• Proposes a MFD-based hierarchical dispatch method for CTaxis in large-scale traffic networks. • This method coordinates dispatch from macro road network to micro intersection. • This method provides an updatable way of match relationship between passengers and CTaxis. • Proposed method improves the service efficiency compared to NCD, CCD and MPCD. The dynamic dispatch of taxis is an important way to improve the service efficiency of taxis and reduce cruising distance. However, in a complex urban road network with extensive and stochastic roadside service demands, current macroscopic dispatch methods can hardly establish accurate and real-time matching relationships between taxis and passengers, and microscopic dispatch methods have the disadvantage of excessive computational complexity and nonupdatable match relationships. To this end, this paper proposes a hierarchical and cooperative macroscopic and microscopic dynamic dispatching approach for real-time urban network taxis in a connected taxi (CTaxi) information environment. First, network traffic flow dynamic evolution is described based on a macroscopic fundamental diagram (MFD), and the optimal macroscopic dispatching model is proposed to optimize the distribution of CTaxis for large-scale urban road networks with multiple MFD sub-regions. Second, to avoid the phenomenon of CTaxis blindly searching for passengers, a microscopic and relationship-updatable dispatching strategy between CTaxis and passengers is established within each MFD sub-region considering stochastic service demands and the signal timing of intersections. Finally, numerical experiments are conducted in a real road network to compare the performance of four methods: no CTaxi dispatch (NCD), conventional CTaxi dispatch (CCD), MPC dispatching (MPCD) and the dispatching strategy cooperating macro and micro (DSCMM) proposed in this paper. The results show that the DSCMM method can reduce the idle driving distance by 26.8%, 10.8% and 8.3% and the passenger waiting time by 11.0%, 13.4% and 6.4% compared to NCD, CCD and MPCD, respectively. Additionally, the market allocation of CTaxis under four scenarios is discussed, and the results demonstrate that the proposed DSCMM method is effective in improving the efficiency of CTaxis operation and reducing the waste of service resources in urban road networks with different service scales. 

Dynamic dispatch of connected taxis for large-scale urban road networks with stochastic demands: An MFD-enabled hierarchical and cooperative approach

Purpose The literature review aims to facilitate a broader understanding of on-demand service platform operations management and proposes potential research directions for scholars. Design/methodology/approach This study searches four databases for relevant literature on on-demand service platform operations management and selects 72 papers for this review. According to the research context, the literature can be divided into research on “a single platform” and research on “multiple platforms”. According to the research methods, the literature can be classified into “Mathematical Models”, “Empirical Studies”, “Multiple Methods” and “Literature Review”. Through comparative analysis, we identify research gaps and propose five future research agendas. Findings This paper proposes five research agendas for future research on on-demand service platform operations management. First, research can be done to combine classic research problems in the field of operations management with platform characteristics. Second, both the dynamic and steady-state issues of on-demand service platforms can be further explored. Third, research employing mathematical models and empirical analysis simultaneously can be more fruitful. Fourth, more research efforts on the various interactions among two or more platforms can be pursued. Last but not least, it is worthwhile to examine new models and paths that have emerged during the latest development of the platform economy. Originality/value Through categorizing the literature into two research contexts as well as classifying it according to four research methods, this article clearly shows the research progresses made so far in on-demand service platform operations management and provides future research directions. 

On-demand service platform operations management: a literature review and research agendas

