Reorganization by Combining with Traffic Congestion Data of The School District

In recent years, traffic congestion has become increasingly serious, and typical tidal mode of transportation is particularly prominent in urban traffic. This brings some troubles to people’s daily travel. There is no doubt that the increasing number of private cars is the main cause of traffic congestion. Therefore, some measures were taken in [1] to reduce traffic congestion, like limiting the number of trips through the license plate tail number. However, there are also hidden factors that can cause traffic congestion, such as the way of school district plan. In Beijing, the school that the children can choose mainly depends on where their parents live. In addition, due to the attention of children’s education and travel safety, parents pick up their children by private car at the time of upper and lower school [2], [3]. The centralized division by residential areas is easy to cause local traffic congestion. In [4], the congestion data of school working days and rest days were compared by using the two-stage least squares regression method, which showed that the traffic congestion index increased by 20% when children were transported to and from school by private cars. The work in [5] established the relationship model between a middle school in Beijing and the surrounding traffic condition. By using the traffic situation in the peak period, the model proved that this kind of school district division method increased the pressure of local traffic network to some extent. However, there is no other effective way to solve the problem of time waste caused by traffic congestion for parents and students. Therefore, in the context of transportation and education, this paper proposes an innovative application scenario to discuss the school district re-planning combining with traffic congestion data. This replanning method not only eases the imbalance of educational resources, but also alleviates the pressure of traffic network. It has important practical significance although it ignores students’ personal wishes. In order to alleviate the traffic congestion caused by students’ going to school and back, the authors in [6] established a temporary parking model for a primary school. The model was simulated with the data of speed, acceleration and queuing, and a plan was put forward to alleviate the congestion. Aiming at the phenomenon of private car picking up children, an efficient parameter-free GRASP-VND metaheuristic in [7] was developed. This way found optimal or close-to-optimal solutions in very limited computing times, and has achieved the effect of easing congestion. However, the above methods are solutions to alleviate local traffic congestion for a single school, and the alleviation of traffic congestion in the whole region remains to be discussed. In [8], an intelligent school bus transportation system was proposed. This system detected the arrival time of children through automatic communication to reduce waiting time, and proved the effectiveness in subsequent tests. Nevertheless, in China, the safety of school buses has been a hot issue. In terms of reducing congestion, the work in [9] proposed a continuous road nodes model, and applied its strong self-similarity to schedule the traffic systems dynamically. Otherwise, to optimize travel mode, [10] used a big data system to enable vehicles access to the Internet platform in order to achieve real-time management and promoted optimal adjustment of urban traffic structures. The application scenario proposed in this paper can be inspired