Unlimited-ride bike-share pass pricing revenue management for casual riders using only public data

Yoon, Gyugeun; Chow, Joseph Y.J.

doi:10.1016/j.ijtst.2020.01.003

Cited by 6 publications

(7 citation statements)

References 9 publications

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“…However, implementing a poorly designed dynamic pricing or rebalancing model can be detrimental, as it can increase costs without improving performance. Bike Docked OD Linear Regression [257] Simulation RL [252] Essentially, the application of incentives or dynamic pricing is applicable to all forms of sharing, as long as controlling the customer's behavior is a desired outcome. For instance, rebalancing operations for docked or dockless systems can be reduced by rewarding customers for returning vehicles to high-traffic areas.…”

Section: ) Pricingmentioning

confidence: 99%

“…For instance, rebalancing operations for docked or dockless systems can be reduced by rewarding customers for returning vehicles to high-traffic areas. However, there is limited research on this topic, with most studies focusing on ride-sharing and using data from origin-destination [253,254,255,257]. Dynamic pricing is often examined in conjunction with related issues such as matching and repositioning using Deep-RL methods [252,253,254,255].…”

Section: ) Pricingmentioning

confidence: 99%

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A Systematic Literature Review on Machine Learning in Shared Mobility

Teusch,

Gremmel,

Koetsier

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels.

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Section: ) Pricingmentioning

confidence: 99%

Section: ) Pricingmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning in Shared Mobility

Teusch,

Gremmel,

Koetsier

et al. 2023

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

show abstract

“…Most available data structures provide readings on an hourly, or occasionally per-minute, basis. The BSS literature is rich in studies that emphasize big data ( [21], [34], [37], [41], [44], [59], [60], [62], [63], [68]). Zhao et al [68] examined weather variations to develop a comprehensive model for inferring the relationship between weather variability and cycling.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Bike Usage and Optimizing Operations at Repair Shops in Bike Sharing Systems

Alzaman

Aljuneidi

2023

IEEE Access

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Supply chain responsiveness and big data analytics (BDA) have garnered considerable interest in academia and among practitioners. BDA helps researchers understand the current challenges in data management, including the high volume, velocity, and variety of data. This study is concerned with improving the responsiveness of supply chain networks to bike-sharing systems (BSS), which exhibit BDA characteristics. To address the challenges of forecasting bike usage and accordingly optimizing repair shop operations, we analyze multi-factor BSS data (Data from Washington D.C. BSS available to public), wherein attributes, such as weather conditions, registration, humidity, date, and time, are present. We use machine learning algorithms, such as neural networks, decision-tree-based regression, K-nearest neighbor, support vectors, and ensemble random forest, to predict bike usage and repair. This work contests the results and demonstrates the effectiveness of combining machine learning with supply chain network design. Supply chain networks model bike repairs by means of capacity extensions, which entails a nonlinear problem. In this study, we utilize a gradient search to solve a nonlinear supply chain network model. By enabling capacity extension, bike repair shops within the BSS exhibit a promising 50 % reduction in lead repair time. Furthermore, a 25 % overall throughput increase in BSS is achieved. Ultimately, this study demonstrates the importance of operational flexibility in responding to big data challenges.INDEX TERMS Big data, bike sharing, flexibility, machine leaning, supply chain network design.

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“…Profits have a close relationship with costs and revenues. Yoon et al investigate the impacts of different pricing plans in several cities on membership demand and ridership, and present a new pricing plan based on the estimated cost per trip and price sensitivity of customers to improve revenue [13]. Estrada et al focus on how to determine operational cost based on defined performance, expected functionality of a system and cost driver analysis [14].…”

Section: System Analysismentioning

confidence: 99%

Modeling and Optimization in Resource Sharing Systems: Application to Bike-Sharing with Unequal Demands

Liu

Chan

2021

Algorithms

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The imbalanced distribution of shared bikes in the dockless bike-sharing system (a typical example of the resource-sharing system), which may lead to potential customer churn and lost profit, gradually becomes a vital problem for bike-sharing firms and their users. To resolve the problem, we first formulate the bike-sharing system as a Markovian queueing network with higher-demand nodes and lower-demand nodes, which can provide steady-state probabilities of having a certain number of bikes at one node. A model reduction method is then designed to reduce the complexity of the proposed model. Subsequently, we adopt an operator-based relocation strategy to optimize the reduced network. The objective of the optimization model is to maximize the total profit and act as a decision-making tool for operators to determine the optimal relocation frequency. The results reveal that it is possible for most of the shared bikes to gather at one low-demand node eventually in the long run under the influence of the various arrival rates at different nodes. However, the decrease of the number of bikes at the high-demand nodes is more sensitive to the unequal demands, especially when the size of the network and the number of bikes in the system are large. It may cause a significant loss for operators, to which they should pay attention. Meanwhile, different estimated values of parameters related with revenue and cost affect the optimization results differently.

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Unlimited-ride bike-share pass pricing revenue management for casual riders using only public data

Cited by 6 publications

References 9 publications

A Systematic Literature Review on Machine Learning in Shared Mobility

A Systematic Literature Review on Machine Learning in Shared Mobility

Predicting Bike Usage and Optimizing Operations at Repair Shops in Bike Sharing Systems

Modeling and Optimization in Resource Sharing Systems: Application to Bike-Sharing with Unequal Demands

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