Exploiting Real-Time Traffic Light Scheduling with Taxi Traces

Zongjian He, Daqiang Zhang, Jiannong Cao, Xuefeng Liu, Xiaopeng Fan, Chengzhong Xu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)


Traffic lights in urban area can significantly influence the efficiency and effectiveness of transportation. The real-time scheduling information of traffic lights is fundamentally important for many intelligent transportation applications, such as shortest-time navigation and green driving advisory. However, existing traffic light scheduling identification systems either entail dedicated infrastructures or depend on specialized traffic traces, which hinders the popularity and real world deployment. Differently, we propose to identify real-time traffic light scheduling by analyzing taxi traces that are widely accessible from taxi companies. The key idea is to exploit the periodicity in traffic patterns, which is directly affected by traffic lights. We also develop advanced algorithms to identify red/green lights duration and signal change time. We evaluate our solution using over one billion taxi records from Shenzhen, China. The evaluation results validate the effectiveness of our system.
Original languageEnglish
Title of host publicationProceedings - 45th International Conference on Parallel Processing, ICPP 2016
Number of pages10
ISBN (Electronic)9781509028238
Publication statusPublished - 21 Sept 2016
Event45th International Conference on Parallel Processing, ICPP 2016 - Philadelphia, United States
Duration: 16 Aug 201619 Aug 2016


Conference45th International Conference on Parallel Processing, ICPP 2016
Country/TerritoryUnited States


  • Data analysis
  • Intelligent traffic
  • Signal processing
  • Traffic light

ASJC Scopus subject areas

  • Software
  • Mathematics(all)
  • Hardware and Architecture


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