Tag Archives: travel demand management

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.

Recent research

Pricing carpool rides

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The initial idea of the paper was proposed by Ruijie Li, then a visiting student from Southwest Jiaotong University. He read about the mechanism design issues in ride-sharing, and was convinced that more research is needed in this direction.  In this paper we focus on a feature that many ridesharing users care about: the schedule displacement (i.e., the difference between the desired and actual arrival time) in matching.   By assuming the users bid for shared rides by reporting their valuation of the displacement, we are able to analyze the matching and pricing problem using the auction theory, including the well-known VCG scheme.    The paper was published in Transportation Science in 2020.    A preprint may be downloaded here.

Abstract: This paper considers a carpool matching (CaMa) problem in which participants price shared rides based on both operating cost and schedule displacement (i.e, the absolute difference between the desired and actual arrival times). By reporting their valuation of this displacement, each participant in effect bids for every possible shared ride that generates a unique value to her. The CaMa problem can be formulated as a mixed integer program (MIP) that maximizes the social welfare by choosing matching pairs and a departure time for each pair. We show the optimal departure time can be determined for each pair a prior, independent of the matching problem. This result reduces the CaMa problem to a standard bipartite matching problem. We prove that the classical Vickrey-Clarke-Groves (VCG) pricing policy ensures no participant is worse off or has the incentive to misreport their valuation of schedule displacement. To control the large deficit created by the VCG policy, we develop a single-side reward (SSR) pricing policy, which only compensates participants who are forced by the system to endure a schedule displacement. Under the assumption of overpricing tendency (i.e., no participant would want to underreport their value), we show the SSR policy not only generates substantial profits, but also retains the other desired properties of the VCG policy, notably truthful reporting. Even though it cannot rule out underreporting, our simulation experiments confirm that the SSR policy is a robust and deficit-free alternative to the VCG policy. Specifically, we find that (1) underreporting is not a practical concern for a carpool platform as it never reduces the number of matched pairs and its impact on profits is largely negligible; and (2) participants have very little to gain by underreporting their value.

Recent research

Travel demand management practice in China

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This article stemmed from a research report commissioned by World Bank back in 2019.   I’ve never tried to publish it in a journal, though my friend Daizong Liu had helped translate it into Chinese and published it online through his very successful Wechat public platform (一览众山小).  The article reviews the travel demand management practice in China and attempts to draw some useful lessons from it.  You may read the abstract below and download the Egnlish version at ChinaTDM.

Lessons Learned from China’s Travel demand management practice

China’s car ownership has been expanding at a staggering pace in the past two decades. The rapid motorization brought unprecedented level of traffic to its densely populated cities
unprepared to accommodate it, causing severe congestion and air pollution problems. Chinese cities have responded to these challenges with sweeping travel demand management (TDM) measures. The practice of TDM in China is unique not only because it is large in scale and broad in scope, but also because it occurs against the backdrop of a fast and historical transition of the most populous country on earth. The objective of this note is to review and document this practice, discuss its outcomes and lessons, and examine what the rest of the world could learn from it.

Recent research

A-PASS for Travel Demand Management

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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.