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Transportation in the Sharing Economy
Abstract
Traditionally, individuals and firms have bought and owned resources. For instance, owning a car was a status symbol for people. In recent years, this focus has changed. Young people just want to have some convenient means of transportation available. Sharing cars, bikes, or rides is also acceptable and has the advantage of increased sustainability compared with everyone owning such a device. A similar development can also be observed in industry. Already for decades, smaller farmers in rural areas have shared some expensive harvesting machines, via cooperatives. In recent years, cost pressures have incentivized all firms to use their equipment more efficiently and to avoid idle capacities. This has led, among other things, to the exchange of transportation requests between smaller carriers, when one of them faces an overload situation and another has idle resources. Similarly, some production firms share storage space and logistics service providers operate this storage jointly with greater efficiency than each firm could do separately. All these tendencies have led to the development of a new paradigm, the “Sharing Economy.” In transportation, these resources are the vehicles used to deliver goods or move passengers. Given an increasing pressure to act economically and ecologically sustainable, efficient mechanisms that help to benefit from idle capacities are on the rise. They are typically organized through digital platforms that facilitate the efficient exchange of goods or services and help cope with data privacy issues. Both transportation companies and heavy users of transportation services need to learn how to play in a world of shared idle capacities. “Transportation in the Sharing Economy” was the theme of the 2019 Transportation Science and Logistics Workshop held in Vienna, and it is therefore also the focus of this associated special issue. The papers included cover several forms of shared resources in transportation, where the first part deals with freight, whereas in the second part, the focus lies on passenger transportation. Each study highlights the benefits of sharing resources. We received 40 submissions, of which 10 papers were accepted for this special issue after the reviewing process.
... The sharing economy in transportation is a strategy that helps businesses reduce costs, minimize environmental impacts, and optimize resource usage by sharing equipment, storage space, and logistics services. Such concepts are typically organized through digital platforms that facilitate efficient exchange of goods or services (Daglis, 2022;Gansterer et al., 2022). ...
... As highlighted by Gansterer et al. (2022), collaboration approaches based on the employment of the auction mechanism had a very limited attention with respect to other collaborative approaches. Consequently, the originality of this paper lies in the development of an auctionbased mechanism for the horizontal collaboration among a group of coalited carriers for the management of shared customers. ...
... For example, in Europe, Timocom is a known provider of a digital marketplace for shipping. It works like an auctioneer, charging fees to users who can either offer their available cargo for transport or bid on shipments that need moving (Gansterer et al., 2022). ...
Companies are constantly looking for new strategies to improve their logistics performance and ensure their competitiveness in the global market. This article provides a new scheme for managing the selection of shared customers for a logistics company. The new mechanism proposes the use of the auction as a tool to manage the selection of shared clients through the coalition pool. Thus, all unprofitable shared customers will be pushed to the pool for outsourcing by the other collaborating carriers. Then, some profitable auctioned ones will be selected. The selection system is designed based on solving a vehicle routing problem that aims to maximize the carrier's profit in a decentralized context. At first, a mixed integer linear programing model is derived to solve the deterministic version of the problem. Then in order to efficiently address the stochastic version of the problem, a simulation-based optimization model is developed. This model is employed to solve a real case study of a parcel delivery company, considering the travel times as a bimodal distribution. A comparative study is conducted to demonstrate the effectiveness of the auction approach in managing shared customers. The results of our study reveal that the proposed auction approach efficiently manages the shared customers which leads to the substantial increase of 22.65% in profits for the delivery company. These findings have significant implications for logistics companies seeking to improve their performance and competitiveness in the global market.
... Many notable firms have used social media and advanced technology to create platforms and applications focused on shared consumption. The concept of shared consumption is centred on maximising the value of assets not completely used by their owners [1,2]. In addition to meeting the diverse needs of customers, these platforms increase the worth of surplus personal possessions. ...
