukkapatnam, and x required strategically located personnel to make the control decisions (Abkowitz and Lepofsky 1990). Most of those early studies assumed that the controller had little or no real-time information on the position of vehicles along the line and the holding strategy was applied at pre-specified control points on the basis of the timetable and possibly the distance between consecutive vehicles (Carrel et al 2010).…”

“…The objective function may consider only waiting passengers or incorporate also on-board passengers (Berrebi, Watkins, and Laval 2015;Delgado, Muñoz, and Giesen 2012;Delgado et al 2009;Eberlein, Wilson, and Bernstein 2001;Sáez et al 2012;Sánchez-Martínez, Koutsopoulos, and Wilson 2016) or even consider transferring passengers and their ability to successfully complete a direct transfer (Hadas and Ceder 2010;Hall, Dessouky, and Lu 2001;Manasra 2015;Yu et al 2011). Zolfaghari, Azizi, and Jaber (2004) were the first to include in the objective function the extra waiting time of passengers who failed to board the first-arriving vehicle due to binding capacity constraints.…”