Connected vehicle penetration rate for estimation of arterial measures of effectiveness

Most of the existing connected vehicle (CV)-based traffic control models require a critical penetration rate. If the critical penetration rate cannot be reached, then data from traditional sources (e.g., loop detectors) need to be added to improve the performance. However, it can be expected that over the next 10 years or longer, the CV penetration will remain at a low level. This paper presents a real-time detector-free adaptive signal control with low penetration of CVs ([Formula: see text]10%). A probabilistic delay estimation model is proposed, which only requires a few critical CV trajectories. An adaptive signal control algorithm based on dynamic programming is implemented utilizing estimated delay to calculate the performance function. If no CV is observed during one signal cycle, historical traffic volume is used to generate signal timing plans. The proposed model is evaluated at a real-world intersection in VISSIM with different demand levels and CV penetration rates. Results show that the new model outperforms well-tuned actuated control regarding delay reduction, in all scenarios under only 10% penetrate rate. The results also suggest that the accuracy of historical traffic volume plays an important role in the performance of the algorithm.

Download Full-text

Coordination strategy for vehicles passing multiple signalized intersections: A connected vehicle penetration rate study

2017 American Control Conference (ACC) ◽

10.23919/acc.2017.7963722 ◽

2017 ◽

Cited By ~ 4

Author(s):

Zhiyuan Du ◽

Baisravan HomChaudhuri ◽

Pierluigi Pisu

Keyword(s):

Penetration Rate ◽

Signalized Intersections ◽

Connected Vehicle ◽

Coordination Strategy ◽

Rate Study

Download Full-text

Platoon Signal Priority in Connected-Autonomous Vehicle Environments: Algorithm Development and Testing

Put i saobraćaj ◽

10.31075/pis.65.04.01 ◽

2019 ◽

Vol 65 (4) ◽

pp. 1-9

Author(s):

Milan Zlatkovic ◽

Andalib Shams

Keyword(s):

Traffic Congestion ◽

Autonomous Vehicles ◽

Penetration Rate ◽

Autonomous Vehicle ◽

Transportation Systems ◽

Vehicle Technologies ◽

Measures Of Effectiveness ◽

Urban Corridor ◽

Intersection Control ◽

Day By Day

As traffic congestion increases day by day, it becomes necessary to improve the existing roadway facilities to maintain satisfactory operational and safety performances. New vehicle technologies, such as Connected and Autonomous Vehicles (CAV) have a potential to significantly improve transportation systems. Using the advantages of CAVs, this study developed signalized intersection control strategy algorithm that optimizes the operations of CAVs and allows signal priority for connected platoons. The algorithm was tested in VISSIM microsimulation using a real-world urban corridor. The tested scenarios include a 2040 Do-Nothing scenario, and CAV alternatives with 25%, 50%, 75% and 100% CAV penetration rate. The results show a significant reduction in intersection delays (26% - 38%) and travel times (6% - 20%), depending on the penetration rate, as well as significant improvements on the network-wide level. CAV penetration rates of 50% or more have a potential to significantly improve all operational measures of effectiveness.

Download Full-text

Arterial Performance Measures in a Connected Vehicle Environment

10.31224/osf.io/sr39y ◽

2020 ◽

Author(s):

Noah J. Goodall ◽

Brian L. Smith ◽

Ramkumar Venkatanarayana

Keyword(s):

Performance Metrics ◽

Lateral Acceleration ◽

Connected Vehicle ◽

Transit Signal Priority ◽

Traffic Calming ◽

Arterial Performance ◽

Data Elements ◽

Measures Of Effectiveness ◽

Point Detection ◽

Regulation Compliance

Wireless communication between vehicles and the transportation infrastructure will provide significantly more timely and comprehensive information about arterials and their performance. However, most measures-of-effectiveness were developed based on data available from traditional “point” sensors. The information made available in a connected vehicle environment requires new metrics that can fully utilize the data. This paper identifies several new arterial performance metrics made available in a connected vehicle environment, as well as several existing metrics that can be evaluated more accurately and frequently than before. The new metrics are person-delay, sudden deceleration, change in lateral acceleration, and aggregate regulation compliance. Person-delay measures a vehicle’s lost time multiplied by the number of passengers, and allows for more efficient movement of high-occupancy vehicles and sophisticated transit signal priority. Sudden deceleration and change in lateral acceleration measure activities such as unexpected braking and swerving, which may be leading indicators of unsafe conditions. Aggregate regulation compliance detects unsafe driving behavior that is difficult to collect in the field, such as speeding and illegal U-turns. Engineers can address problem areas through signal timing changes traffic calming, and other measures. The proposed metrics all require high-resolution detection, and are difficult or impossible to measure with existing point detection. For each new metric, its compatibility with connected vehicles is discussed, and required SAE J2735 DSRC Message Set Dictionary data elements are identified.

