MARIO: A spatio-temporal data mining framework on Google Cloud to explore mobility dynamics from taxi trajectories

Ghosh, Shreya; Ghosh, Soumya K.; Buyya, Rajkumar

doi:10.1016/j.jnca.2020.102692

Cited by 32 publications

(18 citation statements)

References 17 publications

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“…relative = (I 0 − I)/I × 100% (11) where relative is the relative percentage, I 0 is the value of indicator obtained from the proposed RL method, I is the value obtained from the compared method. The proposed method achieves an increase of 4.8% in answer rate, an increase of 6.2% in total revenue and a decrease of 27.27% in waiting times at most (primary RL method is not compared due to its error optimization).…”

Section: Simulated Results and Discussionmentioning

confidence: 99%

“…They studied the dynamic taxi route recommendation problem as a sequential decision-making problem and designed an effective two-step method to tackle it. A taxi monitoring method is presented to detect anomalous driving behaviours, improving the taxi service [11]. A spatiotemporal data mining framework was proposed to explore mobility dynamics from taxi trajectories [12].…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement Learning for Optimizing Driving Policies on Cruising Taxis Services

et al. 2020

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As the key element of urban transportation, taxis services significantly provide convenience and comfort for residents’ travel. However, the reality has not shown much efficiency. Previous researchers mainly aimed to optimize policies by order dispatch on ride-hailing services, which cannot be applied in cruising taxis services. This paper developed the reinforcement learning (RL) framework to optimize driving policies on cruising taxis services. Firstly, we formulated the drivers’ behaviours as the Markov decision process (MDP) progress, considering the influences after taking action in the long run. The RL framework using dynamic programming and data expansion was employed to calculate the state-action value function. Following the value function, drivers can determine the best choice and then quantify the expected future reward at a particular state. By utilizing historic orders data in Chengdu, we analysed the function value’s spatial distribution and demonstrated how the model could optimize the driving policies. Finally, the realistic simulation of the on-demand platform was built. Compared with other benchmark methods, the results verified that the new model performs better in increasing total revenue, answer rate and decreasing waiting time, with the relative percentages of 4.8%, 6.2% and −27.27% at most.

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Section: Simulated Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Optimizing Driving Policies on Cruising Taxis Services

et al. 2020

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“…As an example of such data, the time series depicted in Figure 5 shows how the number of passengers evolves over time. Some recent and relevant works exploiting taxi trajectories for the study of mobility dynamics can be found in [58,59].…”

Section: Taxi Trajectories Datamentioning

confidence: 99%

Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches

Cecaj

Lippi

Mamei

et al. 2021

IoT

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The possibility of sensing and predicting the movements of crowds in modern cities is of fundamental importance for improving urban planning, urban mobility, urban safety, and tourism activities. However, it also introduces several challenges at the level of sensing technologies and data analysis. The objective of this survey is to overview: (i) the many potential application areas of crowd sensing and prediction; (ii) the technologies that can be exploited to sense crowd along with their potentials and limitations; (iii) the data analysis techniques that can be effectively used to forecast crowd distribution. Finally, the article tries to identify open and promising research challenges.

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“…They established a model to describe the spatial-temporal characteristics and drivers' passenger-search behaviour. Ghosh et al [23] used taxi trajectory data to generate a dynamic network for spatial-temporal trip analysis. They proposed a framework named MARIO to analyse travel demand variations in different regions [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis on cruising process for on‐street parking using an spectral clustering method

Qin

Pang

et al. 2020

IET intell. transp. syst

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Parking problems caused by a lack of parking spaces have exacerbated traffic congestion and worsened environmental pollution. An analysis of the cruising process for parking can provide new perspectives to reduce cruising. Based on a parking survey conducted in Beijing, the authors collected a large amount of trajectory data of cruising vehicles. Then, fluctuation indexes of trajectories were proposed to analyse travellers' cruising processes for parking. The spectral clustering method based on a hidden Markov model (HMM) was used to recognise the cruising trajectories. The recognition performance for three-dimensional trajectory data is better. Cruising trajectories for Clusters 1, 2, 3, 4, and 6 have large fluctuations and a weightier effect on road traffic. These groups can be taken as target groups for intelligent parking guidance and recommendations. The recognition accuracies for parking location and parking status increase with increasing intercepted trajectory lengths. 150 m from far to near the desired destination can be used as a threshold of the cruising trajectory length to accurately predict travellers' parking location and status. These research results can be applied in intelligent parking systems to dynamically predict parking situations, formulate parking guidance schemes and information release strategies, and improve parking efficiency.

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MARIO: A spatio-temporal data mining framework on Google Cloud to explore mobility dynamics from taxi trajectories

Cited by 32 publications

References 17 publications

Reinforcement Learning for Optimizing Driving Policies on Cruising Taxis Services

Reinforcement Learning for Optimizing Driving Policies on Cruising Taxis Services

Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches

Analysis on cruising process for on‐street parking using an spectral clustering method

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