Improvement Of Traffic Efficiency Research Articles

Dynamic Multi-Function Lane Management for Connected and Automated Vehicles Considering Bus Priority

Bus lanes are commonly implemented to ensure absolute priority for buses at signalized intersections. However, while prioritizing buses, existing bus lane management strategies often exacerbate traffic demand imbalances among lanes. To address this issue, this paper proposes a dynamic Multi-Function Lane (MFL) management strategy. The proposed strategy transforms traditional bus lanes into Multi-Function Lanes (MFLs) that permit access to Connected and Automated Vehicles (CAVs). By fully utilizing the idle right-of-way of the MFL, the proposed strategy can achieve traffic efficiency improvement. To evaluate the proposed strategy, some experiments are conducted under various demand levels and CAV penetration rates. The results reveal that the proposed strategy (i) improves the traffic intensity balance degree by up to 52.9 under high demand levels; (ii) reduces delay by up to 80.56% and stops by up to 89.35% with the increase in demand level and CAV penetration rate; (iii) guarantees absolute bus priority under various demand levels and CAV penetration rates. The proposed strategy performs well even when CAV penetration is low. This indicates that the proposed strategy has the potential for real-world application.

Trajectory Planning for Multiple Autonomous Vehicles at Short‐Distance Tandem Signalized Intersections Based on Rule‐Free Framework

High‐level autonomous vehicles (AVs) have more possibilities for improving traffic efficiency. The improvement of traffic efficiency for mixed flow at near‐saturated short‐distance tandem signalized intersections (STSI) needs attention. Most of the existing studies design a generalized control rule for AVs, ignoring the heterogeneity among different AVs. Herein, a multivehicle trajectory planning framework based on a multiagent reinforcement learning (MRL) algorithm is designed to heuristically explore the optimal traffic efficiency of mixed flow at STSI. The core algorithm of this framework is improved from the classical MRL algorithm multi‐agent proximal policy optimization based on the idea of the virtual group instead of designing control rules. The trajectories planned by the framework show outstanding performance in improving throughputs and reducing emissions at the global system level, comparing natural driving, classic adaptive cruise control model and cooperative adaptive cruise control model. The framework can be used to explore optimal traffic efficiency for mixed flow and better heterogeneous rules for high‐level AVs.

Life cycle assessment of carbon emissions for bridge renewal decision and its application for Maogang Bridge in Shanghai

The renewal of old bridges is sometimes restricted to avoid the high carbon emissions caused by materials and construction, which however might not be conductive to climate change mitigation due to their insufficient functions. In order to facilitate the optimization of renewal timing and strategy from the perspective of reducing the comprehensive global warming impacts, we put forward the low-carbon bridge renewal decision method referring to the ideas of consequential life cycle assessment (LCA) and dynamic LCA. In the proposed method, a comparative system boundary matching the bridge renewal characteristics is constructed, where the carbon emission reduction owing to the traffic efficiency improvement by functional updating is incorporated as the low-carbon benefits of renewal, and three types of time-dependent factors, i.e., time-dependent characterization factor, trend factors and changes of traffic demands, are introduced to characterize the influence of renewal timing. The proposed method provided guidance for the renewal of Maogang Bridge, the first long-span cable-stayed bridge in Shanghai. Results illustrate that the global warming impacts of renewal construction activities and the low-carbon benefits from functional updating decrease with the renewal timing. For Maogang Bridge, the rapidly increasing traffic demands inevitably advocated renewal in advance, and the reduction of global warming impacts from emission reduction or carbon uptake could be manifested for renewal in advance. Adopting material recycling in the disposal of old bridge helped reconstructing a new bridge while deconstructing the old one in 2019 becoming the finally selected low-carbon renewal strategy, which is expected to reduce 15.04 kt CO2e-2019 compared to the essential renewal in 2050 with the landfill disposal method. The robustness of the strategy choice has been justified, given the variations of analytical time horizon, expected emission trends and repair emissions. In this way, the low-carbon renewal decision method could build a quantitative basis for judging the urgency of functional updating and verifying the low-carbon benefits of immediate concrete recycling in up-coming urban renewals.

