scholarly journals Freeway Traffic Speed Estimation of Mixed Traffic Using Data from Connected and Autonomous Vehicles with a Low Penetration Rate

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shanglu He ◽  
Xiaoyu Guo ◽  
Fan Ding ◽  
Yong Qi ◽  
Tao Chen
Keyword(s):  
Small Proportion ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Estimation Accuracy ◽  
Distribution Data ◽  
Speed Estimation ◽  
Mixed Traffic ◽  
Fundamental Diagram ◽  
Using Data ◽  
The Impact

Connected and autonomous vehicles (CAVs) are on the way to the field application. In the beginning stage, there will be a mixed traffic flow, containing the regular human-driven vehicles and CAVs with a low penetration rate. Recently, the discussion about the impact of a small proportion of CAVs in the mixed traffic is controversial. This paper investigated the possibility of applying the limited data from these lowly penetrated CAVs to estimate the average freeway link speeds based on the Kalman filtering (KF) method. First, this paper established a VISSIM-based microsimulation model to mimic the mixed traffic with different CAV penetration rates. The characteristics of this mixed traffic were then discussed based on the simulation data, including the sample size distribution, data-missing rate, speed difference, and fundamental diagram. Accordingly, the traditional KF-based method was introduced and modified to adapt data from CAVs. Finally, the evaluations of the estimation accuracy and the sensitive analysis of the proposed method were conducted. The results revealed the possibility and applicability of link speed estimation using data from a small proportion of CAVs.

scholarly journals Impacts of Autonomous Vehicles on Traffic Flow Characteristics under Mixed Traffic Environment: Future Perspectives

2021 ◽  
Vol 13 (19) ◽  
pp. 11052
Author(s):  
Mohammed Al-Turki ◽  
Nedal T. Ratrout ◽  
Syed Masiur Rahman ◽  
Imran Reza
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Flow Characteristics ◽  
Driving Behavior ◽  
Mixed Traffic ◽  
Traffic Characteristics ◽  
Traffic Environment ◽  
Traffic Flow Characteristics ◽  
The Impact

Vehicle automation and communication technologies are considered promising approaches to improve operational driving behavior. The expected gradual implementation of autonomous vehicles (AVs) shortly will cause unique impacts on the traffic flow characteristics. This paper focuses on reviewing the expected impacts under a mixed traffic environment of AVs and regular vehicles (RVs) considering different AV characteristics. The paper includes a policy implication discussion for possible actual future practice and research interests. The AV implementation has positive impacts on the traffic flow, such as improved traffic capacity and stability. However, the impact depends on the factors including penetration rate of the AVs, characteristics, and operational settings of the AVs, traffic volume level, and human driving behavior. The critical penetration rate, which has a high potential to improve traffic characteristics, was higher than 40%. AV’s intelligent control of operational driving is a function of its operational settings, mainly car-following modeling. Different adjustments of these settings may improve some traffic flow parameters and may deteriorate others. The position and distribution of AVs and the type of their leading or following vehicles may play a role in maximizing their impacts.

scholarly journals An Efficiency Enhancing Methodology for Multiple Autonomous Vehicles in an Urban Network Adopting Deep Reinforcement Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1514 ◽  
Author(s):  
Quang-Duy Tran ◽  
Sang-Hoon Bae
Keyword(s):  
Reinforcement Learning ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Effective Means ◽  
Urban Network ◽  
Learning Agents ◽  
Policy Optimization ◽  
The Impact

To reduce the impact of congestion, it is necessary to improve our overall understanding of the influence of the autonomous vehicle. Recently, deep reinforcement learning has become an effective means of solving complex control tasks. Accordingly, we show an advanced deep reinforcement learning that investigates how the leading autonomous vehicles affect the urban network under a mixed-traffic environment. We also suggest a set of hyperparameters for achieving better performance. Firstly, we feed a set of hyperparameters into our deep reinforcement learning agents. Secondly, we investigate the leading autonomous vehicle experiment in the urban network with different autonomous vehicle penetration rates. Thirdly, the advantage of leading autonomous vehicles is evaluated using entire manual vehicle and leading manual vehicle experiments. Finally, the proximal policy optimization with a clipped objective is compared to the proximal policy optimization with an adaptive Kullback–Leibler penalty to verify the superiority of the proposed hyperparameter. We demonstrate that full automation traffic increased the average speed 1.27 times greater compared with the entire manual vehicle experiment. Our proposed method becomes significantly more effective at a higher autonomous vehicle penetration rate. Furthermore, the leading autonomous vehicles could help to mitigate traffic congestion.

