Different Number Of Vehicles Research Articles

Multiple Table Models Based on Queuing Theory for Firefighting Traffic Problems

With the development of global urbanization, urban planning has been a hot spot of most concern. Since the traditional urban fire station setting method has been gradually unable to meet the emergency needs of a city. In this paper, a series of methods are developed to achieve the best balance between economic expenditure and reliability. According to the heat maps about emergency calls in a city in recent years, different types of alarm phones have obvious regional distribution characteristics. Inspired by the Queuing Theory algorithm, we abstract this complex problem into a parallel multi-server waiting queuing model to find each average queue length of three kinds of alarm calls and success in converting the multiplicity problem into solving the vehicle number and location problems respectively. Based on this planning problem, new combinations with different numbers of vehicles can be gotten. Then based on the assignment model, the optimal deployment plan can also be different with different numbers and geographical locations of the three kinds of stations. Our method significantly improves the firefighting traffic problems and can be applied in the actual situation.

Employing the maximum likelihood fitting method in the principle of probability and statistics to continually apply the original FHWA road traffic noise prediction model

The original federal highway administration (FHWA) model for road traffic noise (RTN) prediction in the United States (US) has been widely applied in many countries. However, the condition was that the intensities of vehicle noise sources at the reference distance must be determined based on the empirical formula related to vehicle speed and road surface obtained by applying the measured results of a large number of different vehicles in the US. Because the dominant vehicle types and road pavement structures in other countries are not the same, the classification of vehicles and the calculation formula for the source intensity will be different. Even in the US, because of the older modeling data, the current vehicles and their changed noise source, and the new traffic noise model (TNM), the original FHWA model may produce errors if applied in other countries. Therefore, by employing the maximum likelihood method in probability and statistics, this study proposes a method that can continue to directly apply the original FHWA model according to the local vehicle traffic flow, together with a simple method of measuring the attenuation of RTN with distance, to eliminate the uncertainty of the source intensity.

Constraint Programming Approach to Coverage-Path Planning for Autonomous Multi-UAV Infrastructure Inspection

This article presents a constraint modeling approach to global coverage-path planning for linear-infrastructure inspection using multiple autonomous UAVs. The problem is mathematically formulated as a variant of the Min–Max K-Chinese Postman Problem (MM K-CPP) with multi-weight edges. A high-level constraint programming language is used to model the problem, which enables model execution with different third-party solvers. The optimal solutions are obtained in a reasonable time for most of the tested instances and different numbers of vehicles involved in the inspection. For some graphs with multi-weight edges, a time limit is applied, as the problem is NP-hard and the computation time increases exponentially. Despite that, the final total inspection cost proved to be lower when compared with the solution obtained for the unrestricted MM K-CPP with single-weight edges. This model can be applied to plan coverage paths for linear-infrastructure inspection, resulting in a minimal total inspection time for relatively simple graphs that resemble real transmission networks. For more extensive graphs, it is possible to obtain valid solutions in a reasonable time, but optimality cannot be guaranteed. For future improvements, further optimization could be considered, or different models could be developed, possibly involving artificial neural networks.

Secure Intrusion Detection by Differentially Private Federated Learning for Inter-Vehicle Networks

Along with providing several benefits, the unprecedented growth of connected and automated vehicles brings worries about damaging cyber attacks. Network-based intrusion detection systems (IDSs) using deep learning methods can effectively mitigate the threats by promptly detecting malicious behaviors. However, the centralized learning mode may cause data leverage. Federated learning has emerged as a new distributed machine learning training paradigm to preserve data privacy by allowing clients to train and validate models locally with their data and then send the model parameters to the central server. First, we propose a new framework named DPFL-F2IDS scheme for an edge inter-vehicle network that transmits Basic Safety Messages, which consists of Differentially Private Federated Learning (DPFL) and F2IDS (Framework for IDS). DPFL can defend against the member inference attacks faced by the standard federated learning, but difficulties still exist in making a tradeoff on the utility metrics and privacy metrics. Second, experiments by centralized learning methods were performed on the VeReMi Extension dataset. Third, the performance of federated learning by different numbers of vehicles and different optimizers is evaluated. DPFL by different noise values is also evaluated. Results clarified that the F1-scores reached 0.9915 and 0.9700 by long short-term memory (LSTM)-based intrusion detection for binary classification and multi-classification in the centralized learning mode, respectively. The utility results achieved by federated averaging (FedAvg) with Adabound optimizer were closer to the centralized learning mode than the classical FedAvg algorithms. The optimal values of the noise multipliers were also found without degrading the model quality and preserving privacy.

