Taxi Dispatch with Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach (1603.04418v1)

Abstract: Traditional taxi systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and customer demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding customer demand and system status can be collected in real time. This information provides opportunities to perform various types of control and coordination for large-scale intelligent transportation systems. In this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/supply models and real-time GPS location and occupancy information. The objectives include matching spatiotemporal ratio between demand and supply for service quality with minimum current and anticipated future taxi idle driving distance. Extensive trace-driven analysis with a data set containing taxi operational records in San Francisco shows that our solution reduces the average total idle distance by 52%, and reduces the supply demand ratio error across the city during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a wide variety of predictive models and optimization problem formulations. This compatibility property allows us to solve robust optimization problems with corresponding demand uncertainty models that provide disruptive event information.

• The paper presents a receding horizon control framework using real-time data to optimize taxi dispatch by matching supply and demand while minimizing idle distance.
• Evaluated using San Francisco data, the approach reduced total idle distance by 52% and supply-demand error by 45% compared to traditional methods.
• This flexible methodology improves efficiency, reduces emissions, and enhances passenger satisfaction, advancing control techniques in intelligent transportation systems.

Taxi Dispatch with Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach

This paper presents a novel framework for taxi dispatch systems in metropolitan areas, leveraging real-time sensing data and advanced control methodologies. The receding horizon control (RHC) approach forms the core of the system, focusing on optimizing the distribution and movement of vacant taxis to better match passenger demand while minimizing idle driving distances. The research identifies two primary objectives within the dispatch system: matching the supply-demand ratio across different city regions and minimizing idle driving distances.

In the proposed framework, data derived from networked sensors, including GPS location and occupancy status, are used to dynamically inform dispatch decisions. The authors emphasize the importance of balancing system-level supply and demand and utilize predictive models to estimate spatial and temporal demand, thereby reducing the mismatch that typically characterizes uncoordinated dispatch systems. The choice of RHC allows for a balance between current dispatch optimization and anticipated future conditions, which is crucial for maintaining high service quality.

The methodology is evaluated through a detailed trace-driven analysis using taxi operational records from San Francisco. The authors report strong numerical results, indicating that the RHC approach reduces the average total idle distance of taxis by 52%, a significant improvement over traditional methods. Additionally, the supply-demand ratio error was reduced by 45% during one experimental time slot, demonstrating enhanced service fairness across urban areas.

Another notable aspect of this paper is its compatibility with various predictive models and optimization formulations, allowing for robust problem solving in the presence of demand uncertainties. This flexibility is particularly beneficial in dealing with unexpected events that may impact passenger demand, such as city-wide festivals or severe weather conditions.

The implications of this research are substantial, both practically and theoretically. Practically, integrating real-time data into dispatch systems can lead to more efficient taxi operations, reduced emissions from idle driving, and improved passenger satisfaction due to shorter wait times. On a theoretical level, the approach enhances the applicability of control techniques to large-scale intelligent transportation systems, showcasing the potential for RHC frameworks to address complex urban mobility challenges.

In future studies, the authors plan to extend the framework to incorporate additional types of data and refine control algorithms to accommodate an even broader range of urban environments. This work contributes to the growing intersection of cyber-physical systems, data-driven control, and transportation logistics, positioning AI at the forefront of evolving intelligent transportation systems. As cities continue to grow and mobility demands increase, such methodologies are crucial in developing responsive and sustainable urban transport networks.