No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Multi-party ride-matching problem in the ride-hailing market with bundled option services
Abstract
As demands for convenient and comfortable mobility grow, transportation network companies (TNCs) begin to diversify the ride-hailing services they offer. Modes that are offered now include ride-pooling (RP), non-ride-pooling (NP), and a third “bundled” option, which combines RP and NP. This emerging bundled option allows riders to be served via either RP or NP mode, whichever becomes available first. This paper examines the added complexity that a ride-hailing service platform faces when it introduces a third bundled option. Incorporating the predicted pooling information in the near future, a ride-matching problem is dedicated to matching vehicles with riders under various scenarios over a number of matching iterations. We formulate the multi-period ride-matching problem using an integer linear programming model with multiple objectives and to make dispatching decisions based on certain matching criteria. The complexity of the problem requires resolution via a two-stage Kuhn-Munkres (2-KM) algorithm, whose robustness is verified by computational tests. Some interesting insights are obtained: (1) how the bundled option impacts system performance metrics depends on whether the supply is sufficient or not; (2) there is an optimal value of the criterion of the maximum pickup time that maximizes the ride-pooling time savings.
... Unlike traditional travel forms, the ride-hailing app offers various types of service options, such as express, comfort, commercial, or luxury. Ride-hailing apps allow riders to choose one-to-one service or bundled service (Wang and Yang 2019;Qin et al. 2021). One-to-one service means that riders select only one of the service options when placing an order (see Fig. 1(a)). ...
... To the best of our knowledge, few studies have addressed these heterogeneous ordering behaviors among riders. One is Qin et al. (2021), who formulate an integer linear programming model to examine a multi-period ride-matching problem with three mode choice behaviors: ride-pooling (RP), non-ride-pooling (NP), and a third "bundled" option that combines both RP and NP. In accordance with their model, riders can ordering a single service or a bundled service. ...
... This setting allows the platform with the authority to assign orders based on the service load of each service option as well as lower the average waiting time for all riders. Therefore, different from Qin et al. (2021) who address a matching problem with different travel modes, we explore an order assignment problem where the platform allows riders to choose single or multiple service options in one travel mode. However, our study is similar to Wong et al. (2008) who extend the model of urban taxi services in congested networks to the case of multiple user classes, multiple taxi modes, and customer hierarchical modal choice. ...
We study an order assignment problem in a ride-hailing system with two classes of riders (i.e., single-choice riders and multi-choice riders) and n types of vehicles. We regard the system as an M/G/n queueing system and develop an order assignment strategy based on the service loads of the n servers. We construct a nonlinear programming model to solve the order assignment problem with the objective of minimizing the average waiting time for riders. Using Lagrange duality theory, we derive the closed form of the optimal solution with a Lagrangian multiplier. Using parameter optimization theory, we carry out optimality analyses and theoretically demonstrate the Lipschitz stability of the feasible region and optimal objective value, the Ho¨lder\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H\ddot{o}lder$$\end{document} stability of the optimal solution, and the existence and differentiability of the optimal solution. To understand the theoretical results intuitively, we conduct two groups of numerical experiments. The first one is implemented in an M/G/2 queueing system to graph the effects of parameter variation on the optimal order assignment strategy. The second one is conducted in an M/G/n queueing system to show the applicability of this study in systems with multiple heterogeneous service options. The results demonstrate that the service load of each server is directly related to its service capacity and order arrival rate. And the service load is a main factor influencing the optimal order assignment strategy. Management insights for the optimal order assignment strategy can be generated to inform real ride-hailing platforms.
... Ridehailing has become one of the most active and exciting research topics in the transportation sector [7,8]. Ride-hailing platforms established a technology and market structure that is more effective than traditional taxi services, enabling passengers to request a vehicle on short notice [9][10][11][12][13]. The ability for passengers and drivers to connect via mobile smartphone applications is made possible by many factors, including social networks, real-time information, and mobile technology [14]. ...
... In order to assess the effects of assignment time intervals and assignment radius, the authors of [12] provided a model that defines the assignment process in ride-sourcing markets. An assignment model with several service alternatives, such as a bundled option, was proposed by [13]. A discrete-time geometric assignment and the effects of spatial pricing were described by [26]. ...
... The success of assignments, while minimizing denied requests, is affected by drivers, distance, socioeconomic features, and land use [16]. In general, the assignment considers the pick-up travel distance and pick-up travel time [9,12,13,15,24,26,[34][35][36]. Some studies considered minimizing the number of unsatisfied requests [24,32]. ...
Uber, Gojek, and Grab are companies providing new massive job opportunities for driver partners. Ride-hailing provides convenient services because passengers can determine the position of the vehicle picking the, up in real time. Ride-hailing also provides security because passengers can quickly determine the driver’s identity. However, the rapid development of ride-hailing has led to increased congestion and emissions. This study proposes pick-up strategies to reduce fuel consumption and emissions, formulated as an assignment model. The assignment problem is abstracted into a linear programming model by considering the uncertainty of the parameters represented by fuzzy numbers. The proposed assignment model can handle the uncertainty of travel delays caused by unpredictable traffic conditions. The assignment aims to minimize fuel consumption, travel delays, and unserviced requests. The assignment model is designed to work for platforms that allow passengers to walk according to their readiness and the maximum walking distance. The numerical simulation results show that allowing passengers to walk to the vehicle can maintain optimality and significantly reduce fuel consumption. The proposed model’s implementation is expected to enable sustainable transport and significantly mitigate emissions caused by vehicle mobility in picking up passengers.
... Meanwhile, to assign the ride-sharing vehicles while considering network traffic dynamics, Zhou et al. [33] proposed a scalable dynamic ride-sharing method that can solve the endogenous congestion effect. While optimizing vehicle assignment and routing simultaneously, Qin et al. [34] investigated the ride-sharing matching problem in conjunction with predicted near-term pooled information for matching vehicles and passengers in various scenarios over multiple matching iterations. ...
Reasonable matching of capacity resources and transported cargoes is the key to realizing intelligent scheduling of less-than-truck-load (LTL) logistics. In practice, there are many types and numbers of participating objects involved in LTL logistics, such as customers, orders, trucks, unitized implements, etc. This results in a complex and large number of matching schemes where truck assignments interact with customer order service sequencing. For the truck–cargo online matching problem under real-time demand, it is necessary to comprehensively consider the online matching process of multi-node orders and the scheduling of multi-types of trucks. Combined with the actual operation scenario, a mixed-integer nonlinear programming model is introduced, and an online matching algorithm with a double-layer nested time window is designed to solve it. By solving the model in a small numerical case using Gurobi and the online matching algorithm, the validity of the model and the effectiveness of the algorithm are verified. The results indicate that the online matching algorithm can obtain optimization results with a lower gap while outperforming in terms of computation time. Relying on the realistic large-scale case for empirical analysis, the optimization results in a significant reduction in the number of trips for smaller types of trucks, and the average truck loading efficiency has reached close to 95%. The experimental results demonstrate the general applicability and effectiveness of the algorithm. Thus, it helps to realize the on-demand allocation of capacity resources and the timely response of transportation scheduling of LTL logistics hubs.