Driven by the extensive implementation of information communication technology, collaborative consumption has become more popular. Historically, people have always thought that the best way to get something is to obtain the ownership of it. However, collaborative consumption has recently seen a meteoric rise in popularity due to that obtaining the right to use rather than own. More research into this emerging phenomenon is necessary, notwithstanding the huge impact that collaborative consumption activities have had on companies and individuals. Existing research indicates a lack of knowledge on the factors that motivate or impede user engagement in collaborative consumption. Building on the cost and benefit framework, this research presents a model that examines the effects of perceived benefits (enjoyment and economic reward), perceived costs (privacy risk and security risk) and perceived platform quality (system quality, service quality and information quality) on the intention to engage in the collaborative economy. Using a structural equation modelling approach, 524 active users with experience in car sharing evaluated the research model.The results show that perceived benefits and platform quality positively influence CC participation, the perceived cost reveals a partial support relationship to participate in CC, where security risks are supported but privacy risks are not. This research results will contribute to the research and practice on sharing economy.
... In the field of logistics, the concept of the sharing economy is widely advocated, such as supply chains [22,23], logistics service providers [24], platforms [25,26]. Joint distribution is a way to implement sharing economy in logistics, by facilitating the efficient utilization and allocation of fleet and facility resources. ...
This article introduces the concept of joint distribution context into the location-routing problem (LRP) to tackle the issue of resource waste and high costs in distribution centers (DCs) for multiple companies. Fixed costs of constructing DCs, fixed costs of hiring vehicles, and routing costs are taken into account when constructing the mixed integer programming (MIP) model. A two-stage algorithm is designed to solve this problem. In the first stage, K-means clustering is used to group demand nodes with vehicle capacity constraints. In the second stage, the simplified LRP model is solved by Lingo, and locations of DCs and routing schemes are obtained. Additionally, a cost-sharing model based on the desirability function is developed to address the cost allocation problem among companies. The results and sensitivity analysis demonstrate that the joint distribution can effectively reduce costs.
... However, future research should consider the potential of minimizing distance travelled by allowing the vehicles to switch between depots in a dynamic setting due to the density of delivery points and depots in urban quick commerce areas. One could even reflect on allowing lateral transhipment between MFCs to redistribute inventory (Hartl and Romauch 2016) or collaboration between shippers (Gansterer and Hartl 2018;Gansterer et al. 2022), or across retailers and their warehouses (see, e.g., Mancini and Gansterer 2021). Lastly, we assume a known forecasted demand, which is static, as a starting point for this strategic problem. ...
In 2020, the first quick commerce businesses in grocery retail emerged in the European market. Customers can order online and receive their groceries within 15 min in the best case. The ability to provide short lead times is, therefore, essential. However, the ambitious service promises of quick deliveries further complicate order fulfillment, and many retailers are struggling to achieve profitability. Quick commerce retailers need to establish an efficient network of micro-fulfillment centers (MFCs) in customer proximity, i.e., urban areas, to master these challenges. We address this strategic network problem and formulate it as a location routing problem. This enables us to define the number, location, type, and size of MFCs based on setup, replenishment, order processing, and transportation costs. We solve the problem using a cluster-first-route-second heuristic based on agglomerative clustering to approximate transportation costs. Our numerical experiments show that our heuristic solves the problem effectively and provides efficient decision support for quick commerce retailing. We generate managerial insights by analyzing key aspects of a quick commerce business, such as lead times and problem-specific cost factors. We show, for example, that allowing slightly higher delivery flexibility (e.g., offering extended lead times) enables bundling effects and results in cost savings of 50% or more of fulfillment costs. Furthermore, using multiple small MFCs is more efficient than larger, automated MFCs from a lead time and cost perspective.
... Based on the aforementioned considerations, shared parking has become a hot topic in the research on parking difficulties due to its advantages of low cost and high utilization rate. It holds the potential to profit from the business model of the sharing economy [3,4]. Shared parking refers to the efficient utilization of scattered idle parking resources. ...