On gap matching for lane-changing vehicles: Coordinated strategies to improve traffic flow at weave bottlenecks

The weaving section on the freeway will be an active bottleneck due to vehicles’ mandatory or discretionary lane-changing (LC) conflicts. In the early stages of the development of intelligent connected transportation, destination information collection of weaving vehicles is possible, which provides an opportunity to improve performance in weaving traffic. In this paper, a simulation system with coordinated strategies embedded to satisfy the LC needs is presented under the weaving traffic flow. First, an algorithm is proposed to evaluate the balance between the demand for LC vehicles and the available gaps provided in weaving traffic, which can be adjusted by variable speed limit (VSL) and ramp metering strategies. Second, to validate the proposed algorithm, a typical weave section with a merge and a diverge connected through a single auxiliary lane is researched, and a simulation platform based on cellular automata has been developed. The optimal control strategies are obtained by model predictive control (MPC) and taking the minimum travel time as an objective. Third, the advantages of coordinated control strategies are studied and discussed in traffic efficiency improvement and the success of LC proportions under different combinations of weaving ratios and large vehicle traffic mix.

Time optimization of urban arterial coordinated control based on multidimensional neighbourhood artificial bee colony algorithm

ABSTRACT Considering the traffic flow of the inner and outer intersection approach of urban arterial roads, an optimization control model of traffic arterial road signal parameters with signal cycle, green light time, and phase offset as the optimization variables is proposed. In order to solve the optimization model of timing parameters, the swarm evolution mechanism of artificial bee colony algorithm is improved and applied. The case study shows that the optimization model of timing parameters can collaboratively optimize the signal cycle, green light time and phase offset of the arterial coordinated control system, and the timing results of the optimization model by comprehensive consideration of inner and outer approach traffic flow are more beneficial to the improvement of traffic efficiency than the decoupling optimization algorithm. The proposed optimization model and solution algorithm can improve the effect of arterial coordinated control.

Multi-Objective Optimal Deployment of Road Traffic Monitoring Cameras: A Case Study in Wujiang, China

This study presents a multi-objective optimal framework for deploying traffic monitoring cameras at road networks. Compared with previous studies that focused on addressing single traffic problem such as OD estimation, link flow observation, path flow reconstruction, and travel time estimation, this study aims to address a comprehensive traffic management problem, including crash prevention, traffic violation governance, and traffic efficiency improvement. First, a potential principle for selecting the location of traffic monitoring deployment is determined, taking into account the key signalized intersections, areas prone to traffic congestion, crash-prone spots, and areas prone to traffic violations. Then, a multi-objective optimal model is developed to minimize the ATFM (i.e., average traffic volume of each five minutes), TCF (i.e., traffic crash frequency), and TVF (i.e., traffic violation frequency) while adhering to cost constraints. Finally, RVEA and NSGA-II algorithms are used to solve the multi-objective optimal model, respectively, and a comprehensive metric is proposed to evaluate the deployment schemes. The case study results demonstrate that the solutions obtained by the RVEA algorithm outperform those of the NSGA-II algorithm, and the best traffic monitoring deployment rate is 62.79%, under cost constraints. In addition, the comparison using the FAHP method also illustrates that the RVEA scheme is superior to the NSGA-II scheme. The above research results could potentially be used to optimize the locations of traffic cameras in road networks, which help to improve traffic management.

Modeling an ITS Management Solution for Mixed Highway Traffic With Eclipse MOSAIC

Mixed traffic of conventional and automated vehicles entails many challenges concerning the safe coexistence and the improvement of traffic efficiency. As a solution, a Traffic Management Center (TMC) combines information from physical sensors and communicated data to enable advanced traffic controllers. Since there is no sufficient penetration rate of automated vehicles nowadays, such a solution could be developed and tested with synthetically generated data using simulation, which models the future Intelligent Transportation System (ITS). This paper addresses the challenges for evaluation of such a system by the means of simulation. For this purpose, we present the Eclipse MOSAIC Multi-Domain Simulation Framework, which provides the possibility to couple best-in-class simulators of the domains traffic, application, and communication, as well as to include further models and external native code libraries. Moreover, we discuss modeling extensions for traffic control algorithms and our method to create a highway scenario based on real traffic data. As primary result, many research questions for mixed traffic and ITS could be investigated using the MOSAIC framework. In an exemplary study, we analyze the deployment effort for infrastructure and communication technologies in order to facilitate a Mainstream Traffic Flow Controller in a realistic scenario with mixed traffic. Simulations performed with MOSAIC confirmed that traffic efficiency improvement is feasible even with fewer infrastructure components, thus with less cost.

