Article
Transportation
Murat Bayrak, Zhengyao Yu, Vikash V. Gayah
Summary: This paper proposes a population-based incremental learning (PBIL) algorithm to determine left-turn restrictions at intersections in order to maximize the network's operational performance. The algorithm is effective at identifying near-optimal configurations, suggesting that left turns should generally be restricted at intersections with the highest flow. This approach provides additional intersection capacity and reduces additional travel distance.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
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Mansour Johari, Shang Jiang, Mehdi Keyvan-Ekbatani, Dong Ngoduy
Summary: This paper investigates the partitioning problem of bi-modal networks and proposes a three-step partitioning algorithm based on NMFD. The role played by mode differentiation in bi-modal network partitioning is studied through the lens of speed-NMFDs.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Correction
Transportation
Kam K. H. Ng
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Correction
Transportation
Jiyeon Lee, Ilkyeong MoonJ
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Jiaming Liu, Zhen Guo, Bin Yu
Summary: This paper proposes an integrated model to simultaneously deal with gate assignment and taxiway planning, considering practical constraints. Through state analysis and sensitivity analysis, the results show that the model can balance resource allocation.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Yu Jiang, Zhitao Hu, Zhenyu Liu, Honghai Zhang
Summary: The Airport Gate Assignment Problem (AGAP) is an optimization problem with multiple constraints. In this study, a NSGA-II-LNS algorithm is proposed to minimize taxiing costs and passenger walking distance, while considering fairness among different airlines. The numerical study at Nanjing Lukou International Airport demonstrates that the proposed algorithm outperforms previous ones in terms of solution quality, with acceptable computing time.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Ruixia Yang, Baoming Han, Qi Zhang, Zhenyu Han, Yuxuan Long
Summary: This study proposes an integrated optimization approach based on AFC data to develop a route plan and timetable that can meet dynamic passenger demand. The improved non-dominated sorted genetic algorithm-II is applied to solve the bi-objective problem, and a numerical experiment proves the effectiveness and efficiency of the approach.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Hossein Moradi, Sara Sasaninejad, Sabine Wittevrongel, Joris Walraevens
Summary: This paper proposes a three-module framework that collects relevant information of Connected Vehicles (CVs), performs initial partitioning based on rational considerations, and identifies optimal protected regions through evaluation, improvement, and iteration. Experimental results show that the proposed framework enhances the efficiency of perimeter control systems, even at low CVs' penetration rates.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
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X. Jin, W. F. Ma, R. X. Zhong, G. G. Jiang
Summary: Fundamental diagrams (FDs) are essential in traffic flow theory, and efficient model calibration is important to describe traffic flow characteristics. This study proposes a probabilistic sensitivity analysis guided variational Bayesian (PSA-VB) framework to calibrate the parameters of FDs. The proposed method shows lower computational cost and faster convergence speed compared to existing methods, and it can capture traffic flow characteristics by explicitly considering traffic dynamics.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Xiongwen Qian, Jianfeng Mao, Yuan Wang, Meng Qiu
Summary: A column generation-based framework is proposed for air traffic flow management (ATFM) incorporating a user-driven prioritization process (UDPP). Airspace Users' (AUs') preferences and priorities can be explicitly reflected in the framework. The proposed framework efficiently solves the ATFM problem considering UDPP features with often zero or small optimality gaps.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Wanda Ma, Peng Li, Jing Zhao
Summary: This study introduces an innovative bi-level model to address the challenges of managing traffic flow in construction work zones in urban transportation networks. The model integrates ramp closure, lane reorganization, and signal timing strategies within a network-level framework, thereby capturing the interdependencies between these strategies and enhancing the overall performance of the transportation network. A Genetic Algorithm (GA)-based heuristic method is proposed to solve the optimization problem, and a case study demonstrates the effectiveness of the proposed approach. This study offers a comprehensive and innovative solution to mitigate the negative impacts of detour traffic on urban transportation networks, assist transportation agencies in effectively managing traffic flow, and improve the overall system performance.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
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Enyi Chen, Qin Luo, Jingjing Chen, Yuxin He
Summary: The analysis of passenger travel choice behaviours under train delays is crucial for urban rail transit operation management. This paper analyzes the travel choices of affected regular passengers using data collected through an automatic fare collection (AFC) system and train delay log records. A data-driven four-stage framework is proposed for studying regular passengers' responses under delays, including data profiling, regular passenger screening, affected regular passenger identification, and affected passenger behaviour prediction modeling. Experiments conducted using the Shenzhen Metro in China validate the proposed framework and provide insights for analyzing passenger behavior and train delay-related tasks through multi-source heterogeneous data mining.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Jiarong Yao, Hao Wu, Keshuang Tang