Cooperation Method of Connected and Automated Vehicles at Unsignalized Intersections: Lane Changing and Arrival Scheduling

Connected and automated vehicles (CAVs) have shown great potential in improving traffic efficiency in the area of intersection management. Numerous researches have been accomplished to solve the CAV scheduling problem at the intersection scenario. However, most of them focus on intersections where lane-changing behavior is prohibited, which is impractical for real-world implementation. To address this problem, this paper proposes a novel cooperation framework to solve the CAV arrival scheduling problem in unsignalized intersections where lane changing behavior is permitted. In the first stage, an iterative framework is proposed to solve target assignment and path planning problems, which prevents the deadlock of multiple CAVs with different target lanes. In the second stage, a graph-based minimum clique cover method is then established to obtain the optimal scheduling plan for the CAVs with a low calculation burden. Extensive numerical simulations for different numbers of vehicles and traffic volumes validate the effectiveness of the proposed algorithm.

Deep Reinforcement Learning for Vehicle Platooning at a Signalized Intersection in Mixed Traffic with Partial Detection

The intersection management system can increase traffic capacity, vehicle safety, and the smoothness of all vehicle movement. Platoons of connected vehicles (CVs) use communication technologies to share information with each other and with infrastructures. In this paper, we proposed a deep reinforcement learning (DRL) model that applies to vehicle platooning at an isolated signalized intersection with partial detection. Moreover, we identified hyperparameters and tested the system with different numbers of vehicles (1, 2, and 3) in the platoon. To compare the effectiveness of the proposed model, we implemented two benchmark options, actuated traffic signal control (ATSC) and max pressure (MP). The experimental results demonstrated that the DRL model has many outstanding advantages compared to other models. Through the learning process, the average waiting time of vehicles in the DRL method was improved by 20% and 28% compared with the ATSC and MP options. The results also suggested that the DRL model is effective when the CV penetration rate is over 20%.

Ground vibration induced by high speed trains on an embankment with pile-board foundation: Modelling and validation with in situ tests

To investigate the train-induced ground vibration, an explicit time-domain, three dimensional (3D) finite element (FE) model is developed. The train, track, embankment, pile-board structure and nearby ground soils are all fully coupled in this model. The complex geometries involving the track components and pile-board structure are all modelled in detail, which makes the simulation of wave propagation more realistic from the train to the ground. The model is validated with in situ tests data collected in the Beijing-Shanghai high speed railway line. Good agreements have been achieved between the numerical results and experimental results both in time domain and frequency domain. The proposed model is thus capable of reproducing the dynamic ground response induced by a typical high speed train. Soil responses induced by different number of vehicles are compared. With more vehicles, the spectral peaks of soil responses are more prominent at the integral multiples of the vehicle passing frequency. Too few vehicles will not bring about such phenomenon, thus sufficient number of vehicles should be included in a train to properly model train-induced ground vibration. With the proposed model, the influence of the pile-board foundation on the ground vibration is investigated. It is found that the pile-board foundation can significantly attenuate the low frequency ground vibration. The attenuation of the ground vibration as a function of distance from the track is simulated and the influential factors to the local vibration amplification are investigated. It is found that soil Young’s modulus and soil impedance contrast are the two main factors influential to the local vibration amplification. The softer the natural soil, the larger the amplification. The larger soil impedance contrast makes the amplification more obvious. The soil stratification and geometric discontinuity at ground surface are not the main cause of the local vibration amplification in this work.

Research on Manhattan Distance Based Trust Management in Vehicular Ad Hoc Network

In recent years, Vehicular Ad Hoc Network (VANET) has developed significantly. Coordination between vehicles can enhance driving safety and improve traffic efficiency. Due to the high dynamic characteristic of VANET, security has become one of the challenging problems. Trust of the message is a key element of security in VANET. This paper proposes a Manhattan Distance Based Trust Management model (MDBTM) in VANET environment which solves the problem in existing trust management research that considers the distance between the sending vehicle and event location. In this model, the Manhattan distance and the number of building obstacles are calculated by considering the movement relationship between the sending vehicle and event location. The Dijkstra algorithm is used to predict the path with the maximum probability, when the vehicle is driving toward the event location. The message scores are then calculated based on the Manhattan distance and the number of building obstacles. Finally, the scores are fused to determine whether to trust the message. The experimental results show that the proposed method has better performance than similar methods in terms of correct decision probability under different proportions of malicious vehicles, different numbers of vehicles, and different reference ranges.

