Intelligent Driving Strategy of Expressway Based on Big Data of Road Network and Driving Time

Wang, Shuoqi; Wang, Zhanzhong

doi:10.1109/access.2023.3273304

Cited by 2 publications

(3 citation statements)

References 31 publications

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“…In [87], Jiang and Fei studied the integration of traffic and vehicle driving data for individual vehicle speed prediction along specific driving routes, and proposed a novel two-level non-parametric data-driven model to improve prediction accuracy. Moreover, some studies used Baidu, Gaode, or other online map API to obtain future traffic information [88,89]. Practically, we have conducted studies on how to predict future driving characteristics based on the Gaode map API.…”

Section: Driving Characteristic Predictionmentioning

confidence: 99%

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Wang,

Hao,

et al. 2023

Sensors

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Energy management methods (EMMs) utilizing sensing, communication, and networking technologies appear to be one of the most promising directions for energy saving and environmental protection of fuel cell vehicles (FCVs). In real-world driving situations, EMMs based on driving cycle information are critical for FCVs and have been extensively studied. The collection and processing of driving cycle information is a fundamental and critical work that cannot be separated from sensors, global positioning system (GPS), vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), intelligent transportation system (ITS) and some processing algorithms. However, no reviews have comprehensively summarized the EMMs for FCVs from the perspective of driving cycle information. Motivated by the literature gap, this paper provides a state-of-the-art understanding of EMMs for FCVs from the perspective of driving cycle information, including a detailed description for driving cycle information analysis, and a comprehensive summary of the latest EMMs for FCVs, with a focus on EMMs based on driving pattern recognition (DPR) and driving characteristic prediction (DCP). Based on the above analysis, an in-depth presentation of the highlights and prospects is provided for the realization of high-performance EMMs for FCVs in real-world driving situations. This paper aims at helping the relevant researchers develop suitable and efficient EMMs for FCVs using driving cycle information.

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Section: Driving Characteristic Predictionmentioning

confidence: 99%

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Wang,

Hao,

et al. 2023

Sensors

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“…According to the literature [31], the average speed of freight vehicles on the expressway is between 63 and 67 km/h, generally running in the slow lane. Taking the average speed as 65 km/h, the highest speed is 90 km/h, and the lowest speed is 40 km/h.…”

Section: Case Analysis and Simulationmentioning

confidence: 99%

“…According to the literature [31], the average speed of freight vehicles on the expressway is between 63 and 67 km/h, generally running in the slow lane. Taking the average speed The regulation and control strategy of the other expressways is the same as that of the G12 expressway.…”

Section: Case Analysis and Simulationmentioning

confidence: 99%

Collaborative Development and Transportation Volume Regulation Strategy for an Urban Agglomeration

Wang,

Wang

2023

Sustainability

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Transportation plays an important role in urban development and the formation of urban agglomerations. Indexes including urban centrality, urban development intensity, and degree of urban development imbalance are defined to measure the level of urban development, and are taken as the basis for transportation regulation within urban agglomerations. Regulation of transportation volume is divided into static and dynamic regulation, and this paper studies static regulation. There are two purposes for static regulation. One is to solve the problems of unbalanced urban development and collaborative development, and the other is to solve the problem of rational utilization of the highway transport network in an urban agglomeration so that the total transportation volume of the urban agglomeration does not exceed the total transportation volume that the transport network can bear, realizing coordinated transport, improving transport efficiency, and reducing traffic congestion and traffic accidents. A distributed intelligent regulation model based on the principle of game control is proposed, which is divided into three layers: macro-regulation (government layer), meso-regulation (urban agglomeration layer), and micro-regulation (individual city game layer). The regulation strategies and methods of the urban agglomeration layer and individual city game layer are given, and are verified and illustrated using as the research object called the Jilin Province urban agglomeration in the northeast of China. The paper contributes to the field by presenting innovative research and provides important theories and methods for collaborative development and transportation within urban agglomerations.

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Intelligent Driving Strategy of Expressway Based on Big Data of Road Network and Driving Time

Cited by 2 publications

References 31 publications

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Collaborative Development and Transportation Volume Regulation Strategy for an Urban Agglomeration

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