The accuracy of the regression model of ridership of subway stations depends on the scale range of the built environment around the subway stations. Previous studies have not considered the Modifiable Area Unit Problem (MAUP) to establish the regression model of subway station ridership. Taking Beijing as an example, this paper expanded the built environment variables from “5D” category to “7D” category, added indicators such as parking fee standard and population density factor, and proposed a Multi-Scale Geographical Weighted Regression (MGWR) model of outbound ridership of subway stations with standardized variables. The goodness of fit of regression models under 10 spatial scales or built environment around subway stations are compared, and the spatial heterogeneity of built environment factors under the optimal spatial scale of outbound ridership of subway stations during the morning peak on weekdays is discussed. The results show that: (1) the scale range overlapped by 1000 m radius circular buffer zone and Thiessen polygon has the highest explanatory power for the regression model, and is regarded as the optimal scale range of built environment; (2) the density of office facilities, sports and leisure facilities, medical service facilities, building density and floor area ratio (FAR) has a significant impact on the outbound ridership of all subway stations; (3) office facilities, catering facilities, FAR, number of parking lots, and whether subway stations are transfer stations have a positive impact on outbound ridership. The number of medical service facilities, sports and leisure facilities, bus stops and building density have a negative impact on outbound ridership; (4) the two added factors in this study: parking charge standard and population density, as the influencing factors of the built environment, have a significant impact on the outbound ridership of some subway stations; and (5) the different local coefficients of the built environment factors at different stations are discussed, which indicate the spatial heterogeneity on the outbound ridership. The results can provide an important theoretical basis for the prediction and analysis of demand of ridership at subway stations and the integration of the built environment around the stations.

A flexible bus route optimization scheduling model that considers the dynamic changes of passenger demand is proposed to address the large difference in demand for flexible bus passengers and real-time variability. This model uses the heuristic algorithm based on gravity model to determine the following: passenger booking; vehicle passenger capacity; team known conditions such as size, according to the dynamic changes of passenger demand for real-time iterative update shuttle travel time; vehicle operating costs (vehicle); and time cost for passengers (passengers waiting time for the vehicle, actual time of arrival, and the difference between expected and actual times of arrival) before minimization as the target. Finally, the practicabilities of the model and algorithm are verified by an example. Analysis results show that for 102 travel demands of 15 randomly generated demand points, completing all services requires 17–21 vehicles with average travel time of 24.59 minutes each. The solution time of 100 groups of data is within 25 seconds and the average calculation time is 12.04 seconds. Under the premise of real-time adjustment of connection planning time, this optimization model can thus better meet the dynamic demand of passengers compared with the current scenario. The model effectively reduces the planning path error, shortens the travel distance and passenger travel time, and achieves better results than the flexible bus scheduling model that ignores changes of connection travel time.

This study comprehensively analyses the robustness of urban road networks through topological indices based on the complex network theory and operational indices based on traffic assignment theory: User Equilibrium (UE), System Optimum (SO), and Price of Anarchy (POA). Analysing topological indices may pin down the most important nodes for URNs from the perspective of connectivity, while more sophisticated operational indices are helpful to examine the importance of nodes for URNs by taking into account link capacity, travel demand, and drivers’ behaviour. The previous way is calculated in a static way, which reduces the computation times and increases the efficiency for quick assessment of the robustness of URNs, while the latter is in a dynamic way, namely, calculating is based on removal of individual nodes, although this way is more likely to capture realistic meanings but consumes huge amount of time. The efforts made in this study try to find the relationship between topological and operational indices so as to assist the assessment of robustness of URNs to local disruptions. Seven realistic urban road networks such as Sioux Falls and Anaheim are used as network examples, and results show that different indices reflect robustness characteristics of urban road networks from different ways, and rank correlations between any two indices are poor although small network such as Sioux Falls have better correlations than others.

The promising potential of distributed and interconnected lightweight devices that can jointly generate superior information-collecting and problem-solving abilities has long fostered various significant and ubiquitous techniques, from wireless sensor networks (WSNs) to Internet of Things (IoT). Although related applications have been widely used in different domains in attempting to collect and harness the ever-growing information flows, one major issue that impedes the further advancement of WSNs or IoT-based applications is the restricted battery power. Previous research mainly focuses on investigating novel protocols to save energy by reducing data traffic with the aid of optimal or heuristic algorithms. However, data packet behaviours and significant parameters involved are mostly preconfigured in a supervised-learning fashion rather than using an unsupervised learning paradigm and therefore may not adapt to uncertain or fast-changing environments. Hence, this paper concentrates on optimising the behaviours of data packets and significant parameters in a widely tested routing protocol, namely, Cognitive Packet Network (CPN), with the aid of several bio-inspired algorithms to increase the efficiency of energy usage and information acquisition. Two novel packet behaviours are introduced, and an on-line parameter calibration scheme is proposed to realise packet time-to-live (TTL) adjustment and rate adaptation. The simulation results show that the introduction of the bioinspired algorithms can improve the efficiency of information sharing and reduce the energy consumption.

Considering pedestrian preferences for the minimum distance, the minimum number of queuing pedestrians and the shortest estimated time, three pedestrian choice strategies of ticket gate machine are proposed. Pedestrian choice strategies of ticket gate machine are added into pedestrian simulation model which is based on cellular automata, and pedestrian choice behavior simulation model of ticket gate machine is obtained. On the platform of MATLAB simulation software, pedestrian choice behavior is simulated. Simulation results indicate that choice strategies of ticket gate machine proposed in this paper describe pedestrian choice behavior well, and it needs to consider the ratio of bidirectional pedestrian generation rate in the process of setting ticket gate machines in the bidirectional passage.