As an important service industry in cities, taxis provide people with an all-weather travel mode. And its demand is greatly affected by the internal functions of the city. It is very important to understand the relationship between the mixed degree of urban internal functions and the residents’ taxi travel demand to alleviate traffic congestion and formulate corresponding urban traffic strategies. This paper combined two heterogeneous data in the main urban area of Xi’an, urban points of interest (POIs) and taxi GPS. Firstly, a spatial information entropy model was constructed to quantitatively evaluate the mixed degree of functions in different spaces within the city. Secondly, the kernel density estimation method was used to analyze the spatial distribution evolution characteristics of residents’ taxi travel demand. A geographically weighted regression (GWR) model was further used to study the spatial and temporal influences of the mixed degree of urban internal functions on taxi travel demand. Results indicate that there is an obvious spatiotemporal pattern in the impact of the mixed degree of urban functions on taxi travel demand. And the GWR model is used to study the impact is superior to the ordinary least squares (OLS). In more developed areas, improving the mixed degree of urban functions will be more attractive than backward areas. It is also found that although the single function of the city has an impact on the taxi travel demand, the result of the single function is not ideal. This study can provide a reference for the optimal combination of basic units of urban space in urban planning, promote the balance of supply and demand of urban taxis, rationalize urban taxis’ operation and allocation, and solve the problems of urban transportation systems.

Although a wide range of literature has investigated the network-level highway maintenance plans and policies, few of them focused on the maintenance scheduling problem. This study proposes a methodology framework to model and compare two different maintenance scheduling strategies for highway networks, i.e., minimal makespan strategy (MMS) and minimal increased travel delay strategy (MITDS). We formulate MMS as a mixed integer linear programming model subject to the constraints of the quantity of manpower and the worst-first maintenance sequence. A bi-level programming model is proposed to quantify and optimize MITDS. The upper level model determines the optimal scheduling to minimize the increased traffic delays during the maintenance makespan. In the lower level, a modified day-to-day traffic assignment model is put forward to reflect the traffic evolution dynamics by simulating travelers’ route choice behaviors. A simulated annealing algorithm and augmented Lagrange algorithm are employed to solve the two proposed models, respectively. Finally, a numerical example using a highway network is developed. The two proposed strategies are tested considering different traffic demands, numbers of engineering teams, and travelers’ sensitivities to traffic congestion. The experiment results reveal that compared with MMS, MITDS extends makespan by 2 days though, it reduces the total increased travel delays by 4% and both MMS and MITDS can obtain the minimum total increased travel delays when the number of engineering teams is 6. The sensitivity analysis indicates that both the two strategies have the maximum and minimum total increased travel delays when the weight of prediction in travelers’ perception is 0.3 and 0.7, respectively. The proposed framework has the potential to provide reference in implementing highway maintenance activities reasonably.

Over the past few decades, taxi drivers’ income has received extensive attention from scholars. Previous studies have investigated the factors affecting taxi drivers’ income from multiple perspectives. However, less attention has been paid to road network topology, which has a direct impact on taxis’ operation efficiency and drivers’ income. To fill this gap, this paper examines the relationship between taxi drivers’ income and urban road network topology; we employed various methods, namely, spatial design network analysis (sDNA), bivariate Moran’s I, and geographically weighted regression (GWR). The results show the following. (1) The total order income (TOI) of taxi drivers has a certain degree of positive spatial correlation with closeness and betweenness. (2) The impact of urban road network topology on the average order income (AOI) of taxi drivers is stable. Specifically, closeness and betweenness have significant impacts on the AOI of taxi drivers at the medium and larger scales. (3) Closeness has a negative impact on the AOI of taxi drivers, and betweenness has a positive impact on the AOI of taxi drivers. (4) Compared with betweenness, the impact of closeness on the AOI of taxi drivers is greater and more stable. These findings can provide useful reference values for the development of policies aimed at improving both taxi drivers’ income and urban road network efficiency.