Download Full-text

Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections

Sensors ◽

10.3390/s19092059 ◽

2019 ◽

Vol 19 (9) ◽

pp. 2059 ◽

Cited By ~ 18

Author(s):

Kai Gao ◽

Farong Han ◽

Pingping Dong ◽

Naixue Xiong ◽

Ronghua Du

Keyword(s):

Neural Network ◽

Real Time ◽

Bp Neural Network ◽

Queue Length ◽

Penetration Rate ◽

Connected Vehicles ◽

Connected Vehicle ◽

Mixed Traffic ◽

Model Based ◽

Sensing Model

With the development of intelligent transportation system (ITS) and vehicle to X (V2X), the connected vehicle is capable of sensing a great deal of useful traffic information, such as queue length at intersections. Aiming to solve the problem of existing models’ complexity and information redundancy, this paper proposes a queue length sensing model based on V2X technology, which consists of two sub-models based on shockwave sensing and back propagation (BP) neural network sensing. First, the model obtains state information of the connected vehicles and analyzes the formation process of the queue, and then it calculates the velocity of the shockwave to predict the queue length of the subsequent unconnected vehicles. Then, the neural network is trained with historical connected vehicle data, and a sub-model based on the BP neural network is established to predict the real-time queue length. Finally, the final queue length at the intersection is determined by combining the sub-models by variable weight. Simulation results show that the sensing accuracy of the combined model is proportional to the penetration rate of connected vehicles, and sensing of queue length can be achieved even in low penetration rate environments. In mixed traffic environments of connected vehicles and unconnected vehicles, the queuing length sensing model proposed in this paper has higher performance than the probability distribution (PD) model when the penetration rate is low, and it has an almost equivalent performance with higher penetration rate while the penetration rate is not needed. The proposed sensing model is more applicable for mixed traffic scenarios with much looser conditions.

Download Full-text

The Effect of Connected Vehicle Environment on Global Travel Efficiency and Its Optimal Penetration Rate

Journal of Advanced Transportation ◽

10.1155/2017/2697678 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Rongjian Dai ◽

Yingrong Lu ◽

Chuan Ding ◽

Guangquan Lu

Keyword(s):

Penetration Rate ◽

Transportation System ◽

Transportation Systems ◽

Information Environment ◽

Connected Vehicle ◽

Advantages And Disadvantages ◽

Route Network ◽

Travel Efficiency ◽

The Relationship ◽

Global Travel

The effect of connected vehicle environment on the transportation systems and the relationship between the penetration rate of connected vehicle and its efficiency are investigated in this study. An example based on the classical two-route network is adopted in this study, in which the drivers consist of two types: informed and uninformed. The advantages and disadvantages of the connected vehicle environment are analyzed, and the concentration phenomenon is proposed and found to be mitigated when only a fraction of drivers are informed. The simulation tool embodying the characteristics of the connected vehicle environment is developed using the multiagent technology. Finally, different scenarios are simulated, such as the zero-information environment, the full-information environment, and the connected vehicle environment with various penetration rates. Moreover, simulation results of the global performance of the transportation system are compared. The results show that the connected vehicle environment can efficiently improve the performance of the transportation system, while the adverse effects due to concentration rise out from the excessive informed drivers. An optimal penetration rate of the connected vehicles is found to characterize the best performance of the system. These findings can aid in understanding the effect of the connected vehicle environment on the transportation system.

Download Full-text

Estimation of Queue Lengths, Probe Vehicle Penetration Rates, and Traffic Volumes at Signalized Intersections using Probe Vehicle Trajectories

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119856340 ◽

2019 ◽

Vol 2673 (11) ◽

pp. 660-670 ◽

Cited By ~ 7

Author(s):

Yan Zhao ◽

Jianfeng Zheng ◽

Wai Wong ◽

Xingmin Wang ◽

Yuan Meng ◽

...