... This service reduces travelers' waiting time and improves their overall experience. Various matching strategies have been proposed to optimize order matching efficiency (Guo et al., 2021;Qin et al., 2021). However, the impact of order matching efficiency on satisfaction remains unknown. ...
This study proposes an integrated theoretical framework to comprehensively examine the satisfaction and subjective well-being of ridesourcing travelers. A total of 1370 survey responses from China were empirically examined, using partial least squares structural equation modeling and multigroup comparison methods. The findings show that platform safety, driver competence, car comfort, environmental benefit, and social benefit significantly affect travel satisfaction. The influence of order matching efficiency on satisfaction is non-significant but contributes to improving subjective well-being. Environmental concern positively moderates the effect of satisfaction on subjective well-being. Additionally, platform safety significantly improves female satisfaction while environmental concern significantly enhances male satisfaction. Order matching efficiency exerts a stronger impact on improving well-being in the shared travel mode than in the non-shared travel mode. These findings supplement the existing knowledge on shared mobility satisfaction and well-being, and provide valuable practical implications for the sustainable development of ridesourcing.
... The study of ride-hailing platforms mainly focuses on monopoly pricing [3,4], competitive strategies of ride-hailing platforms [5][6][7][8][9][10][11], compatibility strategies of ride-hailing platforms [12][13][14][15], bundling strategies of ride-hailing platforms [16][17][18][19][20][21], regulation strategies of ride-hailing platforms [22], and two-sided matching in ride-hailing platforms [23]. As the research on pricing in ride-hailing platforms is more relevant to our study, we primarily review the research in this area. ...
The pricing of ride-hailing platforms (e.g., Didi Rider and Uber) is heavily and simultaneously influenced by the cross-group network effect and congestion effect. To analyze the bilateral pricing of ride-hailing platforms under the influence of these two effects, in this paper we construct a game-theoretic model under four different scenarios and analyze the equilibrium outcomes. The results show that: (1) when both passengers and drivers are sensitive to hassle costs, if the cross-group network effect on the passenger side is higher than that on the driver side, then the platform’s pricing on both sides increases with the increase in the congestion effect, otherwise the prices on both sides of the platform decrease with the increase in the congestion effect; (2) when passengers are sensitive to hassle costs and drivers are sensitive to price, if the ratio for passengers’ and drivers’ different perceptions of price and hassle cost is greater than a certain threshold, then the platform’s pricing on the passenger side increases with the increase in the congestion effect and the platform’s pricing on the driver side decreases with the increase in the congestion effect, otherwise the platform’s pricing on the passenger side decreases with the increase in the congestion effect and the platform’s pricing on the drivers’ side increases with the increase in the congestion effect; (3) when passengers are sensitive to price and drivers are sensitive to hassle costs, if the ratio for passengers’ and drivers’ different perceptions of price and hassle costs is greater than a certain threshold, then the platform’s pricing on the passenger side decreases with the increase in the congestion effect and the platform’s pricing on the drivers’ side increases with the increase in the congestion effect, otherwise the platform’s pricing on the passenger side increases with the increase of the congestion effect and the platform’s pricing on the driver side decreases with the increase in the congestion effect; (4) when both passengers and drivers are price-sensitive, if the cross-group network effect on the passengers’ side is larger than that on the drivers’ side, then the platform should decrease its pricing on both sides with the increase in the congestion effect, otherwise, if the cross-group network effect on the passengers’ side is less than that on the drivers’ side, the platform should increase its pricing on both sides with the increase in the congestion effect; (5) the platform is able to generate the highest profit in each scenario, and the results of the profit comparison between the four scenarios depends on the cross-group network effects and the congestion effects on both the passengers’ and the drivers’ sides.
... The greedy algorithms have been widely applied in practice but have been proven to be inefficient by Maciejewski et al. (2016). The batch matching optimization can be formulated as a bipartite matching problem (Tong et al., 2016;Qin et al., 2021b). Some literature proposes a dynamic optimization model to determine the optimal matching interval and radius Yang et al., 2020;Qin et al., 2021a). ...
The integrated development of city clusters has given rise to an increasing demand for intercity travel. Intercity ride-pooling service exhibits considerable potential in upgrading traditional intercity bus services by implementing demand-responsive enhancements. Nevertheless, its online operations suffer the inherent complexities due to the coupling of vehicle resource allocation among cities and pooled-ride vehicle routing. To tackle these challenges, this study proposes a two-level framework designed to facilitate online fleet management. Specifically, a novel multi-agent feudal reinforcement learning model is proposed at the upper level of the framework to cooperatively assign idle vehicles to different intercity lines, while the lower level updates the routes of vehicles using an adaptive large neighborhood search heuristic. Numerical studies based on the realistic dataset of Xiamen and its surrounding cities in China show that the proposed framework effectively mitigates the supply and demand imbalances, and achieves significant improvement in both the average daily system profit and order fulfillment ratio.
... The demand of multiple users (passengers and drivers) seeking to share a trip implies defining a relationship between them. Establishing this relationship of users based on their preferences is known as the ridematching problem [15,16]. Current ridesharing systems provided by companies such as Uber (https://www.uber.com/, ...
A ridesharing system is a transport mode where two or more users share the same vehicle and divide the trip’s expenses based on similar routes and itineraries. Popular ridesharing systems, such as Uber, Flinc, and Lyft, define a matching among users based only on the coincidence of routes. However, these systems do not guarantee a stable matching (i.e., a matching in which no user prefers another different from the assigned one). In this work, a new ridesharing system model is proposed, including three types of trips: identical, inclusive, and partial. This model is used to introduce a new algorithm to address the stable matching problem for ridesharing systems. Finally, a set of experimental simulations of the proposed algorithm is conducted. Experimental results show that the proposed algorithm always produces a stable matching.
... In recent years, the concepts of collaboration and sharing have been introduced in many studies to realize the efficient configuration of resources, thereby promoting cost and resource savings through multidepot collaboration [48][49][50]. Many third-party platforms are created to provide sharing services, which promote various collaboration modes [51,52]. Wei et al. [53] investigated a matching problem for ride-sourcing markets. ...
