Tag Archives: ridehail

Recent research

Bucket effect simplifies robust relocation

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Relocating idle assets is a core operational challenge in the transportation industry, particularly for shared mobility services such as ride-hailing, car-sharing, and bike-sharing. The productivity and profitability of these services depend on effectively aligning the spatial and temporal distribution of available assets with fluctuating demand. This often requires routinely repositioning idle assets to anticipated high-demand areas—or “hot spots.” Although relocation is costly and non-revenue-generating in itself, it is essential for mitigating persistent supply–demand imbalances. Efficient relocation strategies are therefore critical to enhancing both user experience and operational performance.

The central challenge of relocation lies in the inherent uncertainty of demand. In ride-hailing and car-sharing markets, for example, passenger arrivals at specific locations vary not only by hour of the day but also from day to day. Such variability is only partially predictable, as travel patterns are often influenced by irregular events such as weather or accidents. To cope with this uncertainty, relocation strategies must be designed to be robust, protecting the service against extreme outcomes. Our study is motivated by the formulation and solution of such a robust relocation problem.

While the basic formulation of the problem is straightforward, solving it efficiently is not. We address this challenge with a novel insight inspired by the well-known “bucket effect.” Specifically, given a fixed relocation routing policy, once a zone is saturated with supply—meaning its demand is fully met at steady state—shifting additional demand elsewhere cannot increase overall profit. This insight, first conceived by Ruijie Li, the lead author and a long-term collaborator who visited my lab, provided the foundation for our approach.

You may read the abstract below or download the full paper.

Abstract: Relocating idle vehicles is a key challenge in autonomous shared mobility systems. This paper develops a robust optimization framework for steady-state relocation under uncertain demand, extending the fluid queuing network model of Braverman et al. [2019] to account for coupled uncertainty-where zone-level demand variations are bounded both locally and by a global budget. Although such uncertainty typically makes robust optimization intractable, we uncover a structural property, which we call the bucket effect: once a zone is saturated with supply (i.e., its demand is fully met) at steady state, shifting demand elsewhere cannot increase overall profit. This insight allows us to reformulate the robust relocation problem as a tractable linear program. Using New York City taxi data, we demonstrate the model’s practical value. The robust relocation strategy delivers higher profit and service availability than no-relocation baselines, while also providing greater stability under demand fluctuations. Importantly, these gains are achieved with sparse relocation patterns, indicating that targeted repositioning-guided by the bucket effect-can yield substantial benefits with modest operational effort.

Recent research

Mitigating TNC-induced traffic congestion

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While the e-hail service offered by TNCs is widely credited for boosting productivity and enhancing level of service, its adverse traffic impact in already-congested city centers has drawn increased scrutiny.   Several cities have started to implement  policies aiming to mitigate the traffic impact induced by excessive TNC operations.   The purpose of this study is to support such policy analysis by developing a model that captures the complex interactions among various stakeholders (riders, drivers and the platform) and those between them and the regulator.   Please read the abstract below for main findings.

The paper was recently published in Transportation Research Part A.   A preprint can be downloaded here.

Abstract: This paper analyzes and evaluates several policies aiming to mitigate the congestion effect a Transportation Network Company (TNC) brings to bear on an idealized city that contains a dense central core surrounded by a larger periphery. The TNC offers both solo and pooling e-hail services to the users of public transport. We develop a spatial market equilibrium model over two building blocks: an aggregate congestion model describing the traffic impact of TNC operations on all travelers in the city, including private motorists, and a matching model estimating the TNC’s level of service based on the interactions between riders and TNC drivers. Based on the equilibrium model, we formulate and propose solution algorithms to the optimal pricing problem, in which the TNC seeks to optimize its profit or social welfare subject to the extra costs and/or constraints imposed by the congestion mitigation policies. Three congestion mitigation policies are implemented in this study: (i) a trip-based policy that charges a congestion fee on each solo trip starting or ending in the city center; (ii) a cordon-based policy that charges TNC vehicles entering the city center with zero or one passenger; and (iii) a cruising cap policy that requires the TNC to maintain the fleet utilization ratio in the city center above a threshold. Based on a case study of Chicago, we find TNC operations may have a significant congestion effect. Failing to anticipate this effect in the pricing problem leads to sub-optimal decisions that worsen traffic congestion and hurt the TNC’s profitability. Of the three policies, the trip-based policy delivers the best performance. It reduces traffic congestion modestly, keeps the TNC’s level of service almost intact, and improves overall social welfare substantially. The cruising cap policy benefits private motorists, thanks to the extra congestion relief it brings about. However, because other stakeholders together suffer a much greater loss, its net impact on social welfare is negative. Paradoxically, the policy could worsen the very traffic conditions in the city center that it is designed to improve.

