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KDDCUP 2017 Travel Time Prediction
SIGKDD stands for the Association for Computing Machinery (ACM) ‘s Special Interest Group on Knowledge Discovery and Data Mining. It is a community for data mining, data science, and analytics. It promotes basic research and development in KDD, adoption of “standards” in the market in terms of terminology, evaluation, methodology and interdisciplinary education among KDD researchers, practitioners, and users. SIGKDD sponsors the KDD Cup competition every year in conjunction with the annual conference. It is aimed at members of the industry and academia, particularly students, interested in KDD.
Highway tollgates are well known bottlenecks in traffic networks. During rush hours, long queues at tollgates can overwhelm traffic management authorities. Effective preemptive countermeasures are desired to solve this challenge. Such countermeasures include expediting the toll collection process and streamlining future traffic flow. The expedition of toll collection could be simply allocating temporary toll collectors to open more lanes. Future traffic flow could be streamlined by adaptively tweaking traffic signals at upstream intersections. Preemptive countermeasures will only work when the traffic management authorities receive reliable predictions for future traffic flow. For example, if heavy traffic in the next hour is predicted, then traffic regulators could immediately deploy additional toll collectors and/or divert traffic at upstream intersections.
Traffic flow patterns vary due to different stochastic factors, such as weather conditions, holidays, time of the day, etc. The prediction of future traffic flow and ETA (Estimated Time of Arrival) is a known challenge. An unprecedented large amount of traffic data from mobile apps such as Waze (in the US) or Amap (in China) can help us take up that challenge. If the contestants in this proposed KDD CUP could design reliable approaches for future traffic flow and ETA prediction, then the traffic management authorities might be able to capitalize on big data & algorithms for fewer congestions at tollgates.
Available datasets are: the road network topology in the target area (Figures 1, 3, and 4, Tables 3 and 4), vehicle trajectories (Table 5), historical traffic volume at tollgates (Table 6), and weather data (Table 7). The contest consists of two tasks with the details below. Task 1: To estimate the average travel time from designated intersections to tollgates For every 20-minute time window, please estimate the average travel time of vehicles for a specific route (shown in Figure 1). a. Routes from Intersection A to Tollgates 2 & 3; b. Routes from Intersection B to Tollgates 1 & 3; c. Routes from Intersection C to Tollages 1 & 3.
For more informations : KDDCUP 2017 official website