This paper investigates the latent classes of parking preference for drivers and the economic effects after implementing Parking Space Comprehensive Utilization (PSC) in traditional business districts (TBD), with a particular focus on the parking preferences of electric vehicle users (EVU). Firstly, Exploratory Factor Analysis (EFA) is used to reduce dimensionality and determine the latent structure. Then, based on the Latent Class Model (LCM), the customers are classified, and the proportion of each class under various latent variables is analyzed. Finally, the paper conducts a quantitative analysis of economic effects by considering different psychological factors across different customer classes. With the data obtained from revealed preference (RP) and stated preference (SP) surveys, this paper identifies the customers’ preferences for the three scenarios presented. The results show that (1) customers can be classified into four classes: core customers (CCS, 34%), potential customers (PCS, 29%), regular customers (RCS, 22%), and marginal customers (MCS, 15%), among which EVU do not show a significant preference for parking charging facilities in TBD; (2) the potential economic improvements for these four classes are: 9%, 12%, 8%, and 10%; (3) CCS has the greatest potential to increase store revenue by ¥7041, while PCS has the greatest potential to increase store customer flow by 31%. These findings provide a valuable reference for decision-making by TBD store managers.
... Ride-hailing systems have additional challenges associated with uncertainties from the supply side (available drivers). Various methods have been proposed to address this issue (Yang et al., 2020;Xu et al., 2018;Gansterer et al., 2022). A review of existing ride-matching algorithms for the ride-hailing problem is provided in Korolko et al. (2018). ...
Shared mobility on demand (MoD) services are receiving increased attention as many high volume ride-hailing companies are offering shared services (e.g. UberPool, LyftLine) at an increasing rate. Also, the advent of autonomous vehicles (AVs) promises further operational opportunities to benefit from these developments as AVs enable a centrally operated and fully connected fleet. There are two fundamental tasks for a shared MoD service: ride-matching and vehicle rebalancing. Traditionally, these two functions are performed sequentially and independently. In this paper, we propose and formulate an integrated ride-matching problem which aims to integrate ride-matching and rebalancing into a single formulation. The integrated problem benefits from interactions between these two tasks. We also propose a methodology to solve the integrated shared ride-matching problem by using supply level information based on a grid representation of the city network. We demonstrate the effectiveness of the proposed methodology through a comparative case study using a benchmark sequential approach and an open source data set. Our results show that the integrated model is able to serve at least the same amount of passengers with significant gains in terms of level of service and sustainability metrics.
Global synchromodal transportation is a promising strategy for providing efficient, reliable, flexible, and sustainable container shipping services across continents. It involves integrating multiple modes and routes owned by various operators to create a comprehensive transport plan. However, these operators often have their own local networks and are hesitant to cede control to a centralized platform. Instead, they prefer to share limited information in a coordinated manner to achieve a common goal without sacrificing their own benefits. This paper proposes a coordinated mechanism for global synchromodal transport planning, in which a global operator proposes incentives to local operators to select the most efficient modes and routes for shipping containers from one continent to another. An augmented Lagrangian relaxation approach is developed for the global operator to generate incentives, and a heuristic algorithm is designed to address the computational complexity of the optimization problems faced by local operators. We incorporate the proposed approaches with a rolling horizon framework to handle dynamic shipment requests received from spot markets and with a buffer strategy to address travel time uncertainties. The coordinated mechanism is tested on a real network between Asia and Europe, and results show that it can significantly increase total profits, reduce request rejections, and reduce infeasible transshipments compared to decentralized global transportation plans currently in use, particularly under scenarios with higher degrees of dynamism and uncertainty.