scholarly journals Applying Machine Learning and Statistical Approaches for Travel Time Estimation in Partial Network Coverage

2019 ◽  
Vol 11 (14) ◽  
pp. 3822 ◽  
Author(s):  
Fahad Alrukaibi ◽  
Rushdi Alsaleh ◽  
Tarek Sayed
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Statistical Approach ◽  
Penetration Rate ◽  
Time Travel ◽  
Machine Learning Algorithms ◽  
Estimation Accuracy ◽  
Travel Times ◽  
Urban Networks ◽  
The Impact

The objective of this study is to estimate the real time travel times on urban networks that are partially covered by moving sensors. The study proposes two machine learning approaches; the random forest (RF) model and the multi-layer feed forward neural network (MFFN) to estimate travel times on urban networks which are partially covered by moving sensors. A MFFN network with three hidden layers was developed and trained using the back-propagation learning algorithm, and the neural weights were optimized using the Levenberg–Marquardt optimization technique. A case study of an urban network with 100 links is considered in this study. The performance of the proposed models was compared to a statistical model, which uses the empirical Bayes (EB) method and the spatial correlation between travel times. The models’ performances were evaluated using data generated from VISSIM microsimulation model. Results show that the machine learning algorithms, e.g., RF and ANN, achieve average improvements of about 4.1% and 2.9% compared with the statistical approach. The RF, MFFN, and the statistical approach models correctly predict real time travel times with estimation accuracies reaching 90.7%, 89.5%, and 86.6% respectively. Moreover, results show that at low moving sensor penetration rate, the RF and MFFN achieve higher estimation accuracy compared with the statistical approach. At probe penetration rate of 1%, the RF, MFFN, and the statistical approach models correctly predict real time travel times with estimation accuracy of 85.6%, 84.4%, and 80.9% respectively. Furthermore, the study investigated the impact of the probe penetration rate on real time neighbor links coverage. Results show that at probe penetration rates of 1%, 3%, and 5%, the models cover the estimation of real time travel times on 73.8%, 94.8%, and 97.2% of the estimation intervals.

Impact of Autonomous-Vehicle-Only Lanes in Mixed Traffic Conditions

2019 ◽  
Vol 2673 (9) ◽  
pp. 430-439 ◽  
Author(s):  
Hwapyeong Yu ◽  
Sehyun Tak ◽  
Minju Park ◽  
Hwasoo Yeo
Keyword(s):  
Autonomous Vehicles ◽  
Road Traffic ◽  
Positive Impact ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Market Penetration ◽  
Mixed Traffic ◽  
Negative Effects ◽  
Average Speed ◽  
Time To Collision

The introduction of autonomous vehicles (AVs) in the near future will have a significant impact on road traffic. AVs may have advantages in efficiency and convenience, but safety can be compromised in mixed operations of manual vehicles and AVs. To deal with the issues associated with mixed traffic and to avoid its negative effects, a special purpose lane reserved for AVs can be proposed to segregate AVs from manual vehicles. In this research, we analyze the effect on efficiency and safety of AVs in mixed traffic and in a situation where an AV-only lane is deployed. In the analysis, we investigate the average speed, the throughput, and the inverse time-to-collision (ITTC). We differentiate the behaviors of manual vehicles and AVs through the reaction time, desired speed, and car-following models. As a result, we observe that the efficiency is improved when the market penetration rate of AVs increases, especially when the highway throughput increases by up to 84% in the case of mixed traffic. However, safety worsens when the market penetration of AVs is under 40%. In this case, the average speed can be improved and the frequency of dangerous situations (ITTC > 0.49) can be reduced drastically in the merging section by making the innermost lane AV-only. Accordingly, we conclude that AV-only lanes can have a significant positive impact on efficiency and safety when the market penetration rate of AVs is low.

