Journal of Advanced Transportation最新文献

In the Connected and Autonomous Vehicle (CAV) environment, road space utilization can be more flexible. This study aims to maximize the allocation of road space for socioeconomic activities without compromising traffic demands. By exploiting the potential of CAVs to improve transportation systems, this paper explores network-level optimization of road space utilization, formulates the problem as a mixed-integer nonlinear programming model, and solves it with a tailored Tabu Search heuristic. We apply the model to a subnetwork of the Wangjing area in Beijing to demonstrate its practicality and effectiveness. The results reveal that initial lane configurations profoundly influence the activity lane planning. Notably, activity lanes are inclined to be arranged in adjacent segments within the network, providing greater socioeconomic benefits due to spatial agglomeration effects. This approach holds significant implications for effectively managing urban traffic flows and maximizing the utility of public spaces.

Against the backdrop of electricity market reform, accurate forecasting of train traction energy consumption can help operating enterprises set energy-saving targets and implement precise energy management. Traction energy consumption prediction models based on traditional influencing factors are prone to uncertainties in future factors and often overlook the seasonal variations inherent in traction energy consumption. This paper proposes a sliding window stacking method that integrates random forest with Holt–Winters, ARIMA, and Prophet models. The method is experimentally validated using 14 years of per-car-kilometer traction energy consumption data from a metro line in a certain city. Experimental results show that the random forest stacking model achieves a mean absolute error (MAE) of 0.037609 kWh/car-km, which represents reductions of 17%, 26%, and 32% compared with using Holt–Winters, ARIMA, and Prophet models alone, respectively. The mean squared error (MSE) reaches 0.002264 kWh/car-km, corresponding to reductions of 33%, 28%, and 46% compared with the individual models. The results demonstrate that the random forest stacking hybrid model can effectively improve the accuracy of train traction energy consumption forecasting.

A literature review indicates that freight demand models (FDMs) covering a large region and multiple categories of commodities and transport modes based on an integrated modeling approach are rare. Compared with traditional models, such models have a much higher utility in decision-making support for long-term planning of regional transport and other related systems, such as economy, land use, and environment. With this, this paper focuses on outlining a methodology for the design and development of such a model based on an integrated modeling framework—PECAS and big data—and then proves its utility by carrying out a case study for a large region in China—the Yangtze River Economic Belt (YREB). The design of such a model starts from a statistical analysis regarding the major types of freight transported over the multimodal transport network of the studied region. Then this, in turn, determines how activities and land uses are classified, synthesized, and represented within the model. The four PECAS modules (such as economic and demographic [ED], activity allocation [AA], space development [SD], and transport [TR]) are then designed, developed, and refined with innovative modeling approaches, such as multiple forecasting techniques, population/employment synthesis at multiple geographies, land use synthesis to address data issues, and estimation of modeling parameters with big data. Study results show that the proposed method is powerful for representing and modeling the impact of several endogenous variables, such as the economy and land use, on freight demand of different transport modes with a high societal, spatial, and temporal resolution. In addition, the estimation errors for the mode shares of the multimodal transport system are found to be less than 10%. The goodness-of-fit (R²) values across each of the three modes of transport network (including highway, railway, and waterway) at the base year are found to be above 0.85. The proposed modeling methods can provide valuable insights into analyzing the complex relationship between several regional elements, including socioeconomic development (by sector), land use regulations and transport supplies (by mode), and multimodal freight demand. An empirical model developed with such a methodology is found to better support planners, engineers, and decision-makers in understanding the complicated relationships among the above regional systems and effectively addressing relevant policy questions.

J. Yang, K. Higuchi, R. Ando, and Y. Nishihori, “Examining the Environmental, Vehicle, and Driver Factors Associated with Crossing Crashes of Elderly Drivers Using Association Rules Mining,” Journal of Advanced Transportation 2020 (2020): 2593410, https://doi.org/10.1155/2020/2593410.

In the article, there are errors in the formulae presented in equation 1.

We apologize for these errors.

This study introduces a novel univariate probability density (UPD) model leveraging partially monotone neural networks to analyze activity durations, with a specific focus on shopping trips by noncommuters in Shanghai. The proposed method ensures the monotonicity of the cumulative distribution function (CDF) with respect to time while enabling flexible modeling of complex distributions influenced by exogenous variables. Simulation experiments validate the model’s robustness and accuracy in capturing distributional patterns and variable effects. Empirical analysis using the 2019 Shanghai Household Travel Survey data demonstrates the model’s capability to reveal nuanced relationships between shopping durations and demographic, household, and locational factors. The results provide valuable insights into activity-based modeling and inform urban planning, transportation systems, and policy-making. By enabling realistic sampling and robust scenario analysis, this approach establishes a flexible, data-driven framework for studying activity durations.