Dynamic customer demands impose new challenges for vehicle routing optimization with time windows, in which customer demands appear dynamically within the working periods of depots. The delivery routes should be adjusted for the new customer demands as soon as possible when new customer demands emerge. This study investigates a collaborative multidepot vehicle routing problem with dynamic customer demands and time windows (CMVRPDCDTW) by considering resource sharing and dynamic customer demands. Resource sharing of multidepot across multiple service periods can maximize logistics resource utilization and improve the operating efficiency of delivery logistics networks. A bi-objective optimization model is constructed to optimize the vehicle routes while minimizing the total operating cost and number of vehicles. A hybrid algorithm composed of the improved k-medoids clustering algorithm and improved multiobjective particle swarm optimization based on the dynamic insertion strategy (IMOPSO-DIS) algorithm is designed to find near-optimal solutions for the proposed problem. The improved k-medoids clustering algorithm assigns customers to depots in terms of specific distances to obtain the clustering units, whereas the IMOPSO-DIS algorithm optimizes vehicle routes for each clustering unit by updating the external archive. The elite learning strategy and dynamic insertion strategy are applied to maintain the diversity of the swarm and enhance the search ability in the dynamic environment. The experiment results with 26 instances show that the performance of IMOPSO-DIS is superior to the performance of multiobjective particle swarm optimization, nondominated sorting genetic algorithm-II, and multiobjective evolutionary algorithm. A case study in Chongqing City, China is implemented, and the related results are analyzed. This study provides efficient optimization strategies to solve CMVRPDCDTW. The results reveal a 32.5% reduction in total operating costs and savings of 29 delivery vehicles after optimization. It can also improve the intelligence level of the distribution logistics network, promote the sustainable development of urban logistics and transportation systems, and has meaningful implications for enterprises and government to provide theoretical and decision supports in economic and social development.
This study introduces an innovative approach to tackle multi-objective linear programming (MOLP) problems amidst uncertainty, employing interval-valued fuzzy numbers. The method is tailored to resolve ride-hailing matching challenges encompassing uncertain travel times. Findings reveal that managing uncertainty parameters within interval-valued fuzzy MOLP is achieved through strategic reformulations, focusing on constraint coefficients, resulting in streamlined linear programming formulations conducive to solution simplicity. The efficacy of the proposed model in efficiently handling ride-hailing matching quandaries is demonstrated. Moreover, this study delves into the prospective applications of the developed method, including its potential for generalization to address non-linear programming (NLP) issues pertinent to the ride-hailing domain. This research advances decision-making processes under uncertainty and paves the way for broader applications beyond ride-hailing.
To cater to the diverse needs of consumers, on-demand service platforms often provide vertically differentiated services and allow consumers to choose their preferred service class. In the traditional service-hailing mode, consumers choose from vertically differentiated services in a deterministic way. However, an emerging service mode bundles options to allow consumers to choose multiple classes of services simultaneously, after which the system assigns one class, which seems probabilistic from the consumer’s point of view. This paper elucidates the differences between the deterministic (DE) and probabilistic (PR) service-hailing modes in terms of the decision-making and profitability of the on-demand platform. It also investigates the platform’s optimal pricing decisions under the DE and PR modes and service mode choices. This paper constructs a stylized model capturing the participation and interaction behaviors of consumers and self-employed service agents, assuming endogenous supply and demand. The model is used to characterize the platform’s optimal price and wage decisions for vertically differentiated services. The results show that the platform’s pricing strategies and service targets are jointly determined by demand-side and supply-side factors, and whether the platform should adopt DE or PR mode is contingent on the consumers’ basic valuation level and its volatility. Furthermore, exploring a more complex hybrid mode suggests that introducing the bundled option does not necessarily boost the platform’s profit. Our findings offer meaningful insights into the pricing strategies and service-hailing mode selections of on-demand platforms offering vertically differentiated services.
Ride-hailing is a creative idea created by transportation supported by science and technology. Ride-hailing services can help daily community activities. The issue with ride-hailing is that traffic conditions are unpredictable, implying that waiting times are uncertain. The time passengers spend waiting from when they book a ride service until the driver arrives at the pick-up location is called waiting time. This study suggests a quadratic programming technique for minimizing waiting time while accounting for the unpredictability of pick-up travel time. The interval-valued fuzzy quadratic programming method handles the uncertainty and imprecision of the anticipated journey time. When allocating drivers to pick up passengers, interval-valued fuzzy numbers can provide a more realistic representation of waiting time uncertainty. As a result, the interval-valued fuzzy quadratic programming model can handle the uncertainty in waiting time for ride-hailing assignment problems. The model's performance is evaluated using waiting time and the number of people served. The model's performance is demonstrated numerically using the simulation-based case study. This study shows how to utilize a mathematical method to solve real-world problems with uncertainty and improve user welfare.
Shared transport is an emerging transportation system where travelers share a vehicle either simultaneously as a group (e.g. ride-sharing, ride-sourcing, or customized bus) and share the cost of the journey in the process, or over time (e.g. car-sharing or bike-sharing) as personal rental. We review shared transport research articles published in major operations research, management science and transportation journals from 2010 to 2021. In reviewing these studies, our objective is to assess and present the operations management research of shared transport from a macro level such that new researchers will be attracted and established researchers will be motivated to contribute to this emerging field. We divide the operations management research of shared transport into eight attributes (shared transport mode, decision level, characteristics of demand, decision function, decision objective, modeling approach, solution method, and type of data). Cross tabulations are done among these eight attributes to gain more profound insights. Furthermore, we divide the decision function attribute into 36 subcategories. Maturity and recent attention are calculated as indicators for evaluating the research potential in the decision function attribute. After analyzing the research status, we look forward to the prospect and provide relevant recommendations for future research.
The current revolutions of automation, electrification, and sharing are reshaping the way we travel, with broad implications for future mobility management. While much uncertainty remains about how these disruptive technologies would exactly impact demand for future mobility and enhancement of transportation supply, it is clear that innovative demand management is equally important as smart supply technology development in solving worsening traffic problems in big cities. In this work, we will discuss the significances, opportunities, and challenges of demand management in the era of smart transportation. Innovative ways of travel demand management for road transportation, public transit, and smart mobility are described, including tradable travel credit schemes for road congestion mitigation, revenue-preserving and Pareto-improving strategies for peak-hour transit demand management, and a novel reward scheme integrated with surge pricing in a ride-sourcing market.
In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed.
In a ride-sharing system, arriving customers must be matched with available drivers. These decisions affect the overall number of customers matched, because they impact whether future available drivers will be close to the locations of arriving customers. A common policy used in practice is the closest driver policy, which offers an arriving customer the closest driver. This is an attractive policy because it is simple and easy to implement. However, we expect that parameter-based policies can achieve better performance. We propose matching policies based on a continuous linear program (CLP) that accounts for (i) the differing arrival rates of customers and drivers in different areas of the city, (ii) how long customers are willing to wait for driver pickup, (iii) how long drivers are willing to wait for a customer, and (iv) the time-varying nature of all the aforementioned parameters. We prove asymptotic optimality of a forward-looking CLP-based policy in a large market regime and of a myopic linear program–based matching policy when drivers are fully utilized. When pricing affects customer and driver arrival rates and parameters are time homogeneous, we show that asymptotically optimal joint pricing and matching decisions lead to fully utilized drivers under mild conditions.
