All posts by yni957

Post War

One of the good things that came out of COVID19 pandemic is I suddenly discovered (or rediscovered?) a new hobby: reading.  Post War is among the first books I read after the pandemic starts. This short review was written in December 2020.


Tony Judt’s Post War is a great read for anyone who is curious about Europe since WWII.  You may be disappointed if you expect a completely objective narrative based on data and stories. Don’t get me wrong—Judt is a good storyteller and he tells a wide range of stories, in fact, so broad he even commented on David Beckham, describing him as “an English player of moderate technical gifts but an unsurpassed talent for self-promotion”….He does, however, insistently make you feel his presence, preference, and emotions in these stories. I love his style, but I realize some may prefer historians without strong opinions.

Judt never hides his love for the “European Social Model”, which recognizes the state has the duty to “shield citizens from the hazards of misfortune or the market”, and “social responsibility and economic advantage should not be mutually exclusive”.  At the end of the book, he passionately compares Europe with America and China, writing, “America would have the biggest army and China would make more, and cheaper, goods. But neither America nor China had a serviceable model to propose for universal emulation. In spite of the horrors of their recent past—and in large measure because of them—it was Europeans who were now uniquely placed to offer the world some modest advice on how to avoid repeating their own mistakes. Few would have predicted it sixty years before, but the twenty-first century might yet belong to Europe.”

An Efficiency Paradox of Uberization

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

A-PASS for Travel Demand Management

Auction-Based Permit Allocation and Sharing System (A-Pass) for Travel Demand Management, co-authored by Ruijie Li and Xiaobo Liu (both at Southwest Jiaotong University, China).

This is a follow up to another paper related to the mechanism design problems arising from ridesharing.  In this paper, we try to show the promise of integrating ridesharing with quantity-based travel demand management.  One of the main insights is that by auctioning out permits (e.g., to use a road facility), we can eliminate the deficits that are otherwise unavoidable in classical  Vickrey-Clark-Gloves mechanism.  The paper just came out in Transportation Science. You may read the abstract below and download a preprint here.


We propose a novel quantity-based demand management system aiming to promote ride-sharing. The system sells the permit to access a facility (conceptualized as a bottleneck) by auction but encourages commuters to share the permits with each other. The permit is classified according to access time and the commuters may be assigned one of the three roles: solo driver, ride-sharing driver, or rider. At the core of this auction-based permit allocation and sharing system (A-PASS) is a trilateral matching problem (TMP) that matches permits, drivers and riders. We formulate TMP as an integer program, and prove it can be reduced to an equivalent linear program. A pricing policy based on the classical Vickrey-Clark-Gloves (VCG) mechanism is proposed to determine the payment for each commuter. We prove, under the VCG policy, different commuters will pay exactly the same price as long as their role and access time are the same. We also show A-PASS can eliminate any deficit that may arise from the VCG policy by controlling the number of shared rides. Results of numerical experiment suggest A-PASS strongly promote rider-sharing. As ride-sharing increases, all stake holders are better off: the ride-sharing platform receives greater profits, the commuters enjoy higher utility, and the society benefits from more efficient utilization of infrastructure.

A Physical Model of Street Ride-Hail

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.

My first post

I finally decided that I need a research+blog type place to share my work and writing.   My student told me  Northwestern provides a web-hosting service based on WordPress.  I have a few hours to spare since it is a MLK day. The tool seems quite reasonable and hence I took the plunge.  Let’s see how it goes…