Commercial complex is a compositional scenario that involves pedestrians with different travel purposes such as commuting, shopping and business. Simulation of pedestrian behavior in a commercial complex is difficult due to the heterogeneity of pedestrians and different attractions from various kinds of shops. Inspired from the law of universal gravitation and the pedestrian dynamics theory, we propose an attractive potential based social force framework for pedestrian simulation in commercial complexes. Our framework consists of an attractive potential model and an attractive potential based social force model. The former model evaluates the attractive force of different types of shops towards heterogeneous pedestrians, by associating the attributes of pedestrians with the types of shops. The latter model effectively evaluates the travel direction of pedestrians by incorporating the attractive force with the social force model via a synthesis force criterion. We conduct the simulation experiment based on 600 groups of pedestrian tracking data collected in the China World Mall, Beijing, a typical commercial complex. The simulation results show that the model can effectively simulate not only the trajectory of real pedestrians, but also the behavior of entering shops. Our research significantly improves the authenticity of pedestrian simulation and provides support for travel behavior modeling of heterogeneous pedestrians in the commercial complex.

Vehicle trajectories from different directions within intersections intersect and create conflicts on a two-dimensional plane. Compared to highways, the lateral movement of vehicles within intersections is irregular and difficult to predict. This risk from lateral trajectories extends to the surrounding areas, impacting other vehicles within the intersection and subsequently reducing both safety and efficiency. To address this issue, a framework for identifying the conflict risk of vehicle trajectory based on lateral movement at intersections is proposed in this paper. Firstly, the concept of virtual lanes is employed to extract abnormal trajectories of lateral vehicle movement within intersections. Subsequently, a spatio-temporal hexahedral conflict detection algorithm, based on the vehicle border, is developed. Finally, the causes of abnormal lateral trajectories within intersections and their relationship with conflict events are discussed in detail, and hotspot areas of vehicle safety risks within intersections are identified. To validate the proposed model, typical intersection vehicle trajectory data captured by uncrewed aerial vehicles is utilized. The research results indicate that abnormal lateral movements of vehicles within intersections are common and pose significant risks to neighboring vehicles. The data reveal a specific pattern in the abnormal trajectory and trajectory density at intersections, with abnormal lateral trajectories of left-turning vehicles accounting for as much as 95.02% of cases. Furthermore, abnormal lateral movement in risky trajectories accounted for up to 86.6%. The proposed method in this paper provides support for intersection safety assessments, abnormal lateral trajectory warning systems, and real-time risk calculations for connected autonomous vehicles and Car2Car communication.

As large passenger flow commonly arises in metropolitan metro stations during peak hours, most of bottleneck congestion appears in the entrance halls of metro stations. Previous research work on mitigating metro congestion mostly focus on optimizing single facility and lack a comprehensive analysis on how congestion arises. We propose a system dynamics based framework for optimizing the aisle length and mitigating bottleneck congestion in the entrance halls. We first define the key bottleneck region (KBR) of metro stations consisting of security check area, automatic fare gate area, and the area between them. Then we propose a system dynamics model for simulating passenger flows in KBR by determining the boundary and using a causal analysis of a KBR system. Finally we conduct a case study on the optimization of aisle length, based on the field observation data from three metro stations of Beijing, China. The results reveal that the minimum aisle length of commuter metro stations for acceptable system’s passing efficiency is 6m, and suggest the optimized aisle length for three different commuter metro stations. Our study provides a feasible and effective approach to alleviate passenger flow bottleneck without changing the number of facilities, and can improve the efficiency of metro stations effectively.

Subway security check systems (SSCS), which play an important role for smooth operation of subways of metropolises, exhibit a high complexity and randomness due to their multiple queues, mixed services, and heterogeneous passengers. Traditional approaches for evaluating configurations of SSCS either assume a single-queueing model or ignore various attributes of passengers for the sake of simplicity of models, leading to unsatisfactory results. In this paper, we propose a robust framework for evaluating the performance of configurations of SSCS. By considering passenger attributes (e.g., with or without bags) and special services (e.g., special check, additional search), we regard SSCS as two subsystems each of which is modeled as an iterative probabilistic formula, and introduce a hybrid queueing model to evaluate the waiting time of each passenger in SSCS. The parameters of the model are calibrated and validated using the field observation data collected in 24 subway stations of Beijing, which serve as the input of simulation studies. We conduct two simulation studies to evaluate three indices of SSCS: passengers’ waiting time in SSCS,$K$-systematic time of SSCS, and density flow map, and draw three conclusions. (a) 1X2D, 2X3D, 2X4D configurations are suitable for small, medium and large passenger flows, respectively, where$m\text{X}n\text{D}$denotes the configuration of$m$X-ray machines and$n$detector doors; (b) acceptable efficiency of SSCS is achieved when the ratio between the numbers of X-ray machines and detector doors is in the range of 1:2-1:1, and the efficiency achieves highest at the ratio 1:2; (c) Increasing additional detector doors for no-bag channels can improve the efficiency of SSCS. However, switching existing doors to no-bag channels leads to opposite results. Our findings are beneficial to improving both the capacity and efficiency of SSCS, as well as allocating security check facilities reasonably.

BitTorrent has been a very successful peer-to-peer (P2P) file-sharing application, and several BitTorrent-based P2P video streaming systems have been proposed in the literature. Nowadays, network address translation (NAT) has been widely used since it reduces the usage of IP addresses, but it is also considered as a factor that degrades the performance of P2P systems because NAT limits the direction of connectivity. In order to understand what impact NAT has on the performance of BitTorrent-based P2P video streaming systems, we build an analytical model which can be used to predict the average continuity index, a video streaming performance metric, when a fraction of the participating peers are behind NAT devices. A software simulator is written to validate our analytical model, and the simulation results also give some insights on the fairness issue of P2P video streaming systems in the presence of NAT devices. In this paper, both our analytical model and simulation results are presented and verified.