Link speed estimation using GPS data: an empirical investigation of some issues

2020 ◽  
Vol 47 (1) ◽  
pp. 37-49
Author(s):  
Mohamed El Esawey ◽  
Khaled Nasr
Keyword(s):  
Estimation Methods ◽  
Estimation Accuracy ◽  
Gps Data ◽  
Slight Variation ◽  
Speed Estimation ◽  
Position Information ◽  
Instantaneous Speed ◽  
Tracking Devices ◽  
The Impact ◽  
Speed Method

Probe vehicles equipped with tracking devices such as global positioning system receivers (GPS) can be utilized for real-time link speed estimation. In this empirical research, the impact of the data collection resolution, also known as the polling interval, on the network coverage and link speed estimation accuracy was explored. Furthermore, a comparison was made between different methods that currently exist for average link speed estimation using GPS data. The study made use of a 1 s resolution GPS dataset that covered 100 trips in Vancouver, BC. The dataset was sub-sampled 36 times to simulate cases of 5–180 s sampling intervals. An existing map-matching algorithm was used to match the GPS points to the correct travel links for the 36 datasets. Consequently, average link speed was calculated for each link in the dataset using the time stamp difference method and the average instantaneous speed method. A slight variation of the average instantaneous speed method was also tested where instantaneous speeds were computed from position information only. An improvement was further applied to the latter method by using a path inference technique to compensate for the lack of GPS points on some links. The speed estimation methods were compared at different polling intervals and the results were discussed. In general, it was shown that the average instantaneous speed method provides the highest estimation accuracy while the path inference method provides the highest coverage compared to all other methods.

scholarly journals Traffic Equilibrium for Mixed Traffic Flows of Human-Driven Vehicles and Connected and Autonomous Vehicles in Transportation Networks under Tradable Credit Scheme

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Fang Zhang ◽  
Jian Lu ◽  
Xiaojian Hu
Keyword(s):  
Autonomous Vehicles ◽  
Network Performance ◽  
Penetration Rate ◽  
User Equilibrium ◽  
Unit Price ◽  
Mixed Traffic ◽  
Traffic Flows ◽  
Stochastic User Equilibrium ◽  
Tradable Credit Scheme ◽  
Tradable Credit

In this paper, the traffic equilibriums for mixed traffic flows of human-driven vehicles (HDV) and connected and autonomous vehicles (CAV) under a tradable credit scheme (TCS) are established and formulated as two variational inequality (VI) problems with exogenous and endogenous CAV penetration rate, respectively. A modified Lagrangian dual (MLD) method embedded with a revised Smith’s route-swapping (RSRS) algorithm is proposed to solve the problems. Based on the numerical analysis, the impacts of CAV penetration and the extra expense of using a CAV on network performance are investigated. A novel driveway management, autonomous vehicle/credit charge (AVCC) link, is put forward to improve the efficiency of TCS. Under the TCS with exogenous CAV penetration rate, a logit-based model is applied to describe the stochastic user equilibrium for mixed traffic flow. It is found that the penetration of CAV gives rise to a better network performance and it can be further improved by the deployment of AVCC link. Under the TCS with endogenous penetration rate, a nested-logit model is applied to describe travelers’ choices of vehicle types and routes. It is found that the deployment of AVCC links can slow down the decline rate of CAV penetration with increasing expense and thus ensure a lower average travel time for CAVs. In both cases, the deployment of AVCC links can stimulate credit trading and drop down its unit price.

Utilizing Lane-Based Strategy to Incorporate Mixed Traffic in Interchange Control for Connected and Autonomous Vehicles

2019 ◽  
Vol 2673 (5) ◽  
pp. 454-465 ◽  
Author(s):  
Majeed Algomaiah ◽  
Zhixia Li
Keyword(s):  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Traffic Signals ◽  
Heavy Vehicle ◽  
Market Penetration ◽  
Mixed Traffic ◽  
Traffic Demand ◽  
Communication Range ◽  
Throughput And Delay ◽  
Lane Based

This work examines the next-generation interchange control system (NIC) that aims to control connected and autonomous vehicles (CAV) at interchanges with the consideration of different mixed traffic cases. The first objective of the paper is to test several parameters including traffic demand, heavy vehicle percentage, communication range, and advance stop line (ASL) to investigate their impact on throughput and delay. The second objective is to incorporate mixed traffic in the NIC, utilizing a lane-based strategy that is responsive to market penetration rates. The NIC coordinates vehicles to traverse the interchange terminal by using a reservation-based control strategy with a first-come-first-served (FCFS) reservation protocol. The algorithm of this system was modeled in the simulation software package VISSIM using a slightly modified real-world scenario of interchange. The evaluation of the system starts with testing some key variables when market penetration rate is 100%. The results demonstrate that the increase in traffic demand and heavy vehicle percentage affects the performance of the NIC by increasing the delay. Although the effects of communication range and advance stop location do not have clear patterns, the communication range of 600 ft and ASL of 100 ft indicate a relatively lower delay. Throughput and delay results reveal that the NIC outperforms traffic signals when the market penetration rate is 75%, whereas a 25% market penetration rate provides similar performance to traffic signals.