Previous research

How can the taxi industry survive the tide of ridesourcing?

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This  paper makes two empirical findings and one prediction. First, it reveals the intensity and scope of the impact of ridesourcing on the conventional taxi industry. Second, it uncovers evidence that taxis may be competitive in densely populated areas.  The second finding leads to a follow-up study you can read here.

I predict that the taxi industry is here to stay in the foreseeable future.    Here is what I wrote in the conclusion:

“Beyond e-hailing, economy of scale and aggressive pricing, ridesourcing does not seem to have other means at present to drive its expansion in the market. E-hailing is no longer the secret weapon that once glorifies the cause of TNCs – it can be easily picked up by a taxi dispatcher that owns and operates its own fleet. Aggressive pricing, on the other hand, has proven at best a double-edged sword, as Uber’s recent bitter defeat in China has vividly demonstrated. The scale of TNCs, which gives outside visitors a brand to stick to, is indeed an important competitive advantage. Even this lead is not that difficult to catch up, however, if a mobile platform, presumably operated by a third party, can unify taxi dispatchers around the world. Such a platform can easily work within cities’ existing regulatory structure, rather than against it, because it utilizes a dedicated and existing fleet. It can also improve the experience of street-hailing, a decisive advantage it holds against ridesourcing, by offering customers the amenities considered only available to e-hailing users, such as paying the fare on-line and rating drivers, all in real-time. An obvious solution may be allowing customers, as they board the taxi hailed off street, to open up an electronic transaction session similar to those seen on e-haling platforms, by e.g. scanning a QR code attached to the taxis or the driver’s smart phone.”… therefore, “The revolution of ridesourcing is unlikely to eliminate the necessity of a dedicated service fleet, and for years to come we will continue to live in a world with both ridesourcing and (upgraded) taxis.”.

The Journal of Transportation Research Part C selected this paper to receive the Best Paper Award in 2018. You may download a preprint here.

Abstract:  This paper aims to examine the impact of ridesourcing on the taxi industry and explore where, when and how taxis can compete more effectively. To this end a large taxi GPS trajectory data set collected in Shenzhen, China is mined and more than 2,700 taxis (or about 18% of all registered in the city) are tracked in a period of three years, from January 2013 to November 2015, when both e-hailing and ridesourcing were rapidly spreading in the city. The long sequence of GPS data points is first broken into separate “trips”, each corresponding to a unique passenger state, an origin/destination zone, and a starting/ending time. By examining the trip statistics, we found that: (1) the taxi industry in Shenzhen has experienced a significant loss in its ridership that can be indisputably credited to the competition from ridesourcing. Yet, the evidence is also strong that the shock was relatively short and that the loss of the taxi industry had begun to stabilize since the second half of 2015; (2) taxis are found to compete more effectively with ridesourcing in peak period (6-10 AM, 5-8 PM) and in areas with high population density. (3) e-hailing helps lift the capacity utilization rate of taxis. Yet, the gains are generally modest except for the off-peak period, and excessive competition can lead to severely under-utilized capacities; and (4) ridesourcing worsens congestion for taxis in the city, but the impact was relatively mild. We conclude that a dedicated service fleet with exclusive street-hailing access will continue to co-exist with ridesourcing and that regulations are needed to ensure this market operate properly.

Recent research

To pool or not to pool

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To Pool or Not to Pool: Equilibrium,  Pricing and Regulation

This paper was the first published based on  Kenan’s PhD research. It introduces ride-pooling into the equilibrium analysis of the ride-hail market and analyzes the effect of pricing strategies and various regulations on pooling.

After the first draft is completed in the Spring of 2019,  it took almost two years  to move the paper through various stages of the review process, first at Management Science, then at Transportation Research Part B (three rounds). While the long waiting was no doubt frustrating, the quality of the paper might have benefited from intensive scrutiny and repeated revisions.  For a preprint, please check here; the link to the final version is here.

