In the context of China’s recent urbanization, the agglomeration and diffusion of the strip-city spatial network are gradually being reconstructed. The ways in which the street network structure affects the underlying logic of economic and social development is worthy of in-depth consideration. This study takes Lanzhou (a typical strip city in China) as a case study, using dynamic, geographic, big data and spatial syntactic-theory models to explore the influence of street network accessibility and structure on the spatial and temporal distribution of strip-city spatial vitality. We use Hotspot Analysis (Getis-Ord Gi*) to analyze the dispersal characteristics of street space vitality. In addition, the spatial and temporal heterogeneity characteristics and mechanism of the influence of street accessibility on spatial vitality are evaluated using the spatial Durbin model (SDM). The results show that: the temporal and spatial performance of urban vitality on weekdays and weekends conforms to people’s daily activities, offering similar spatial agglomeration and dispersion effects; accessibility and pedestrian-friendly streets have better urban spatial vitality clustering; street network integration significantly affects the reshaping of urban vitality, but there is apparent temporal heterogeneity in the degree of impact.

The present study investigates the dynamic evolution characteristics of urban spatial morphology by analyzing real road network data from 2000, 2010, and 2020, along with nighttime lighting data employing spatial analysis methods and spatial syntax models. Accordingly, two separate dimensions of urban morphology: internal and external, are covered. First, the integration and synergy of interior morphology features are analyzed using spatial syntactic modeling. Subsequently, the spatial compactness, fractal dimension, and level of center of gravity shift of the city are assessed by combining the nighttime lighting data with the earlier dataset. This analysis facilitated the deep exploration of the spatiotemporal evolution of the city’s external morphology. Building upon this foundation, the interaction between the "internal and external" domains was analyzed further. The main findings of the study reveal a synchronous pattern of urban expansion throughout the evolution of urban spatial morphology. Furthermore, the urban form was observed to undergo a progressive transformation, transitioning from a "single core" morphology to a "primary and secondary double core" morphology. Over time, this development progressed and evolved into a "belt-like multi-core" structure. Additionally, the coupling characteristics further validate the relationship between the structure of the road network and the urban morphology in river valley-type cities. In particular, accessibility of dense and horizontally distributed transportation network was found to significantly influence the spatial development of these cities. As observed, the findings provides valuable insights into understanding the characteristics of internal and external associations regarding urban spatial patterns.

Accurate traffic flow prediction is essential for the effective management of Intelligent Transportation Systems (ITS). However, traditional methods based on static graph structures often fail to address the complex and nonlinear spatiotemporal dependencies in evolving traffic conditions. To address this challenge, we propose a Dynamic Graph Convolutional Recurrent Network with Temporal Self‐Attention (DGCRN‐TSA), which integrates a temporal attention mechanism to jointly capture dynamic spatial topologies and long‐range temporal patterns. The model incorporates a graph generation module that adaptively learns time‐varying adjacency matrices from traffic signals and introduces a trend‐aware attention module enhanced by residual‐guided decomposition for distinguishing between normal and anomalous traffic behaviours. Experiments on real traffic datasets confirm that DGCRN‐TSA achieves superior performance in both short‐ and medium‐to‐long‐term forecasts. Notably, it reduces MAE by 19.4% on PeMS04 and improves MAPE by 12.2% on PeMS08. The model also ensures high prediction accuracy with strong computational efficiency and an inference speed comparable to AGCRN. DGCRN‐TSA offers an efficient and reliable solution for dynamic spatiotemporal modelling and large‐scale real‐time traffic prediction.

Under the perspective of “flow space”, the text of this paper constructs an interactive spatial connection network within 30 county towns units of the Lanzhou-Xining urban agglomeration. Further, with the help of social network and ArcGIS (which is the spatial analysis method), the quantitative analysis is used to analyze the structural characteristics of multi-dimensional spatial network of the Lan-Xi urban agglomeration and their mutual relations. Moreover, the synergetic degree model is used to measure the synergetic development level of the “traffic-industry” compound system in urban agglomeration at present. The results indicate that the Lanzhou-Xining urban agglomeration forms a double-core structure that Lanzhou and Xining as the center, and the space of county towns presents the correlated characteristic of “dumbbell-like” expansion. Furthermore, the central power of county towns which are restricted by the traffic condition and industrial structure indicates some outstanding differences. Further, it shows that the center develops to the periphery in a low-level but balanced way. In addition, the random disequilibrium of order degree of traffic and industry in urban agglomeration restricts the synergetic development of county towns, and the compound system of urban agglomeration is in the low-medium stage of synergetic development. The results of this study can provide some references for improving the traffic infrastructure construction and industrial functional structure of Lan-Xi urban agglomeration. This study also can shed some lights on the construction of the Silk Road Economic Belt.

This study focuses on the challenges of resident mobility in low-density areas. Amid China’s rapid urbanization, rural landscapes and travel patterns are undergoing significant transformation. Using Lanzhou’s rural areas as a representative case study, this research employs questionnaire surveys to collect data. It applies a multi-nominal logit (MNL) model to examine factors influencing travel mode choices and utilizes structural equation modeling (SEM) to assess travel satisfaction—a composite metric derived from residents’ subjective evaluations of convenience, cost, time, and comfort. Findings indicate that private cars and public transportation are the primary travel modes. The MNL model reveals that age and destination accessibility significantly influence travel choices. SEM path analysis further shows that annual household income has a direct positive effect on satisfaction, while age exerts an indirect negative influence through mediating variables. Female satisfaction levels were significantly lower than those of males. Both road density and perceived infrastructure quality significantly enhanced satisfaction, while destination accessibility may exert a slight negative indirect effect by increasing travel expectations. The study theoretically enriches research on rural travel patterns and provides practical insights into rural transportation planning and infrastructure development.