The Impact of Service Refusal to the Supply–Demand Equilibrium in the Taxicab Market

Wei, Dali; Cao, Yuan; Liu, Hongchao; Wu, Dayong; Kumfer, Wesley

doi:10.1007/s11067-016-9324-z

Cited by 8 publications

(9 citation statements)

References 27 publications

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“…As a special component in the taxi fare structure, congestion premium accounts for the extra operational cost for a taxicab in congested traffic. In recent years, traffic congestion is getting worse and worse in many cities, especially in some megacities (e.g., Beijing, a city with 21 million people and 5 million vehicles) [1,2]. The mileage per unit time that a taxicab travels declines rapidly and congestion premium has therefore become an increasingly important source of income for taxi drivers in congested cities.…”

Section: Introductionmentioning

confidence: 99%

“…In some extreme cases, for example, during peak hours, taxi drivers prefer being out of service than working inside the city, which in turn exacerbates the imbalance between the supply and demand in the taxicab market. A recent survey in Beijing found that as many as nearly 10,000 taxi vehicles were at rest during the afternoon peak hours, evading service in congested hours [2][3][4]. Thus, it is very necessary to analyze the adjustment mechanism of congestion premium in congested cities.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Analyzing Taxi Congestion Premium in Congested Cities

Cao

Wei

et al. 2017

Journal of Advanced Transportation

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Traffic congestion is a significant problem in many major cities. Getting stuck in traffic, the mileage per unit time that a taxicab travels will decline significantly. Congestion premium (or so-called low-speed fare) has become an increasingly important income source for taxi drivers. However, the impact of congestion premium on the taxicab market is not widely understood yet. In particular, modeling and analyzing of the taxi fare structure with congestion premium are extremely limited. In this paper, we developed a taxi price equilibrium model, in which the adjustment mechanism of congestion premium on optimizing the taxi driver's income, balancing the supply and demand, and eventually improving the level of service in the whole taxicab market was investigated. In the final part, we provided a case study to demonstrate the feasibility of the proposed model. The results indicated that the current taxi fare scheme in Beijing is suboptimal, since the gain from the raise of congestion premium cannot compensate for the loss from the demand reduction. Conversely, the optimal fare scheme suggested by our model can effectively reduce the excessive demand and reach the supply-demand equilibrium, while keeping the stability of the driver's income to the maximum extent.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Analyzing Taxi Congestion Premium in Congested Cities

Cao

Wei

et al. 2017

Journal of Advanced Transportation

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“…e system will match the requests and vehicles in each region at regular intervals. Wei et al [17] studied the impact of service refusal on the balance of supply and demand in the taxi market.…”

Section: Related Workmentioning

confidence: 99%

“…However, the corresponding operation strategy did not develop with the increase of the number of taxis, there are still many shortcomings, such as the difficulty in finding taxis in peak hours, uneven distribution of taxis, and the drivers' refusal of service [17]. Taxi drivers' strategies of seeking passengers are mostly empirical and substantially vary among each other [18,19], which leads to low service efficiency and low income.…”

Section: Introductionmentioning

confidence: 99%

Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand

Yang

Wang

et al. 2020

Journal of Advanced Transportation

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With the improvement of people’s living standards, people’s demand of traveling by taxi is increasing, but the taxi service system is not perfect yet; taxi drivers usually rely on their operational experience or cruise randomly to find passengers. Without macroguidance, the role of the taxi system cannot be fully utilized. Many scholars have studied taxi behaviors to find better operational strategies for drivers, but their researches rely on local optimization methods to improve the profit of drivers, which will lead to imbalance between supply and demand in the city. To solve this problem, we propose a Multiagent Reinforcement Learning- (MARL-) based taxi predispatching model through analyzing the running data of 13,000 taxis. Different from other methods of scheduling taxis based on the real-time location of orders, our model first predicts the demand for taxis in different regions in the next period and then dispatches taxis in advance to meet the future requirement; thus, the number of taxis needed and available in different regions can be balanced. Besides, in order to reduce computational complexity, we propose several methods to reduce the state space and action space of reinforcement learning. Finally, we compare our method with another taxi dispatching method, and the results show that the proposed method has a significant improvement in vehicle utilization rate and passenger demand satisfaction rate.

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“…Especially in the rush hour periods before and after work hours, as well as on congested roads, slow speeds cause a further decrease in supply. Drivers refusing to take on passengers or taking breaks will affect the relationship between supply and demand in the market, as well as their income [ 6 – 8 ]. A city’s transportation network is a massive and complex network containing complex, nonlinear relationships.…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding the unbalance of interest in taxi market based on drivers' service profit margins

Kong

Sun

et al. 2018

PLoS ONE

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Nowadays, ride-hailing services have been established as a part of the urban transportation. Their arrival has remade the profit structure and resulted in the unbalance of interest in taxi market. Here, we establish the service models of taxis, carpooling, and car-hailing under “Internet +” from the perspective of profit margins, to perform a comparative analysis among the different services. Results: First, Profit margins are generally higher for short trips than for long trips, though empty cruise fee to a certain degree make up for the driver’s decreased profit margins. Second, the profit margin for carpooling is roughly 1.85 times that of ride-hailing, and 1.75 times that of taxis. This shows that the sharing economy has a certain advantage. Third, Profit margins are higher and fluctuations are lower on non-work days than on work days. At last, Profit margins are roughly 1.3 times higher on non-congested roads than on congested roads. The reduced profitability on congested roads makes it even harder to catch a ride during rush hours and on congested roads. We suggest that the relevant departments make appropriate efforts to make it more attractive for drivers to take on passengers during rush hours and on congested roads, and promote the sharing in the taxi market.

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The Impact of Service Refusal to the Supply–Demand Equilibrium in the Taxicab Market

Cited by 8 publications

References 27 publications

Modeling and Analyzing Taxi Congestion Premium in Congested Cities

Modeling and Analyzing Taxi Congestion Premium in Congested Cities

Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand

Understanding the unbalance of interest in taxi market based on drivers' service profit margins

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