Keyword(s):

Large Scale ◽

Penetration Rate ◽

Signalized Intersections ◽

Real Field ◽

Connected Vehicle ◽

Trajectory Data ◽

Probe Vehicles ◽

Probe Vehicle ◽

Traffic Volumes ◽

Queue Lengths

With the development of connected vehicle technologies and the emergence of e-hailing services, a vast amount of vehicle trajectory data is being collected every day. This massive amount of trajectory data could provide a new perspective for sensing, diagnosing, and optimizing transportation networks. There has been some literature estimating traffic volumes and queue lengths at intersections using the data collected from these probe vehicles. Nevertheless, some of the existing methods only work when the penetration rate of the probe vehicles is high enough. Some other methods require two critical inputs, the distribution of the queue lengths and the penetration rate of the probe vehicles. However, these two inputs might vary a lot both spatially and temporally and are not usually known in the real world. To fill the gap, this paper proposes a novel method for the estimation of queue lengths, probe vehicle penetration rates, and traffic volumes at signalized intersections. The key step is to estimate the penetration rate of the probe vehicles from the distribution of their stopping positions at the intersections. Then, scaling up the number of probe vehicles in the queues and in the traffic according to the estimated penetration rate will give an estimate of the total queue length and the total traffic volume, respectively. The proposed method has been validated by both simulation data and real-field data. The testing results have shown that the method is ready for large-scale real-field applications.

Download Full-text

Analysis of highway performance under mixed connected and regular vehicle environment

Journal of Intelligent and Connected Vehicles ◽

10.1108/jicv-10-2020-0011 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Zhao Zhang ◽

Xianfeng (Terry) Yang

Keyword(s):

Penetration Rate ◽

Evaluation Criteria ◽

Speed Control ◽

Operational Performance ◽

Connected Vehicle ◽

Mixed Traffic ◽

Highway Capacity ◽

Content Type ◽

Traffic Demand ◽

Traffic Pattern

Purpose This study aims to study the connected vehicle (CV) impact on highway operational performance under a mixed CV and regular vehicle (RV) environment. Design/methodology/approach The authors implemented a mixed traffic flow model, along with a CV speed control model, in the simulation environment. According to the different traffic characteristics between CVs and RVs, this research first analyzed how the operation of CVs can affect highway capacity under both one-lane and multi-lane cases. A hypothesis was then made that there shall exist a critical CV penetration rate that can significantly show the benefit of CV to the overall traffic. To prove this concept, this study simulated the mixed traffic pattern under various conditions. Findings The results of this research revealed that performing optimal speed control to CVs will concurrently benefit RVs by improving highway capacity. Furthermore, a critical CV penetration rate should exist at a specified traffic demand level, which can significantly reduce the speed difference between RVs and CVs. The results offer effective insight to understand the potential impacts of different CV penetration rates on highway operation performance. Originality/value This approach assumes that there shall exist a critical CV penetration rate that can maximize the benefits of CV implementations. CV penetration rate (the proportion of CVs in mixed traffic) is the key factor affecting the impacts of CV on freeway operational performance. The evaluation criteria for freeway operational performance are using average travel time under different given traffic demand patterns.

Download Full-text

Impact of connected vehicle guidance information on network-wide average travel time

Advances in Mechanical Engineering ◽

10.1177/1687814016683356 ◽

2017 ◽

Vol 9 (1) ◽

pp. 168781401668335 ◽

Cited By ~ 1

Author(s):

Jiangfeng Wang ◽

Jiarun Lv ◽

Qian Zhang ◽

Xuedong Yan

Keyword(s):

Travel Time ◽

Data Exchange ◽

Positive Impact ◽

Penetration Rate ◽

Transportation Systems ◽

Time Variability ◽

Connected Vehicle ◽

Vehicle Guidance ◽

Average Travel Time ◽

The Impact

With the emergence of connected vehicle technologies, the potential positive impact of connected vehicle guidance on mobility has become a research hotspot by data exchange among vehicles, infrastructure, and mobile devices. This study is focused on micro-modeling and quantitatively evaluating the impact of connected vehicle guidance on network-wide travel time by introducing various affecting factors. To evaluate the benefits of connected vehicle guidance, a simulation architecture based on one engine is proposed representing the connected vehicle–enabled virtual world, and connected vehicle route guidance scenario is established through the development of communication agent and intelligent transportation systems agents using connected vehicle application programming interface considering the communication properties, such as path loss and transmission power. The impact of connected vehicle guidance on network-wide travel time is analyzed by comparing with non-connected vehicle guidance in response to different market penetration rate, following rate, and congestion level. The simulation results explore that average network-wide travel time in connected vehicle guidance shows a significant reduction versus that in non–connected vehicle guidance. Average network-wide travel time in connected vehicle guidance have an increase of 42.23% comparing to that in non-connected vehicle guidance, and average travel time variability (represented by the coefficient of variance) increases as the travel time increases. Other vital findings include that higher penetration rate and following rate generate bigger savings of average network-wide travel time. The savings of average network-wide travel time increase from 17% to 38% according to different congestion levels, and savings of average travel time in more serious congestion have a more obvious improvement for the same penetration rate or following rate.

Download Full-text