Urban street networks derive their complexity not only from their hierarchical structure, but also from their tendency to simultaneously exhibit properties of both grid-like and tree-like networks. Using topological indicators based on planning parameters, we develop a method of network division that makes classification of such intermediate networks possible. To quantitatively describe the differences between street network patterns, we first carefully define a tree-like network structure according to topological principles. Based on the requirements of road planning, we broaden this definition and also consider three other types of street networks with different microstructures. We systematically compare the structure variables (connectivity, hierarchy, and accessibility) of selected street networks around the world and find several explanatory parameters (including the relative incidence of through streets, cul-de-sacs, and T-type intersections), which relate network function and features to network type. We find that by measuring a network’s degree of similarity to a tree-like network, we can refine the classification system to more than four classes, as well as easily distinguish between the extreme cases of pure grid-like and tree-like networks. Each indicator has different distinguishing capabilities and is adapted to a different range, thereby permitting networks to be grouped into corresponding types when the indicators are evaluated in a certain order. This research can further improve the theory of interaction between transportation and land use.

Drivers could be more careful when vehicles move in the non-standard narrow lane. This might cause the drivers to behave differently from other normal-size lanes. To investigate the characteristics of the traffic flow in narrow lanes, this paper collected the vehicle trajectory data on four-lane road segments with 2.8m width and 3.25m width respectively. Firstly, the spatial distribution of wheel tracks in the lane under low traffic volume is studied, and the results show that the wheel trails’ distribution is obviously affected by three factors: speed, lane width, and lane in cross-section position, which are significantly different from normal-size lanes. Secondly, statistical analysis shows that vehicle-following status in narrow lane environment can be divided into three types: staggered following, parallel following, and transitional following. Finally, a multivariate logistic regression model is constructed to predict the driver’s following strategy under different driving micro-environment parameters. The research results can support the fine management of lanes and help to improve the driving strategy of autonomous vehicles.

Large hospitals in China typically face high-density patient flows, highly concentrated medical service demands, and significant impacts on surrounding transportation systems. To explore this issue in depth, this study selected a sample of 135 hospitals from the Beijing–Tianjin–Hebei region and constructed a structural equation model based on multi-source data to empirically analyze the key factors and mechanisms influencing traffic attraction for different types of hospitals. The findings reveal that medical level is the most critical factor affecting hospital traffic attraction, with significant differences in the factor pathways between general hospitals and specialized hospitals. For general hospitals, hospital size remains an important factor, but the influence of traffic attraction has shifted from traditional static indicators such as construction area and number of beds to more dynamic service diversity indicators, such as the number of medical departments. For specialized hospitals, the second most influential factor is traffic accessibility, particularly the availability of parking spaces, which significantly impacts their traffic attraction, reflecting patients’ strong demand for convenient transportation when accessing specialized care. This study highlights the dynamic evolution of traffic attraction factors across different hospital types and provides new insights and theoretical support for optimizing hospital transportation planning and resource allocation.

Reducing road width can not only save land, but also increase traffic safety. However, the mechanism of traffic flow caused by lane narrowing is not clear, and the car following behavior may also c...