This paper schedules the zonal-based flexible bus service (ZBFBS) considering elastic stochastic demand, stochastic location, time-dependent travel time, and passenger time window constraints based on a scheduled-based formulation. Unlike a traditional time–space network that stipulates the precise arrival and departure times on specific nodes, a zonal-based time–space network is proposed to define the routes in terms of zonal visits of the flexible buses while allowing for flexibility in their arrival and departure times to cater for randomness. The ZBFBS scheduling problem is formulated as a two-stage decision-dependent stochastic problem with recourse. The first stage schedules the zonal visits of flexible buses and the second stage matches each passenger with either flexible bus or ad hoc service, with the latter incurring extra cost to carry the unmatched passengers. To effectively solve the problem, a state-augmented network, that integrates time and zone, is proposed to reduce the number of variables. Moreover, relaxation formulations based on vehicle types and routes are introduced, with an insertion heuristic implemented for vehicle scheduling. The problem is solved by a gradient-based solution approach. Numerical studies demonstrate the efficiency and quality of the solution methods under a variety of ride requests, as well as its advantage over the frequency-based approach in substantially reducing the ad hoc service cost. The applicability of the model is validated by solving an instance of Chengdu, China, with real data. 

Scheduling zonal-based flexible bus service under dynamic stochastic demand and Time-dependent travel time

Dynamic Adjustment Policy of Search Driver Matching Distance via Markov Decision Process

PurposeCouriers are in an unequal relationship with on-demand logistic platforms with regards to order assignment and delivery-related information acquisition, which leads to high courier turnover rates. Based on social cognitive theory and justice theory, this research investigates the impact of order assignment and delivery-related information disclosure strategy on couriers' perceived justice and continuous participation intention and presents managerial suggestions to on-demand logistic platforms to lower the courier turnover rate.Design/methodology/approachTaking Chinese couriers as experimental subjects, this study conducts experiments by constructing an order receiving scene of order assignment strategy (performance-priority vs distance-priority) and delivery-related information disclosure strategy (detailed-information vs brief-information) and analyzes the results of 452 valid respondents.FindingsThe results indicate that the interaction between order assignment and delivery-related information disclosure strategy in on-demand logistics significantly affects couriers’ continuous participation intention, specifically under performance-priority order assignment and detailed-information (vs brief-information) disclosure strategy. Informational justice and distribution justice play mediating roles, and work experience and proactive personality moderate the relationship interactions.Practical/Social implicationsThe research helps us to understand the order-receiving justice demand and delivery-related information demand of couriers in on-demand logistics platforms and sheds light on cutting down turnover rates through different strategies designs and justice environment construction.Originality/valueThis research integrates social cognitive theory into on-demand logistics and combines with justice theory to fill platform strategies, couriers’ justice perception and characteristics, as well as behavior into “triadic reciprocal causation.” Meanwhile, it investigates different impacts and interactive relationships of order assignment and delivery-related information disclosure strategy, expands strategies from the impact of operational efficiency to the impact of couriers’ participation and extends the literature of justice perception and individual characteristics in on-demand logistics.

Continuous participation intention in on-demand logistics: interactive effects of order assignment and delivery-related information disclosure strategies

Online platforms for matching supply and demand, as part of the sharing economy, are becoming increasingly important in practice and have seen a steep increase in academic interest. Especially in the taxi/travel industry, platforms such as Uber, Lyft, and Didi Chuxing have become major players. Some of these platforms, including Didi Chuxing, operate two matching systems: Inform, where multiple drivers receive ride details and the first to respond is selected; and Assign, where the platform assigns the driver nearest to the customer. The Inform system allows drivers to select their destinations, but the Assign system minimizes driver-customer distances. This research is the first to explore: (i) how a platform should allocate customer requests to the two systems and set the maximum matching radius (i.e., customer-driver distance), with the objective to minimize the overall average waiting times for customers; and (ii) how taxi drivers select a system, depending on their varying degrees of preference for certain destinations. Using approximate queuing analysis, we derive the optimal decisions for the platform and drivers. These are applied to real-world data from Didi Chuxing, revealing the following managerial insights. The optimal radius is 1-3 kilometers, and is lower during rush hour. For most considered settings, it is optimal to allocate relatively few rides to the Inform system. Most interestingly, if destination selection becomes more important to the average driver, then the platform should not always allocate more requests to the Inform system. Although this may seem counter-intuitive, allocating too many orders to that system would result in many drivers opting for it, leading to very high waiting times in the Assign system.

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Tian Wu

Papers

Taxi-hailing platforms: Inform or Assign drivers?