Automatic Vehicle Route Prediction based on Multi-Sensor Fusion

To solve the problem of inaccurate results of vehicle routing prediction caused by a large number of uncertain information collected by different sensors in previous automatic vehicle route prediction algorithms, an automatic vehicle route prediction algorithm based on multi-sensor fusion is studied. The process of fusion of multi-sensor information based on the D-S evidence reasoning fusion algorithm is applied to automatic vehicle route prediction. According to the contribution of a longitudinal acceleration sensor and yaw angular velocity sensor detection information to the corresponding motion model, the basic probability assignment function of each vehicle motion model is obtained; the basic probability assignment function of each motion model is synthesized by using D-S evidence reasoning synthesis formula. The new probability allocation of each motion model is obtained under all evidence and then deduced according to the decision rules. Guided by the current optimal motion model, the optimal motion model at each time is used to accurately predict the vehicle movement route. The simulation results show that the prediction error of the algorithm is less than 4% in the process of 30 minutes of automatic vehicle route prediction.

Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles

Due to the limitations of data transfer technologies, existing studies on urban traffic control mainly focused on isolated dimension control such as traffic signal control or vehicle route guidance to alleviate traffic congestion. However, in real traffic, the distribution of traffic flow is the result of multiple dimensions whose future state is influenced by each dimension’s decisions. Presently, the development of the Internet of Vehicles enables an integrated intelligent transportation system. This paper proposes an integrated intelligent transportation model that can optimize predictive traffic signal control and predictive vehicle route guidance simultaneously to alleviate traffic congestion based on their feedback regulation relationship. The challenges of this model lie in that the formulation of the nonlinear feedback relationship between various dimensions is hard to describe and the design of a corresponding solving algorithm that can obtain Pareto optimality for multi-dimension control is complex. In the integrated model, we introduce two medium variables—predictive traffic flow and the predictive waiting time—to two-way link the traffic signal control and vehicle route guidance. Inspired by game theory, an asymmetric information exchange framework-based updating distributed algorithm is designed to solve the integrated model. Finally, an experimental study in two typical traffic scenarios shows that more than 73.33% of the considered cases adopting the integrated model achieve Pareto optimality.

Research on vehicle scheduling based on C-W algorithm

Vehicle scheduling is one of the most important links in logistics distribution, a large number of demands also lead to logistics companies have to reasonably arrange the transportation of vehicle routes to save costs. By optimizing the distribution routes of vehicles, logistics companies can save transportation mileage and thus save transportation costs. In this paper,10 demand points of an agricultural products logistics company in Beijing are selected, and the location of the distribution center is obtained through the center of gravity method. Finally, the route allocation is completed by using the mileage saving algorithm, and the scheduling of transport vehicles is realized. The example shows that the mileage saving algorithm can optimize the vehicle route and realize route allocation.

Optimal Operation Solution for Public Bicycles Based on Genetic Algorithm

Aiming at the various problems with the scheduling of urban public bicycles, this paper conducted a research on the shortest path between rental points and employed the Floyd algorithm to find the optimal route. Based on the conditions of limited number of bicycle transportation vehicles and in different time slots the bicycle rental points were required to restore to the original number of bicycles, a constraint scheduling model was established according to the bicycle supply-demand relationships of the rental points, and the Genetic Algorithm (GA) was used to solve the model to find the shortest path. In terms of balancing the bicycles at each rental point, this paper re-distributed the initial bicycles according to the different demands of each rental point in different time slots, and solved the problem using the solution of the first problem to obtain the optimal vehicle route. This research is a useful reference for solving difficulties in public bicycle scheduling.

Analysis of Overweight Truck Permit Policy in New Jersey

The roadway infrastructure constantly deteriorates because of environmental conditions, but other factors such as exposure to heavy trucks exacerbates the rate of deterioration. Therefore, decision-makers are constantly searching for ways to optimize allocation of the limited funds for repair, maintenance, and rehabilitation of New Jersey’s infrastructure. New Jersey legislation requires operators of overweight (OW) trucks to obtain a permit to use the infrastructure. The New Jersey Department of Transportation (NJDOT) issues a variety of permits based on the types of goods carried. These permits allow OW trucks to use the infrastructure either for a single trip or for multiple trips. Therefore, one major concern is whether the permit revenue of the agency can recoup the actual cost of damage to the infrastructure caused by these OW trucks. This study investigates whether NJDOT’s current permit fee program can collect enough revenue to meet the actual cost of damage to the infrastructure caused by these heavy-weight permit trucks. The infrastructure damage is estimated by using pavement and bridge deterioration models and New Jersey permit data from 2013 to 2018 containing vehicle configuration and vehicle route. The analysis indicates that although the cost of infrastructure damage can be recovered for certain permit types, there is room for improvement in the permit program. Moreover, based on permit rules in other states, the overall rank of the New Jersey permit program is evaluated and possible revisions are recommended for future permit policies.