With the availability of the location information of drivers and passengers, ride-sourcing platforms can now provide increasingly efficient online matching compared with physical searching and meeting performed in the traditional taxi market. The matching time interval (the time interval over which waiting passengers and idle drivers are accumulated and then subjected to peer-to-peer matching) and matching radius (or maximum allowable pick-up distance, within which waiting passengers and idle drivers can be matched or paired) are two key control variables that a platform can employ to optimize system performance in an online matching system. By appropriately extending the matching time interval, the platform can accumulate large numbers of waiting (or unserved) passengers and idle drivers and thus match the two pools with a reduced expected pick-up distance. However, if the matching time interval is excessively long, certain passengers may become impatient and even abandon their requests. Meanwhile, a short matching radius can reduce the expected pick-up distance but may decrease the matching rate as well. Therefore, the matching time interval and matching radius should be optimized to enhance system efficiency in terms of passenger waiting time, vehicle utilization, and matching rate. This study proposes a model that delineates the online matching process in ride-sourcing markets. The model is then used to examine the impact of the matching time interval and matching radius on system performance and to jointly optimize the two variables under different levels of supply and demand. Numerical experiments are conducted to demonstrate how the proposed modeling and optimization approaches can improve the real-time matching of ride-sourcing platforms.
Ride‐hailing platforms such as Uber, Lyft, and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research topics in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing (DP) algorithms—the two key levers in ride‐hailing—have received tremendous attention from the research community and are continuously being designed and implemented at industrial scales by ride‐hailing platforms. We provide a review of matching and DP techniques in ride‐hailing, and show that they are critical for providing an experience with low waiting time for both riders and drivers. Then we link the two levers together by studying a pool‐matching mechanism called dynamic waiting (DW) that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We show using data from Uber that by jointly optimizing DP and DW, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. We also highlight several key practical challenges and directions of future research from a practitioner's perspective.
This paper studies the supply curve of ride-hailing systems under different market conditions. The curve defines a relationship between the throughput of trips of the system and the cost of riders it serves. We first focus on isotropic markets by revisiting a matching failure identified recently that matches a requesting rider with an idle driver very far away. The failure will cause the supply curve of an e-hailing market to be backward bending, but it is proved that the backward bend does not arise in the street-hailing market. By constructing a double-ended queuing model, we prove that the supply curve of an e-hailing system with finite matching radius is always backward bending, but a smaller matching radius leads to a weaker bend. We further reveal the possibility of completely avoiding the bend by adaptively adjusting the matching radius. We then turn to the anisotropic markets and identify another type of matching failure due to indiscriminate matching between drivers and riders, which again causes a backward bending supply. Given the prevalence of such a matching failure in real-world operations, we discuss how to avoid it using price or rationing discrimination. A conceptualized two-node network is constructed to facilitate the discussion.
In this paper, we propose a novel, computational efficient, dynamic ridesharing algorithm. The beneficial computational properties of the algorithm arise from casting the ridesharing problem as a linear assignment problem between fleet vehicles and customer trip requests within a federated optimization architecture. The resulting algorithm is up to four times faster than the state-of-the-art, even if it is implemented on a less dedicated hardware, and achieves similar service quality. Current literature showcases the ability of state-of-the-art ridesharing algorithms to tackle very large fleets and customer requests in almost near real-time, but the benefits of ridesharing seem limited to centralized systems. Our algorithm suggests that this does not need to be the case. The algorithm that we propose is fully distributable among multiple ridesharing companies. By leveraging two datasets, the New York city taxi dataset and the Melbourne Metropolitan Area dataset, we show that with our algorithm, real-time ridesharing offers clear benefits with respect to more traditional taxi fleets in terms of level of service, even if one considers partial adoption of the system. In fact, e.g., the quality of the solutions obtained in the state-of-the-art works that tackle the whole customer set of the New York city taxi dataset is achieved, even if one considers only a proportion of the fleet size and customer requests. This could make real-time urban-scale ridesharing very attractive to small enterprises and city authorities alike. However, in some cases, e.g., in multi-company scenarios where companies have predefined market shares, we show that the number of vehicles needed to achieve a comparable performance to the monopolistic setting increases, and this raises concerns on the possible negative effects of multi-company ridesharing.
Real-time peer-to-peer ridesharing is a promising mode of transportation that has gained popularity during the recent years thanks to the widespread use of smart phones, mobile application development platforms, and online payment systems. An assignment of drivers to riders, known as the ride-matching problem, is a central component of a peer-to-peer ridesharing system. In this paper, we discuss the features of a flexible ridesharing system, and propose an algorithm to optimally solve the ride-matching problem in a flexible ridesharing system in real-time. We generate random instances of the problem, and perform sensitivity analysis over some of the important parameters in a ridesharing system. Furthermore, we discuss two novel approaches to increase the performance of a ridesharing system.
Significance
Ride-sharing services can provide not only a very personalized mobility experience but also ensure efficiency and sustainability via large-scale ride pooling. Large-scale ride-sharing requires mathematical models and algorithms that can match large groups of riders to a fleet of shared vehicles in real time, a task not fully addressed by current solutions. We present a highly scalable anytime optimal algorithm and experimentally validate its performance using New York City taxi data and a shared vehicle fleet with passenger capacities of up to ten. Our results show that 2,000 vehicles (15% of the taxi fleet) of capacity 10 or 3,000 of capacity 4 can serve 98% of the demand within a mean waiting time of 2.8 min and mean trip delay of 3.5 min.
Kuhn-Munkres algorithm is one of the most popular polynomial time algorithms for solving clas- sical assignment problem. The assignment problem is to find a n a ssignment o f t he j obs t o t he w orkers that has minimum cost, given a cost matrix X 2 R mn , where the element in the i-th row and j-th column rep- resents the cost of assigning the i-th job to the j-th worker. the time complexity of Kuhn-Munkres algorithm is O(mn 2 ), which brings prohibitive computational burden on large scale matrices, limiting the further usage of these methods in real applications. Motivated by this observation, a series of acceleration skills and paral- lel techniques have been studied on special structure. In this paper, we improve the original Kuhn-Munkres algorithm by utilizing the sparsity structure of the cost matrix, and propose two algorithms, sparsity based KM(sKM) and parallel KM(pKM). Furthermore, numerical experiments are given to show the efficiency of our algorithm. We empirically evaluate the proposed algorithm sKM) and (pKM) on random generated largescale datasets. Results have shown that sKM) greatly improves the computational performance. At the same time, (pKM) provides a parallel way to solve assignment problem with considerable accuracy loss.
