This article presents an overview of the agent-based modeling and simulation approach and its recent developments in transport fields, with the purpose of discovering the advantages and gaps and encouraging more valuable investigations and applications of agent-based models. We clarify the agent-based model from agents, the background of development, and the basic structure applied in transport systems. Then, the agent-based transport modeling toolkits are discussed. The applications of agent-based models in transport systems are reviewed in three time scale models followed by an additional discussion of hybrid modeling approaches. The extensive modeling of the beliefs, desires, learning, and adaptability of individuals and the optimization problems using agent-based models are explored. Besides, we point out some limitations in terms of calibration and validation procedure, agents’ behavior modeling, and computing efficiency. In conclusion, some recommendations are given and suggest potential and insightful directions such as Big Data and Digital Twin for future research.

The demand-responsive transit (DRT) service is an emerging and flexible transit mode to enhance the mobility of the urban transit system by providing personalized services. Passengers can make advanced appointments through smartphone applications. In this paper, an analytical model is proposed for the many-to-one DRT system. The agency and user costs are approximated by closed-form expressions. The agency cost, which is also the operation cost, is approximated by the continuum approximation technique. A nearest-neighbor routing strategy is applied, whereby the vehicle always collects the nearest passenger waiting in the system. The Vickrey queueing theory is adopted as the basis for approximating each component of the user cost, which is composed of the out-of-vehicle and in-vehicle waiting times and schedule deviations, which also depend on the service quality of the DRT system. The results of the numerical experiment show that (1) the agency and user costs are influenced significantly by the demand density, and (2) the DRT operator cannot further decrease the operating cost by solely deploying larger vehicles.

In this study, a nanostructured TiO₂ coating was successfully prepared on a large-scale titanium plate by a simple one-step surface mechanical alloying (SMA) treatment. The coating was characterised to determine the morphological and composition changes with SMA. The coating was ∼60 μm thick and composed of 5 nm diameter rutile TiO₂ particles. The photocatalytic activity of the nanostructured TiO₂ coating was studied via the ultraviolet–visible (UV–vis) decolorisation of aqueous rhodamine-B. The results showed that the photocatalytic degradation effect in 5 h of the φ30 mm nanostructured TiO₂ coating was comparable to that of 20 mg raw TiO₂ under similar UV–visible light irradiation. One-step SMA was demonstrated as an appropriate method to produce a high-performance photocatalytic plate.

Analyzing pedestrian flow characteristics and the interaction mechanism among pedestrians or between pedestrians and environment, we proposed a pedestrian flow simulation model based on the multi-agent technology. In the model, the hierarchical structure of multi agents was designed according to communication relations, and inner structures including network agent, grid agents, pedestrian agents and service interface. Furthermore, a superimposed shortest path algorithm was proposed to cater for the modelling of complex topological network with multiple exits and inner barriers. Pedestrian decision-making rules were established synthetically by considering the distance factor and pedestrian herd mentality factors. A scenario was studied in which pedestrians tried to evacuate from a large room with inner barriers and four exits. The results show that the weight factor of the network edge impacts the crowds shape at exits. In addition, simulation experiments show the trend of evacuation times with respect to the impact factor of pedestrian herd mentality.

Crowd simulation is a convenient method to evaluate pedestrians’ status and their corresponding management strategies in large public spaces. However, the performance of real-time simulation can be limited by the model’s large-scale computational cost. In order to overcome this difficulty, this study proposes PaCS (Parallel Computation for Crowd Simulation), a parallel computation framework for field-based crowd simulation, based on an enhanced status update method and an efficient task assignment strategy. Parallel computing is introduced with synchronous updates, task division and multiprocessing calculation mechanisms. The movement model is split into the smallest and independent computational units. The field model for simulating the crowd movement has also been improved in terms of weighted multi-direction choice and multi-field environment division. The experiments confirmed that the parallel synchronous algorithm has a significant advantage at the computational scale of more than 10,000 pedestrians. The speedup ratio of the parallel approach can be more than 5 times when simulating one million pedestrians. This framework can help to establish the essential methods for multi-modal transportation systems that require fast simulations for a large-scale crowd. It would also help future digital twin systems to evaluate and validate any potential management strategies when applied in metro stations, railway stations, and other transportation hubs.

The usage of mobile phones has undergone tremendous growth in the past decades. Large amounts of mobile-phone signaling data (MSD) are generated while using various mobile phone applications. The large-scale MSD presents opportunities for transport planners to utilize it for better planning and management of the transportation system. In this paper, we use MSD to analyze subway passengers’ travel behavior and extract their complete travel trajectories. The complete travel trajectories of subway passengers include their trajectories, both inside and outside the subway system. In the first stage, the MSD from the subway base stations is selected, sorted by time, and the rough trajectory in the subway system is extracted. The ground base stations around the subway station are then considered to correct the boarding and alighting subway stations in order to obtain a more detailed trajectory. In the second stage, the service range of the base station is determined according to the Thiessen polygon, and a temporal dynamic threshold is proposed to extract the passenger’s stop point outside the subway system. Finally, the complete trajectories of subway passengers are obtained. The proposed algorithms are verified using a set of MSD collected in Suzhou, China. The results show that the proposed algorithms can effectively extract the complete travel trajectory of subway passengers.