There usually exist a few big customers at ports of near-sea container shipping routes who have preferences on the weekly ship arrival times due to their own production and sale schedules. Therefore, in practice, when designing ship schedules, carriers must consider such customers’ time preferences, regarded as weekly soft-time windows, to improve customer retention, thereby achieving sustainable development during a depression in the shipping industry. In this regard, this study explores how to balance the tradeoff between the ship total operating costs and penalty costs from the violation of the weekly soft-time windows. A mixed-integer nonlinear nonconvex model is proposed and is further transformed into a mixed-integer linear optimization model that can be efficiently solved by extant solvers to provide a global optimal solution. The proposed model is applied to a near-sea service route from China to Southeast Asia. The results demonstrate that the time preferences of big customers affect the total cost, optimal sailing speeds, and optimal ship arrival times. Moreover, the voyage along a near-sea route is generally short, leaving carriers little room for adjusting the fleet size.

This paper proposes to address the problem of the simultaneous optimization of the liner shipping route and ship schedule designs by incorporating port time windows. A mathematical programming model was developed to minimize the carrier’s total operating cost by simultaneously optimizing the port call sequence, ship arrival time per port of call, and sailing speed per shipping leg under port time window constraints. In view of its structure, the nonlinear nonconvex optimization model is further transformed into a mixed-integer linear programming model that can be efficiently solved by extant solvers to provide a global optimal solution. The results of the numerical experiments performed using a real-world case study indicated that the proposed model performs significantly better than the models that handle the design problems separately. The results also showed that different time windows will affect the optimal port call sequence. Moreover, port time windows, bunker price, and port efficiency all affect the total operating cost of the designed shipping route.

To reduce the operating cost and running time of demand responsive transit between urban and rural areas, a DBSCAN K-means (DK-means) clustering algorithm, which is based on the density-based spatial clustering of applications with noise (DBSCAN) and K-means clustering algorithm, was proposed to cluster pre-processing and station optimization for passenger reservation demand and to design a new variable-route demand responsive transit service system that can promote urban–rural integration. Firstly, after preprocessing the reservation demand through DBSCAN clustering algorithm, K-means clustering algorithm was used to divide fixed sites and alternative sites. Then, a bus scheduling model was established, and a genetic simulated annealing algorithm was proposed to solve the model. Finally, the feasibility of the model was validated in the northern area of Yongcheng City, Henan Province, China. The results show that the optimized bus scheduling reduced the operating cost and running time by 9.5% and 9.0%, respectively, compared with those of the regional flexible bus, and 4.5% and 5.1%, respectively, compared with those of the variable-route demand response transit after K-means clustering for passenger preprocessing.

Combining traffic micro-simulation models and emission models is a primary method to estimate traffic emissions. However, there has been limited research on the impact of traffic micro-simulation model parameters on simulator outputs, which are critical to emissions calculations. Based on combining VISSIM and MOVES, this study uses the Morris and Sobol methods to explore the impact of parameters in VISSIM on operating mode distribution and travel time distribution. Taking an urban signal control intersection with three traffic scenarios in Chengdu, China as an example, this study verifies the methods’ feasibility. Apart from these parameters, which have been proved to be necessary calibration parameters, including the desired speed distribution, the desired acceleration function, and the desired deceleration function, an additional 24 parameters related to simulation setting and driving behaviour models are selected as the initial parameters. The number of interaction objects, maximum look-ahead distance, average standstill distance, additive part of safety distance, and safety distance reduction factor close to a stop line, are considered to be the important parameters for this case study. The impact of these five parameters on the bins of operating mode distribution and travel time distribution are further analyzed with One-at-a-time, and these parameters are compared with those reported in previous studies. It is concluded that the important parameters selected in this study are reasonable and can support the calibration of VISSIM parameters for this case’s traffic emissions. GRAPHICAL ABSTRACT

In real-world traffic environments, complex factors often cause small targets to be missed or occluded targets to be misidentified during vehicle and pedestrian detection, making this task a challenging problem in the field of object detection. To address the challenges of visual perception in complex traffic scenarios, this paper redefines vehicle and pedestrian detection as a geometric measurement problem based on object detection and proposes YOLO-TAD (TriResMSConv, ARSA, and D² Head), an intelligent perception and detection network designed to enhance measurement accuracy and spatiotemporal consistency. This improved network incorporates three key innovations: TriResMSConv (Triple Residual Multi-Scale Segmentation Convolution), ARSA (Axial Rotation Spatial Attention), and a fully decoupled D² Head (Densely Distributed Perception Detection Head) based on distributed regression. TriResMSConv enhances feature representation through multi-scale residual branches; ARSA performs axial decomposition in the spatial dimension and applies RoPE (Rotation Position Encoding) to emphasize orientation dependence; and the D² Head integrates IoU perception with distributed regression mechanisms to improve localization accuracy. Additionally, this method employs full projection IoU (CPIoU) to increase the accuracy and stability of bounding box recognition. Experimental results demonstrate that the proposed method significantly improves detection performance on the SODA-10M autonomous driving perception dataset. Compared with YOLO11s, this model achieves improvements of 3.0% and 2.2% in mAP@50 and mAP@50-95, respectively. Subsequent generalization experiments conducted on the KITTI and BDD100K datasets confirmed this finding.

A shipping company operates multiple routes. Different routes between the same origin and destination ports may result in varying fuel consumption, shipping times, and inventory costs. Therefore, this research examines the optimization of sailing speed for a set of near-sea shipping services while taking container shipping routes into account. This problem is designed to reduce the overall cost. A Mixed-Integer Linear Programming (MILP) model is developed, which is then utilized for a group of practical service routes. The outcomes indicate that the best solution can be achieved rapidly, showcasing the efficiency and effectiveness of the model proposed.