Taxi is certainly the most popular type of on-demand transportation service in urban areas because taxi-dispatching systems offer more and better services in terms of shorter wait times and passenger travel convenience. However, a shortage of taxicabs has always been critical in many urban contexts especially during peak hours and taxi has great potential to maximize its efficiency by employing the shared-ride concept. There are recent successes in dynamic ride-sharing projects that are expected to bring substantial benefits arising from energy consumption and operation efficiency and thus, it is essential to develop advanced shared-taxi-dispatch algorithms and investigate the collective benefits of dynamic ride-sharing by maximizing occupancy and minimizing travel times in real-time. This paper investigates how taxi services can be improved by proposing shared-taxi algorithms and what type of objective functions and constraints could be employed to prevent excessive passenger detours. Hybrid Simulated Annealing (HSA) is applied to dynamically assign passenger requests efficiently. A series of simulations are conducted with two different taxi operation strategies. The simulation results reveal that allowing ride-sharing for taxicabs increases productivity over the various demand levels and HSA can be considered as a suitable solution to maximize the system efficiency of dynamic ride-sharing.
In this paper we study a dynamic problem of ridesharing and taxi sharing with time windows. We consider a scenario where people needing a taxi or interested in getting a ride use a phone app to designate their source and destination points in a city, as well others restrictions (such as maximum allowable time to be at the destination). On the other hand, we have taxis and people interested in giving a ride, with their current positions and also some constraints (vehicle capacity, destination, maximum time to destination). We want to maximize the number of shared trips: in the case of taxis, people going to close locations can share the costs of the trip, and in case of rides, the driver and passengers can share costs as well. This problem is dynamic since new calls for taxis or calls for rides arrive on demand. This gives rise to an optimization problem which we prove to be NP-Hard. We then propose heuristics to deal with it. We focus on the taxi sharing problem, but we show that our model is easily extendable to model the ridesharing situation or even a situation where there are both taxis and car owners. In addition, we present a framework that consists basically of a client application and a server. The last one processes all incoming information in order to match vehicles to passengers requests. The entire system can be used by taxi companies and riders in a way to reduce the traffic in the cities and to reduce the emission of greenhouse gases.
Smartphone technology enables dynamic ride-sharing systems that bring together people with similar itineraries and time schedules to share rides on short-notice. This paper considers the problem of matching drivers and riders in this dynamic setting. We develop optimization-based approaches that aim at minimizing the total system-wide vehicle miles incurred by system users, and their individual travel costs. To assess the merits of our methods we present a simulation study based on 2008 travel demand data from metropolitan Atlanta. The simulation results indicate that the use of sophisticated optimization methods instead of simple greedy matching rules substantially improve the performance of ride-sharing systems. Furthermore, even with relatively low participation rates, it appears that sustainable populations of dynamic ride-sharing participants may be possible even in relatively sprawling urban areas with many employment centers.
Modern societies rely on efficient transportation systems for sustainable mobility. In this paper, we perform a large-scale and empirical evaluation of a dynamic and distributed taxi-sharing system. The novel system takes advantage of nowadays widespread availability of communication and computation to convey a cost-efficient, door-to-door and flexible system, offering a quality of service similar to traditional taxis. The shared taxi service is assessed in a real-city scenario using a highly realistic simulation platform. Simulation results have shown the system's advantages for both passengers and taxi drivers, and that trade-offs need to be considered. Compared with the current taxi operation model, results show a increase of 48% on the average occupancy per traveled kilometer with a full deployment of the taxi-sharing system.
Carpooling is the concept of people sharing a vehicle for a ride when their departure and destination locations are similar. Dynamic car pool [1] is the dynamic coordination of ride offerings and ride requests based on the, in real time created, transportation and offering needs. Taxi pool [2] is a mode of transport that falls between private transport and conventional bus transport. The routes are fixed or semi-fixed, but with the added convenience of stopping anywhere to pick or drop passengers and not having fixed time schedules. Positioning Systems are systems for finding the location of a mobile device using several different positioning technologies. This paper describes how Positioning Systems can be utilized in order to support a dynamic network of car and taxi pool services that will maximize the exploitation of empty seats traveling with tenable advantages.
Bipartite matching markets pair agents on one side of a market with agents, items, or contracts on the opposing side. Prior work addresses online bipartite matching markets, where agents arrive over time and are dynamically matched to a known set of disposable resources. In this paper, we propose a new model, Online Matching with (offline) Reusable Resources under Known Adversarial Distributions (OM-RR-KAD), in which resources on the offline side are reusable instead of disposable; that is, once matched, resources become available again at some point in the future. We show that our model is tractable by presenting an LP-based adaptive algorithm that achieves an online competitive ratio of 1/2 − ε for any given ε > 0. We also show that no non-adaptive algorithm can achieve a ratio of 1/2 + o(1) based on the same benchmark LP. Through a data-driven analysis on a massive openly-available dataset, we show our model is robust enough to capture the application of taxi dispatching services and ride-sharing systems. We also present heuristics that perform well in practice.
We develop an event-driven Receding Horizon Control (RHC) scheme for a Ride Sharing System (RSS) where vehicles are shared to pick up and drop off passengers so as to minimize a weighted sum of passenger waiting and traveling times. The RSS is modeled as a discrete event system and the event-driven nature of the controller significantly reduces the complexity of the vehicle assignment problem, thus enabling its real-time implementation. Simulation results using actual city maps and real traffic data illustrate the effectiveness of the RH controller in terms of online implementation and performance relative to known heuristics.
With the rapid development of mobile-internet technologies, on-demand ride-sourcing services have become increasingly popular and largely reshaped the way people travel. Demand prediction is one of the most fundamental components in supply-demand management systems of ride-sourcing platforms. With an accurate short-term prediction for origin-destination (OD) demand, the platforms make precise and timely decisions on real-time matching, idle vehicle reallocations, and ride-sharing vehicle routing, etc. Compared to the zone-based demand prediction that has been examined in many previous studies, OD-based demand prediction is more challenging. This is mainly due to the complicated spatial and temporal dependencies among the demand of different OD pairs. To overcome this challenge, we propose the Spatio-Temporal Encoder-Decoder Residual Multi-Graph Convolutional network (ST-ED-RMGC), a novel deep learning model for predicting ride-sourcing demand of various OD pairs. Firstly, the model constructs OD graphs, which utilize adjacent matrices to characterize the non-Euclidean pair-wise geographical and semantic correlations among different OD pairs. Secondly, based on the constructed graphs, a residual multi-graph convolutional (RMGC) network is designed to encode the contextual-aware spatial dependencies, and a long-short term memory (LSTM) network is used to encode the temporal dependencies, into a dense vector space. Finally, we reuse the RMGC networks to decode the compressed vector back to OD graphs and predict the future OD demand. Through extensive experiments on the for-hire-vehicles datasets in Manhattan, New York City, we show that our proposed deep learning framework outperforms the state-of-arts by a significant margin.