Summary: This study proposes a SPaT estimation method using license plate recognition (LPR) data for fixed-time controlled intersections. The SPaT estimation problem is formulated as a bi-level programming model to find the optimal match between the phase boundaries and the LPR passing time series. Evaluation with an empirical case and comparison with an existing method demonstrate the potential for practical application, with phase duration estimation accuracies reaching 90.0%.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Xiaoyan Lei
Summary: This study establishes a vertical dynamics model for high-speed train-track coupling system considering nonlinear wheel-rail contact and proposes a cross iteration method to solve the dynamics equations of the nonlinear coupling system. Using this model, four types of track irregularities (smooth track, track random irregularity, short-wave irregularity, and combined track random irregularity and short-wavelength irregularity) are analyzed for their effects on the vibration responses of the train and the track. The results show that the vehicle and the bogie passing frequencies are the main sources of excitation for the track vibration displacement and velocity, while the short-wave irregularity is the main source of excitation for the track vibration acceleration. The track random irregularity, short-wave irregularity, and sleeper spacing have significant influences on the wheel-rail force, wheelset and bogie vibration acceleration, while the car-body vibration acceleration is mainly affected by the track random irregularity.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Wenxin Teng, Bi Yu Chen, William H. K. Lam, Weishu Gong, Chaoyang Shi, Mei Lam Tam
Summary: This study proposes a reliable eco-routing model that considers uncertainties in travel time and fuel consumption. It develops a stochastic fuel consumption formula and a bi-objective model to minimize travel time and fuel consumption while satisfying constraints on reliability. An efficient path ranking algorithm is also developed.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Douglas Zechin, Helena Beatriz Bettella Cybis
Summary: This paper proposes a framework that uses a Variational LSTM neural network model to calculate the probability of short-term traffic breakdown. Unlike standard deterministic recurrent neural networks, this framework is designed to produce output distributions, considering that traffic breakdown is a stochastic event. The framework includes the robustness of neural networks and the stochastic characteristics of highway capacity. It consists of building and training a probabilistic speed forecasting neural network, forecasting speed distributions using Monte Carlo dropout for Bayesian approximation, and establishing a speed threshold for breakdown occurrence and calculating breakdown probabilities based on the speed distributions. The proposed framework efficiently controls traffic breakdown, handles multiple independent variables or features, and can be combined with traffic management strategies.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Kequan Chen, Victor. L. Knoop, Pan Liu, Zhibin Li, Yuxuan Wang
Summary: This study investigates the impact of anticipation on the behavior of a new follower (NF) in a target lane using new trajectory datasets. The results show that anticipation significantly affects the NF's movement, resulting in gap creation and speed reduction. The developed binary logistic models can predict the NF's behavior with good performance.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Hao Fu, William H. K. Lam, H. W. Ho, Wei Ma
Summary: This study proposes a method to optimize the locations of multi-type traffic sensors in order to improve the accuracy of link travel time estimation. By integrating data from different types of sensors, link travel times in an entire road network can be better estimated. The method takes into account constraints such as total financial budget, measurement errors, and cost ratio.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Engineering, Electrical & Electronic
Dehua Shi, Sheng Liu, Yujie Shen, Shaohua Wang, Chaochun Yuan, Long Chen
Summary: This study investigates the impact of different clutch collaboration manners on the characteristics of transient mode switching in power split hybrid electric vehicles (HEVs) and formulates an optimization problem for control parameters related to the clutch collaboration. Simulation results demonstrate that optimized control parameters can greatly improve the performance of transient mode switching. This research provides valuable insights for the dynamic coordinated control of power split HEVs with complex clutch collaboration mechanisms.
AUTOMOTIVE INNOVATION
(2023)
Article
Engineering, Electrical & Electronic
Jiuyu Chen, Zhongli Wang, Jian Wang, Baigen Cai
Summary: This study introduces Q-EANet, a trajectory prediction network that combines GRU encoders and attention modules to enhance interpretability and reduce complexity in prediction models for self-driving vehicles. By introducing a new explanatory rule, it models the entire trajectory prediction process via an implicit social modeling formula. Q-EANet achieves state-of-the-art performance on the nuScenes benchmark while maintaining a simple module design.
IET INTELLIGENT TRANSPORT SYSTEMS
(2023)