Automated Traffic State Optimization in the Weaving Area of Urban Expressways by a Reinforcement Learning-Based Cooperative Method of Channelization and Ramp Metering

The traffic congestion problem on urban expressways, especially in the weaving areas, has become severe. Some cooperative methods have been proven to be more effective than a separate approach in optimizing the traffic state in weaving areas on urban expressways. However, a cooperative method that combines channelization with ramp metering has not been presented and its effectiveness has not been examined yet. Thus, to fill this research gap, this study proposes a reinforcement learning-based cooperative method of channelization and ramp metering to achieve automated traffic state optimization in the weaving area. This study uses an unmanned aerial vehicle to collect the real traffic flow data, and four control strategies (i.e., two kinds of channelization methods, a ramp metering method, and a cooperative method of channelization and ramp metering) and a baseline (without controls) are designed in the simulation platform (Simulation of Urban Mobility). The speed distributions of different control strategies on each lane were obtained and analyzed in this study. The results show that the cooperative method of channelization and ramp metering is superior to other methods, with significantly higher increases in vehicle speeds. This cooperative method can increase the average vehicle speeds in lane-1, lane-2, and lane-3 by 14.51%, 14.81%, and 37.03%, respectively. Findings in this study can contribute to the improvement of traffic efficiency and safety in the weaving area of urban expressways.

TRUCON: Blockchain-Based Trusted Data Sharing With Congestion Control in Internet of Vehicles

The Internet of vehicles (IoV) has a substantial impact on traffic efficiency improvement and accidents avoidance. Due to restricted resources, vehicles must share observed data with RSUs and other vehicles to execute some time-tolerant computing tasks. However, data provided by vehicles cannot always be trusted due to the presence of attackers. Fake messages could have catastrophic ramifications, such as vehicle collisions. Furthermore, extensive data sharing might cause channel congestion, resulting in the loss of vital messages during delivery. To overcome the aforementioned issues, we propose TRUCON, a blockchain-based trusted data sharing mechanism with congestion control in IoV. Firstly, we propose a Kademlia algorithm-based traffic data forwarding method to control channel congestion state. By adjusting the bucket size and distance threshold, source vehicles can limit the number of reference vehicles forwarded. Secondly, we present a cuckoo filter-based traffic data deduplication and discrimination approach. To avoid repetitive sharing, vehicles and RSUs can check their local filters to verify if the current data report has been shared. Based on the foregoing, we propose a blockchain-based trust management mechanism with congestion control. RSUs serve as full nodes while vehicles are light nodes in the blockchain. Finally, we develop a trust management prototype system with congestion control that incorporates both on-chain and off-chain parts. It signifies that our scheme is both feasible and effective.

Eco-driving policy for connected and automated fuel cell hybrid vehicles platoon in dynamic traffic scenarios

Cooperation of platooning vehicles enabled by eco-driving and connected and automated technology has shown considerable potential in energy saving and traffic efficiency improvement. However, affected by the impacts of the randomly changed states of traffic vehicles and the uncertainty of future road conditions, the adoption of eco-driving for the connected and automated fuel cell vehicle (CAFCHV) platoon in a dynamic traffic environment will encounter significant challenges. Additionally, improper inter-vehicle spacing can significantly affect operating safety and energy efficiency. To address this problem, a centralized variable spacing control strategy that adaptively adjusts with velocity and slope for the CAFCHV platoon is designed to calculate the optimal speed and promote energy allocation efficiency. In the upper control layer, gradient-based model prediction control (GRAMPC) is leveraged to calculate the optimal speed of the platoon, where the velocity of each vehicle can be regulated via its own and the surrounding vehicle's state and relative position. In the bottom layer, each vehicle in the platoon solves an MPC-based energy management strategy (EMS) to achieve power allocation in various power sources. Compared with the adaptive cruise control (ACC) driving manner, the simulation results regulated by the Planning manner display a considerable improvement in terms of the global cost, hydrogen consumption, battery degradation, and SOC by 1.36%, 1.48%, 2.47%, and 1.56%, respectively.

