Search Results (13)

Electric-powered unmanned guidance vehicles provide surface taxiing guidance for arriving and departing aircraft within the airport movement area, enabling sustained safety under complex operational conditions and improving overall operational efficiency, particularly under low-visibility scenarios. In this context, how to design scientifically rigorous operational trajectories for the three phases of unmanned guidance vehicle operations—dispatch, guidance, and recovery—remains an open and important research problem. This study proposes a three-stage trajectory-planning method for unmanned guidance vehicles, including initial trajectory planning, conflict prediction, and conflict resolution. First, the Guidance Unit—composed of the unmanned guidance vehicle and the guided aircraft—is defined, and a standard speed-profile design model is established for this unit. Then, considering airport operational-safety constraints, a conflict prediction algorithm for the guidance process is developed, which identifies potential conflicts in guidance trajectory planning based on time-window overlap analysis. Subsequently, under operational safety constraints, an optimization model aiming to minimize the maximum guidance time is formulated, and a trajectory planning algorithm for unmanned guidance vehicles based on the improved A* algorithm is designed to generate conflict-free operational trajectories. Finally, a simulation study is conducted using a major airport in Southwest China as a case study. The results show that (1) the speed-profile design and airport operational-rule constraints affect the operational trajectories of unmanned guidance vehicles; (2) the proposed algorithm enables coordinated planning of both speed control and path selection, thereby improving overall operational efficiency by 43.65% compared with conventional operations, while ensuring conflict-free airport surface taxiing, due to the adoption of an improved A* trajectory-planning algorithm for unmanned guidance vehicles; (3) under the electric-powered guidance-vehicle scheme proposed in this study, the method achieves a 34.52% reduction in total energy consumption during the guidance phase compared with traditional Follow-Me guidance, enabling the simultaneous optimization of operational efficiency and energy consumption. Full article

With the rapid growth of urban transportation demand, traditional taxi systems face challenges such as supply–demand imbalances and low dispatch efficiency. These methods, which rely on static data and predefined strategies, struggle to adapt to dynamic traffic environments. To address these issues, this paper proposes a dual-layer Taxi Dispatch and Empty-Vehicle Repositioning (TDEVR) optimization framework based on Multi-Agent Reinforcement Learning (MARL). The framework separates the tasks of taxi matching and repositioning, enabling efficient coordination between the decision-making and execution layers. This design allows for the real-time integration of both global and local supply–demand information, ensuring adaptability to complex urban traffic conditions. A Multi-Agent Dual-Attention Reinforcement Learning (MADARL) algorithm is proposed to enhance decision-making and coordination, combining local and global attention mechanisms to improve local agents’ decision-making while optimizing global resource allocation. Experiments using a real-world New York City taxi dataset show that the TDEVR framework with MADARL leads to an average improvement of 20.63% in the Order Response Rate (ORR), a 15.29 increase in Platform Cumulative Revenue (PCR), and a 22.07 improvement in the Composite Index (CI). These results highlight the significant performance improvements achieved by the proposed framework in dynamic scenarios, demonstrating its ability to efficiently adapt to real-time fluctuations in supply and demand within urban traffic environments. Full article

The rise of ride-hailing services has brought new riding experiences for passengers and exerted a profound impact on the traditional taxi market. To enhance patrol efficiency, increase revenue, and promote sustainable development in the taxi industry, traditional taxis have actively undergone transformation and adopted an integrated “online-offline” operating model, combining online order acceptance with offline order-taking. Meanwhile, a considerable number of orders are canceled by passengers after being accepted, leading to a waste of platform capacity, reduced order dispatch efficiency, and additional empty-running costs for drivers. This issue is particularly prominent during peak hours with order overflow. Based on the changes in taxi order acceptance during order overflow, this paper constructs a model for passenger order cancellation probability during peak hours, examines the relationship between regional order density and the proportion of offline taxi order acceptance, discusses the impact of regional order density changes on the passenger order cancellation probability and stakeholder returns, and proposes optimal order dispatch strategies for ride-hailing platforms with different order densities. Additionally, it analyzes more optimal taxi operating models under varying arrival states. The research findings provide more scientific and efficient operational recommendations for ride-hailing platforms and taxis, promoting sustainable development in the entire travel market and thereby contributing to a greener and more efficient travel environment. Full article

The objective imbalance between the taxi supply and demand exists in various areas of the city. Accurately predicting this imbalance helps taxi companies with dispatching, thereby increasing their profits and meeting the travel needs of residents. The application of Graph Convolutional Networks (GCNs) in traffic forecasting has inspired the development of a spatial–temporal model for grid-level prediction of the taxi demand–supply imbalance. However, spatial–temporal GCN prediction models conventionally capture only static inter-grid correlation features. This research aims to address the dynamic influences caused by taxi mobility and the variations of other transportation modes on the demand–supply dynamics between grids. To achieve this, we employ taxi trajectory data and develop a model that incorporates dynamic GCN and Gated Recurrent Units (GRUs) to predict grid-level imbalances. This model captures the dynamic inter-grid influences between neighboring grids in the spatial dimension. It also identifies trends and periodic changes in the temporal dimension. The validation of this model, using taxi trajectory data from Shenzhen city, indicates superior performance compared to classical time-series models and spatial–temporal GCN models. An ablation study is conducted to analyze the impact of various factors on the predictive accuracy. This study demonstrates the precision and applicability of the proposed model. Full article

