Transportation Research Part C: Emerging Technologies

Journal: Transportation Research Part C: Emerging Technologies

Abbreviation

Transp. Res., Part C Emerg. Technol.

Publisher

Elsevier

ISSN

0968-090X

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Publications 1 - 10 of 59

A rolling-horizon quadratic-programming approach to the signal control problem in large-scale congested urban road networks
Aboudolas, Konstantinos; Papageorgiou, Markos; Kouvelas, Anastasios; et al. (2010)
Transportation Research Part C: Emerging Technologies
The optimality condition of the multiple-cycle smoothed curve signal timing model
Li, Li; Yang, Kaidi; Li, Zhiheng; et al. (2013)
Transportation Research Part C: Emerging Technologies
Developing a passive GPS tracking system to study long-term travel behavior
Marra, Alessio Daniele; Becker, Henrik; Axhausen, Kay W.; et al. (2019)
Transportation Research Part C: Emerging Technologies
Modeling, estimation, and control in large-scale urban road networks with remaining travel distance dynamics
Sirmatel, Isik Ilber; Tsitsokas, Dimitrios; Kouvelas, Anastasios; et al. (2021)
Transportation Research Part C: Emerging Technologies
City-scale control of urban road traffic poses a challenging problem. Dynamical models based on the macroscopic fundamental diagram (MFD) enable development of model predictive perimeter control methods for large-scale urban networks, representing an advanced control solution carrying substantial potential for field implementation. In this paper we develop a multi-region approximation of the trip-based model, which describes in more details the trip length characteristics compared to existing accumulation-based models. The proposed M-model includes effects of the total remaining travel distance on the transfer flows driving the vehicle accumulation dynamics, potentially yielding improved accuracy over the standard production-over-trip length approximation of the outflow MFD considered in many works on MFD-based modeling and control. We explain that to properly perform perimeter control, boundary queue dynamics have to be integrated. Furthermore, model-based parameter estimation (MBPE), nonlinear moving horizon observer (MHO), and model predictive control (MPC) formulations for the proposed models are presented, forming an integrated traffic control framework. Microsimulation-based case studies, considering an urban network with 1500 links, where the model parameters obtained by MBPE method are used in MHO and MPC design, demonstrate the efficient operation of the proposed framework.
Integrated speed modeling and traffic management to precisely model the effect and dynamics of temporary speed restrictions to high-speed railway traffic
Long, Sihui; Meng, Lingyun; Wang, Yihui; et al. (2023)
Transportation Research Part C: Emerging Technologies
This paper investigates the integrated optimization problem of train rescheduling and train control for high-speed railway lines, where perturbations occur and cause Temporary Speed Restriction (TSR) to trains. We consider microscopic details (i.e., block sections) for ensuring the feasibility of the solution from a train signaling point of view, and an even higher level of detail, to accurately represent the train speed profiles. Running time and headway time are variable, at the same time depending from, and affecting, traffic. We optimize train speed profiles by considering vehicle and infrastructure constraints (e.g., traction, slopes). The model naturally considers the transition along the normal, disturbed and the recovery operation periods. A mixed-integer nonlinear programming (MINLP) model is first developed to simultaneously optimize train orders, routes, and departure and arrival times, as well as train speed profiles, aiming at reducing total train deviation time. The MINLP model is difficult to solve; thus we further reformulate it into a mixed-integer linear programming (MILP) model by means of piecewise linear approximation. A two-step approach is designed to speed up the solving procedure of the MILP model: first estimate the upper/lower bounds of train speeds and then solve the MILP model based on the estimated bounds of train speeds. Three instances (i.e., a small-scale line, a medium-scale line, and a large-scale network) are used to highlight the performance of the approach, verify the benefit of the integration, and its dependence on the parameters used. According to the experimental results, our integrated optimization method leads to an average improvement of 3%-36% in solution quality, compared with the integrated approach without train rerouting measure. Moreover, the integrated optimization method outperforms the sequential approach, achieving 6%-9% improvement in solution quality.
Stochastic prediction of train delays in real-time using Bayesian networks
Corman, Francesco; Kecman, Pavle (2018)
Transportation Research Part C: Emerging Technologies
A Bayesian network model to predict the effects of interruptions on train operations