Public bicycle can be a disease-resilient travel mode during the coronavirus disease 2019 (COVID-19) pandemic. Nonetheless, its evidence on public bicycle sharing is still inconclusive. This study used Bayesian structural time series models and causal impact inference for the data on the daily ridership of public bicycles in Seoul, South Korea, for 1826 days from January 1, 2017, to December 31, 2021. The study found that the usage of public bicycles was robust against the COVID-19 pandemic even in densely populated Seoul. Compared with the pre-pandemic period, public bicycles' usage was unaffected on days when weather conditions, such as snow, rain, and wind speed were not as severe, as well as on days with non-seasonal event factors, such as weekdays, public holidays, and traditional Korean holidays. In addition, its robustness against the pandemic became more pronounced as the number of bicycle racks increased and the intensity of social distancing increased. However, public bicycles were in demand primarily for leisure and exercise, not for travel, during the pandemic. Public bicycle sharing can be a disease-resilient travel mode. Continuous investment in infrastructure such as bicycle paths and public bicycle is required to become a more resilient travel mode against infectious diseases.
Viability of electric car-sharing operations depends on rebalancing algorithms. Earlier methods in the literature suggest a trend toward non-myopic algorithms using queueing principles. We propose a new rebalancing policy using cost function approximation. The cost function is modeled as a p-median relocation problem with minimum cost flow conservation and path-based charging station capacities on a static node-charge graph structure. The cost function is NP-complete, so a heuristic is proposed that ensures feasible solutions that can be solved in an online system. The algorithm is validated in a case study of electric carshare in Brooklyn, New York, with demand data shared from BMW ReachNow operations in September 2017 (262 vehicle fleet, 231 pickups per day, 303 traffic analysis zones (TAZs)) and charging station location data (18 charging stations with 4 port capacities). The proposed non-myopic rebalancing heuristic reduces the cost increase compared to myopic rebalancing by 38%. Other managerial insights are further discussed.
Current mobility services cannot compete on equal terms with self-owned mobility products concerning service quality. Due to supply and demand imbalances, ridesharing users invariably experience delays, price surges, and rejections. Traditional approaches often fail to respond to demand fluctuations adequately since service levels are, to some extent, bounded by fleet size. With the emergence of autonomous vehicles, however, the characteristics of mobility services change and new opportunities to overcome the prevailing limitations arise. In this paper, we consider an autonomous ridesharing problem in which idle vehicles are hired on-demand in order to meet the service level requirements of a heterogeneous user base. In the face of uncertain demand and idle vehicle supply, we propose a learning-based optimization approach that uses the dual variables of the underlying assignment problem to iteratively approximate the marginal value of vehicles at each time and location under different availability settings. These approximations are used in the objective function of the optimization problem to dispatch, rebalance, and occasionally hire idle third-party vehicles in a high-resolution transportation network of Manhattan. The results show that the proposed policy outperforms a reactive optimization approach in a variety of vehicle availability scenarios while hiring fewer vehicles. Moreover, we demonstrate that mobility services can offer strict service level contracts (SLCs) to different user groups featuring both delay and rejection penalties.
Horizontal collaboration has been considered as effective practice for sustainable logistics and freight transport and it has gained increased attention in recent years. This paper aims to provide a survey of the development of horizontal collaborative transport (HCT) over the past ten years, to identify research trends and gaps, then to propose some research opportunities. The paper also aims to provide guidelines to logistics companies who wish to embark on HCT, to help them choose which HCT solution to implement. To provide a comprehensive and structured review, the paper follows the methodology for Systematic Literature Review proposed in the literature. A total of 120 scientific papers published between 2007 and 2017 were reviewed. A survey framework based on two axes-HCT solutions and implementation issues-is developed to analyse and position the papers. The results show that, regarding HCT solutions, carrier alliance and flow controller collaboration were the most frequently studied. But recent innovative solutions such as pooling and physical internet are also gaining increased attention. Regarding implementation issues, the focus of the literature has been on the development of decision-making models, including transport planning, lane exchange, and gain sharing. Conversely, managerial and technological issues have received less attention.
