Pengling Wang

Last Name

Wang

First Name

Pengling

ORCID

0000-0002-2632-4260

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Publications 1 - 10 of 17

Train trajectory optimization methods for energy-efficient railway operations
Wang, Pengling (2017)
Train trajectory optimization in the presence ofexternal factors: The example of wind
Trivella, Alessio; Wang, Pengling; Corman, Francesco (2019)
The impact of wind on energy-efficient train control
Trivella, Alessio; Wang, Pengling; Corman, Francesco (2020)
EURO Journal on Transportation and Logistics
An energy-efficient train trajectory corresponds to the speed profile of a train between two stations that minimizes energy consumption while respecting the scheduled arrival time and operational constraints such as speed limits. Determining this trajectory is a well-known problem in the operations research and transport literature, but has so far been studied without accounting for stochastic variables like weather conditions or train load that in reality vary in each journey. These variables have an impact on the train resistance, which in turn affects the energy consumption. In this paper, we focus on wind variability and propose a train resistance equation that accounts for the impact of wind speed and direction explicitly on the train motion. Based on this equation, we compute the energy-efficient speed profile that exploits the knowledge of wind available before train departure, i.e., wind measurements and forecasts. Specifically, we: (i) construct a distance-speed network that relies on a new non-linear discretization of speed values and embeds the physical train motion relations updated with the wind data, and (ii) compute the energy-efficient trajectory by combining a line-search framework with a dynamic programming shortest path algorithm. Extensive numerical experiments reveal that our “wind-aware” train trajectories present different shape and reduce energy consumption compared to traditional speed profiles computed regardless of any wind information.
Train trajectory optimization in the presence ofexternal factors: The example of wind
Trivella, Alessio; Wang, Pengling; Corman, Francesco (2019)
The train trajectory optimization problem consists in determining the speed profile of a train between two stations that minimizes energy consumption while respecting the scheduled arrival time and operational constraints such as speed limits. The problem is well-known in the literature but has so far been studied without accounting for external factors as weather conditions or train load that in reality vary in each journey. These factors have an impact on the train resistance, which in turn can affect energy consumption. In this paper, we focus on wind uncertainty and propose a novel train resistance equation that accounts for the impact of wind intensity and direction. For different wind conditions, we determine optimal trajectories as dynamic programs defined on a space-speed network that embeds the physical train motion relations updated with the actual wind information. Numerical experiments show that our “wind-aware” train trajectories are more energy-efficient than traditional solutions computed independent of wind information.
Robust cooperative train trajectory optimization with stochastic delays under virtual coupling
Wang, Pengling; Zhu, Yongqiu; Zhu, Wei (2023)
IET Intelligent Transport Systems
Virtual coupling technology was recently proposed in railways, which separates trains by a relative braking distance (or even shorter distance) and moves trains synchronously to increase capacity at bottlenecks. This study proposes a real-time cooperative train trajectory planning algorithm for coordinating train movements under virtual coupling by considering stochastic initial delays. The algorithm uses mixed-integer programming models to estimate the delay propagation among trains, detect feasible coupled-running locations, and optimize the trajectories of the two trains such that they coordinate their speeds to achieve energy-efficient, punctual movements, as well as a safe coupled-running process. A robust optimization method is proposed to capture the stochastic delays as an uncertainty set, which is reformulated to its dual problem. Case studies of planning train trajectories for the classical virtual-coupling scenario suggest that (1) the coupled-running distance is greatly affected by the coordination of train timetables, delays, and safe separation constraints at switches; (2) the coordination of train movements for a coupled-running process imposes extra energy costs; and (3) the proposed method can detect feasible coupled-running locations and produce cooperative speed profiles in short computational times.
Two-train trajectory optimization with a green-wave policy
Wang, Pengling; Goverde, Rob M.P. (2016)
Transportation Research Record
Improving the Utilization of Regenerative Energy and Shaving Power Peaks by Railway Timetable Adjustment
Wang, Pengling; Bešinović, Nikola; Goverde, Rob M.P.; et al. (2022)
IEEE Transactions on Intelligent Transportation Systems
Employing regenerative braking in trains contributes to reducing the amount of energy used, especially when applied to commuter trains and to those used on very dense suburban networks. This paper presents a method to fine-tune the periodic timetable to improve the utilization of regenerative energy and to shave power peaks while maintaining the structure and robustness of the original timetable. First, a mixed-integer linear programming model based on the periodic event scheduling framework is proposed. A set of feasible timetables is determined and optimized with the aim of increasing synchronized acceleration and braking events at the same station, and maintaining the timetable robustness at the specified level. Next, a local search algorithm is developed to optimize the timetable such that the power peak value is minimized. The max-plus system model is adopted to estimate the delay propagation. Monte Carlo simulation is used to evaluate the utilization of regenerative energy and power peaks in random delayed circumstances. The proposed method was adopted to fine-tune the 2019 timetable for a sub-network of the Dutch railway. In the case of on-time scenarios, the optimized timetable increases the regenerative energy usage by almost 290% and decreases the 15-minute power peaks by 8.5%. In the case of delay scenarios, the optimized timetable outperforms the original timetable in terms of using regenerative energy and shaving power peaks.
Rescheduling trains using petri nets and heuristic search
Wang, Pengling; Ma, Lei; Goverde, Rob M.P.; et al. (2016)
IEEE Transactions on Intelligent Transportation Systems
Train trajectory optimization for improved on-time arrival under parametric uncertainty
Wang, Pengling; Trivella, Alessio; Goverde, Rob M.P.; et al. (2020)
Transportation Research Part C: Emerging Technologies
In this paper we study the problem of computing train trajectories in an uncertain environment in which the values of some system parameters are difficult to determine. Specifically, we consider uncertainty in traction force and train resistance, and their impact on travel time and energy consumption. Our ultimate goal is to be able to control trains such that they will arrive on-time, i.e. within the planned running time, regardless of uncertain factors affecting their dynamic or kinematic performance. We formulate the problem as a Markov decision process and solve it using a novel numerical approach which combines: (i) an off-line approximate dynamic programming (ADP) method to learn the energy and time costs over iterations, and (ii) an on-line search process to determine energy-efficient driving strategies that respect the real-time time windows, more in general expressed as train path envelope constraints. To evaluate the performance of our approach, we conducted a numerical study using real-life railway infrastructure and train data. Compared to a set of benchmark driving strategies, the trajectories from our ADP-based method reduce the probability of delayed arrival, and at the same time are able to better use the available running time for energy saving. Our results show that accounting for uncertainty is relevant when computing train trajectories and that our ADP-based method can handle this uncertainty effectively.
Multiple-phase train trajectory optimization with signalling and operational constraints
Wang, Pengling; Goverde, Rob M.P. (2016)
Transportation Research Part C: Emerging Technologies

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