Huang, Ping; Lessan, Javad; Wen, Chao; et al. (2020)
Transportation Research Part C: Emerging Technologies
Based on the Bayesian network (BN) paradigm, we propose a hybrid model to predict the three main consequences of disruptions and disturbances during train operations, namely, the primary delay (L), the number of affected trains (N), and the total delay times (T). To obtain an effective BN structure, we first analyze the dependencies of the involved factors on each station and among adjacent stations, given domain knowledge and expertise about operational characteristics. We then put forward four candidate BN structures, integrating expert knowledge, the interdependencies learned from real-world data, and real-time prediction and operational requirements. Next, we train the candidate structures based on a 5-fold cross-validation method, using the operational data from Wuhan-Guangzhou (W-G) and Xiamen-Shenzhen (X-S) high-speed railway (HSR) lines in China. The best performing structure is nominated to predict the consequences of disruptions and disturbances in the two HSR lines. Comparisons results show that the proposed model outperforms three other commonly used predictive models, reaching an average prediction accuracy of 96.6%, 74.8%, and 91.0% on the W-G HSR line, and 94.8%, 91.1%, and 87.9% on the X-S HSR line for variables L, N, and T, respectively.
Integrated optimization of train timetabling and rolling stock circulation problem with flexible short-turning and energy-saving strategies
Gong, Congcong; Luan, Xiaojie; Yang, Lixing; et al. (2024)
Transportation Research Part C: Emerging Technologies
In daily operations, passenger demand for metro lines traversing city centers often exhibits pronounced tidal characteristics, particularly during morning and evening peak hours. Given the unbalanced spatial and temporal distribution of passenger demand in a bi-directional metro line, this paper investigates an integrated optimization method for train timetabling and rolling stock circulation plans with flexible short-turning and energy-saving strategies. In particular, this approach simultaneously considers constraints such as limited train capacity, turnaround operations, the finite number of available trains, and regenerative energy utilization. Firstly, by introducing decisions involving service frequency, service headway, train route selection, rolling stock circulation plan, and the overlap time indicator, a nonlinear integer programming (NLIP) model is formulated to minimize the weighted sum of passenger waiting time and energy costs, accounting for both passenger and operator perspectives. Subsequently, the model is reformulated into a quadratically constrained quadratic programming (QCQP) model which can be solved directly by commercial solvers. To address large-scale real-world experiments, an adaptive large neighborhood search (ALNS) algorithm is developed. Finally, numerical experiments are conducted on a simplified metro line and Fuzhou Metro Line 1. The results demonstrate that, compared to the full-length strategy, the proposed method reduces total passenger waiting time and energy costs by approximately 8.7% and 5.7%, respectively. Moreover, the methods could support decision-makers with different passenger and operator preferences.
Enhancing the interaction of railway timetabling and line planning with infrastructure awareness
Fuchs, Florian; Trivella, Alessio; Corman, Francesco (2022)
Transportation Research Part C: Emerging Technologies
Planning a railway system is done in multiple stages that are typically intractable to optimize in an integrated manner. This work develops a novel iterative approach to tackle two of these stages jointly: line planning and timetabling. Compared to existing approaches that iteratively ban a whole conflicting line plan when the timetable is found infeasible, our method can accurately identify the smallest set of incompatible services. Besides, by efficiently exploiting the available railway infrastructure, our method accounts for all the possible routing options of trains, a feature commonly neglected to reduce complexity but that helps gaining timetable feasibility. Using real data from a railway company in Switzerland, we find that our approach is (i) practical for solving real-life instances, (ii) an order of magnitude faster than existing benchmarks, and (iii) able to solve more instances. Our insights shed light on the necessity of considering infrastructure and banning conflicts rather than line plans in the joint line planning and timetabling problem.
Real-time merging traffic control for throughput maximization at motorway work zones
Tympakianaki, Athina; Spiliopoulou, Anastasia D.; Kouvelas, Anastasios; et al. (2014)
Transportation Research Part C: Emerging Technologies

Publications 1 - 10 of 59

Journal: Transportation Research Part C: Emerging Technologies

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