Collaboration has been one of the important trends in vehicle routing. A typical mechanism to enable carrier collaboration is to use combinatorial auctions, where requests are not traded individually but are combined into bundles. Previous literature on carrier collaboration has focused on issues such as bundle formation or winner determination, typically assuming truthfulness of all agents and absence of any strategic behavior. This article considers the interdependencies and problems that arise from bidders acting as buyers and sellers of requests at the same time. From standard auction theory, desirable properties of exchange mechanisms are identified as efficiency, incentive compatibility, individual rationality, and budget balance. It is shown that these desirable properties cannot be fulfilled at the same time. In particular, the properties efficiency and incentive compatibility induce that budget balance is violated, that is, an outside subsidy is required. We propose two incentive compatible exchange mechanisms. One is more closely related to the classical VCG approach, while the other one uses a more complicated concept for computing payments to participants. A numerical study investigates how frequently desired properties are violated. We show that both mechanisms can be acceptable in practical situations, but none of them can satisfy all desired properties.
In horizontal collaborations, carriers form coalitions in order to perform parts of their logistics operations jointly. By exchanging transportation requests among each other, they can operate more efficiently and in a more sustainable way. Collaborative vehicle routing has been extensively discussed in the literature. We identify three major streams of research: (i) centralized collaborative planning, (ii) decentralized planning without auctions, and (ii) auction-based decentralized planning. For each of them we give a structured overview on the state of knowledge and discuss future research directions.
An equilibrium network design model is formulated to determine the optimal configuration of a bicycle sharing system in Washington, D.C. Such systems involve a fleet of bicycles positioned at various stations across the network. A user is permitted to check out a bicycle at any station and return it to a station close to her destination. Operators of such systems need to determine the optimal configuration that maximizes utilization. Users employ shared-bicycles for their trips only if their travel utilities improve through their use. An equilibrium approach results in a design that supports the flows it attracts. The model takes the form of a bi-level, mixed-integer program that has a non-convex feasible region. A metaheuristic solution scheme is developed to overcome the intractability of exact solution methods for large networks. System usage patterns and travel time improvements through the use of bicycle sharing are estimated.
In this study, we propose improving the performance of one-way vehicle sharing systems by incorporating parking reservation policies. In particular, we study a parking space reservation policy in which, upon rental, the users are required to state their destination and the system then reserves a parking space for them until they arrive at their destinations. We measure the performance of the vehicle sharing system by the total excess time users spend in the system. The excess time is defined as the difference between the actual journey time and the shortest possible travel time from the desired origin to the desired destination. A Markovian model of the system is formulated. Using this model, we prove that under realistic demand rates, this policy improves the performance of the system. This result is confirmed via a simulation study of a large real system, Tel-O-Fun, the bike-sharing system in Tel-Aviv. For all the tested demand scenarios, the parking reservation policy reduces the total excess time users spend in the system, with a relative reduction varying between 14% and 34%. Through the simulation we examine additional service-oriented performance measures and demonstrate that they all improve under the parking reservation policy.
Bike-sharing systems allow people to rent a bicycle at one of many automatic rental stations scattered around the city, use them for a short journey and return them at any station in the city. A crucial factor for the success of a bike-sharing system is its ability to meet the fluctuating demand for bicycles and for vacant lockers at each station. This is achieved by means of a repositioning operation, which consists of removing bicycles from some stations and transferring them to other stations, using a dedicated fleet of trucks. Operating such a fleet in a large bike-sharing system is an intricate problem consisting of decisions regarding the routes that the vehicles should follow and the number of bicycles that should be removed or placed at each station on each visit of the vehicles. In this paper, we present our modeling approach to the problem that generalizes existing routing models in the literature. This is done by introducing a unique convex objective function as well as time-related considerations. We present two mixed integer linear program formulations, discuss the assumptions associated with each, strengthen them by several valid inequalities and dominance rules, and compare their performances through an extensive numerical study. The results indicate that one of the formulations is very effective in obtaining high quality solutions to real life instances of the problem consisting of up to 104 stations and two vehicles. Finally, we draw insights on the characteristics of good solutions.
