Category Archives: Previous research

Hyperpath Truck Routing

My work in this area was resulted from my collaborations  with an online freight exchange platform in China between 2017 and 2019.  When I began to work with the firm in 2017, through Xiaobo Liu at SWJTU,  it was called Truck Gang (货车帮).  Soon after that it was merged with Yunmanman (运满满),  and the merged company was named Manbang (满帮).   When Manbang eventually went public in 2021, it was valued at nearly $24B.    The results reported in this paper were produced using data provided by Truck Gang, and the paper was published in Transportation Science a couple of years ago, co-authored by my former student John Miller and Xiaobo.


Abstract:  Online freight exchange (OFEX) platforms serve the purpose of matching demand and supply for freight in real time. This paper studies a truck routing problem that aims to leverage the power of an OFEX platform. The OFEX routing problem is formulated as a Markov decision problem, which we solve by finding the bidding strategy at each possible location and time along the route that maximizes the expected profit. At the core of the OFEX routing problem is a combined pricing and bidding model that simultaneously (1) considers the probability of winning a load at a given bid price and current market competition, (2) anticipates the future profit corresponding to the current decision, and (3) prioritizes the bidding order among possible load options. Results from numerical experiments constructed using real-world data from a Chinese OFEX platform indicate that the proposed routing model could (1) improve a truck’s expected profit substantially, compared with the benchmark solutions built to represent the state of the practice, and (2) enhance the robustness of the overall profitability against the impact of market competition and spatial variations.

Paired-Line Hybrid Transit

Paired-Line Hybrid Transit was the first in a series of “hybrid-transit” studies conducted by my group using a stylized design model.  This line of work, funded by an National Science Foundation between   2013 and 2016, was initiated by Peng Chen in his PhD thesis.  The main idea is to pair a demand-adaptive service with a fixed-route service so that the transit system can leverage the advantages of both while avoiding their drawbacks.  The paper was published in Transportation Research Part B in 2017.

For preprint, check  Hybrid Transit System Design_Journal_2.0


Abstract: This paper proposes and analyzes a new transit system that integrates the traditional fixed-route service with a demand-adaptive service. The demand-adaptive service connects passengers from their origin/destination to the fixed-route service in order to improve accessability. The proposed hybrid design is unique in that it operates the demand-adaptive service with a stable headway to cover all stops along a paired fixed-route line. Pairing demand-adaptive vehicles with a fixed-route line simplifies the complexity of on-demand routing, because the vehicles can follow a more predictable path and can be dispatched on intervals coordinated with the fixed-route line. The design of the two services are closely
coupled to minimize the total system cost, which incudes both the transit agency’s operating cost and the user cost. The optimal design model is formulated as a mixed integer program and solved using
a commercially available metaheuristic. Numerical experiments are conducted to compare the demand adaptive paired-line hybrid transit (DAPL-HT) system with two related transit systems that may be considered its special cases: a fixed-route system and a flexible-route system. We show that the DAPL-HT system outperforms the other two systems under a wide range of demand levels and in various scenarios of input parameters. A discrete-event simulation model is also developed and applied to confirm the correctness of the analytical results.

Planning EV charging infrastructure

This paper was our first on sustainability-related topics.  As mentioned in the Acknowledgment, it was  inspired by  Professor David Boyce’s 2012 trip from Chicago, IL to Madison, WI.  At the time, he just bought a Nissan Leaf (one of the first successful battery electric vehicle models, with a whooping range  of 70 miles!), and was eager to prove it can be used for long-distance travel.     Due to the limited availability of charging stations back then, however, he was forced to spend one night at a hotel that was less than 20 miles from Madison, turning a four-hour trip to an overnight one.

David’s adventure got me into the EV infrastructure planning, which eventually led to this paper, and a PhD thesis completed by Mehrnaz Ghamami.  The core idea  of this paper is the consideration of the tradeoff between battery cost and charging stations in EV infrastructure planning. That is, from a system point of view, how should social  resources be allocated between manufacturing larger batteries and building more charging facilities?  Check the abstract below for our main findings, and you can also download  Preprint  here.

The paper was published in Transportation Research Part B in 2013.


Abstract: The transition to electric vehicles (EV) faces two major barriers. On one hand, EV batteries are still expensive and limited by range, owing to the lack of technology breakthrough. On the other hand, the underdeveloped supporting infrastructure, particularly the lack of fast refueling facilities, makes EVs unsuitable for medium and long distance travel. The primary purpose of this study is to better understand these hurdles and to develop strategies to overcome them. To this end, a conceptual optimization model is proposed to analyze travel by EVs along a long corridor. The objective of the model is to select the battery size and charging
capacity (in terms of both the charging power at each station and the number of stations needed along the corridor) to meet a given level of service in such a way that the total social cost is minimized. Two extensions of the base model are also considered. The first relaxes the assumption that the charging power at the stations is a continuous variable. The second variant considers battery swapping as an alternative to charging. Our analysis suggests that (1) the current paradigm of charging facility development that focuses on level 2 charging delivers extremely poor level of service; (2) the level 3 charging method is necessary not only to achieve a reasonable level of service, but also to minimize the social cost, (3) investing
on battery technology to reduce battery cost is likely to have larger impacts on reducing the charging cost; and (4) battery swapping promises high level of service, but it may not be socially optimal for a modest level of service, especially when the costs of constructing swapping and charging stations are close.

How can the taxi industry survive the tide of ridesourcing?

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.