Effects of advanced traffic signal status warning systems on vehicle emission reductions at signalized intersections

ABSTRACT

Signalized intersections have been identified as vehicle emission hotspots, where drivers decelerate, idle, and accelerate their vehicles in response to signal changes. Advanced traffic signal status warning systems (ATSSWSs) can be applied to reduce traffic emissions at intersections by mitigating unnecessary braking and acceleration. In this study, two types of ATSSWSs, variable message sign (VMS) based and vehicle-to-infrastructure (V2I) based, were designed, and their environmental effectiveness was evaluated through driving simulator-based experiments. Three scenarios were designed and tested: (1) baseline without an ATSSWS, (2) with the VMS-based ATSSWS, and (3) with the V2I-based ATSSWS. The Motor Vehicle Emission Simulator model was used to evaluate and compare the environmental effectiveness of these two types of ATSSWSs. The results indicate that the proposed ATSSWSs can reduce traffic emissions at signalized intersections. In particular, the V2I-based ATSSWS can substantially reduce CO₂, NOx, CO, and HC emissions. The results will help transportation practitioners with implementing advanced driver information systems and decision making on emission reduction policies.

Implications: Signalized intersection has been identified as one of hottest spots for vehicle emissions where signal control causes vehicles to frequently decelerate, idle, and accelerate. Advanced Traffic Signal Status Warning Systems (ATSSWS) can be applied to reduce traffic emission at intersections by decreasing vehicles’ unnecessary brakes and accelerations. The results of this study will assist transportation practitioners in implementing advanced driver information systems and making decisions on emission reduction policies.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (71373018, 71871013) and partially supported by the National Science Foundation (1137732).

Notes on contributors

Xiaofei Sun

Xiaofei Sun is a graduate research assistant at the MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, in Beijing, People’s Republic of China.

Xumei Chen

Xumei Chen is a professor at the MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, in Beijing, People’s Republic of China and an adjunct professor at Xuchang University, Xuchang, Henan Province, People’s Republic of China.

Yi Qi

Yi Qi is a professor at the College of Science, Engineering and Technology, Texas Southern University, in Houston, TX.

Bimin Mao

Bimin Mao is a graduate research assistant at the College of Science, Engineering and Technology, Texas Southern University, in Houston, TX.

Lei Yu

Lei Yu is a professor at the MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, in Beijing, P.R. China, and is a research professor at the College of Science, Engineering and Technology, Texas Southern University, in Houston, TX.

Peijia Tang

Peijia Tang is a graduate research assistant at the College of Science, Engineering and Technology, Texas Southern University, in Houston, TX.