Bike sharing systems have been installed in many cities around the world and are increasing in popularity. A major operational cost driver in these systems is rebalancing the bikes over time such that the appropriate number of bikes and open docks are available to users. We combine two aspects that have previously been handled separately in the literature: determining service level requirements at each bike sharing station, and designing (near-)optimal vehicle routes to rebalance the inventory. Since finding provably optimal solutions is practically intractable, we propose a new cluster-first route-second heuristic, in which a polynomial-size Clustering Problem simultaneously considers the service level feasibility and approximate routing costs. Extensive computational results on real-world data from Hubway (Boston, MA) and Capital Bikeshare (Washington, DC) are provided, which show that our heuristic outperforms a pure mixed-integer programming formulation and a constraint programming approach.
With recent advances in mobile technology, public transit agencies around the world have started actively experimenting with new transportation modes, many of which can be characterized as on-demand public transit. Design and efficient operation of such systems can be particularly challenging, because they often need to carefully balance demand volume with resource availability. We propose a family of models for on-demand public transit that combine a continuous approximation methodology with a Markov process. Our goal is to develop a tractable method to evaluate and predict system performance, specifically focusing on obtaining the probability distribution of performance metrics. This information can then be used in capital planning, such as fleet sizing, contracting, and driver scheduling, among other things. We present the analytical solution for a stylized single-vehicle model of first-mile operation. Then, we describe several extensions to the base model, including two approaches for the multivehicle case. We use computational experiments to illustrate the effects of the inputs on the performance metrics and to compare different modes of transit. Finally, we include a case study, using data collected from a real-world pilot on-demand public transit project in a major U.S. metropolitan area, to showcase how the proposed model can be used to predict system performance and support decision making.
The growth of e-commerce has increased demand for last-mile deliveries, increasing the level of congestion in the existing transportation infrastructure in urban areas. Crowdsourcing deliveries can provide the additional capacity needed to meet the growing demand in a cost-effective way. We introduce a setting where a crowd-shipping platform sells heterogeneous products of different sizes from a central depot. Items sold vary from groceries to electronics. Some items must be delivered within a time window, whereas others need a customer signature. Furthermore, customer presence is not guaranteed, and some deliveries may need to be returned to the depot. Delivery requests are fulfilled by a fleet of professional drivers and a pool of crowd drivers. We present a crowd-shipping platform that standardizes crowd drivers’ capacities and compensates them to return undelivered packages back to the depot. We formulate a two-stage stochastic model, and we propose a branch and price algorithm to solve the problem exactly and a column generation heuristic to solve larger problems quickly. We further develop an analytical method to calculate upper bounds on the supply of vehicles and an innovative cohesive pricing problem to generate columns for the pool of crowd drivers. Computational experiments are carried out on modified Solomon instances with a pool of 100 crowd vehicles. The branch and price algorithm is able to solve instances of up to 100 customers. We show that the value of the stochastic solution can be as high as 18% when compared with the solution obtained from a deterministic simplification of the model. Significant cost reductions of up to 28% are achieved by implementing crowd drivers with low compensations or higher capacities. Finally, we evaluate what happens when crowd drivers are given the autonomy to select routes based on rational and irrational behavior. There is no cost increase when crowd drivers are rational and select routes that have a higher compensation first. However, when crowd drivers are irrational and select routes randomly, the cost can increase up to 4.2% for some instances.