Traffic Oscillation using Stochastic Lagrangian Dynamics: Simulation and Mitigation via Control of Autonomous Vehicles

2019 ◽  
Vol 2673 (7) ◽  
pp. 1-11
Author(s):  
Fangfang Zheng ◽  
Liang Lu ◽  
Ruijie Li ◽  
Xiaobo Liu ◽  
Youhua Tang
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Control Strategies ◽  
Dynamics Simulation ◽  
Lagrangian Model ◽  
Mixed Traffic ◽  
Mixed Traffic Flow ◽  
Sample Paths ◽  
Stop And Go ◽  
The Impact

The phenomenon of stop-and-go waves is frequently observed in congested traffic. With the development of connected and autonomous vehicle (CAV) technologies, it is possible to reduce traffic oscillation via control of CAVs in a mixed traffic flow with both human drivers and autonomous vehicles (AVs). This paper introduces a stochastic Lagrangian model which is capable of simulating stop-and-go traffic considering the heterogeneity of drivers. The sample paths of the stochastic process are smooth without aggressive oscillation. The model is further extended to the mixed traffic flow condition, considering stochastic human driving behavior and deterministic behavior of AVs. With the proposed model, the variation of performance of AV control strategies can be quantified in addition to the average performance. A numerical example with a single lane circular road is used to investigate the impact of the AV control strategy on mitigating stop-and-go waves. Both qualitative and quantitative results show that the phenomenon of stop-and-go waves can be reduced significantly with only one AV, while the increase of AVs from 10% (two AVs) to 50% (10 AVs) offers just marginal improvement in relation to the ensemble-averaged performance and 95% confidence interval of the ensemble-averaged performance. The proposed simulation approach based on the stochastic Lagrangian model can effectively investigate the impact of AV control strategies on traffic oscillation, considering in particular the uncertainty of human driver behavior.

scholarly journals Estimating Destination of Bus Trips Considering Trip Type Characteristics

2021 ◽  
Author(s):  
Soongbong Lee ◽  
Jongwoo Lee ◽  
Bumjoon Bae ◽  
Daisik Nam ◽  
Seunghoon Cheon
Keyword(s):  
Decision Support ◽  
Local Governments ◽  
Evaluation Criteria ◽  
Policy Decision ◽  
Estimation Accuracy ◽  
Travel Patterns ◽  
Accurate Identification ◽  
Using Data ◽  
Stage Estimation ◽  
The Impact

In recent years, local governments have been using transportation card data to monitor the use of public transport and improve the service. However, local governments that are applying a single-fare scheme are experiencing difficulties in using data for accurate identification of real travel patterns, policy decision support, etc. because the information on alighting stops of users is missing. This policy limits its functionality of utilizing data such as accurate identification of real travel patterns, policy decision support, etc. Various studies to overcome this limitation have been conducted in South Korea and other countries to develop es-timation methodologies of alighting stops. Even existing studies introduce an advanced method, we found the margin for better accuracy by combining various estimation methodologies for estimating alighting stops. This study reviewed previously conducted studies to classify data with missing alighting stop information into trip types and then applied an appropriate alighting stop estimation methodology for the characteristics of each trip type by stage. The proposed method is evaluated by utilizing transportation card data of the Seoul metropolitan area and checked the accuracy for each standard of allowable error for sensitivity analysis. Furthermore, the number of trips, accuracy, and valid tag rate were checked for each type to examine the need for classifying the trip types. Finally, our evaluation also examines the impact of classifying trip types on estimation accuracy. The evaluation criteria are accuracy of the number of trips and valid tag rate. The analysis shows that the stage-by-stage estimation methodology based on the trip type proposed in this study can es-timate users’ destinations more accurately than previous studies. Furthermore, based on the construction of nearly 100% valid tag data, this study differs from prior studies.

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