Hindrance-Aware Platoon Formation for Connected Vehicles in Mixed Traffic

Aiming at traffic efficiency improvement, vehicle platoon becomes an essential driving technique to control the synchronous driving of connected and automated vehicles. Since the mature automated driving technology and cooperative willingness cannot be ensured for all vehicles, a mixed traffic paradigm that incorporates both cooperative vehicles (CVs) and non-cooperative vehicles (NCVs) will be inevitable. Due to the traffic hindrance and intermittent connections caused by the randomly distributed NCVs, traffic efficiency would be limited in the mixed traffic. Therefore, a hindrance-aware platoon formation scheme is proposed for the mixed traffic in this paper, which aims at coordinating CVs to maximize overall traffic velocity while minimize collision risk and fuel consumption. Specifically, a vehicle connection model is first established to construct the hindrance-aware relationships among vehicles, based on which, the intermittent connections of CVs are formulated as a mixed integer nonlinear programming (MINLP) problem. To reduce the search space, a novel cross-vehicle matrix is designed to represent the hindrances that each CV faces, then a traffic-maneuver-tree construction algorithm is proposed to search for feasible and hindrance-aware platoon formation connections. A length-dynamic inverse influence zone is constructed, then two decision indicators of platoon leader and follower are decoupled by evaluating leadership qualifications of all CVs. Given the hindrance-aware connections and leadership qualifications, a hindrance-aware platoon formation algorithm is proposed to jointly optimize the speed, safety and energy efficiency of CVs. To verify the effectiveness of the platoon formation mechanism, extensive simulations are conducted, and the results reveal that the road utilization, traffic capacity improvement and safety assurance in mixed traffic can be enhanced.

A V2P Collision Risk Warning Method based on LSTM in IOV

With the evolution of communication networks, the Internet of Vehicles (IOV) continues to accelerate the safe and rapid development of autonomous vehicles. Vehicle-to-Pedestrian (V2P) communication is a key technology in autonomous vehicles and a potential solution to realize collaborative intelligence between vehicles and pedestrians. However, the existing V2P communication early warning system does not consider the uncertainty of pedestrian trajectory, and the determination of the collision area is limited to a single point, resulting in an inaccurate system judgment and limited improvement of traffic efficiency. This paper designs a new autonomous-oriented V2P communication network architecture and completes a V2P communication early warning system based on Long Range (LoRa). A V2P anticollision model is established, and a new V2P collision risk early warning method is proposed. In this method, danger index is introduced into the early warning of collision between pedestrian and vehicle. The long short-term memory (LSTM) artificial neural network is used to predict the pedestrian’s trajectory, so as to deduce the pedestrian-vehicle collision risk area when the pedestrian trajectory is uncertain. Meanwhile, the confidence probability is used to judge whether the pedestrian and vehicle are warned. The simulation shows that the V2P collision risk warning method proposed in this paper has good performance, which can accurately warn the pedestrian and vehicle under different vehicle speeds and Global Positioning System (GPS) positioning errors. At the same time, it reflects the characteristics of intelligence brought by using LSTM methods. Using the V2P communication early warning system based on LoRa to verify the experimental results show that when the GPS positioning accuracy is submeter level, the prediction accuracy is greater than 98%. The results of the proposed method show good performance and high detection rate.