Ride-hailed shared autonomous vehicles (SAV) have emerged recently as an economically feasible way of introducing autonomous driving technologies while serving the mobility needs of under-served communities. There has also been corresponding research work on optimization of the operation of these SAVs. However, the current state-of-the-art research in this area treats very simple networks, neglecting the effect of a realistic other traffic representation, and is not useful for planning deployments of SAV service. In contrast, this paper utilizes a recent autonomous shuttle deployment site in Columbus, Ohio, as a basis for mobility studies and the optimization of SAV fleet deployment. Furthermore, this paper creates an SAV dispatcher based on reinforcement learning (RL) to minimize passenger wait time and to maximize the number of passengers served. The created taxi-dispatcher is then simulated in a realistic scenario while avoiding generalization or over-fitting to the area. It is found that an RL-aided taxi dispatcher algorithm can greatly improve the performance of a deployment of SAVs by increasing the overall number of trips completed and passengers served while decreasing the wait time for passengers. Full article

The increasing attention of opinion towards climate change has prompted public authorities to provide plans for the containment of emissions to reduce the environmental impact of human activities. The transport sector is one of the main ones responsible for greenhouse emissions and is under investigation to counter its burdens. Therefore, it is essential to identify a strategy that allows for reducing the environmental impact produced by aircraft on the landing and take-off cycle and its operating costs. In this study, four different taxiing strategies are implemented in an existing Italian airport. The results show advantageous scenarios through single-engine taxiing, reduced taxi time through improved surface traffic management, and onboard systems. On the other hand, operating towing solutions with internal combustion cause excessive production of pollutants, especially HC, CO, NOX, and particulate matter. Finally, towing with an electrically powered external vehicle provides good results for pollutants and the maximum reduction in fuel consumption, but it implies externalities on taxiing time. Compared to the current conditions, the best solutions ensure significant reductions in pollutants throughout the landing and take-off cycle (−3.2% for NOx and −44.2% for HC) and economic savings (−13.4% of fuel consumption). Full article

This paper addresses the taxi fleet dispatch problem, which is critical for many transport service platforms such as Uber, Lyft, and Didi Chuxing. We focus on maximizing the revenue and profit a taxi platform can generate through the dispatch approaches designed with various criteria. We consider determining the proportion of taxi fleets to different destination zones given the expected rewards from the future states following the distribution decisions learned through reinforcement learning (RL) algorithms. We also take into account more straightforward greedy algorithms that look ahead fewer decision time steps in the future. Our dispatch decision algorithms commonly leverage contextual information and heuristics using a data structure called Contextual Matching Matrix (CMM). The key contribution of our paper is the insight into the trade-off between different design criteria. Primarily, through the evaluation with actual taxi operation data offered by Seoul Metropolitan Government, we challenge the natural expectation that the RL-based approaches yield the best result by showing that a lightweight greedy algorithm can have a competitive advantage. Moreover, we break the norm of dissecting the service area into sub-zones and show that matching passengers beyond arbitrary boundaries generates significantly higher operating income and profit. Full article

Demand for different modes of transportation clearly interacts. If public transit is delayed or out of service, customers might use mobility on demand (MoD), including taxi and carsharing for their trip, or discard the trip altogether, including a first and last mile that might otherwise be covered by MoD. For operators of taxi and carsharing services, as well as dispatching agencies, understanding increasing demand, and changing demand patterns due to outages and delays is important, as a more precise demand prediction allows for them to more profitably operate. For public authorities, it is paramount to understand this interaction when regulating transportation services. We investigate the interaction between public transit delays and demand for carsharing and taxi, as measured by the fraction of demand variance that can be explained by delays and the changing OD-patterns. A descriptive analysis of the public transit data set yields that delays and MoD demand both highly depend on the weekday and time of day, as well as the location within the city, and that delays in the city and in consecutive time intervals are correlated. Thus, demand variations must by corrected for these external influences. We find that demand for taxi and carsharing increases if the delay of public transit increases and this effect is stronger for taxi. Delays can explain at least 4.1% (carsharing) and 18.8% (taxi) of the demand variance, which is a good result when considering that other influencing factors, such as time of day or weather exert stronger influences. Further, planned public transit outages significantly change OD-patterns of taxi and carsharing. Full article

