Design scheme of Distributed Dynamic Route Guidance System

Fan, Yuezhen; Lu, Dunmin; Li, Yunfeng; Jiang, Fachao

doi:10.1109/icetc.2010.5529585

Cited by 1 publication

(1 citation statement)

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“…Existing route guidance research mainly focuses on route guidance algorithms [16,17,[27][28][29], guidance strategy [30][31][32], and system design [33] using traditional data detectors. CV technologies allow for some innovative means for data acquisition, and many scholars have attempted to study the dynamic route guidance models in a CV environment [32,34,35].…”

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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