Understanding the conditions that affect takeover time (TOT) in conditional autonomous driving systems remains a challenging issue. The takeover process requires a seamless transition of control from the autonomous system to the driver when the system encounters situations it cannot manage. This study examines the effects of traffic conditions, road geometry, and weather on TOT using a linear mixed model to quantify their influence. Preliminary findings indicate that factors such as rain, gender, and age significantly extend control transition duration. These insights highlight the need for personalized designs in automated driving systems (ADSs) and takeover request protocols to accommodate diverse driver characteristics and environmental conditions. While the research utilizes a driving simulator, suggesting the need for field validation, it offers a foundational understanding that can enhance the safety and efficiency of conditional automation systems. This study contributes to safer ADS design and supports the commercial viability of conditional autonomous vehicles.

Vehicle lateral motion control is one of the critical issues in intelligent vehicle control. We design a vehicle lateral motion controller by combining the adaptive fading Sage–Husa Kalman filter (AFSH-KF) with the robust model predictive algorithm to address the problem of vehicle lateral motion control. Due to the influence of process and measurement noise on the estimation results, the AFSH-KF is employed to estimate the vehicle state parameters to improve the estimation accuracy and compared with the Kalman filter (KF). Simultaneously considering the influence of the uncertainties or perturbations appearing in the feedback loop (vehicle state parameters) on vehicle lateral motion control, a robust model predictive controller (RMPC) is designed for vehicle lateral motion. The performance of the designed controller is verified by co-simulating with MATLAB/Simulink and CarSim in double-lane, S-shape, and Fishhook conditions. The results show that the AFSH-KF can effectively estimate the states (yaw rate and sideslip angle) of the vehicle. Compared to the MPC controller, the RMPC controller significantly reduced the maximum and mean square error of the lateral deviation of the vehicle tracking target trajectory at different speeds.

In the oversaturated metro system, the mismatch between supply and demand leads to unequal allocation of train capacity at different stations, resulting in a transportation inequity issue. This paper proposes a collaborative optimization method to use train carriage flexible release strategy and passenger flow control strategy, which is described as a mixed-integer nonlinear programming (MINLP) model considering the trade-off between equity and efficiency. To solve this model, it is reformulated into a mixed-integer linear programming (MILP) model, which is solved by the GUROBI solver. An efficient variable neighborhood search algorithm is then proposed to find a high-quality solution to the proposed problem. Finally, two sets of numerical experiments, including a small-scale case and a real-world case of Chengdu metro system, are conducted to verify the proposed model. The experimental results show that the train release scheme and passenger flow control scheme generated by our proposed method can perform well on the trade-off between equity and efficiency.

Passenger pushing behavior during emergency evacuations on roll-on/roll-off (Ro-Ro) passenger ships is a critical yet overlooked factor in evacuation modeling. This study investigates the impact of pushing behavior on evacuation dynamics by employing an improved social force model (SFM) that integrates pushing forces and the ship’s inclination angle. Four evacuation scenarios are simulated to evaluate the impacts of pushing behavior and falling incidents. Results show that (1) moderate pushing can slightly shorten evacuation time without significantly increasing the risk of falling; (2) excessive pushing induces localized congestion, elevates the probability of falls, and ultimately prolongs evacuation time—under severe pushing conditions, total evacuation time increased by 45.4% compared with the no-pushing baseline; and (3) ship inclination significantly affects passenger stability, particularly near exit bottlenecks and in narrow passages. The findings enhance the realism of evacuation simulations and provide practical insights for optimizing crowd management strategies on Ro-Ro passenger ships.

This study was conducted to ensure traffic continuity at an adaptive signalized intersection by developing a SUMO-based digital twin of the Heybe Intersection in Antalya, using real traffic data obtained from the Antalya Traffic Control Center (covering 165 days of observations). To address potential sensor failure scenarios, a solution integrating traffic forecasting and reinforcement learning was developed. After applying data cleaning techniques, multiple deep learning models were trained to forecast traffic volumes, and their outputs were used to generate an origin-destination (O/D) matrix that served as input to a Deep Q-Learning (DQL) control model. Three scenarios were evaluated in the simulation: (i) baseline adaptive signal control under normal operating conditions, (ii) the existing system under sensor failure reverting to a fixed-time plan, and (iii) the proposed DQL-based intersection management. Results demonstrated that, under sensor failure conditions, the DQL-based system achieved substantial improvements compared to the fixed-time baseline: the average delay was reduced by 61.3%, the average speed increased by 134.6%, and the level of service improved from E to B. These findings highlight the potential of integrating forecasting models with DQL to enhance the resilience of smart intersections against sensor malfunctions.