Information-traffic coupled cell transmission model for information spreading dynamics over vehicular ad hoc network on road segments

Abstract

Vehicular Ad Hoc Network (VANET) makes real-time traffic information accessible to vehicles en routes, thus possesses a great potential to improve traffic safety and mobility in the near future. Existing literature shows that we are still lack of approaches to track information spreading dynamics via VANET, which will prevent the potential applications from success. Motivated by this view, this research develops an information-traffic coupled cell transmission model (IT-CTM) to capture information spreading dynamics via VANET. More exactly, this study considers information spreading over a road segment forms a wave with a front and tail, each of which goes through the road segment following an intermittent transmission pattern due to traffic flow dynamics. The approach of IT-CTM discretizes a road segment into a number of cells. Each cell covers several intermittent transmissions. Mathematical methods are developed to capture the inner-cell and inter-cell movements of information front and tail, which enable us to track the information spreading dynamics along cells. Numerical experiments based on simulation and field data indicate that the IT-CTM can closely track the dynamic movements of information front and tail as well as the dynamic information coverage as a single or multiple piece(s) of information propagating via VANET on a one-way or two-way road segment. The mean absolute error (MAE) for tracking dynamic information coverage is less than 5% across all experiments in this study.

Research on the Optimal Deployment of Expressway Roadside Units under the Fusion Perception of Intelligent Connected Vehicles

“…Based on the intelligent expressway sensor network in the coexistence environment of V2V and V2I communication, this paper constructs the optimal deployment model of roadside RSU including roadside RSU information coverage income, information transmission deficit in the life cycle, and the construction and maintenance cost [11,18] from the perspective of intelligent expressway sensor network builder; it determines the deployment spacing under different permeability to achieve the purpose of reducing the deployment cost of roadside RSU. Moreover, this paper reconstructs the model based on the traffic flow-information flow coupling theory [19][20][21][22][23] and the WSN node energy loss model [24], combining the permeability characteristics to determine the deployment adjustment of roadside RSU under different influences, that is, the deployment interval under different income states.…”

“…Based on the above objectives, this paper firstly constructs the cluster length model of vehicle clusters under different permeabilities according to the information flow-traffic flow coupling theory [19][20][21][22][23]. Secondly, based on assumption (2), the time expectation model of information transmission in the life cycle is constructed under the consideration of opposite traffic flow.…”

Exploring the Influence of Vehicle Mobility on Information Spreading in VANETs

“…At present, many researchers have studied the instantaneous information transmission in VANETs, and related research topics such as propagation speed, network connectivity, propagation distance and opportunity communication. Lili Du et al [21] proposed an information-traffic-coupled cell transmission model () to capture information based on VANETs transmission dynamics. The IT-CTM method discretizes the road segment into multiple units and can track the dynamic movement of information, thus increasing its coverage.…”

MAGIC Dataset: Multiple Conditions Unmanned Aerial Vehicle Group-Based High-Fidelity Comprehensive Vehicle Trajectory Dataset

“…From the macro perspective, the data can be used to calibrate a traffic flow model ( 6 , 7 ) or identify the crash black spots or convergence hot spots in the traffic flow ( 8 ) by extracting the time-domain or frequency-domain features of the datasets. In addition, some studies have regarded the trajectory of the dataset as the trajectory of the connected vehicles (CVs), and further simulated CVs with different penetration rates, sampling frequencies to validate some safety-oriented or efficiency-oriented emerging driving strategies, or both ( 9 , 10 ).…”