State of data platforms for connected vehicles and infrastructures

Lim, Kai Li; Whitehead, Jake; Jia, Dongyao; Zheng, Zuduo

doi:10.1016/j.commtr.2021.100013

Cited by 43 publications

(11 citation statements)

References 42 publications

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“…These devices provide advanced notification of the incidents ahead, travel time and re-recommending routes to the drivers and are likely to supplement the VMS information. Similarly, this study also did not consider the impact of the connected vehicles, where drivers are warned about the crash and other hazards ahead via basic safety messaging (Lim et al , 2021), on the diversion rate, as the number of connected vehicles in the US roads is increasing (Acharya and Mekker, 2021). This limitation could be a future research avenue.…”

Section: Discussionmentioning

confidence: 99%

The verbiage in variable message signs and traffic diversion during crash incidents

Acharya

Mekker

2022

JICV

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Purpose WIth limited research on the effects of variable message sign (VMS) message content and verbiage on revealed driver behavior, this study aims to investigate how different verbiage of crash-related messages are related to the diversion rate. Design/methodology/approach Using ordered logit models, the associations of message verbiage with diversion rates during crash incidents were assessed using five years of VMS message history within a section of I-15 in the state of Utah. Findings A significant impact of message verbiage on the diversion rate was observed. Based on the analysis results, the crash message verbiage with the highest diversion was found to be miles to crash + “prepare to stop,” followed by crash location + delay information, miles to crash + “use caution” + lane of the crash, etc. In addition, the diversion rate was found to be correlated to some roadway characteristics (e.g. occupancy in mainline, weather condition and light condition) along with the temporal variations. Research limitations/implications These findings could be used by transportation agencies (e.g. state department of transportation [DOTs]) to make informed decisions about choosing the message verbiage during future crash incidents. This study also revealed that higher diversion rates are associated with a shorter distance between the crash location and VMS device location, recommending increasing the number of VMS devices, particularly in crash-prone areas.

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Section: Discussionmentioning

confidence: 99%

The verbiage in variable message signs and traffic diversion during crash incidents

Acharya

Mekker

2022

JICV

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“…Lim et al. (2021) reviewed the recent progress in connected mobility and established a development need for a combination of connected data sources and data applications. However, only few researchers have attempted to integrate TSE and signal optimization.…”

Section: Introductionmentioning

confidence: 99%

Traffic signal optimization for partially observable traffic system and low penetration rate of connected vehicles

Zhang

Guo

et al. 2022

Computer aided Civil Eng

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Observability and controllability are two critical requirements for a partially observable transportation system. This paper proposes a stepwise signal optimization framework with connected vehicle (CV) data as input to solve both challenges. First, a Bayesian deduction method based on low‐penetration CV data is established to estimate the traffic volume. Thereafter, an offline signal optimization model is constructed to simultaneously optimize the flexible lane settings and signal timings, which are set as the prior information for the third step. In the third step, an online deep recurrent Q‐learning (DRQN) signal optimization model dynamically adjusts signal settings based on real‐time traffic information. Numerical experiments demonstrate that the model outperforms the actuated control, the online DQRN model without offline filter, and the back‐pressure model by 9%–66% and 7%–29% in two networks. This study innovatively combines traffic state estimation and traffic signal control as an integrated process. It contributes to an improved understanding of traffic control in a CV environment and lays a solid foundation for future traffic control strategies.

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“…There have been increasing research efforts to improve the simulation-aided approaches for autonomous driving studies (Rosique et al , 2019; Lim et al , 2021). Virtual reality (VR) and augmented reality-based simulations, in particular, attracted attention and applications.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the inconsistency in driving patterns between manual and autonomous modes under complex driving scenarios with a VR-enabled simulation platform

Fang

Zheng

et al. 2022

JICV

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Purpose With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios. Design/methodology/approach The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment. Findings Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow. Research limitations/implications Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react. Practical implications This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving. Social implications This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology. Originality/value A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

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State of data platforms for connected vehicles and infrastructures

Cited by 43 publications

References 42 publications

The verbiage in variable message signs and traffic diversion during crash incidents

The verbiage in variable message signs and traffic diversion during crash incidents

Traffic signal optimization for partially observable traffic system and low penetration rate of connected vehicles

Analyzing the inconsistency in driving patterns between manual and autonomous modes under complex driving scenarios with a VR-enabled simulation platform

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