This paper uses a neural network approach transformer of taxi driver behavior to predict the next destination with geographical factors. The problem of predicting the next destination is a well-studied application of human mobility, for reducing traffic congestion and optimizing the electronic dispatching system’s performance. According to the Intelligent Transport System (ITS), this kind of task is usually modeled as a multi-class problem. We propose the novel model Deep Wide Spatial-Temporal-Based Transformer Networks (DWSTTNs). In our approach, the encoder and decoder are the transformer’s primary units; with the help of Location-Based Social Networks (LBSN), we encode the geographical information based on visited semantic locations. In particular, we trained our model for the exact longitude and latitude coordinates to predict the next destination. The benefit in the real world of this kind of research is to reduce the customer waiting time for a ride and driver waiting time to pick up a customer. Taxi companies can also optimize their management to improve their company’s service, while urban transport planner can use this information to better plan the urban traffic. We conducted extensive experiments on two real-word datasets, Porto and Manhattan, and the performance was improved compared to the previous models. Full article

As the key element of urban transportation, taxis services significantly provide convenience and comfort for residents’ travel. However, the reality has not shown much efficiency. Previous researchers mainly aimed to optimize policies by order dispatch on ride-hailing services, which cannot be applied in cruising taxis services. This paper developed the reinforcement learning (RL) framework to optimize driving policies on cruising taxis services. Firstly, we formulated the drivers’ behaviours as the Markov decision process (MDP) progress, considering the influences after taking action in the long run. The RL framework using dynamic programming and data expansion was employed to calculate the state-action value function. Following the value function, drivers can determine the best choice and then quantify the expected future reward at a particular state. By utilizing historic orders data in Chengdu, we analysed the function value’s spatial distribution and demonstrated how the model could optimize the driving policies. Finally, the realistic simulation of the on-demand platform was built. Compared with other benchmark methods, the results verified that the new model performs better in increasing total revenue, answer rate and decreasing waiting time, with the relative percentages of 4.8%, 6.2% and −27.27% at most. Full article

The global market for MoD services is in a state of rapid and challenging transformation, with new market entrants in Europe, such as Uber, MOIA, and CleverShuttle, competing with traditional taxi providers. Rapid developments in available algorithms, data sources, and real-time information systems offer new possibilities of maximizing the efficiency of MoD services. In particular, the use of demand predictions is expected to contribute to a reduction in operational costs and an increase in overall service quality. This paper examines the potential of predictive fleet management strategies applied to a large-scale real-world taxi dataset for the city of Munich. A combination of state-of-the art dispatching algorithms and a predictive RHC optimization for idle vehicle rebalancing was developed to determine the scale by which a fleet size can be reduced without affecting service quality. A simulation study was conducted over a one-week period in Munich, which showed that predictive fleet strategies clearly outperform the present strategy in terms of both service quality and costs. Furthermore, the results showed that current taxi fleets could be reduced to 70% of their original size without any decrease in performance. In addition, the results indicated that the reduced fleet size of the predictive strategy was still 20% larger compared to the theoretical optimum resulting from a bipartite matching approach. Full article

Mobile dispatching applications have become popular for at least two major reasons. The first reason is a more mobile-centric usage pattern, where users relate to apps for fulfilling different needs that they have. In this respect, a vehicle dispatching application for mobile phones is perceived as a modern way of booking a vehicle. The second reason has to do with the advantages that this method has over traditional dispatching systems, such as being able to see the vehicle approaching on a map, being able to rate a driver and the most importantly spurring customer retention. The taxi dispatching business, one of the classes of dispatching businesses, tends to be a medium to lower class fidelity service, where users mostly consider the closest taxi as opposed to quality, which is regarded as being at a relatively consistent level. We propose a new approach for the taxi ordering application , a mobile dispatching system, which allows for a more engaged user base and offers fidelity rewards that are used to enhance the customer retention level based on a built in social customer relationship management (CRM) component. With this approach, we argue that in a business world which is shifting from a consumer-centric marketing to a human-centric model, this apps will allows taxi businesses to better interact with their clients in a more direct and responsible manner. Also this distributed system helps taxi drivers, which can receive orders directly from their clients and will be able to benefit from offering quality services as they can get higher ratings. Full article

Currently, electric taxis have been deployed in many cities of China. However, the charging unbalance in both temporal and spatial scale has become a rising problem, which leads to low charging efficiency or charging congestion in different stations or time periods. This paper presents a multi-objective coordinated charging strategy for electric taxis in the temporal and spatial scale. That is, the objectives are maximizing the utilization efficiency of charging facilities, minimizing the load unbalance of the regional power system and minimizing the customers’ cost. Besides, the basic configuration of a charging station and operation rules of electric taxis would be the constraints. To tackle this multi-objective optimizing problems, a fuzzy mathematical method has been utilized to transfer the multi-objective optimization to a single optimization issue, and furthermore, the Improved Particle Swarm Optimization (IPSO) Algorithm has been used to solve the optimization problem. Moreover, simulation cases are carried out, Case 1 is the original charging procedure, and Cases 2 and 3 are the temporal and spatial scale optimized separately, followed with Case 4, the combined coordinated charging. The simulation shows the significant improvement in charging facilities efficiency and users’ benefits, as well as the better dispatching of electric taxis’ charging loads. Full article