In free float: Developing Business Analytics support for carsharing providers

Wagner, Sebastian; Brandt, Tobias; Neumann, Dirk

doi:10.1016/j.omega.2015.02.011

Cited by 42 publications

(43 citation statements)

References 39 publications

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“…The quantiles are 0.79 and 6.33, respectively, which means that the grids with average daily order numbers that are less than 0.79 and larger than 6.33 are chosen as the samples. [3,19]. Parking Lots has a positive impact on the probability of existence of demand, which implies that more space provide more opportunities to build car-sharing stations.…”

Section: Resultsmentioning

confidence: 99%

Optimizing Location of Car-Sharing Stations Based on Potential Travel Demand and Present Operation Characteristics: The Case of Chengdu

Cheng

Ding

et al. 2019

Journal of Advanced Transportation

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Car-sharing is becoming an increasingly popular travel mode in China and many companies invest plenty of money on that including vehicle enterprises and Internet companies. But most of them build car-sharing stations by their experience or randomly as long as there is parking space in the early development of their business. This results in many stations with low operational efficiency and causes capital loss. This study aims to use different data source with statistical models and machine learning algorithm to help car-sharing operator to choose the optimal location of new stations and adjust the location of existing stations. We select Chengdu where there are huge amounts of car-sharing travel demand and several large car-sharing operators as the research area and two main operators as the research objects. Chengdu is divided into 58724 squared grids each of which is 0.5km * 0.5km instead of focusing on the buffers generated by stations. We try to find a model to estimate a potential travel demand value for each small grid with three data sources: order data, population data, and Point of Interest (POI) data. This problem is transformed into a binary form and five different methods, Logistic Regression, Logistic Regression with LASSO, Naive Bayes, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, are implemented. The optimal model, Logistic Regression with LASSO, is chosen to estimate the probability of existence of demand in all grids. With car-sharing order data from different operators, an existing order heat value is also computed for each grid. Then we analyze and classify all the grids into four groups. For different groups of grids, we give different suggestions on the optimal location of stations. This study focuses on a more competitive market and finds the influential factors on order number. Suggestions on the optimal location of stations are given in consideration of competitors. We hope that our research can help operators improve their business and make rational plans.

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Section: Resultsmentioning

confidence: 99%

Optimizing Location of Car-Sharing Stations Based on Potential Travel Demand and Present Operation Characteristics: The Case of Chengdu

Cheng

Ding

et al. 2019

Journal of Advanced Transportation

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“…The current references mainly focus on RTC [11][12][13][14][15][16] and FFC [17][18][19][20][21][22], but the study on OWC is insufficient. In basic analysis unit, the user is always selected for the RTC model [11][12][13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In basic analysis unit, the user is always selected for the RTC model [11][12][13]. The spatial scales of a single station, the station cluster to land parcel (including grid, census tracts, transportation analysis zone, and district), are in increasing order [14][15][16][17][18][19][20][21][22][23][24]. The smaller the spatial scale of the analysis unit is, the greater the model can reflect the demand characteristics of the local space.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The vehicle usage rate can be used as dependent variable [14]. Without station in FFC, one method to obtain usage frequency is by calculating the bookings in area unit, which makes booking density the most common variable in FFC [17,[20][21][22] including usage duration. However, no study has been found on station usage imbalance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Given that dependent variables meet with other exponential family distribution, the generalized linear model is used [12]. Moreover, given that an independent variable has large zero value, the zero-inflated model [13,20] is used. The Dynamic Ordered Probit Model is used because decisions of multiple frequencies of usage are cumulatively related to each other [13].…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Locating Station of One-Way Carsharing Based on Spatial Demand Characteristics

Chen

Cheng

et al. 2018

Journal of Advanced Transportation

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This study aims to explore factors that affect carsharing demand characteristics in different time periods based on EVCARD transaction data, which is the largest station-based one-way carsharing program in Shanghai, China. Monthly usage intensity and degree of usage imbalance are used as proxies of demand. This study uses three groups of independent variables: carsharing station attributes, built environment (density, diversity, design, and destination accessibility), and transportation facilities. The adaptive elastic net regression is developed to identify factors that influence carsharing usage intensity and degree of usage imbalance after factor selection using extra-randomized-tree algorithm. Finally, a station layout is proposed according to both usage intensity and degree of imbalance. The main results of this study are presented as follows: (1) different effects of built environment and transportation factors cause dynamic demand across different time periods; (2) factors with positive and negative effect on carsharing demand are divided clearly for guidance of the carsharing station layout; (3) public parking space leads to more personal vehicle trip compared to a carsharing trip; and (4) as public transportation, the relationship of the metro and carsharing is complementary. However, the bus stop and carsharing have a competitive relationship. This study provides a carsharing layout method based on both usage intensity and degree of imbalance. Furthermore, several policies concerning carsharing are proposed.

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Reinforcement learning for the optimization of electric vehicle virtual power plants

Al‐Gabalawy

2021

Int Trans Electr Energ Syst

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Summary Integrating weather‐dependent renewable energy sources into the electricity system impose challenges on the power grid. Balancing services are needed, which can be provided by virtual power plants (VPP) that aggregate distributed energy resources (DER) to consume or produce electricity on demand. Electric vehicle (EV) fleets can use idle cars' batteries as combined storage to offer balancing services on smart electricity markets. However, there are risks associated with this business model extension. The fleet faces severe imbalance penalties if it cannot charge the offered amount of balancing energy due to the vehicles' unpredicted mobility demand. Ensuring the fleet can fulfill all market commitments risks denying profitable customer rentals. We study the design of a decision support system that estimates these risks, dynamically adjusts the composition of a VPP portfolio, and profitably places bids on multiple electricity markets simultaneously. Here we show that a reinforcement learning agent can optimize the VPP portfolio by learning from favorable market conditions and fleet demand uncertainties. In comparison to previous research, in which the bidding risks were unknown and fleets could only offer conservative amounts of balancing power to a single market, our proposed approach increases the amount of offered balancing power by 48% to 82% and achieves a charging cost reduction of the fleet by 25%. In experiments with real‐world carsharing data of 500 EVs, we found that mobility demand forecasting algorithms' accuracy is crucial for a successful bidding strategy. Moreover, we show that recent advancements in deep reinforcement learning decrease the convergence time and improve the results' robustness. Our results demonstrate how modern RL algorithms can be successfully used for fleet management, VPP optimization, and demand response in the smart grid. We anticipate that DER, such as EVs, will play an essential role in providing reliable backup power for the grid and formulate market design recommendations to allow easier access to these resources.

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In free float: Developing Business Analytics support for carsharing providers

Cited by 42 publications

References 39 publications

Optimizing Location of Car-Sharing Stations Based on Potential Travel Demand and Present Operation Characteristics: The Case of Chengdu

Optimizing Location of Car-Sharing Stations Based on Potential Travel Demand and Present Operation Characteristics: The Case of Chengdu

Locating Station of One-Way Carsharing Based on Spatial Demand Characteristics

Reinforcement learning for the optimization of electric vehicle virtual power plants

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