We develop an event-driven Receding Horizon Control (RHC) scheme for a Mobility-on-Demand System (MoDS) in a transportation network where vehicles may be shared to pick up and drop off passengers so as to minimize a weighted sum of passenger waiting and traveling times. Viewed as a discrete event system, the event-driven nature of the controller significantly reduces the complexity of the vehicle assignment problem, thus enabling its real-time implementation. Simulation results using actual city maps and real taxi traffic data illustrate the effectiveness of the RH controller in terms of real-time implementation and performance relative to known greedy heuristics.
With the recent rapid growth of technology-enabled mobility services, ride-sourcing platforms, such as Uber and DiDi, have launched commercial on-demand ride-pooling programs that allow drivers to serve more than one passenger request in each ride. Without requiring the prearrangement of trip schedules, these programs match on-demand passenger requests with vehicles that have vacant seats. Ride-pooling programs are expected to offer benefits for both individual passengers in the form of cost savings and for society in the form of traffic alleviation and emission reduction. In addition to some exogenous variables and environments for ride-sourcing market, such as city size and population density, three key decisions govern a platform's efficiency for ride-pooling services: trip fare, vehicle fleet size, and allowable detour time. An appropriate discounted fare attracts an adequate number of passengers for ride-pooling, and thus increases the successful pairing rate, while an appropriate allowable detour time prevents passengers from giving up ride-pooling service. This paper develops a mathematical model to elucidate the complex relationships between the variables and decisions involved in a ride-pooling market. We find that the monopoly optimum, social optimum and second-best solutions in both ride-pooling and non-pooling markets are always in a normal regime rather than the wild goose chase (WGC) regime—an inefficient equilibrium in which drivers spend substantial time on picking up passengers. Besides, in general, a unit decrease in trip fare in a ride-pooling market attracts more passengers than would a non-pooling market, because it not only directly increases passenger demand due to the negative price elasticity, but also reduces actual detour time, which in turn indirectly increases ride-pooling passenger demand. As a result, we prove that monopoly optimum, social optimum and second-best solution trip fares in a ride-pooling market are lower than that in a non-pooling market under certain conditions. These theoretical findings are further verified by a set of numerical studies.
Recent works on ride-sharing order dispatching have highlighted the importance of taking into account both the spatial and temporal dynamics in the dispatching process for improving the transportation system efficiency. At the same time, deep reinforcement learning has advanced to the point where it achieves superhuman performance in a number of fields. In this work, we propose a deep reinforcement learning based solution for order dispatching and we conduct large scale online A/B tests on DiDi's ride-dispatching platform to show that the proposed method achieves significant improvement on both total driver income and user experience related metrics.
In particular, we model the ride dispatching problem as a Semi Markov Decision Process to account for the temporal aspect of the dispatching actions. To improve the stability of the value iteration with nonlinear function approximators like neural networks, we propose Cerebellar Value Networks (CVNet) with a novel distributed state representation layer. We further derive a regularized policy evaluation scheme for CVNet that penalizes large Lipschitz constant of the value network for additional robustness against adversarial perturbation and noises. Finally, we adapt various transfer learning methods to CVNet for increased learning adaptability and efficiency across multiple cities. We conduct extensive offline simulations based on real dispatching data as well as online AB tests through the DiDi's platform. Results show that CVNet consistently outperforms other recently proposed dispatching methods. We finally show that the performance can be further improved through the efficient use of transfer learning.
We study the following multi-period multi-objective online ride-matching problem. A ride-sourcing platform needs to match passengers and drivers in real time without observing future information, considering multiple
objectives such as platform revenue, pick-up distance, and service quality. We develop an efficient online matching policy that adaptively balances the trade-offs among multiple objectives in a dynamic setting, and provide theoretical performance guarantee for the policy. We prove that the proposed adaptive matching policy can achieve a "target-based optimal solution", i.e., a solution that minimizes the Euclidean distance to any pre-determined multi-objective target. Specifically, the outcome under our policy converges to the "compromise solution" if we set the utopia point as the target. Through numerical experiments and industrial testing using real data from a ride-sourcing platform, we demonstrate that our approach indeed obtains solutions that are closest to the pre-determined targets under various settings, in comparison to existing approaches. The policy presents solutions with delicate balance among multiple objectives and brings value
to all the stakeholders in the ride-sourcing ecosystem comparing to benchmark policies: (1) drivers with higher service scores are dispatched with more orders and receive higher incomes; (2) passengers are more
likely to be served by drivers with higher service scores, and passengers with higher order revenues are served with higher answer rates, at the expense of a small increase in pick-up distance; (3) the platform obtains a
higher total revenue.
A fundamental question in any peer-to-peer ridesharing system is how to, both effectively and efficiently, dispatch user's ride requests to the right driver in real time. Traditional rule-based solutions usually work on a simplified problem setting, which requires a sophisticated hand-crafted weight design for either centralized authority control or decentralized multi-agent scheduling systems. Although recent approaches have used reinforcement learning to provide centralized combinatorial optimization algorithms with informative weight values, their single-agent setting can hardly model the complex interactions between drivers and orders. In this paper, we address the order dispatching problem using multi-agent reinforcement learning (MARL), which follows the distributed nature of the peer-to-peer ridesharing problem and possesses the ability to capture the stochastic demand-supply dynamics in large-scale ridesharing scenarios. Being more reliable than centralized approaches, our proposed MARL solutions could also support fully distributed execution through recent advances in the Internet of Vehicles (IoV) and the Vehicle-to-Network (V2N). Furthermore, we adopt the mean field approximation to simplify the local interactions by taking an average action among neighborhoods. The mean field approximation is capable of globally capturing dynamic demand-supply variations by propagating many local interactions between agents and the environment. Our extensive experiments have shown the significant improvements of MARL order dispatching algorithms over several strong baselines on the accumulated driver income (ADI), and order response rate measures. Besides, the simulated experiments with real data have also justified that our solution can alleviate the supply-demand gap during the rush hours, thus possessing the capability of reducing traffic congestion.
With the emergence of ride-sharing companies that offer transportation on demand at a large scale and the increasing availability of corresponding demand data sets, new challenges arise to develop routing optimization algorithms that can solve massive problems in real time. In this paper, we develop an optimization framework, coupled with a novel and generalizable backbone algorithm, that allows us to dispatch in real time thousands of taxis serving more than 25,000 customers per hour. We provide evidence from historical simulations using New York City routing network and yellow cab data to show that our algorithms improve upon the performance of existing heuristics in such real-world settings.
