Connected Vehicle Infrastructure University Transportation Center
The final report for Field Implementation Feasibility Study of Cumulative Travel-Time Responsive Intersection Control Algorithm under Connected Vehicle Technology, submitted by Dr. Saerona Choi, Dr. Byungkyu Brian Park, and Dr. Joyoung Lee has been released.
Report Abstract:
This project utilized the Connected Vehicle (CV) environment, which provides two-way wireless communications between vehicles and infrastructure, to (1) improve the Cumulative Travel-time Responsive (CTR) Intersection Control Algorithm under low CV market penetration by utilizing Bluetooth technology, and (2) assess potential benefits of the CTR algorithm by examining mobility, energy, and greenhouse emissions measures. The project team developed and evaluated a hardware-in-the-loop simulation to ensure that the developed CTR algorithm will work with an existing traffic controller on the Northern Virginia Connected Vehicle Test Bed.
The team enhanced the CTR algorithm and evaluated its impact to verify the feasibility of field implementation. Two prediction techniques, a standard Kalman filter (SKF) and an adaptive Kalman filter (AKF), were applied to estimate cumulative travel time for each phase in the CTR algorithm. In addition, traffic demand, the market penetration rate (MPR), and the types of available data were also considered in evaluating CTR algorithm performance. The Lee Highway and Nutley Street intersection on the Northern Virginia Connected Vehicle Test Bed was selected for a case study and simulated within VISSIM.
The results showed that the CTR algorithm’s performance depends on the MPR, as the information collected from CVs is a key CTR algorithm-enabling factor. However, this study found that the MPR could be relaxed (1) when the data were collected from both CV and infrastructure sensors, and (2) when an AKF was adopted in the CTR algorithm. The minimum MPRs required to outperform the current actuated traffic signal control were empirically found for each prediction technique and types of available data—data from both Connected Vehicle and infrastructure sensors, or Connected Vehicle’s data only. Even without the infrastructure sensors, the CTR algorithm could be considered for implementation at an intersection with high traffic demand and a 50% to 60% MPR. As the MPR for this field evaluation was around 14%, much lower than the minimum 20% required with an AKF incorporated, the project team could not implement the proposed algorithm. Instead, the team developed an implementation plan that can be easily adopted by traffic engineers once the MPR reaches 20% or higher.
Click here to learn more about this project and read the final report.
The final report for A Connected Vehicle-Enabled Virtual Dynamic Message Sign System Demonstration and Evaluation on the Virginia Connected Vehicle Testbed, submitted by Dr. Hyungjun Park, Simona Babiceanu, Robert Kluger, Dr. Brian Smith, and David Recht has been released.
Report Abstract:
Dynamic message signs (DMSs) are widely used to deliver traveler information. While these have proven to be effective, key limitations exist: (1) the locations of DMSs are fixed, (2) reading a DMS message is distracting to drivers, and (3) installation and maintenance of DMSs is expensive. To address these limitations, a smartphone-based virtual DMS (VDMS) application was developed in the first round of Connected Vehicle/Infrastructure University Transportation Center (CVI-UTC) projects. This application used smartphones to provide audible “reading” of DMS messages to drivers.
This project built upon previous work to develop a more advanced, second generation of the VDMS system, that is fully integrated in the Dedicated Short Range Communications (DSRC) environment of the Virginia Connected Vehicle Test Bed. The highlights of the enhanced VDMS system include (1) use of four of 40+ DSRC-based roadside equipment units (RSEs) on the Virginia Connected Vehicle Test Bed, and (2) software (VDMS Manager) that has the capability to virtually “build” new DMSs and to create modified and new messages for those DMSs.
To evaluate the VDMS system as an information dissemination tool to support advanced traffic management, operational testing (including three surveys, entrance, post-incident, and exit) was carried out with actual operators at the McConnell Public Safety and Traffic Operations Center. It was observed that operators preferred the VDMS system due to its capability of providing more detailed and customized messages at more appropriate locations for motorists.
Click here to learn more about this project and read the final report.
The final report for Connected Vehicle Freeway Speed Harmonization Systems, submitted by Dr. Hesham Rakha, and Dr. Hao Yang, has been released.
Report Abstract:
The capacity drop phenomenon, which reduces the maximum bottleneck discharge rate following the onset of congestion, is a critical restriction in transportation networks that causes additional traffic congestion. Consequently, preventing or reducing the occurrence of the capacity drop not only mitigates traffic congestion, but can also produce environmental and traffic safety benefits. To address this issue, this project developed and evaluated a speed harmonization (SH) algorithm based on a bi-level feedback control system with the assistance of vehicle-to-infrastructure (V2I) communications. The algorithm computes advisory speed limits for individual vehicles to prevent the breakdown of downstream bottleneck discharge by regulating traffic flow approaching the bottleneck, which in turn reduces traffic stream delay, emissions and fuel consumption levels. To assess the benefits of the algorithm, a section of Interstate 66 in Northern Virginia was simulated with the INTEGRATION microscopic traffic simulation model, and five trailers were installed on the road to collect real-time traffic data for each vehicle equipped with V2I communications to implement the SH algorithm. The simulations demonstrated that the algorithm significantly mitigated road congestion when a capacity drop occurred at a bottleneck. Also, the study results showed that higher market penetration rates (MPRs) of vehicles equipped with the SH algorithm led to higher SH algorithm benefits. In particular, at 100% MPR, the bottleneck discharge flow rate increased by up to 1.5%, and the vehicular delay decreased by about 22%. Moreover, with the SH algorithm, CO2 and fuel consumption levels were reduced by up to 3.5%. A 100% MPR is the best-case scenario. However, the results also demonstrated that an MPR of even 10% is sufficient to produce overall emission and fuel consumption savings.
Click here to learn more about this project and read the final report.