On the stochastic fundamental diagram for freeway traffic: Model development, analytical properties, validation, and extensive applications

Qu, Xiaobo; Zhang, Jin; Wang, Shuaian

doi:10.1016/j.trb.2017.07.003

Cited by 144 publications

(71 citation statements)

References 52 publications

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“…The selected routes are spatially (intercity and intra-city) and hierarchically (expressway, arterial, and freeway) representative. Data collection and aggregation play essential roles in the credibility of traffic model development (Qu et al, 2017). Although the data collection system is very advanced, some noises still exist due to a variety of reasons such as device temporary malfunction and/or earth magnetic field jamming.…”

Section: Data Descriptionmentioning

confidence: 99%

Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

Wei

Easa

et al. 2018

Computer aided Civil Eng

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In this research, by taking advantage of dynamic fuel consumption–speed data from Internet of Vehicles, we develop two novel computational approaches to more accurately estimate truck fuel consumption. The first approach is on the basis of a novel index, named energy consumption index, which is to explicitly reflect the dynamic relationship between truck fuel consumption and truck drivers’ driving behaviors obtained from Internet of Vehicles. The second approach is based on a Generalized Regression Neural Network model to implicitly establish the same relationship. We further compare the two proposed models with three well‐recognized existing models: vehicle specific power (VSP) model, Virginia Tech microscopic (VT‐Micro) model, and Comprehensive Modal Emission Model (CMEM). According to our validations at both microscopic and macroscopic levels, the two proposed models have stronger performed in predicting fuel consumption in new routes. The models can be used to design more energy‐efficient driving behaviors in the soon‐to‐come era of connected and automated vehicles.

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Section: Data Descriptionmentioning

confidence: 99%

Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

Wei

Easa

et al. 2018

Computer aided Civil Eng

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“…The mixture of AVs and RVs may reveal some complicated traffic flow characteristics unlike that with only RVs [11,12]. So far, studies on such mixed traffic flow are still quite limited [13,14]. Appropriate models and modeling approaches are needed, at all (microscopic, mesoscopic, and macroscopic) levels, which could allow for a proper reflection of evolving dynamics and capabilities of mixed traffic flow.…”

Section: Introductionmentioning

confidence: 99%

Characteristic Analysis of Mixed Traffic Flow of Regular and Autonomous Vehicles Using Cellular Automata

Liu

Guo

Taplin

et al. 2017

Journal of Advanced Transportation

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The technology of autonomous vehicles is expected to revolutionize the operation of road transport systems. The penetration rate of autonomous vehicles will be low at the early stage of their deployment. It is a challenge to explore the effects of autonomous vehicles and their penetration on heterogeneous traffic flow dynamics. This paper aims to investigate this issue. An improved cellular automaton was employed as the modeling platform for our study. In particular, two sets of rules for lane changing were designed to address mild and aggressive lane changing behavior. With extensive simulation studies, we obtained some promising results. First, the introduction of autonomous vehicles to road traffic could considerably improve traffic flow, particularly the road capacity and free-flow speed. And the level of improvement increases with the penetration rate. Second, the lane-changing frequency between neighboring lanes evolves with traffic density along a fundamental-diagram-like curve. Third, the impacts of autonomous vehicles on the collective traffic flow characteristics are mainly related to their smart maneuvers in lane changing and car following, and it seems that the car-following impact is more pronounced.

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“…Note that the proposed test platform has a very high flexibility in simulating real world traffic operations, including those with purely traditional vehicles. For example, it can be used for conducting traffic capacity analysis [28][29][30], Long Distance Commuter lane , traffic oscillations [31,32], traffic safety analysis [33][34][35], and others. At the end of this paper, we use a simple case study of high-speed U-turn to build the tracking control function.…”

Section: Resultsmentioning

confidence: 99%

PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking

Wang

Zhang³

et al. 2017

Journal of Advanced Transportation

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With the advent of autonomous vehicles, in particular its adaptability to harsh conditions, the research and development of autonomous vehicles attract significant attention by not only academia but also practitioners. Due to the high risk, high cost, and difficulty to test autonomous vehicles under harsh conditions, the hardware-in-the-loop (HIL) scaled platform has been proposed as it is a safe, inexpensive, and effective test method. This platform system consists of scaled autonomous vehicle, scaled roadway, monitoring center, transmission device, positioning device, and computers. This paper uses a case of the development process of tracking control for high-speed U-turn to build the tracking control function. Further, a simplified vehicle dynamics model and a trajectory tracking algorithm have been considered to build the simulation test. The experiment results demonstrate the effectiveness of the HIL scaled platform.

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On the stochastic fundamental diagram for freeway traffic: Model development, analytical properties, validation, and extensive applications

Cited by 144 publications

References 52 publications

Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

Characteristic Analysis of Mixed Traffic Flow of Regular and Autonomous Vehicles Using Cellular Automata

PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking

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