We present a novel order dispatch algorithm in large-scale on-demand ride-hailing platforms. While traditional order dispatch approaches usually focus on immediate customer satisfaction, the proposed algorithm is designed to provide a more efficient way to optimize resource utilization and user experience in a global and more farsighted view. In particular, we model order dispatch as a large-scale sequential decision-making problem, where the decision of assigning an order to a driver is determined by a centralized algorithm in a coordinated way. The problem is solved in a learning and planning manner: 1) based on historical data, we first summarize demand and supply patterns into a spatiotemporal quantization, each of which indicates the expected value of a driver being in a particular state; 2) a planning step is conducted in real-time, where each driver-order-pair is valued in consideration of both immediate rewards and future gains, and then dispatch is solved using a combinatorial optimizing algorithm. Through extensive offline experiments and online AB tests, the proposed approach delivers remarkable improvement on the platform's efficiency and has been successfully deployed in the production system of Didi Chuxing.
Bipartite matching markets pair agents on one side of a market with agents, items, or contracts on the opposing side. Prior work addresses online bipartite matching markets, where agents arrive over time and are dynamically matched to a known set of disposable resources. In this paper, we propose a new model, Online Matching with (offline) Reusable Resources under Known Adversarial Distributions (OM-RR-KAD), in which resources on the offline side are reusable instead of disposable; that is, once matched, resources become available again at some point in the future. We show that our model is tractable by presenting an LP-based adaptive algorithm that achieves an online competitive ratio of 1/2 - eps for any given eps greater than 0. We also show that no non-adaptive algorithm can achieve a ratio of 1/2 + o(1) based on the same benchmark LP. Through a data-driven analysis on a massive openly-available dataset, we show our model is robust enough to capture the application of taxi dispatching services and ride-sharing systems. We also present heuristics that perform well in practice.
Ride-sharing (RS) has great values in saving energy and alleviating traffic pressure. Existing studies can be improved for better efficiency. Therefore, we propose a new ride-sharing model, where each driver has a requirement that if the driver shares a ride with a rider, the shared route percentage (i.e., the ratio of the shared route's distance to the driver's total travel distance) exceeds an expectation rate of the driver, e.g., 0.8. We consider two variants of this problem. The first considers multiple drivers and multiple riders and aims to compute driver-rider pairs to maximize the overall shared route percentage (SRP). We model this problem as the maximum weighted bigraph matching problem, where the vertices are drivers and riders, edges are driver-rider pairs, and edge weights are driver-rider's SRP. However, it is rather expensive to compute the SRP values for large numbers of driver-rider pairs on road networks. To address this problem, we propose an efficient method to prune many unnecessary driver-rider pairs and avoid computing the SRP values for every pair. To improve the efficiency, we propose an approximate method with error bound guarantee. The basic idea is that we compute an upper bound and a lower bound for each driver-rider pair in constant time. Then, we estimate an upper bound and a lower bound of the graph matching. Next, we select some driver-rider pairs, compute their real shortest-route distance, and update the lower and upper bounds of the maximum graph matching. We repeat above steps until the ratio of the upper bound to the lower bound is not larger than a given approximate rate. The second considers multiple drivers and a single rider and aims to find the top-
$k$
drivers for the rider with the largest SRP. We first prune a large number of drivers that cannot meet the SRP requirements. Then, we propose a best-first algorithm that progressively selects the drivers with high probability to be in the top-
$k$
results and prunes the drivers that cannot be in the top-
$k$
results. Extensive experiments on real-world datasets demonstrate the superiority of our method.
This study proposes equilibrium models under different behavioral assumptions of labor supply in a ride-sourcing market and then investigates the performance of surge pricing. A time-expanded network is first proposed to delineate possible work schedules of drivers. Based on the proposed network, we provide formulations and algorithms for both neoclassical and income-targeting hypotheses to characterize the labor supply of ride-sourcing drivers, i.e., their choices of work hours. We then investigate the impact of surge pricing using a bi-level programming framework, with the lower-level problem capturing the equilibrium work hour choices while the upper-level one representing revenue-maximizing surge pricing. Compared to static pricing, the platform and drivers are found to generally enjoy higher revenue while customers may be made worse off during highly surged periods. Lastly, a simple regulation scheme to reduce market power is discussed.
Dynamic ride-sharing systems enable people to share rides and increase the efficiency of urban transportation by connecting riders and drivers on short notice. Automated systems that establish ride-share matches with minimal input from participants provide convenience and the most potential for system-wide performance improvement, such as reduction in total vehicle-miles traveled. Indeed, such systems may be designed to match riders and drivers to maximize system performance improvement. However, system-optimal matches may not provide the maximum benefit to each individual participant. In this paper, we consider a notion of stability for ride-share matches and present several mathematical programming methods to establish stable or nearly stable matches, where we note that ride-share matching optimization is performed over time with incomplete information. Our numerical experiments using travel demand data for the metropolitan Atlanta region show that we can significantly increase the stability of ride-share matching solutions at the cost of only a small degradation in system-wide performance.
In this paper, we present an ensemble learning approach for better understanding ridesplitting behavior of passengers of ridesourcing companies who provide prearranged and on-demand transportation services. An ensemble learning model is a weighted combination of multiple classification models or week classifiers to form a strong classification model. The goal of ensemble learning is to combine decisions or predictions of several base classifiers to improve prediction, generalizability, and robustness over a single classifier. This paper employs the Boosting ensemble by growing individual decision trees sequentially and then assembling these trees to produce a powerful classification model. To improve the prediction accuracy of ridesplitting choices, we explored real-world individual level data extracted from the on-demand ride service platform of DiDi in Hangzhou, China. Over one million trips of the four service types, i.e., Taxi Hailing Service, Express, Private Car Service, and Hitch, are explored with descriptive statistics. A variety of features that may impact ridesplitting behavior are ranked and selected by using the ReliefF algorithm, such as trip travel time, trip costs, trip length, waiting time fee, travel time reliability of origins/destinations and so on. The Boosting ensemble trees with full features and selected features are trained and validated using two independent datasets. This paper also verifies that ensemble learning is particularly useful and powerful in the ridesplitting analysis and outperforms three other widely used classifiers. This paper is one of the first quantitative studies that empirically reveal the real-world demand and supply pattern by exploring the city-wide data of an on-demand ride service platform.
