Reducing congestion and emissions via roadside unit deployment under mixed traffic flow

It is expected that for a long time the future road trafc will be composed of both regular vehicles(RVs)and connected autonomous vehicles(CAVs).As a vehicle-to-infrastructure technology dedicated to facilitating CAV under the mixed trafc fow,roadside units(RSUs)can also improve the quality of information received by CAVs,thereby infuencing the routing behavior of CAV users.This paper explores the possibility of leveraging the RSU deployment to afect the route choices of both CAVs and RVs and the adoption rate of CAVs so as to reduce the network congestion and emissions.To this end,we frst establish a logit-based stochastic user equilibrium model to capture drivers’route choice and vehicle type choice behaviors provided the RSU deployment plan is given.Particularly,CAV users’perception error can be reduced by higher CAV penetration and denser RSUs deployed on the road due to the improved information quality.With the established equilibrium model,the RSU deployment problem is then formulated as a mathematical program with equilibrium constraints.An active-set algorithm is presented to solve the deployment problem efciently.Numerical results suggest that an optimal RSU deployment plan can efectively drive the system towards one with lower network delay and emissions.

An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

Self-driving and semi-self-driving cars play an important role in our daily lives.The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information.However,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution notifications.In this case,roadside units are considered one of themost important communication peripherals.Random distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage area.In this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these cars.Therefore,this approach is based primarily on k-means to find the optimal location of each roadside unit.In addition,this approach supports dynamicmobility with a long period of connectivity for each car.Further,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and effectively.Thus,the system can achieve a high connectivity rate with a low error rate while reducing costs.

End-to-End Rate Adaptation to Support Heterogeneous Services for Infrastructure-Based Vehicular Networks

Vehicle to Infrastructure （V2I） communications aim to provide mobile users on the road low-cost Internet and driver safety services. However, to meet Quality of Service （QoS） requirements of various applications and officiently utilize limited wireless channel resourc- es, the transport layer protocol has to perform effective rate control in low channel quality and frequent changing topology communica- tion environment. In this paper, we propose a novel rate-control scheme in infrastructure based vehicular networks that avoids conges- tion and starvation and promotes fairness in end-to-end V2I communications. In vehicular networks, a bottleneck roadside unit （RSU） keeps track of its buffer size, aggregate incoming rate, and link throughput, and appropriately allocates bandwidth to traversing flows. With feedback information from the RSU, source nodes dynamically adjust their sending rates to avoid buffer overflow or starvation at the bottleneck RSU. Simulation results show that the proposed scheme can reduce not only packet losses owing to buffer overflow but also buffer starvation time, which improves the utilization efficiency of wireless channel resource.

Cluster-based RSU deployment strategy for vehicular ad hoc networks with integration of communication,sensing and computing

The integration of communications,sensing and computing(I-CSC)has significant applications in vehicular ad hoc networks(VANETs).A roadside unit(RSU)plays an important role in I-CSC by performing functions such as information transmission and edge computing in vehicular communication.Due to the constraints of limited resources,RSU cannot achieve full coverage and deploying RSUs at key cluster heads of hierarchical structures of road networks is an effective management method.However,direct extracting the hierarchical structures for the resource allocation in VANETs is an open issue.In this paper,we proposed a network-based renormalization method based on information flow and geographical location to hierarchically deploy the RSU on the road networks.The renormalization method is compared with two deployment schemes:genetic algorithm(GA)and memetic framework-based optimal RSU deployment(MFRD),to verify the improvement of communication performance.Our results show that the renormalization method is superior to other schemes in terms of RSU coverage and information reception rate.