Intersection Vehicular Traffic Flow Scheduling Datasets

Background

Vehicular traffic congestion remains a major problem in most modern cities of the world. Therefore, efforts to minimize these congestions and their accompanying effects continuously receive much attention from researchers, traffic engineers, policymakers, etc.

One important element required in the efforts of traffic engineering for the minimization of traffic congestion is data. Data influence the modeling and deployment of traffic scheduling systems. Most importantly, the objectives of modeling and deploying the traffic scheduling system influence the nature of the data to be used.

Aim

The aim of this study was to generate and provide a dataset that may be used in the training of computationally intelligent systems having basic objects of minimizing waiting time, travel time, etc. at various intersections (isolated intersections or roundabouts).

Methodology

The dataset (.csv) consisted of waiting time (W), queue length (Q), and phase duration (P). The waiting time is the time duration vehicles have waited at an intersection/roundabout before being scheduled to utilize the intersection/roundabout. The queue length refers to the number of vehicles (vehicular count) waiting at an intersection/roundabout. Phase duration is the time period a given vehicular flow (lane) is assigned the green wave to utilize the intersection/roundabout. The dataset was obtained through repeated training, testing, and modification of phase duration and the Adaptive Neuro-Fuzzy Inference System (ANFIS) model.

The dataset considered bounded conditions on the three parameters (W, Q, P). The W and Q were bounded between zero and ninety – [0, 90] and the P is [13, 50]. That is, when W and Q are greater than or equal to the upper bound, the upper bound is used. Every flow may be assigned a minimum P of 13s and a maximum of 50s. The P-bounds assumed that the lower bound is large enough for vehicles on the assigned traffic flow to move to the safe region of the intersection before the scheduling system switches assignment to another traffic flow. 

Conclusion

The dataset may be used as a benchmark dataset for the improvement of traffic flow controllers as well as other datasets.