The implementation of Vehicle-to-Grid technology enables bidirectional communication and power flow in the Internet of Electric Vehicles (IoEV) context, facilitating the extensive application of electric vehicles in the logistics industry. In response to escalating urban traffic congestion, simultaneously, many cities have implemented widespread traffic restriction policies. Scientifically optimizing the charging strategies for electric logistics fleets and formulating rational distribution plans are pivotal pathways for developing more efficient and intelligent IoEV. To address the problem, an Electric Vehicle Routing Problem of heterogeneous fleet with time window under traffic constraints is formulated, featuring strategies for demand-driven charging within the IoEV and staggered traffic restriction periods. Given the intricate nature of this mathematical model, it is divided into two subproblems, from which two integer programming models are derived. To tackle this model, a two-tier optimization approach is employed, and an improved Ant Colony Optimization algorithm integrated with Variable Neighborhood Search is proposed. Experimental results show that the proposed model reduces the cost by 9.80%-15.68%, confirming the effectiveness of the proposed charging and staggered traffic restriction strategies, as well as the influence of different traffic restriction factors. This research holds practical and theoretical significance in aiding local governments in formulating rational traffic restriction policies, assisting businesses in effectively reducing the costs of electric logistics fleets, and advancing the development of IoEVs.

Due to the influence of altitude change on a driver’s heart rate, it is difficult to estimate driving fatigue using heart rate variability (HRV) at a road segment with frequent and rapid altitude change. Accordingly, a novel method of driving fatigue estimation for driving at plateau area with frequent altitude changes is proposed to provide active safety monitoring in real time. A naturalistic driving experiment at Qinghai-Tibet highway was conducted to collect drivers’ electrocardiogram data and eye movement data. The results of the eye movement-based method were selected to enhance the HRV-based driving fatigue degree estimation method. A correction factor was proposed to correct the HRV-based method at the plateau area so that the estimation can be made via common portable devices. The correction factors for both upslope and downslope segments were estimated using the field experiment data. The results on the estimation of revised driving fatigue degree can describe the driver’s fatigue status accurately for all the road segments at the plateau area with altitudes from 3540 to 4767 m. The results can provide theoretical references for the design of the devices of active safety prevention.