Path Planning for Intelligent Vehicle Collision Avoidance of Dynamic Pedestrian Using Att-LSTM, MSFM, and MPC at Unsignalized Crosswalk

In this article, path planning for intelligent vehicle collision avoidance of dynamic pedestrian using attention mechanism-long short-term memory network (Att-LSTM), modified social force model (MSFM), and model predictive control (MPC) is systematically investigated, and pedestrian-dynamic vehicle conflict scene at an unsignalized crosswalk is covered. First, a data-driven stacking fusion model based on the Att-LSTM and MSFM is proposed for pedestrian path prediction. Pedestrian heterogeneity (age and gender) is taken into account for the first time. The data-driven stacking fusion model is verified with the existing methods. Then an MPC-based path planning-tracking system considering pedestrian path prediction is developed. The predicted path of pedestrian is treated as a pedestrian-safety region with the combination of a semiellipse and semicircle, and the front wheel steering angle is calculated to prevent the intelligent vehicle from colliding with the dynamic pedestrians. Simulink–Carsim simulations are presented to simulate the real dynamic environment where crossing pedestrians exist. The results illustrate that the developed MPC system considering pedestrian path prediction can provide dynamic path planning performance acceptably and effectively, and make it possible for the intelligent vehicle to present the great feasibility for pedestrian safety protection and traffic efficiency improvement.

A Distributed and Hierarchical Optimal Control Method for Intelligent Connected Vehicles in Multi-Intersection Road Networks

Intelligent connected vehicles (ICVs) technologies will bring significant changes to future transportation, and urban intersections will be an important scenario for the application of ICVs. There exists one significant challenge to address for the control of ICVs in unsignalized, multi-intersection road networks, that is, how to realize the comprehensive optimization of traffic efficiency and energy saving. To solve this problem, the distributed and hierarchical optimal control architecture is first established in this paper, consisting of a cloud decision layer and a vehicle control layer. For the cloud decision layer, the distributed model predictive control (DMPC) method is utilized for distributed optimization control of multi-intersection road network systems, to achieve optimization in terms of traffic efficiency. For the vehicle control layer, based on the reference speed optimized from the cloud decision layer, the DMPC method is further utilized for distributed optimal control of each vehicle platoon, to achieve optimization in terms of energy saving. Finally, the comparative simulation tests are carried out based on MATLAB and SUMO. The feasibility and effectiveness of the proposed method were verified, and the improvement of traffic efficiency and energy saving was achieved.

Optimization of lane-changing advisory of connected and autonomous vehicles at a multi-lane work zone

The work zone with lane closure will be an active bottleneck due to vehicles’ mandatory lane-changing conflicts. The emerging Connected Autonomous Vehicle (CAV) technology provides opportunities for vehicle motion planning to improve traffic performance. However, the literature using CAV technology mainly focuses on single-lane lane-changing control in the merging area. The algorithm dealing with multi-lane lane-changing control is absent. In this paper, a simulation system with a lane-changing optimal strategy embedded for the multi-lane work zone is presented under the heterogeneous traffic flow. First, the road upstream of the work zone is divided into several segments, and an optimal multi-lane lane-changing algorithm is designed. It is recommended that CAVs, on the closure lane and the merged lane, change lanes on each segment to balance traffic distribution and minimize traffic delay. Second, to validate the algorithm proposed, a typical three-lane freeway with one-lane closed for the work zone is researched, and the simulation platform based on cellular automata is developed. Third, the advantages of multi-lane control strategies are studied and discussed in traffic efficiency improvement and collision risk reduction by comparing previous lane-changing control algorithms.

Eco-Driving of Freight Vehicles With Signal Priority on Congested Arterial Roads

This paper investigates a cooperative optimization framework of eco-driving and signal priority for freight vehicles in signalized arterial road. Considering the congestion of passenger vehicles at the signalized intersections, the problem is modeled as a multi-stage mixed integer optimization problem containing continuous variables (speed trajectory) and discrete ones (signal timing). The optimal speed trajectory is obtained with the objective of minimizing the fuel consumption of freight vehicles. Meanwhile, freight-priority optimal signal timing is achieved by minimizing the queueing delay of passenger vehicles at the intersections, while ensuring freight vehicles to avoid stopping and idling. The cooperative optimization problem is non-convex due to constraints from the queues and traffic signals in the network; therefore, a sub-optimal strategy restoring convexity is proposed to solve the problem. Comparison studies of the result and some benchmark methods are performed via numerical simulations, showing significant improvement of traffic efficiency and fuel economy with faster calculation.

