A Trial-and-Error Method with Autonomous Vehicle-to-Infrastructure Traffic Counts for Cordon-Based Congestion Pricing

We propose a bi-level network design model comprising automated vehicle (AV) links and congestion pricing to improve traffic congestion. As upper-level road planners strive to minimize total travel-time costs by optimizing both the network design and the congestion pricing, lower-level travelers make choices about their routes to minimize their individual travel costs. Our proposed model integrates a network design and congestion pricing to improve traffic congestion and we use a relaxation-based method to solve the model. We conducted a series of numerical tests to analyze the proposed model and solution method. Our results indicate that network design is more effective than congestion pricing when the AV market penetration is high and the opposite is true when AV penetration is low. More importantly, we find that a network design of automated vehicle links with congestion pricing is superior to a single network design or congestion pricing, especially when both AVs and conventional vehicles have a relatively large market penetration.

Section: Network Descriptionmentioning

confidence: 99%

Section: Journal Of Advanced Transportationunclassified

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Optimal Design of Transportation Networks with Automated Vehicle Links and Congestion Pricing

Wang

2018

“…Zong et al applied a multiobjective stability control algorithm to improve the vehicle stability of a tractor semitrailer by using differential braking [35]. Recently, more and more people pay attention to intelligent vehicle based on Cooperative Vehicle Infrastructure System [36][37][38][39][40][41][42][43]. Besides, vehicle control and system model was combined with transportation simulation [44][45][46][47][48][49][50][51][52].…”

Section: And Wu Investigated Lyapunov Concept Exponents and Applicatimentioning

confidence: 99%

Bifurcation of Lane Change and Control on Highway for Tractor-Semitrailer under Rainy Weather

Peng

Guan

Zhang

et al. 2017

A new method is proposed for analyzing the nonlinear dynamics and stability in lane changes on highways for tractor-semitrailer under rainy weather. Unlike most of the literature associated with a simulated linear dynamic model for tractor-semitrailers steady steering on dry road, a verified 5DOF mechanical model with nonlinear tire based on vehicle test was used in the lane change simulation on low adhesion coefficient road. According to Jacobian matrix eigenvalues of the vehicle model, bifurcations of steady steering and sinusoidal steering on highways under rainy weather were investigated using a numerical method. Furthermore, based on feedback linearization theory, taking the tractor yaw rate and joint angle as control objects, a feedback linearization controller combined with AFS and DYC was established. The numerical simulation results reveal that Hopf bifurcations are identified in steady and sinusoidal steering conditions, which translate into an oscillatory behavior leading to instability. And simulations of urgent step and single-lane change in high velocity show that the designed controller has good effects on eliminating bifurcations and improving lateral stability of tractor-semitrailer, during lane changing on highway under rainy weather. It is a valuable reference for safety design of tractor-semitrailers to improve the traffic safety with driver-vehicle-road closed-loop system.

“…Neural network is an important method to detect driving risk [12]. With the fast development and adoption of vehicle-to-infrastructure (V2I) facilities, it is very convenient to detect driving risk, and the accuracy of the detection model is a key factor [13][14][15]. So, a neural network model of high accuracy will be established to identify driving risk in this paper.…”

Section: Introductionmentioning

confidence: 99%

Driving Risk Detection Model of Deceleration Zone in Expressway Based on Generalized Regression Neural Network

Wang

Tang

et al. 2018

Drivers’ mistakes may cause some traffic accidents, and such accidents can be avoided if prompt advice could be given to drivers. So, how to detect driving risk is the key factor. Firstly, the selected parameters of vehicle movement are reaction time, acceleration, initial speed, final speed, and velocity difference. The ANOVA results show that the velocity difference is not significant in different driving states, and the other four parameters can be used as input variables of neural network models in deceleration zone of expressway, which have fifteen different combinations. Then, the detection model results indicate that the prediction accuracy rate of testing set is up to 86.4%. An interesting finding is that the number of input variables is positively correlated with the prediction accuracy rate. By applying the method, the dangerous state of vehicles could be released through mobile internet as well as drivers' start of risky behaviors, such as fatigue driving, drunk driving, speeding driving, and distracted driving. Numerical analyses have been conducted to determine the conditions required for implementing this detection method. Furthermore, the empirical results of the present study have important implications for the reduction of crashes.