Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Zhenliang Ma"'
Publikováno v:
IET Intelligent Transport Systems, Vol 18, Iss 10, Pp 1895-1909 (2024)
Abstract Individual mobility is driven by activities and thus restricted geographically, especially for trip destination prediction in public transport. Existing statistical learning based models focus on extracting mobility regularity in predicting
Externí odkaz:
https://doaj.org/article/12fbb4eb132940c9beb242af5576d659
A deep learning approach for robust traffic accident information extraction from online chinese news
Publikováno v:
IET Intelligent Transport Systems, Vol 18, Iss 10, Pp 1847-1862 (2024)
Abstract Road traffic accidents are the leading causes of injuries and fatalities. Understanding the traffic accident occurrence pattern and its contributing factors are prerequisites for effective traffic safety management. The paper proposes a deep
Externí odkaz:
https://doaj.org/article/9e7064a44cfe4e57ad4d5cf9cdea5327
Autor:
Daria Ivina, Zhenliang Ma
Publikováno v:
European Transport Research Review, Vol 16, Iss 1, Pp 1-13 (2024)
Abstract Ensuring the reliability of railway transportation is heavily dependent on the quality of its infrastructure. In this regard, renewal and maintenance of the railway track infrastructure, referred to as trackwork, play a vital role. However,
Externí odkaz:
https://doaj.org/article/d78a5f843fa745efa08d89c88cb9ec8e
Publikováno v:
Urban Rail Transit, Vol 9, Iss 3, Pp 266-279 (2023)
Abstract Many urban rail systems operate near capacity given the rapid increase in passenger demand, and unplanned disruptions are unavoidable. From a passenger perspective, the duration of trip delays is a major concern, and passenger trip delays ma
Externí odkaz:
https://doaj.org/article/f3fd2bae491b4f86a3f8e101a1436734
Publikováno v:
Communications in Transportation Research, Vol 3, Iss , Pp 100093- (2023)
Online demand prediction plays an important role in transport network services from operations, controls to management, and information provision. However, the online prediction models are impacted by streaming data quality issues with noise measurem
Externí odkaz:
https://doaj.org/article/7c8f915f845d49c287cd60ac1597f17d
Publikováno v:
IET Intelligent Transport Systems, Vol 17, Iss 4, Pp 744-753 (2023)
Abstract Reinforcement learning (RL)‐based models have been widely studied for traffic signal control with objectives, such as minimizing vehicle delay and queue length, maximizing vehicle throughput, and improving road safety, through tailored rew
Externí odkaz:
https://doaj.org/article/42b3bb2612c74a70a826ef629d8574c9
Autor:
Zhenliang Ma, Pengfei Zhang
Publikováno v:
Multimodal Transportation, Vol 1, Iss 1, Pp 100002- (2022)
The ‘sharing’ business models and on-demand services have been altering city dwellers’ travel habits from buying the means of transport to buying mobility services based on needs. The capability to proactively provide personalized services (e.g
Externí odkaz:
https://doaj.org/article/bf5a88fc239e4474be6efebc3daf323c
Publikováno v:
IEEE Access, Vol 8, Pp 107876-107886 (2020)
Accurate prediction of short-term passenger flow is vital for real-time operations control and management. Identifying passenger demand patterns and selecting appropriate methods are promising to improve prediction accuracy. This paper proposes a hyb
Externí odkaz:
https://doaj.org/article/80dd2c803d16410981cdec33db40b1ab
Autor:
Seyed Mohammad Hossein Moosavi, Zhenliang Ma, Danial Jahed Armaghani, Mahdi Aghaabbasi, Mogana Darshini Ganggayah, Yuen Choon Wah, Dmitrii Vladimirovich Ulrikh
Publikováno v:
Applied Sciences, Vol 12, Iss 18, p 9392 (2022)
Electric vehicles (EVs) have been progressing rapidly in urban transport systems given their potential in reducing emissions and energy consumptions. The Shared Free-Floating Electric Scooter (SFFES) is an emerging EV publicized to address the first-
Externí odkaz:
https://doaj.org/article/b98a258b13b14280860961675beeb816
Publikováno v:
Journal of Advanced Transportation, Vol 2021 (2021)
Transit network simulation models are often used for performance and retrospective analysis of urban rail systems, taking advantage of the availability of extensive automated fare collection (AFC) and automated vehicle location (AVL) data. Important
Externí odkaz:
https://doaj.org/article/b3a7c0bc419b4dbfbee4067de8c1783a