A Coupled Vehicle-Signal Control Method at Signalized Intersections in Mixed Traffic Environment

Signalized intersections play a vital role in addressing the issue of transportation efficiency and vehicle fuel economy in urban areas. Meanwhile, with the development of Connected and Automated Vehicles (CAVs), the mixed traffic environment composed of traffic participants with differing intelligent levels will become an important stage of the intelligent transportation system. Considering the changes in the mixed traffic environment, this paper proposes a Coupled Vehicle-Signal Control (CVSC) method to optimize the traffic signal timing and driving trajectories of CAVs at the same time, with the goals of traffic efficiency improvement and energy saving respectively. The signal timing is continuously optimized to minimize the total delay at the intersection. CAVs generate eco-driving trajectories using the received signal timing information and the planned arrival time to reduce fuel consumption. Finally, simulation experiments were been carried out to verify the control effect of the proposed CVSC. On the one hand, the influence of the involved parameters on the optimization results was analyzed and discussed. On the other hand, the proposed CVSC method was compared with the traditional CACC control and the classic eco-driving model, GlidePath. The research results show that the proposed CVSC method can effectively improve the performance of signalized intersections. When the penetration rate of CAVs is greater than 40%, this method can save fuel consumption by 6%-14% and increase the average speed by 1%-5%.

Comparison of two methods for subchannel seizure in gsm cell using vamos technics

Abstract This paper considers two methods for voice channel seizure in one GSM cell, which uses VAMOS technics for traffic efficiency improvement. An important characteristic for models comparison is call loss caused by unsuccessful connection pairing in one timeslot. The first method gives priority to voice connection quality, and the other one to traffic efficiency. The methods are estimated by the simulation, which is first tested by comparing its results to the calculation results for a very small system. The influences of traffic intensity, allowed power difference of two paired connections and attenuation coefficient on the loss caused by unsuccessful pairing are assessed.

A Mac Protocol Implementation for Wireless Sensor Network

IEEE 802.15.4 is an important standard for Low Rate Wireless Personal Area Network (LRWPAN). The IEEE 802.15.4 presents a flexible MAC protocol that provides good efficiency for data transmission by adapting its parameters according to characteristics of different applications. In this research work, some restrictions of this standard are explained and an improvement of traffic efficiency by optimizing MAC layer is proposed. Implementation details for several blocks of communication system are carefully modeled. The protocol implementation is done using VHDL language. The analysis gives a full understanding of the behavior of the MAC protocol with regard to backoff delay, data loss probability, congestion probability, slot effectiveness, and traffic distribution for terminals. Two ideas are proposed and tested to improve efficiency of CSMA/CA mechanism for IEEE 802.15.4 MAC Layer. Primarily, we dynamically adjust the backoff exponent (BE) according to queue level of each node. Secondly, we vary the number of consecutive clear channel assessment (CCA) for packet transmission. We demonstrate also that slot compensation provided by the enhanced MAC protocol can greatly avoid unused slots. The results show the significant improvements expected by our approach among the IEEE 802.15.4 MAC standards. Synthesis results show also hardware performances of our proposed architecture.

A New Continuous Flow Intersection for Urban Road: Architecture, Design, and Simulation

In the light that heavy left-turn vehicles and oncoming vehicles conflict at conventional intersection(CI), leading to discontinuity and low efficiency of traffic flow, this paper presents a new continuous flow intersection(CFI) for urban roads. The geometry physical model and design principles of the CFI are illustrated and the CFI is designed from following three aspects: traffic space, traffic organization and traffic control. Experiments taking the CI and corresponding the CFI as the reseach objects are carried on, and performance of the proposed the CFI is validated via VISSIM. Extensive simulation results under five traffic conditions have demonstrated the potential of the proposed the CFI for improvement of traffic efficiency, and the applicability of the CFI in China is discussed considering the characteristics of domestic urban intersections and it indicates that the CFI could be applied to domestic suburb roads.