ITS

Your Page: Home > ITS > Projects > Feature Projects in China

Two-way Green Wave Band Based on Traffic Big Data

LOCATION
Shenzhen City, Guangdong Province, China
OWNER/IMPLEMENTATION UNIT
Shenzhen Traffic Police Brigade
KEY WORDS
Smart transportation;traffic big data;online simulation based on big data;green wave band
PROJECT COMPLETION/EXPECTED COMPLETION DATE
July 2017
CASE DESCRIPTION

A green wave band is mainly built to avoid red lights on a given section of road, which reduces waiting time at intersection and ensure continuous flow of traffic. To improve travel efficiency and reduce vehicle wait time at intersections, the Shenzhen traffic police brigade and the Shenzhen traffic research center jointly built a two-way green wave band on a pilot road. This green wave band is 7.6 kilometers long and includes 12 signal control intersections. It is located on Guangqiao Road (Yutang to Huaxia Section 2), which is the main road in Guangming New District.

In addition to the green wave band, Shenzhen traffic police brigade closely follows traffic flow at all intersections through electric police data and floating car big data at each surveillance spot. They also use simulation software supported by big data to carry out online simulations. The online simulations are combined with the embedded signal control algorithm to iteratively optimize the signal coordination parameters.

To verify the operational performance of the green wave band, Shenzhen traffic police analyzed real-time data of vehicles, including average speed of vehicles on the road, delays of vehicles at intersections, and number of stops during the whole journey.

Congestion on Guangqiao Road (Yutang-Huaxia Section Two) was significantly reduced after the two-way green wave band was built. Vehicles can pass almost directly through this section of road without having to wait at intersections, thus greatly reducing delays.

KEY DATA
PROJECT-RELATED PARTIES
CURRENT PROGRESS

Completed

INNOVATION POINT
The green wave band applied transit-related big data to optimize traffic light signals and successfully reduced congestion and delays.
RELATED LINKS