A link-node-based complementarity model for traffic equilibrium with ride-sharing and trunk-sharing

Li, Xingyuan; Cheng, Yan; Guo, Lei; Bai, Jing

doi:10.1080/19427867.2022.2066882

Cited by 2 publications

(6 citation statements)

References 48 publications

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“…The surge in data availability led to the exploration of big data analytics for urban mobility. Li et al (2017) pioneered the integration of traffic data from multiple sources, including GPS devices and social media, to enhance prediction accuracy. The strength lies in the holistic approach to data fusion, yet challenges arise in ensuring data quality and addressing privacy concerns associated with user-generated content.…”

Section: Big Data Analytics In Urban Mobilitymentioning

confidence: 99%

Exploring the Potential of Crowdsourced Traffic Data for Improved Traffic Predictions: A Big Data Approach

Azizi,

Zhang,

Malik

et al. 2024

Preprint

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This paper presents a groundbreaking approach to traffic prediction through the integration of crowdsourced data within a big data analytics framework. The proposed methodology addresses the limitations of traditional traffic prediction models and machine learning approaches by leveraging the collective intelligence from diverse sources, including navigation apps, social media, and connected vehicles. A dynamic algorithm continuously updates the model parameters in real-time, adapting to evolving traffic patterns in urban environments. Comprehensive experiments, conducted on the UrbanTrafficFlow (UTF) and CityMobilityPatterns (CMP) datasets, demonstrate the superior predictive accuracy and adaptability of the proposed model compared to existing methods, including LSTM-based models, spatiotemporal graph convolutional networks (ST-GCN), and traditional statistical models. The integration of diverse data sources showcases the model's ability to enhance accuracy, and the impact of crowdsourced data integration is highlighted. This research contributes to the advancement of intelligent transportation systems and big data analytics, paving the way for more resilient and efficient urban mobility solutions in the era of smart cities.

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Section: Big Data Analytics In Urban Mobilitymentioning

confidence: 99%

Exploring the Potential of Crowdsourced Traffic Data for Improved Traffic Predictions: A Big Data Approach

Azizi,

Zhang,

Malik

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…As analyzed in the Introduction, travelers' destination choice may affect their ridesharing choice, thus the existed ridesharing user equilibrium models ( [17]- [22]) should be reformulated as ridesharing trip-assignment model for calculating the reserve capacity of road network. To facilitate calculation, Di's model [19] is rebuilt as the lower-lever model.…”

Section: Lower-level Model Of Ridesharing Trip-assignment Modelmentioning

confidence: 99%

“…The Model I in [13] is chosen to calculate the reserve capacity of road network without ridesharing, in which the signalcontrolled constraints at the upper level model are removed. To be consistent with the Model I, the proposed model in subsection III-D (hereafter referred to as Model S) will not consider the travelers' destination choice(i.e., Equation (22) needs to be removed in Model S) and the upper-level objective function (19) and the demand conservation (23) should be modified as follows,…”

Section: Subsidizing Ride-sharing Drivers Versus Enhancing Link Capac...mentioning

confidence: 99%

“…To predict the reserve capacity of road network in the context of ridesharing, the lower level model should be replaced by a ridesharing user equilibrium model. Although there have been proposed some ridesharing user equilibrium models, such as Xu et al [17], Di et al [18], [19], Li et al [20], Ma et al [21] and Li et al [22], these models may not be truly suitable for predicting the reserve capacity of road network in the context of ridesharing, since these models assume that the Origin-Destination(OD) matrix is fixed. However, the reserve capacity of road network reflects the number of potential travelers that the road network can accommodate in the future [23], thus it cannot assumes that the OD matrix of these potential travelers is invariant.…”

Section: Introductionmentioning

confidence: 99%

“…However, the reserve capacity of road network reflects the number of potential travelers that the road network can accommodate in the future [23], thus it cannot assumes that the OD matrix of these potential travelers is invariant. Besides, travelers in ridesharing can choose different travel modes, i.e., solo driver, ridesharing driver and ridesharing passenger (or called rider), and the modes selections base on the travel cost of each mode [17]- [22]. Thus, travelers' destination choice will inevitably affect their mode selections.…”

Section: Introductionmentioning

confidence: 99%

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Can Ridesharing Improve the Reserve Capacity of Transportation Network?

Li,

Cheng,

Peng

et al. 2024

IEEE Access

Self Cite

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Ride-sharing is one of the effective method to reduce car ownership, thus it may have a profound impact on the reserve capacity of road network. However, it's unclear the relationships among users' ridesharing behaviors, the reserve capacity of road network and travel demand pattern. To this end, this paper builds a ridesharing trip-assignment model which considers users' ridesharing choice, destination choice and path choice, and further proposes a bi-level programming for reserve capacity of road network with ride-sharing. The bi-level programming is then converted into an equivalent single-layer optimization problem by a conventional relaxation scheme. Finally, numerical experiments are conducted to provide valuable insights and examine the effectiveness of the proposed model. The results show that subsidizing ridesharing drivers can improve almost as much reserve capacity of road network as expanding link capacity without ridesharing. However, retrofitting High-Occupancy Toll (HOT) lane has limited impact on improving reserve capacity of road network.

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A link-node-based complementarity model for traffic equilibrium with ride-sharing and trunk-sharing

Cited by 2 publications

References 48 publications

Exploring the Potential of Crowdsourced Traffic Data for Improved Traffic Predictions: A Big Data Approach

Exploring the Potential of Crowdsourced Traffic Data for Improved Traffic Predictions: A Big Data Approach

Can Ridesharing Improve the Reserve Capacity of Transportation Network?

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