Enabling Safe and Sustainable Medical Deliveries by Connected Autonomous Freight Vehicles Operating within Dangerous Goods Regulations

Health service providers in developed nations are responsible for 5% of their national carbon emissions, much of which originate from transport and supply chains. Connected autonomous freight vehicles (CAV-Fs) offer the potential to reduce this impact and enable lower cost operations, with trials being explored across the world. Transportation and carriage regulations, particularly in relation to the movement of dangerous goods (DG) such as medicines and diagnostic specimens, have not been developed for and applied to this new transport mode, particularly where loads are unaccompanied. Through an audit of current legislation and practice, this paper evaluates current DGs regulations applied to the transportation of medical products and medicines by autonomous road vehicles. Where existing regulations are not appropriate for CAV-Fs, recommendations and adaptations have been proposed to support safe and practical application. Remote monitoring and tracking of vehicles are critical for ensuring load security, quick and effective incident response, and management of documents and communications between parties. Loading/unloading procedures are significantly more important than in crewed vehicles, with load segregation and recording of all activity being of key importance. Other recommendations relate to training provision, vehicle specifications, and product health monitoring.

Estimation of truck reduction factors applicable to calculation of parameters of a regulated intersection

Introduction. The rapid growth of the country’s car park and the increasing rate of traffic volume lead to various types of transport problems. This is especially acute in regulated areas located near logistics parks, as well as in industrial zones of cities. Here, in the general traffic flow, trucks prevail, which differ from passenger cars primarily in their overall dimensions and dynamic characteristics. For the uniformity of the traffic flow, a certain difference is compensated by specialized coefficients of converting to a conventional passenger car. The performed research is aimed at establishing the main methods used in bringing freight vehicles to a conventional passenger car and assessing the current coefficients of converting freight vehicles to a conventional passenger car by determining the value of the reduced traffic intensity.Methods and materials. Within the framework of the study performed, natural methods of data collection were used for the subsequent calculation. When analyzing certain parameters, mathematical models have been established that underlie certain values of the coefficients of converting trucks to a conventional passenger car.Results. In the course of the study, the authors classified the methods for determining the coefficients of bringing a truck to a conventional passenger car. The values of the reduction factors for some of them have been established, which can be used when calculating the parameters of the regulated section. The present value of road traffic on the set object of the study - the regulated intersection - has been calculated using different actuation coefficients established earlier. The results obtained are assessed.Conclusion. The analysis of the values obtained as a result of calculations showed their significant difference from each other with a difference of 12%, 53% and 300% of the value of the total (not reduced) intensity. Based on the results obtained, the main tasks of further research are determined.

Grid-Based Anomaly Detection of Freight Vehicle Trajectory considering Local Temporal Window

The security travel of freight vehicles is of high societal concern and is the key issue for urban managers to effectively supervise and assess the possible social security risks. With continuous improvements in motion-based technology, the trajectories of freight vehicles are readily available, whose unusual changes may indicate hidden urban risks. Moreover, the increasing high spatial and temporal resolution of trajectories provides the opportunity for the real-time recognition of the abnormal or risky vehicle motion. However, the existing researches mainly focus on the spatial anomaly detection, and there are few researches on the real-time temporal anomaly detection. In this paper, a grid-based algorithm, which combines the spatial and temporal anomaly detection, is proposed for tracing the risk of urban freight vehicles trajectory by considering local temporal window. The travel time probability distribution of vehicle historical trajectory is analyzed to meet the time complexity requirements of real-time anomaly calculation. The developed methodology is applied to a case study in Beijing to demonstrate its accuracy and effectiveness.

Traffic Signal Priority Control Strategy for Connected Emergency Vehicles with Dilemma Zone Protection for Freight Vehicles

This paper presents an emergency vehicle priority control system based on connected vehicle technology, called MMITSS priority. Traditional preemption does not consider the effect of the current traffic situation, such as the presence of a freight vehicle in the dilemma zone, on an opposing movement and can have a significant negative impact on the minor movements of vehicles. A mixed integer linear programming model is developed which can consider the priority requests from multiple emergency vehicles and dilemma zone requests from freight vehicles that could be trapped in the dilemma zone. The optimization model provides an optimal schedule that minimizes the total weighted priority request delays and dilemma zone request, as well as some flexibility to adapt to other vehicles in real time. The flexible implementation of the optimal signal timing schedule is designed to improve the mobility of the non-emergency vehicles. The approach has been tested and evaluated using microscopic traffic simulation. The simulation experiments show that the proposed priority control method is able to improve the travel time of the vehicles on the minor street while ensuring safe passage of the freight vehicle at the dilemma zone without significantly delaying the emergency vehicles. The method is implemented at the Maricopa County SMARTDrive ProgramSM test bed in Anthem, Arizona.

Performance Evaluation of Stochastic Systems with Dedicated Delivery Bays and General On-Street Parking

As freight deliveries in cities increase due to retail fragmentation and e-commerce, parking is becoming a more and more relevant part of transportation. In fact, many freight vehicles in cities spend more time parked than they are moving. Moreover, part of the public parking space is shared with passenger vehicles, especially cars. Both arrival processes and parking and delivery processes are stochastic in nature. In order to develop a framework for analysis, we propose a queueing model for an urban parking system consisting of delivery bays and general on-street parking spaces. Freight vehicles may park both in the dedicated bays and in general on-street parking, whereas passenger vehicles only make use of general on-street parking. Our model allows us to create parsimonious insights into the behavior of a delivery bay parking stretch as part of a limited length of curbside. We are able to find explicit expressions for the relevant performance measures, and formally prove a number of monotonicity results. We further conduct a series of numerical experiments to show more intricate properties that cannot be shown analytically. The model helps us shed light onto the effects of allocating scarce urban curb space to dedicated unloading bays at the expense of general on-street parking. In particular, we show that allocating more space to dedicated delivery bays can also make passenger cars better off.

Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles

Whether in developing or developed countries, traffic accidents caused by freight vehicles are responsible for more than 10% of deaths of all traffic accidents. Fatigue driving is one of the main causes of freight vehicle accidents. Existing fatigue driving studies mostly use vehicle operating data from experiments or simulation data, exposing certain drawbacks in the validity and reliability of the models used. This study collected a large quantity of real driving data to extract sample data under different fatigue degrees. The parameters of vehicle operating data were selected based on significant driver fatigue degrees. The k-nearest neighbor algorithm was used to establish the detection model of fatigue driving behaviors, taking into account influence of the number of training samples and other parameters in the accuracy of fatigue driving behavior detection. With the collected operating data of 50 freight vehicles in the past month, the fatigue driving behavior detection models based on the k-nearest neighbor algorithm and the commonly used BP neural network proposed in this paper were tested, respectively. The analysis results showed that the accuracy of both models are 75.9%, but the fatigue driving detection model based on the k-nearest neighbor algorithm is more reliable.