Search Results (9)

Airports are facing significant challenges due to the increasing number of air travel passengers. After a significant downturn during the COVID-19 pandemic, airports are implementing measures to enhance security and improve their level of service in response to rising demand. However, the rising passenger volume has led to increased congestion and longer waiting times, undermining operational efficiency and passenger satisfaction. While most previous studies have focused on static modeling or infrastructure improvements, few have addressed the problem of dynamically allocating passengers in real-time. To tackle this issue, this study proposes a mathematical model with a dynamic rescheduling framework to balance the workload across multiple departure areas where security screening takes place, while minimizing the negative impact on passenger satisfaction resulting from increased walking distances. The proposed model strategically allocates departure areas for passengers in advance, utilizing data-based predictions. A mixed integer linear programming (MILP) model was developed and evaluated through discrete event simulation (DES). Real operational data provided by Incheon International Airport Corporation (IIAC) were used to validate the model. Comparative simulations against four baseline strategies demonstrated superior performance in balancing workload, reducing waiting passengers, and minimizing walking distances. In conclusion, the proposed model has the potential to enhance the efficiency of the security screening stage in the passenger departure process. Full article

The expected growth of electric vehicle (EV) usage will not only increase the energy demand but also bring the requirement to provide the necessary electrical infrastructure to handle the load. While charging infrastructure is becoming increasingly present in urban areas, special attention is required for transit traffic, not just for passengers but also for freight transport. Differences in the nature of battery charging compared to that of classical refueling require careful planning in order to provide a resilient electrical infrastructure that will supply enough energy at critical locations during peak hours. This paper presents a hybrid simulation model for analyzing fast-charging demand based on traffic flow, projected EV adoption, battery characteristics, and environmental conditions. The model integrates a probabilistic model for evaluating the charging requirements based on traffic flows with a discrete-event simulation (DES) framework to analyze charger utilization, waiting queues, and energy demand. The presented case of traffic flow on Slovenian motorways explored the expected power demands at various seasonal traffic intensities. The findings provide valuable insight for planning the charging infrastructure, the electrical grid, and also the layout by anticipating the number of vehicles seeking charging services. The modular design of the model allowed replacing key parameters with different traffic projections, supporting a robust scenario analysis and adaptive infrastructure planning. Replacing the parameters with real-time data opens the path for integration into a digital twin framework of individual EV charging hubs, providing the basis for development of an EV charging hub network digital twin. Full article

One of the most common deficiencies in the public transport system is long waiting times. Currently, in the Guadalajara Metropolitan Area, Mexico, the frequencies of routes are fixed, making it impossible to satisfy a demand with a dynamic variation. An intelligent public transport system is required. The first step to solve this problem is knowing the number of users so that we can respond appropriately to each scenario. In this context, this work focuses on the design and implementation of an embedded system module for passenger counting that can be used to improves public transport service quality. This work presents three contributions. First, a design and experimental validation of the passenger counting system is presented to determine the number of users in an image and send this information to a server suitable for the public transportation system in Guadalajara, Mexico. Second, the generation of two new datasets is reported for training and testing the CSRNet algorithm with images of public transportation systems in Mexican cities. Finally, we make the hardware implementation of the passenger counting system in a Jetson Nano development board. Full article

As tourism demand continues to grow and fluctuate, the problems of increasing empty capacity and high operating costs for tourist shuttle buses have become more acute. Modular vehicles, an emerging transport technology, offer flexible length adjustments and provide innovative solutions to address these challenges. This paper develops a data-driven method to address the problem of scheduling modular vehicles in scenic areas with dynamic passenger demand. The aim is to minimize operating costs and maximize vehicle utilization by exploiting the adjustable capacity of modular vehicles. This approach is applied to tourist shuttle scenarios, and a sensitivity analysis is conducted by varying parameters such as individual vehicle capacity and waiting penalties. Then, we investigate the optimization performance gap between the proposed model and the theoretical global optimum model. The results show that increasing vehicle capacity and varying penalties improve the performance of the data-driven model, and the optimization rate of this model can reach 70.2% of the theoretical optimum, quantifying the effectiveness of the model. The method proposed in this study can effectively reduce the operating cost of shuttle vehicles for scenic areas and meet the challenge of unpredictable passenger demand, which serves as a good reference for fleet management in scenic areas. Full article

This study analyses the potential carrying of bicycles inside a train carriage. To this end, an experimental methodology based on observation and experimentation is implemented. The survey is conducted on the metro system in Valparaíso, Chile, highlighting the importance of intermodality between bicycles and trains. It identifies that the current capacity of the carriages is not adequate to ensure safety and efficiency during boarding and alighting. As a result of the survey, a solution is tested to reduce the number of seats in the carriage and create a designated special waiting area for cyclists. This test is conducted experimentally in a laboratory, using a full-scale model of a metro carriage and its corresponding platform. The experiments show that the designated special waiting area for bicycles reduces boarding time and results in a better distribution of passengers inside the carriage, offering a solution that improves both safety and efficiency. This study could contribute to the development of incentive policies for intermodality, which is a key aspect in achieving sustainability in railway transportation systems. Future research will aim to expand this study by including other carriage configurations and a wider variation of demand levels. Full article

