Prediction of Cancellation Probability of Online Car-Hailing Orders Based on Multi-source Heterogeneous Data Fusion

Sun, Haokai; Lv, Zhiqiang; Li, Jianbo; Xu, Zhihao; Sheng, Zhaoyu; Ma, Zhaobin

doi:10.1007/978-3-031-19214-2_14

Cited by 5 publications

(1 citation statement)

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“…With accelerated urbanization and the continuous increase in population, traffic congestion has gradually become the norm in urban life [1]. In recent years, the demand for urban travel has increased, and the travel modes are diverse [2]. Urban traffic is a global challenge, especially in the context of rapid urbanization and population growth.…”

Section: Introductionmentioning

confidence: 99%

A survey of sustainable development of intelligent transportation system based on urban travel demand

Yan,

2024

Sustain. Social. Dev.

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<p>This paper provides a comprehensive exploration of urban travel demand forecasting and its implications for intelligent transportation systems, emphasizing the crucial role of intelligent transportation systems in promoting sustainable urban development. With the increasing challenges posed by traffic congestion, environmental pollution, and diverse travel needs, accurate prediction of urban travel demand becomes essential for optimizing transportation systems, fostering sustainable travel methods, and creating opportunities for business development. However, achieving this goal involves overcoming challenges such as data collection and processing, privacy protection, and information security. To address these challenges, the paper proposes a set of strategic measures, including advancing intelligent transportation technology, integrating intelligent transportation systems with urban planning, enforcing policy guidance and market supervision, promoting sustainable travel methods, and adopting intelligent transportation technology and green energy solutions. Additionally, the study highlights the role of intelligent transportation systems in mitigating traffic congestion and environmental impact through intelligent road condition monitoring, prediction, and traffic optimization. Looking ahead, the paper foresees an increasingly pivotal role for intelligent transportation systems in the future, leveraging advancements in deep learning and information technology to more accurately collect and analyze urban travel-related data for better predictive modeling. By combining data analysis, public transportation promotion, shared travel modes, intelligent transportation technology, and green energy adoption, cities can build more efficient, environmentally friendly transportation systems, enhancing residents’ travel experiences while reducing congestion and pollution to promote sustainable urban development. Furthermore, the study anticipates that intelligent transportation systems will be intricately integrated with urban public services and management, facilitating efficient and coordinated urban functions. Ultimately, the paper envisions intelligent transportation systems playing a vital role in supporting urban traffic management and enhancing the overall well-being of urban construction and residents’ lives. In conclusion, this research not only enhances our understanding of urban travel demand forecasting and the evolving landscape of intelligent transportation systems but also provides valuable insights for future research and practical applications in related fields. The study encourages greater attention and investment from scholars and practitioners in the research and practice of intelligent transportation systems to collectively advance the progress of urban transportation and sustainable development.<strong></strong></p>

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Section: Introductionmentioning

confidence: 99%