Abstract: We study a monopoly transportation network company (TNC) in an aggregate market that offers on-demand solo and pooling e-hail services, while competing with transit for passengers. The market equilibrium is established based on a spatial driver-passenger matching model that characterizes the passenger wait time for both solo and pooling rides. We prove, under mild conditions, this system always has an equilibrium solution. Built on the market equilibrium, three variants of pricing problems are analyzed and compared, namely, (i) profit maximization, (ii) profit maximization subject to regulatory constraints, and (iii) social welfare maximization subject to a revenue-neutral constraint. A comprehensive case study is constructed using TNC data collected in the city of Chicago. We found pooling is desirable when demand is high, but supply is scarce. However, its benefit diminishes quickly as the average en-route detour time (i.e., the difference between the average duration of solo and pooling trips) increases. Without regulations, a mixed strategy—providing both solo and pooling rides—not only achieves the highest profit and trip production in most scenarios, but also gains higher social welfare. The minimum wage policy can improve social welfare in the short term. However, in the long run, the TNC could react by limiting the size of the driver pool, and consequently, render the policy counterproductive, even pushing social welfare below the unregulated level. Moreover, by maintaining the supply and demand of ride-hail at an artificially high level, the minimum wage policy tends to exacerbate traffic congestion by depressing the use of collective modes (transit and pooling). A congestion tax policy that penalizes solo rides promotes pooling, but consistently harms social welfare. However, it promises to increase both social welfare and pooling ratio, when jointly implemented with the minimum wage policy.

Recent research

An Efficiency Paradox of Uberization

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The main finding is that e-hail (e.g., Uber and Lyft) may not scale as well as street-hailing taxi.  In other words, a thicker market may help improve the performance of taxi more than that of e-hail. We develop a physical model to describe the matching process of both modes. Using the model, we then derive the production function and measure the returns to scale in the matching process.  It indicates that taxi has returns to scale of  2 (implying increasing returns to scale) whereas e-hail has returns to scale of 1 (constant returns to scale).   Empirical data collected in Shenzhen, China largely confirm the theory.

While this paper has yet to be published, I must have spent more time writing and revising it than any of my other papers.  Hongyu convinced me to write it  for a general journal, which, with the benefit of hindsight, might have caused us to oversell an otherwise fine idea.   That being said, I did learn a lot from the process, and I still think it is one of my better works.   The preprint is here, and the abstract follows.

Uberization promises to transform society based on an intuitive proposition: Advanced peer-to-peer matching guarantees greater overall efficiency. Here we show a paradox challenging this proposition in uberized ride-hail service, known as e-hail. By analyzing hundreds of local markets in Shenzhen, China, we discover e-hail is outperformed—in terms of wait time and trip production—by taxis hailed off street in areas with high densities of passengers and drivers. This paradox arises because a quicker match does not always expedite and enhance a service. On the contrary, it can induce competition that undermines the network effect, making a passenger less likely to benefit from more drivers, and vice versa, in e-hail than in taxi service. Consequently, simply attracting more users may not improve e-hail’s efficiency (defined as trip production at a given density of passengers and drivers), because its competitive edge diminishes with scale. The finding implies uberization has a limited impact on efficiency and is unlikely to create a “winner-take-all” in transportation

Recent research

A Physical Model of Street Ride-Hail

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The work was initiated by my former student, Hongyu Chen, as part of his PhD research.  He wanted to build a “physical model” of street-hail taxi matching that can be calibrated and validated with real data (taxi trajectory).  This is very difficult because passenger wait time cannot be directly observed in taxi trajectory data. Hongyu came up with a rather clever and elaborated method to accomplish just that. Read the abstract below and download the paper here.

In this study, we show that the passenger-driver matching process in street ride-hail is dictated by the physical limitation of a passenger’s average eyesight and the preference of cruising taxi drivers for certain locations. Together, these two spatiotemporal features, called effective hail distance (EHD) and local area attractiveness (LAA) respectively, define the number of vacant taxis that a passenger can reach, and accordingly the distribution of her waiting time. To calibrate the waiting time distribution, we extract maximum possible waiting times from taxis GPS trajectory data, by tracking the movements of vacant taxis cruising around a pickup location. Then we prove that, for a given EHD, the extracted maximum possible waiting time follows the same distribution as passenger waiting time. The proposed matching mechanism, along with the novel calibration method, leads to a general model of street ride-hail that can produce reliable estimates of passenger waiting time under a wide variety of market conditions. Moreover, the matching process in the phone-based ride-hail is shown to be a special case of the proposed model, when EHD approaches infinity. This result lays the foundation for understanding and comparing the performance of ride-hail services. It can also help address regulatory and operational questions facing key stake holders in this industry.