This paper proposes a two-tier last-mile delivery model that optimally selects mobile depot locations in advance of full information about the availability of crowd-shippers and then transfers packages to crowd-shippers for the final shipment to the customers. Uncertainty in crowd-shipper availability is incorporated by modeling the problem as a two-stage stochastic integer program. Enhanced decomposition solution algorithms including branch-and-cut and cut-and-project frameworks are developed. A risk-averse approach is compared against a risk-neutral approach by assessing conditional-value-at-risk. A detailed computational study based on the City of Toronto is conducted. The deterministic version of the model outperforms a capacitated vehicle routing problem on average by 20%. For the stochastic model, decomposition algorithms usually discover near-optimal solutions within two hours for instances up to a size of 30 mobile depot locations, 40 customers, and 120 crowd-shippers. The cut-and-project approach outperforms the branch-and-cut approach by up to 85% in the risk-averse setting in certain instances. The stochastic model provides solutions that are 3.35%–6.08% better than the deterministic model, and the improvements are magnified with increased uncertainty in crowd-shipper availability. A risk-averse approach leads the operator to send more mobile depots or postpone customer deliveries to reduce the risk of high penalties for nondelivery.
We study the problem of collaborative less-than-truckload (LTL) transportation in the form of a shipper consortium, which is operated by a third-party logistics provider (3PL) through a cross-dock/pooling network. The 3PL has responsibility for planning the combined loads prior to actual shipments, hiring and routing carriers to execute shipping, and allocating the cost to the shippers in the consortium. Shippers receive substantial cost savings from combined truckload shipments. However, achieving consolidation and realizing this benefit requires addressing two essential issues: (i) how to find an approximately optimal consolidation solution in a large network with many freights and (ii) how to determine a fair cost allocation rule among the shippers’ consolidated freights that ensures budget balance while minimally violating coalitional stability. Our work resolves these two issues. We formulate a time-space network flow model of the problem under both incremental and all-unit discount structures of LTL rates and propose a computationally efficient algorithm based on local search heuristics. We model the problem of allocating cost to the shippers as a cooperative game. We decompose and linearize the Lagrangian dual problem by using total unimodularity and concavity. We propose an efficiently computable cost allocation rule from the linearized dual models. The dual rule ensures stable cooperation but may have underallocation equal to a duality gap. To cover the underallocation, we further develop a budget covering procedure and define an [Formula: see text]-core allocation with desirable properties. Through extensive computational experiments, we find that the shipper consortium reduces total shipping costs by more than 40% in most cases; meanwhile, the [Formula: see text]-core allocation is typically in the core for small-scale networks while violating stability by at most 5% for large-scale networks and provides consolidated freights with more than 50% individual cost savings on average.
Despite their promise, popularity, and rapid growth, the transit implications of ride-hailing platforms (e.g., Uber, Lyft) are not altogether clear. On the one hand, ride-hailing services can provide pooling (i.e., traffic reductions) advantages by efficiently matching customer demand (i.e., trips) with resources (i.e., cars) or by facilitating car-sharing. On the other hand, ride-hailing may also induce extra travel because of increased convenience and travel mode substitution, which may create crowding (i.e., traffic increases). We seek to reconcile these divergent perspectives here, exploring the heterogeneous determinants of ride-hailing’s effects. Taking advantage of Uber’s staggered entry into various geographic markets in California, we execute a regression-based difference-in-differences analysis to estimate the impact of ride-hailing services on traffic volumes. Using monthly micro data from more than 9,000 vehicle detector station units deployed across California, we show that Uber’s effect (either pooling or crowding) on traffic depends on various contextual factors. We find some evidence of pooling effects on weekdays; however, Uber’s entry leads to significant crowding effects on weekends. Furthermore, the crowding effect is amplified on interior roads and in areas characterized by high population density. Although ride-hailing seems to have a substitution effect on public transportation, we find ride-hailing services may have a complementary effect for carpooling users. Finally, we show that premium ride-hailing services (e.g., Uber Black) almost exclusively lead to a crowding effect. We conduct a battery of robustness tests (e.g., propensity score matching, alternative treatment approaches, placebo tests) to ensure the consistency of our findings.