The potential benefits from increased ridesharing are substantial, and impact a wide range of stakeholders. In a properly applied rideshare scheme, drivers and passengers achieve cost savings, they potentially achieve travel time savings, and they benefit from increased travel options. Employers can reduce expensive parking construction or leasing, and benefit from higher worker productivity. Society benefits from congestion reduction, energy security improvements, greenhouse gas (GHG) emission reductions and increased social equity. Unfortunately, ridesharing's historical success has been rather modest, with a substantial decrease in popularity since the 1970's and participation that remains near an all-time low. Clearly there is a disconnect between the purported benefits and the real or perceived challenges associated with sharing rides. This thesis asks why ridesharing is not more popular than current participation suggests, and what can be done to encourage greater participation going forward? After a review of past and present rideshare initiatives, it becomes clear that there is no single challenge to be overcome that will increase interest and participation in ridesharing. Rather, the 'rideshare challenge' is a series of economic, behavioral, institutional and technological obstacles to be addressed. Yet, two opportunities show particular promise at helping overcome these challenges - a focus on large employers, and a technology-based service innovation known as "real-time" ridesharing. Large employers are a unique type of institution that can successfully influence private household travel decisions while simultaneously advancing employer-specific goals and various societal goals. "Real-time" ridesharing extends the range of existing rideshare options available to travelers and it begins to address a number of challenges associated with ridesharing. To increase rideshare participation going forward, this thesis proposes a detailed design for an employer based, technology-focused rideshare trial for the Massachusetts Institute of Technology (MIT), supported by a rigorous, Institute-specific analysis of rideshare viability. The trial is designed to be expanded to other institutions in the MIT/Kendall Square area of Cambridge, MA in the future. The analysis suggests that on an ideal day, approximately 65% of consistent, single occupant commuters could share rides, leading to a 19% reduction in Institute-wide, commuting trip VMT. The trial design focuses on the use of technology, incentives and personalized marketing to overcome the 'rideshare challenge' and realize a significant portion of this best case VMT reduction.
Ridesharing offers the opportunity to make more efficient use of vehicles while preserving the benefits of individual mobility. Presenting ridesharing as a viable option for commuters, however, requires minimizing certain inconvenience factors. One of these factors includes detours which result from picking up and dropping off additional passengers. This paper proposes a method which aims to best utilize ridesharing potential while keeping detours below a specific limit. The method specifically targets ridesharing systems on a very large scale and with a high degree of dynamics which are difficult to address using classical approaches known from operations research. For this purpose, the road network is divided into distinct partitions which define the search space for ride matches. The size and shape of the partitions depend on the topology of the road network as well as on two free parameters. This allows optimizing the partitioning with regard to sharing potential utilization and inconvenience minimization. Match making is ultimately performed using an agent-based approach. As a case study, the algorithm is applied to investigate the potential for taxi sharing in Singapore. This is done by considering about 110 000 daily trips and allowing up to two sharing partners. The outcome shows that the number of trips could be reduced by 42% resulting in a daily mileage savings of 230 000 km. It is further shown that the presented approach exceeds the mileage savings achieved by a greedy heuristic by 6% while requiring 30% lower computational efforts.
With the rising fuel costs, ride sharing is becoming a common mode of transportation. Sharing taxis which has been prominent in several developing countries is also becoming common in several cities around the world. Sharing taxis presents several advantages as it minimizes vacant seats in cars thus reducing costs on taxi operators which results in significantly lower taxi fares for passengers. Besides the economical advantages, taxi sharing is highly important for reducing congestion on the roads and for minimizing the impact of transportation on the environment. In this paper, we formulate the problem of assigning passengers to taxis and computing the optimal routes of taxis as a mixed integer program. To solve the proposed model, we present a Lagrangian decomposition approach which exploits the structure of the problem leading to smaller problems that are solved separately. Furthermore, we propose two heuristics that are used to obtain good quality feasible solutions. The Lagrangian approach along with the heuristics are implemented and compared to solving the full problem using CPLEX. The computational results indicate the efficiency of the methodology in providing tighter bounds than CPLEX in shorter computational time.
Significance
Recent advances in information technologies have increased our participation in “sharing economies,” where applications that allow networked, real-time data exchange facilitate the sharing of living spaces, equipment, or vehicles with others. However, the impact of large-scale sharing on sustainability is not clear, and a framework to assess its benefits quantitatively is missing. For this purpose, we propose the method of shareability networks, which translates spatio-temporal sharing problems into a graph-theoretic framework that provides efficient solutions. Applying this method to a dataset of 150 million taxi trips in New York City, our simulations reveal the vast potential of a new taxi system in which trips are routinely shareable while keeping passenger discomfort low in terms of prolonged travel time.
Taxi ridesharing can be of significant social and environmental benefit, e.g. by saving energy consumption and satisfying people's commute needs. Despite the great potential, taxi ridesharing, especially with dynamic queries, is not well studied. In this paper, we formally define the dynamic ridesharing problem and propose a large-scale taxi ridesharing service. It efficiently serves real-time requests sent by taxi users and generates ridesharing schedules that reduce the total travel distance significantly. In our method, we first propose a taxi searching algorithm using a spatio-temporal index to quickly retrieve candidate taxis that are likely to satisfy a user query. A scheduling algorithm is then proposed. It checks each candidate taxi and inserts the query's trip into the schedule of the taxi which satisfies the query with minimum additional incurred travel distance. To tackle the heavy computational load, a lazy shortest path calculation strategy is devised to speed up the scheduling algorithm. We evaluated our service using a GPS trajectory dataset generated by over 33,000 taxis during a period of 3 months. By learning the spatio-temporal distributions of real user queries from this dataset, we built an experimental platform that simulates user real behaviours in taking a taxi. Tested on this platform with extensive experiments, our approach demonstrated its efficiency, effectiveness, and scalability. For example, our proposed service serves 25% additional taxi users while saving 13% travel distance compared with no-ridesharing (when the ratio of the number of queries to that of taxis is 6).
Taxis make an important contribution to transport in many parts of the world, offering demand-responsive, door-to-door transport. In larger cities, taxis may be hailed on-street or taken from taxi ranks. Elsewhere, taxis are usually ordered by phone. The objective of a taxi dispatcher is to maximize the efficiency of fleet utilization. While the spatial and temporal distribution of taxi requests has in general a high degree of predictability, real time traffic congestion information can be collected and disseminated to taxis by communication technologies. The efficiency of taxi dispatching may be significantly improved through the anticipation of future requests and traffic conditions. A rolling horizon approach to the optimisation of taxi dispatching is formulated, which takes the stochastic and dynamic nature of the problem into account. Numerical experiments are presented to illustrate the performances of the heuristics, taking the time dependency of travel times and passenger arrivals into account.
A practical and applicable taxi-sharing system based on the use of intelligent transportation system (ITS) technologies has been developed in Taipei City. This system is easy for members to use and inexpensive for the service provider to operate. This paper gives an overview of the taxi-sharing service, presents key algorithms for dynamic rideshare matching processes, describes the field trial operation of the system in Taipei Nei-Hu Science and Technology Park and discusses empirical results to provide valuable implications for better taxi-sharing service in the future.
Optimizing online matching for ride-sourcing services with multi-agent deep reinforcement learning
- J Ke
- F Xiao
- H Yang
- J Ye