Nowadays, authorities of large cities in the world implement bus rapid transit (BRT) services to alleviate traffic problems caused by the significant development of urban areas. Therefore, a controller is required to control and dispatche buses in such BRT systems.. However, controllers are facing new challenges due to the inherent uncertainties of passenger parameters such as arrival times, demands, alighting fraction as well as running time of vehicles between stops. Such uncertainties may significantly increase the operational cost and the inefficiencies of BRT services. In this paper, we focus on the controller’s perspective and propose a stochastic mixed-integer nonlinear programming (MINLP) model for BRT scheduling to find the optimal departure time of buses under uncertainty. The objective function of the model consists of passenger waiting and traveling time and aims to minimize total time related to passengers at any stop. From the modeling perspective, we propose a new method to generate scenarios for the proposed stochastic MINLP model. Furthermore, from the computational point of view, we implement an outer approximation algorithm to solve the proposed stochastic MINLP model and demonstrate the merits of the proposed solution method in the numerical results. This paper accurately reflect the complexity of BRT scheduling problem and is the first study, to the best of our knowledge, that presents and solves a mixed-integer nonlinear programming model for BRT scheduling. Full article

The rapid development of intelligent transportation systems and high-speed railways has shortened the waiting time of passengers and the demand for waiting areas. Large-scale stations not only increase the difficulty for passengers when traveling but also waste a great deal of land resources and construction funds. Therefore, this research analyzes passenger waiting area demand according to the characteristics of urban development, passenger travel characteristics and station departure passenger flow. This paper establishes a prediction model for the number of passengers spending time in the waiting room, taking into account passenger traffic and train departure timetables. We used Beijing South Railway Station, Xi’an North Railway Station, Hefei South Railway Station and Zhoukou East Railway Station as examples to predict the numbers of passengers spending time in waiting rooms of different types and scales of station. Research results show that shortening the length of passengers’ early arrival times can effectively reduce the number of passengers gathered in medium-sized stations, which are located in new first-tier cities. Under the influence of the urban traffic environment and passenger flow, the uncertainties regarding travel time and passenger flow in first-tier cities are lower than those in new first-tier cities and higher than those in third-tier and fourth-tier cities. Therefore, the waiting area demand of passengers departing from medium-sized stations in new first-tier cities is lower than that of passengers from large stations in first-tier cities, and higher than that of passengers from small stations in third-tier and fourth-tier cities. Full article

As ridesharing services (including taxi) are often run by private companies, profitability is the top priority in operation. This leads to an increase in the driver’s refusal to take passengers to areas with low demand where they will have difficulties finding subsequent passengers, causing problems such as an extended waiting time when hailing a vehicle for passengers bound for these regions. The study used Seoul’s taxi data to find appropriate surge rates of ridesharing services between 10:00 p.m. and 4:00 a.m. by region using a reinforcement learning algorithm to resolve this problem during the worst time period. In reinforcement learning, the outcome of centrality analysis was applied as a weight affecting drivers’ destination choice probability. Furthermore, the reward function used in the learning was adjusted according to whether the passenger waiting time value was applied or not. The profit was used for reward value. By using a negative reward for the passenger waiting time, the study was able to identify a more appropriate surge level. Across the region, the surge averaged a value of 1.6. To be more specific, those located on the outskirts of the city and in residential areas showed a higher surge, while central areas had a lower surge. Due to this different surge, a driver’s refusal to take passengers can be lessened and the passenger waiting time can be shortened. The supply of ridesharing services in low-demand regions can be increased by as much as 7.5%, allowing regional equity problems related to ridesharing services in Seoul to be reduced to a greater extent. Full article

Modern taxi services are usually classified into two major categories: traditional taxicabs and ride-hailing services. For both services, it is required to design highly efficient recommendation systems to satisfy passengers’ quality of experience and drivers’ benefits. Customers desire to minimize their waiting time before rides, while drivers aim to speed up their customer hunting. In this paper, we propose to leverage taxi service efficiency by designing a generic and smart recommendation system that exploits the benefits of Vehicular Social Networks (VSNs). Aiming at optimizing three key performance metrics, number of pick-ups, customer waiting time, and vacant traveled distance for both taxi services, the proposed recommendation system starts by efficiently estimating the future customer demands in different clusters of the area of interest. Then, it proposes an optimal taxi-to-region matching according to the location of each taxi and the future requested demand of each region. Finally, an optimized geo-routing algorithm is developed to minimize the navigation time spent by drivers. Our simulation model is applied to the borough of Manhattan and is validated with realistic data. Selected results show that significant performance gains are achieved thanks to the additional cooperation among taxi drivers enabled by VSN, as compared to traditional cases. Full article