With the soaring popularity of ride-hailing, the interdependence between transit ridership, ride-hailing ridership, and urban congestion motivates the following question: can public transit and ride-hailing coexist and thrive in a way that enhances the urban transportation ecosystem as a whole? To answer this question, we develop a mathematical and computational framework that optimizes transit schedules while explicitly accounting for their impacts on road congestion and passengers’ mode choice between transit and ride-hailing. The problem is formulated as a mixed integer nonlinear program and solved using a bilevel decomposition algorithm. Based on computational case study experiments in New York City, our optimized transit schedules consistently lead to 0.4%–3% system-wide cost reduction. This amounts to rush-hour savings of millions of dollars per day while simultaneously reducing the costs to passengers and transportation service providers. These benefits are driven by a better alignment of available transportation options with passengers’ preferences—by redistributing public transit resources to where they provide the strongest societal benefits. These results are robust to underlying assumptions about passenger demand, transit level of service, the dynamics of ride-hailing operations, and transit fare structures. Ultimately, by explicitly accounting for ride-hailing competition, passenger preferences, and traffic congestion, transit agencies can develop schedules that lower costs for passengers, operators, and the system as a whole: a rare win–win–win outcome.
Freight consolidation is a logistics practice that improves the cost-effectiveness and efficiency of transportation operations, and also reduces energy consumption and carbon footprint. A “fair” shipping cost-sharing scheme is indispensable to help establish and sustain the cooperation of a group of suppliers in freight consolidation. In this paper, we design a truthful acyclic mechanism to solve the cost-sharing problem in a freight consolidation system with one consolidation center and one common destination. Applying the acyclic mechanism, the consolidation center decides which suppliers’ demands ship via the consolidation center and their corresponding cost shares based on their willingness to pay for the service. The proposed acyclic mechanism is designed based on bin packing solutions that are also strong Nash equilibria for a related noncooperative game. We study the budget-balance of the mechanism both theoretically and numerically. We prove a 2-budget-balance guarantee for the mechanism in general and better budget-balance guarantees under specific problem settings. Empirical tests on budget-balance show that our mechanism performs much better than the guaranteed budget-balance ratio. We also study the economic efficiency of our mechanism numerically to investigate its impact on social welfare under different conditions.
We consider the problem of an operator controlling a fleet of electric vehicles for use in a ride-hailing service. The operator, seeking to maximize profit, must assign vehicles to requests as they arise as well as recharge and reposition vehicles in anticipation of future requests. To solve this problem, we employ deep reinforcement learning, developing policies whose decision making uses [Formula: see text]-value approximations learned by deep neural networks. We compare these policies against a reoptimization-based policy and against dual bounds on the value of an optimal policy, including the value of an optimal policy with perfect information, which we establish using a Benders-based decomposition. We assess performance on instances derived from real data for the island of Manhattan in New York City. We find that, across instances of varying size, our best policy trained with deep reinforcement learning outperforms the reoptimization approach. We also provide evidence that this policy may be effectively scaled and deployed on larger instances without retraining.
We study the regulation of one-way station-based vehicle sharing systems through parking reservation policies. We measure the performance of these systems in terms of the total excess travel time of all users caused as a result of vehicle or parking space shortages. We devise mathematical programming based bounds on the total excess travel time of vehicle sharing systems under any passive regulation (i.e., policies that do not involve active vehicle relocation) and, in particular, under any parking space reservation policy. These bounds are compared to the performance of several partial parking reservation policies, a parking space overbooking policy and to the complete parking reservation (CPR) and no-reservation (NR) policies introduced in a previous paper. A detailed user behavior model for each policy is presented, and a discrete event simulation is used to evaluate the performance of the system under various settings. The analysis of two case studies of real-world systems shows the following: (1) a significant improvement of what can theoretically be achieved is obtained via the CPR policy; (2) the performances of the proposed partial reservation policies monotonically improve as more reservations are required; and (3) parking space overbooking is not likely to be beneficial. In conclusion, our results reinforce the effectiveness of the CPR policy and suggest that parking space reservations should be used in practice, even if only a small share of users are required to place reservations.