Real-Time Vehicle Route Guidance Based on Connected Vehicles

Yuan, Yong J.; Zhou, Jianshan; Wang, Yunpeng; Lu, Guangquan; Xia, Haiying

doi:10.1109/greencom-ithings-cpscom.2013.268

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…Both the public and private sectors are interested in applications and implications of connected vehicles. Applications include intersection signals (Lee and Park, 2012;Christofa et al, 2013;Guler et al, 2014;Hu et al, 2014;Wu et al, 2014;Bagheri et al, 2015;Feng et al, 2015), pavement assessments (Bridgelall, 2013), traffic queue estimation (Li et al, 2013), vehicle routing and travel time estimation (Kianfar and Edara, 2013;Tian et al, 2013;Genders and Razavi, 2015;Moylan and Skabardonis, 2015), driving behavior monitoring and warnings (Sengupta et al, 2007;Goodall et al, 2014;Chrysler et al, 2015;Doecke et al, 2015;Du and Dao, 2015;Osman et al, 2015), and fuel efficiency (Kishore Kamalanathsharma and Rakha, 2014;Liu et al, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Delivering improved alerts, warnings, and control assistance using basic safety messages transmitted between connected vehicles

Liu

Khattak

2016

Transportation Research Part C: Emerging Technologies

105

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When vehicles share their status information with other vehicles or the infrastructure, driving actions can be planned better, hazards can be identified sooner, and safer responses to hazards are possible. The Safety Pilot Model Deployment (SPMD) is underway in Ann Arbor, Michigan; the purpose is to demonstrate connected technologies in a real-world environment. The core data transmitted through Vehicle-to-Vehicle and Vehicle-to-Infrastructure (or V2V and V2I) applications are called Basic Safety Messages (BSMs), which are transmitted typically at a frequency of 10 Hz. BSMs describe a vehicle's position (latitude, longitude, and elevation) and motion (heading, speed, and acceleration). This study proposes a data analytic methodology to extract critical information from raw BSM data available from SPMD. A total of 968,522 records of basic safety messages, gathered from 155 trips made by 49 vehicles, was analyzed. The information extracted from BSM data captured extreme driving events such as hard accelerations and braking. This information can be provided to drivers, giving them instantaneous feedback about dangers in surrounding roadway environments; it can also provide control assistance. While extracting critical information from BSMs, this study offers a fundamental understanding of instantaneous driving decisions. Longitudinal and lateral accelerations included in BSMs were specifically investigated. Varying distributions of instantaneous longitudinal and lateral accelerations are quantified. Based on the distributions, the study created a framework for generating alerts/warnings, and control assistance from extreme events, transmittable through V2V and V2I applications. Models were estimated to untangle the correlates of extreme events. The implications of the findings and applications to connected vehicles are discussed in this paper.

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Section: Literature Reviewmentioning

confidence: 99%

Delivering improved alerts, warnings, and control assistance using basic safety messages transmitted between connected vehicles

Liu

Khattak

2016

Transportation Research Part C: Emerging Technologies

105

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“…(iii) Vehicle Route Recommendation. It collects information about just-driven road segments and traffic congestion situations to introduce better routes for drivers based on existing path algorithms [6][7][8][9][10] (all of these route planning algorithms take traffic congestion situations into account in the process of a vehicle route guidance) without presetting the destination beforehand.…”

Section: (I) Driving Route Predictions Based On Hmmmentioning

confidence: 99%

“…(3) foreach (route in ) (4) Starting point = ( 1 , 1 ); (5) End point = ( , ); (6) Insert and into the set ; 7= Filter( ); (8) Cluster Set = -means++ ( ); / * = { [1], [2], . .…”

Section: Parameter Definitions Of a Hmm For Route Predictionsmentioning

confidence: 99%

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A Method for Driving Route Predictions Based on Hidden Markov Model

Wang

Malekian

et al. 2015

Mathematical Problems in Engineering

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We present a driving route prediction method that is based on Hidden Markov Model (HMM). This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based onK-means++ and the add-one (Laplace) smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of the route predictions that is based on HMM are shown.

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“…Nowadays, scholars have begun to study route guidance in a connected vehicle (CV) environment. Tian et al [22] presented a real-time route guidance system based on CV technologies, and simulation results showed that better routes are found using the V2V and V2I technologies. Paikari et al [23] realized CV guidance by developing a V2V and V2I application interface (API).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Route Choice Prediction Model Based on Connected Vehicle Guidance Characteristics

Lv¹,

Wang²,

Zhang³

2017

Journal of Advanced Transportation

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A route choice prediction model is proposed considering the connected vehicle guidance characteristics. This model is proposed to prevent the delay in the release of guidance information and route planning due to inaccurate timing predictions of the traditional guidance systems. Based on the analysis of the impact of different connected vehicle (CV) guidance strategies on traffic flow, an indexes system for CV guidance characteristics is presented. Selecting five characteristic indexes, a route choice prediction model is designed using the logistic model. A simulation scenario is established by programming different agents for controlling the flow of vehicles and for information acquisition and transmission. The prediction model is validated using the simulation scenario, and the simulation results indicate that the characteristic indexes have a significant influence on the probability of choosing a particular route. The average root mean square error (RMSE) of the prediction model is 3.19%, which indicates that the calibration model shows a good prediction performance. In the implementation of CV guidance, the penetration rate can be considered an optional index in the adjustment of the guidance effect.

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Real-Time Vehicle Route Guidance Based on Connected Vehicles

Cited by 14 publications

References 8 publications

Delivering improved alerts, warnings, and control assistance using basic safety messages transmitted between connected vehicles

Delivering improved alerts, warnings, and control assistance using basic safety messages transmitted between connected vehicles

A Method for Driving Route Predictions Based on Hidden Markov Model

Dynamic Route Choice Prediction Model Based on Connected Vehicle Guidance Characteristics

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