Taxi market equilibrium with third-party hailing service

Qian, Xinwu; Ukkusuri, Satish V.

doi:10.1016/j.trb.2017.01.012

Cited by 78 publications

(37 citation statements)

References 17 publications

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“…In other words, an idle driver's searching process ends only when this driver sees a passenger and the passenger accepts the ride (see Figure (1a)). E-hailing applications (such as Didi and Uber), on the other hand, offer an online platform to match a driver with a passenger even when they are not present in the same space at the same time (He and Shen, 2015;Qian and Ukkusuri, 2017). In other words, even when an idle driver sees a passenger waiting on the roadside, as long as the e-hailing platform does not match them, the driver cannot give a ride to the passenger.…”

Section: Motivationmentioning

confidence: 99%

Optimal passenger-seeking policies on E-hailing platforms using Markov decision process and imitation learning

Shou

Ye³

et al. 2020

Transportation Research Part C: Emerging Technologies

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Vacant taxi drivers' passenger seeking process in a road network generates additional vehicle miles traveled, adding congestion and pollution into the road network and the environment. This paper aims to employ a Markov Decision Process (MDP) to model idle e-hailing drivers' optimal sequential decisions in passenger-seeking. Transportation network companies (TNC) or e-hailing (e.g., Didi, Uber) drivers exhibit different behaviors from traditional taxi drivers because e-hailing drivers do not need to actually search for passengers. Instead, they reposition themselves so that the matching platform can match a passenger. Accordingly, we incorporate e-hailing drivers' new features into our MDP model. The reward function used in the MDP model is uncovered by leveraging an inverse reinforcement learning technique. We then use 44,160 Didi drivers' 3-day trajectories to train the model. To validate the effectiveness of the model, a Monte Carlo simulation is conducted to simulate the performance of drivers under the guidance of the optimal policy, which is then compared with the performance of drivers following one baseline heuristic, namely, the local hotspot strategy. The results show that our model is able to achieve a 17.5% improvement over the local hotspot strategy in terms of the rate of return. The proposed MDP model captures the supply-demand ratio considering the fact that the number of drivers in this study is sufficiently large and thus the number of unmatched orders is assumed to be negligible. To better incorporate the competition among multiple drivers into the model, we have also devised and calibrated a dynamic adjustment strategy of the order matching probability.

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Section: Motivationmentioning

confidence: 99%

Optimal passenger-seeking policies on E-hailing platforms using Markov decision process and imitation learning

Shou

Ye³

et al. 2020

Transportation Research Part C: Emerging Technologies

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“…Zha et al [16] proposed equilibrium models under different behavioral assumptions about the labor supply in a ridesourcing market. Qian and Ukkusuri [17] modelled the taxi market as a multiple-leader-follower game at the network A ft e r A u g . 1 0 t h 0 0 0 0 level and investigated the equilibrium of the taxi market with competition between traditional taxi services and appbased third-party taxi services.…”

Section: E-hailing Services Of Taxicabsmentioning

confidence: 99%

Understanding the Effect of an E-Hailing App Subsidy War on Taxicab Operation Zones

Fang

Lian³

2018

Journal of Advanced Transportation

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Understanding taxicab operation behaviors under various management or market policies (i.e., subsidies) is critical to making informed operating decisions for e-hailing companies and for government surveillance. This paper investigates the change of taxicab operation zones in context of an e-hailing app subsidy war in China, which is an important perspective that reflects changes in taxicab behavior, such as how the operation zones of taxicabs under the e-hailing app subsidy war change and how this change affects their trip distance and cruising time. To investigate this issue, this paper utilizes three indexes to elucidate the change of taxicab operation zones, namely, the repetition ratio of operation zone pairs, the area, and the degree of dispersion in the spatial distribution. A case study using taxicab trajectories during all of the important periods of the e-hailing app subsidy war in Shenzhen, China, was conducted and produced several valuable findings; for example, with respect to taxicabs as a whole, the proportion of habitual operation zone pairs among operation zone pairs in neighboring periods is relatively stable under any subsidy policy, and changes in the operation zones have little effect on changes in the average daily trip distance and average daily cruising time. Four groups of taxicabs divided according to initial change patterns in the operation zones present different change patterns during the subsidy war. By comparing these changes before and after the subsidy war, this paper finds that the subsidy war influences the taxicabs in groups I and II, while it has little influence on the taxicabs in groups III and IV, although all groups were affected during the subsidy war. For the taxicab groups in the period with the highest subsidy, the average daily trip distance and average daily cruising time decreased, whereas, in other periods, they presented different patterns.

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“…He and Shen [21] proposed a spatial equilibrium model to balance the supply and demand of taxi services, and to capture cabdrivers' and passengers' possible adoption of e-hailing apps. Qian and Ukkusuri [22] analyzed the characteristics of traditional taxi services and app-based taxi services, modeled the taxi market as a multiple-leader-follower game at the network level, and investigated the equilibrium of the taxi market with competition between traditional taxi services and app-based third-party taxi services. Zha et al [23] proposed equilibrium models under different behavioral assumptions about the labor supply in a ride-sourcing market and investigated the performance of surge pricing.…”

Section: E-hailing Services Of Taxicabsmentioning

confidence: 99%

Uncovering Spatial Inequality in Taxi Services in the Context of a Subsidy War among E-Hailing Apps

Fang

Hong

et al. 2018

IJGI

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Spatial inequalities in urban public transportation are a major concern in many countries but little of this research has focused specifically on taxi services. The taxi situation has grown more complex, as traditional ride-for-hire services face growing competition from e-hailing apps like Uber in the U.S., or Didi and Kuaidi in China. In 2014, Didi and Kuaidi triggered a nationwide subsidy war, with possible effects on the spatial inequality of taxi services. Taxi trajectory data from Shenzhen collected during the subsidy war shows that this competition reduced spatial inequality in the inner city but aggravated it in the outer city. In this study, a measure of service rate to depict the quantity of taxi services is proposed to calculate a Gini coefficient for evaluating change in the spatial inequality of taxi services. The Theil index and its decomposition were used to distinguish the contribution of Traffic Analysis Zones (TAZs) in the inner and the outer city and compare them to the overall spatial inequality of taxi services in Shenzhen, TAZs in the outer city had greater inequality in taxi services than the inner city. Furthermore, the primary contributor to overall inequality in taxi services was inequality within, rather than between, the inner and outer city. Moreover, the mean values for the changed service rates in the inner city were always larger than the outer city, and the inner city had a more equitable changed service rate than the outer city. These results could serve as a foundation for improving taxi services citywide.

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Taxi market equilibrium with third-party hailing service

Cited by 78 publications

References 17 publications

Optimal passenger-seeking policies on E-hailing platforms using Markov decision process and imitation learning

Optimal passenger-seeking policies on E-hailing platforms using Markov decision process and imitation learning

Understanding the Effect of an E-Hailing App Subsidy War on Taxicab Operation Zones

Uncovering Spatial Inequality in Taxi Services in the Context of a Subsidy War among E-Hailing Apps

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