Efficient urban emergency response is vital for saving lives and property after disasters. However, urban emergency response is challenging because it involves many demands, with a very tight time for making decisions, and is frequently threatened by the disruption of road networks and infrastructures. Taking these challenges into consideration, this article proposes a dynamic truck-UAV (DTU) collaboration strategy to realize efficient and resilient urban emergency response. Specifically, a DTU collaboration strategy is first proposed based on the characteristics of urban emergency management. Then, an integrated truck-UAV collaborative scheduling model is developed and indicated to be an NP-hard problem. Then, a tabu search-based integrated (TSI) scheduling algorithm is developed, with several tailored local search operators and a recursion-based evaluation (RE) algorithm. Finally, comprehensive experiments are conducted to demonstrate the superiority of our proposed strategy over existing truck-UAV collaboration strategies and verify the performance of our proposed solution algorithm. Managerial Relevance Statement—This article proposes a dynamic truck-UAV (DTU) collaboration strategy and a corresponding route planning method for urban emergency response. The findings of this work demonstrate that emergency management agencies can utilize the DTU collaboration strategy for efficient emergency response in urban areas with high-density road networks. Meanwhile, the emergency response under this strategy is resilient to cope with different degrees of road network disruptions. In addition, the DTU collaboration strategy shows a distinct improvement over the parallel and truck-UAV flying sidekick strategies in large-scale emergency response scenarios, and it will perform better when demand density increases. Furthermore, the proposed integrated scheduling algorithm is demonstrated to be suitable and efficient for solving the routing problems associated with the DTU collaboration strategy.

To address the issues of weak correlation between multi-view features, low recognition accuracy of small-scale targets, and insufficient robustness in complex scenarios in underground pipeline detection using three-dimensional ground penetrating radar (3D GPR), this paper proposes a three-dimensional pipeline intelligent detection framework that integrates multi-strategy improved deep learning technology. This paper explores a novel pathway to achieve accurate 3D localization through lightweight joint analysis of multi-view 2D GPR images. First, based on a B/C/D-Scan three-view joint analysis strategy, a three-dimensional pipeline three-view feature evaluation method is established by cross-validating forward simulation results obtained using time-domain finite difference (FDTD) methods with actual measurement data. Second, the DCO-YOLO framework is proposed, which integrates DySample dynamic upsampling, Convolutional gate linear unit (CGLU), and OutlookAttention cross-dimensional correlation mechanisms into the original YOLOv11 algorithm, significantly improving the small-scale pipeline edge feature extraction capability. Furthermore, a 3D-DIoU spatial feature matching algorithm is proposed, which integrates three-dimensional geometric constraints and center distance penalty terms to achieve automated association of multi-view annotations. The three-view fusion strategy resolves inherent ambiguities in single-view detection. Experiments based on 100 kilometers of real urban underground pipeline data show that the proposed method achieves accuracy, recall, and mean average precision of 96.2%, 93.3%, and 96.7%, respectively, in complex multi-pipeline scenarios, which are 2.0%, 2.1%, and 0.9% higher than the baseline model. Ablation experiments validated the synergistic optimization effect of the dynamic feature enhancement module, and Grad-CAM++ heatmap visualization demonstrated that the improved model significantly enhanced its ability to focus on pipeline geometric features. This study integrates deep learning optimization strategies with the physical characteristics of 3D GPR, offering an efficient and reliable novel technical framework for the intelligent recognition and localization of underground pipelines.

With growing traffic congestion and environmental issues, the interactions between travel behaviour and the built environment have drawn attention from researchers and policymakers to take effective measures to encourage more sustainable travel modes and to curb car trips, especially in urbanising areas where travel demand is very complicated. This paper presents how built environmental factors affect public transit choice behaviour in urban villages in China, where a large population of low-income workers are accommodated. This location had a high demand for public transit and special built environmental characteristics. Multinomial logistic regression was employed to examine both the determinants and magnitude of their influence. The results indicate that the impacts of built environments apply particularly in urban villages compared to those in formal residences. In particular, mixed land use generates an adverse effect on public transit choice, a surprising outcome which is contrary to previous common conclusions. This study contributes by addressing a special type of neighbourhood in order to narrow down the research gap in this domain. The findings help to suggest effective measures to satisfy public transit demand efficiently and also provide a new perspective for urban regeneration.

As China’s urbanization process continues to accelerate, the demand for intercity residents’ transportation has increased dramatically. Holiday travel has different demand characteristics, causing serious shortage during peak periods. However, current research barely focuses on the passenger flow prediction along with travel characteristics of intercity shuttles. Accurately predicting passenger flow during the holidays helps to improve operational organization efficiency and residents’ satisfaction, and provides a basis for reasonable resource allocation by the management department. This paper analyzes the spatiotemporal characteristics of intercity shuttles passenger flow in the Pearl River Delta. Separate passenger flow prediction models on non-holiday and holiday are established using an improved genetic algorithm optimized back propagation neural network (IGA-BPNN) based on the characteristics of passenger flow, and the prediction models are validated based on panel data. The results of weekly flow show obvious holiday characteristics, and the hourly traffic flow of holidays is much larger than that of weekends and weekdays. There is a significant difference in the hourly flow between different holidays. The IGA-BPNN model used in this paper achieves lower prediction error relative to the benchmark BPNN approach (leads a two thirds reduction in MAPE, and an over 85% reduction in MSPE).

The joint choice of shopping destination and travel-to-shop mode in downtown area is described by making use of the cross-nested logit (CNL) model structure that allows for potential interalternative correlation along the both choice dimensions. Meanwhile, the traditional multinomial logit (MNL) model and nested logit (NL) model are also formulated, respectively. This study uses the data collected in the downtown areas of Maryland-Washington, D.C. region, for shopping trips, considering household, individual, land use, and travel related characteristics. The results of the model reveal the significant influencing factors on joint choice travel behavior between shopping destination and travel mode. A comparison of the different models shows that the proposed CNL model structure offers significant improvements in capturing unobserved correlations between alternatives over MNL model and NL model. Moreover, a Monte Carlo simulation for a group of scenarios assuming that there is an increase in parking fees in downtown area is undertaken to examine the impact of a change in car travel cost on the joint choice of shopping destination and travel mode switching. The results are expected to give a better understanding on the shopping travel behavior.