Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information

Yang, Yuan; Yan, Jun; Guo, Jing; Kuang, Yujin; Yin, Mingyang; Wang, Shiniu; Ma, Chunrui

doi:10.3390/s21030687

Cited by 18 publications

(7 citation statements)

References 47 publications

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“…For field tests in Nanjing, China, a real-time DGNSS tracking feature for buses was developed, along with the lane-level guidance system centered on a vehicle-mounted terminal. The bus data processing primarily completes management and analysis based on the GPS/DBS data, which elucidate bus status, vehicle trajectories, individual driving behaviors, and road conditions [104].…”

Section: ) On-board Equipment and Diagnostics (Obe And Obd)mentioning

confidence: 99%

“…The use of GPS for lane-level positioning, with higher precision and lower complexity, has been studied in various studies. Meanwhile, the precise positioning of a BeiDou navigation satellite system (BDS) intuitively describes the driving trajectory, speed, and acceleration values of a bus operated by its corresponding driver-for example, Wei et al implemented a decentralized automobile remote positioning based on multiple accessible navigation satellite systems and mobile networks [104].…”

Section: ) Benefits Related To Improving Driver Behavior Decrease Crashes and Ameliorate Traffic Flowmentioning

confidence: 99%

“…Besides that, studies should consider defining bus driver behavior trends and the evaluation of intelligent bus systems, such as traffic light control, bus route planning, and constructive congestion management using assessment methods. The majority of bus vehicle tracking methods concentrate on algorithms and technology, but the application domain is rarely described [104].…”

Section: ) Recommendations Of Traffic Safetymentioning

confidence: 99%

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A Landscape of Research on Bus Driver Behavior: Taxonomy, Open Challenges, Motivations, Recommendations, Limitations, and Pathways Solution in Future

et al. 2021

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Driver behavior is a concerning issue in the area of intelligent transportation system (ITS). Driver behavior is a significant key player in a wide range of unpleasant events during the ride, such as accidents or crashes, traffic congestion, harsh braking, and acceleration/deceleration. Influencing factors of driver behavior have been explored in several studies. It is imperative to investigate these factors in order to provide a comprehensive analysis and to categorize them on the basis of a coherent taxonomy. With that, this study conducted a systematic review on prior studies that focused on bus driver behavior, particularly in the ITS. This study also established a taxonomy on the topic of driver behavior in multiple areas of ITS and their classifications. Different databases, namely ScienceDirect, Web of Science, and IEEE Explore, were utilized to obtain relevant articles from 2008 to 2021 (15 April). Several filtering and scanning stages were performed according to the exclusion/inclusion criteria on all 2,803 articles obtained; however, only 87 articles met the criteria. The final set of articles were categorized into a taxonomy. The first part of the taxonomy focuses on five main factors that influence driver behavior: environmental, demographic, habit, vehicle, and on-road routine factors. The second part of the taxonomy discusses the mapping of data collection methods on the basis of four categories: real-time data collection, survey, simulation, and benchmark. Discussion and analysis were provided to highlight the critical literature gaps on bus driver behavior in the ITS, involving the use of real-time data collection, which is imperative for acquiring highly accurate and sophisticated data. This multi-field systematic review has exposed new research opportunities, motivations, challenges, limitations, and recommendations and highlighted the need for the synergistic integration of interdisciplinary works. Overall, this study presented pathways solution in future direction on the basis of three sequenced phases, namely design, labeling and validation, and machine learning. This study can serve as a guide for future researchers, as it addressed the ambiguities in the ITS-driver behavior domain and provided valuable information on these ITS-driver behavior trends.

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Section: ) On-board Equipment and Diagnostics (Obe And Obd)mentioning

confidence: 99%

Section: ) Benefits Related To Improving Driver Behavior Decrease Crashes and Ameliorate Traffic Flowmentioning

confidence: 99%

Section: ) Recommendations Of Traffic Safetymentioning

confidence: 99%

See 1 more Smart Citation

A Landscape of Research on Bus Driver Behavior: Taxonomy, Open Challenges, Motivations, Recommendations, Limitations, and Pathways Solution in Future

et al. 2021

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“…It can transfer the real-time traffic information to the database by the Global Positioning System (GPS) and modern communication technology, which have gradually become the mainstream approach to probe the intersection operation performance. The GPS is one of the Global Navigation Satellite System (GNSS), which can accurately locate the vehicle trajectory in real time [3]. The GPS system can track vehicle trajectory and collect traffic data, which usually contains temporal information (i.e., timestamps), spatial information (i.e., longitude and latitude) and speed information [4].…”

Section: Introductionmentioning

confidence: 99%

Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting

et al. 2021

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Urban road intersection bottleneck has become an important factor in causing traffic delay and restricting traffic efficiency. It is essential to explore the prediction of the operating performance at intersections in real-time and formulate corresponding strategies to alleviate intersection delay. However, because of the sophisticated intersection traffic condition, it is difficult to capture the intersection traffic Spatio-temporal features by the traditional data and prediction methods. The development of big data technology and the deep learning model provides us a good chance to address this challenge. Therefore, this paper proposes a multi-task fusion deep learning (MFDL) model based on massive floating car data to effectively predict the passing time and speed at intersections over different estimation time granularity. Moreover, the grid model and the fuzzy C-means (FCM) clustering method are developed to identify the intersection area and derive a set of key Spatio-temporal traffic parameters from floating car data. In order to validate the effectiveness of the proposed model, the floating car data from ten intersections of Beijing with a sampling rate of 3s are adopted for the training and test process. The experiment result shows that the MFDL model enables us to capture the Spatio-temporal and topology feature of the traffic state efficiently. Compared with the traditional prediction method, the proposed model has the best prediction performance. The interplay between these two targeted prediction variables can significantly improve prediction accuracy and efficiency. Thereby, this method predicts the intersection operation performance in real-time and can provide valuable insights for traffic managers to improve the intersection’s operation efficiency.

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“…One mode of transportation, which is low cost and affordable, are buses. They have become an essential part of the urban passenger transportation system (Yang et al, 2021). Road safety is important in public transport systems, as millions of passengers, especially in developing countries, use the conventional transportation system to commute (Z.…”

Section: Introductionmentioning

confidence: 99%

A field study of mental workload: conventional bus drivers versus bus rapid transit drivers

et al. 2021

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Road traffic accidents are increasing worldwide and cause a high number of fatalities and injuries. Mental Work Load (MWL) is a contributing factor in road safety. The primary aim of this work was to study important MWL factors and then compare conventional and BRT (Bus Rapid Transit) drivers' MWL. This study evaluated bus drivers' MWL using the Driving Activity Load Index (DALI) questionnaire conducted with 123 bus drivers in Tehran. The results revealed significant differences between conventional and BRT drivers' mental workload. Moreover, data modelling showed that some organizational and environmental factors such as bus type, working hours per day, road maze, and route traffic volume contribute to drivers' mental workload. These findings suggest some essential customized factors that may help measure and offer practical solutions for decreasing the level of bus drivers MWL in real-world road driving.

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Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information

Cited by 18 publications

References 47 publications

A Landscape of Research on Bus Driver Behavior: Taxonomy, Open Challenges, Motivations, Recommendations, Limitations, and Pathways Solution in Future

A Landscape of Research on Bus Driver Behavior: Taxonomy, Open Challenges, Motivations, Recommendations, Limitations, and Pathways Solution in Future

Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting

A field study of mental workload: conventional bus drivers versus bus rapid transit drivers

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