Leveraging Connected Vehicles to Provide Enhanced Roadway Condition Information

Li, Howell; Wolf, Joerg Christian; Mathew, Jijo K.; Navali, Nitin; Zehr, Steven; Hardin, Brandon; Bullock, Darcy M.

doi:10.1061/jtepbs.0000370

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…The measurement of real-time performance is significant for agencies to maintain and sustain their roadways during the rainy season. The maintenance brought by infrastructure can be rather costly and scant in rural areas [34].…”

Section: 5) Road Conditionmentioning

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: 5) Road Conditionmentioning

confidence: 99%

“…Emission rate and the process of fuel consumption are also essential elements that are related to time [12]. Data on braking may also be lost or inaccurate, particularly in severe weather when the driver is forced to brake often.. As a result, it is difficult to assess driver behavior in such conditions [34].…”

Section: ) Concern On Data Collection Managementmentioning

confidence: 99%

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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Leveraging data-driven traffic management in smart cities: Datasets for highway traffic monitoring

Mahmud

Day

2022

The Rise of Smart Cities

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More than smart pavements: connected infrastructure paves the way for enhanced winter safety and mobility on highways

Shi

2020

J Infrastruct Preserv Resil

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Currently, there is an urgent demand for more cost-effective, resource-efficient and reliable solutions to address safety and mobility challenges on highways enduring snowy winter weather. To address this pressing issue, this commentary proposes that the physical and digital infrastructures should be upgraded to take advantage of emerging technologies and facilitate the vehicle-infrastructure integration (VII), to better inform decision-makers at various levels. Driven by the paradigm shift towards more automation and more intelligent transportation, it is time to reimagine the vehicle-infrastructure ecosystem with the cold-climate issues in mind, and to enhance communications and coordination among various highway users and stakeholders. This commentary envisages the deployment of vehicle-to-everything (V2X) technologies to bring about transformative changes and substantial benefits in terms of enhanced winter safety and mobility on highways. At the center of the commentary is a conceptualized design of next-generation highways in cold climates, including the existing infrastructure entities that are appropriate for possible upgrade to connected infrastructure (CI) applications, to leverage the immensely expanded data availability fueled by better spatial and temporal coverage. The commentary also advances the idea that CI solutions can augment the sensing capabilities and confidence level of connected or autonomous vehicles. The application scenarios of VII system is then briefly explored, followed by some discussion of the paradigm shift towards V2X applications and a look to the future including some identified research needs in the arena of CI. This work aims to inspire dialogues and synergistic collaborations among various stakeholders of the VII revolution, because the specific challenges call for systematic, holistic, and multidisciplinary approaches accompanied by concerted efforts in the research, development, pilot testing, and deployment of CI technologies.

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Leveraging Connected Vehicles to Provide Enhanced Roadway Condition Information

Cited by 3 publications

References 5 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

Leveraging data-driven traffic management in smart cities: Datasets for highway traffic monitoring

More than smart pavements: connected infrastructure paves the way for enhanced winter safety and mobility on highways

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