On the Benefits of Using Object-Oriented Programming for the Objective Evaluation of Vehicle Dynamic Performance in Concurrent Simulations

Assessing passenger cars’ dynamic performance is a critical aspect for car industries, due to its impact on the overall vehicle safety evaluation and the subjective nature of the involved handling and comfort metrics. Accordingly, ISO standards, such as ISO 4138 and ISO 3888, define several specific driving tests to assess vehicle dynamics performance objectively. Consequently, proper evaluation of the dynamic behaviour requires measuring several physical quantities, including accelerations, speed, and linear and angular displacements obtained after instrumenting a vehicle with multiple sensors. This experimental activity is highly demanding in terms of hardware costs, and it is also significantly time-consuming. Several approaches can be considered for reducing vehicle development time. In particular, simulation software can be exploited to predict the approximate behaviour of a vehicle using virtual scenarios. Moreover, motion platforms and detail-scalable numerical vehicle models are widely implemented for the purpose. This paper focuses on a customized simulation environment developed in C++, which exploits the advantages of object-oriented programming. The presented framework strives to perform concurrent simulations of vehicles with different characteristics such as mass, tyres, engine, suspension, and transmission systems. Within the proposed simulation framework, we adopted a hierarchical and modular representation. Vehicles are modelled by a 14 degree-of-freedom (DOF) full-vehicle model, capable of capturing the dynamics and complemented by a set of scalable-detail models for the remaining sub-systems such as tyre, engine, and steering system. Furthermore, this paper proposes the usage of autonomous virtual drivers for a more objective evaluation of vehicle dynamic performances. Moreover, to further evaluate our simulator architecture’s efficiency and assess the achieved level of concurrency, we designed a benchmark able to analyse the scaling of the performances with respect to the number of different vehicles during the same simulation. Finally, the paper reports the proposed simulation environment’s scalability resulting from a set of different and varying driving scenarios.

A Motorcyclist-Injury Severity Analysis: A Comparison of Single-, Two-, and Multi-Vehicle Crashes Using Latent Class Ordered Probit Model

Motorcycle crashes increasingly become a high proportion of the overall motorized vehicle fatalities. However, limited research has been conducted to compare the injury severity of single-, two- and multi-vehicle crashes involving a motorcycle. This study aims to investigate the effects of rider characteristics, road conditions, pre-crash situations, and crash features on motorcycle severities with respect to different numbers of vehicles involved. The crash data used was obtained through a comprehensive Motorcycle Crash Causation Study (MCCS) by the Federal Highway Administration. An anatomic injury severity indicator, the New Injury Severity Score (NISS), is utilized to calculate a total score as the sum of squared the abbreviated injury scale scores of each of the rider’s three most severe injuries. A hybrid approach integrating Latent Class Clustering (LCC) and Ordered Probit (OP) models was used to uncover the unobserved heterogeneity and to explore the major factors which significantly affect the injury severities resulting from single-, two- and multi-vehicle crashes involving a motorcycle. The results show that the significant differences in severity exist between different numbers of vehicles involved. More importantly, they also indicate dividing motorcycle crashes into homogeneous classes before modelling helps to discover insightful information. Pre-speed of the motorcycle is found to be a main factor associated with serious and critical injuries in most types of crashes. Findings of the study provide specific and insightful countermeasures targeting at the contributing factors of motorcycle crashes.

Transit-Based Evacuation for Urban Rail Transit Line Emergency

Urban rail systems are the backbone of urban transit networks and are characterized by large passenger volumes, high speeds, punctuality, and low environmental impacts. However, unforeseen events such as rail transit line emergencies can lead to unexpected costs and delays. As a means of disruption management, we divide the decision support system for urban rail transit line emergency situations into two stages—transit-based evacuation and bus bridging management. This paper focuses on the transit-based evacuation under emergency scenarios on a single rail line. The model determines the vehicles and routes within traditional transit systems required to evacuate stranded passengers within a given time window. In addition, the proposed method ensures the reliability of traditional transit systems by considering the operating fleet and reserve fleet in the traditional transit systems. Therefore, the proposed optimization model is established with the objective of maximizing the total number of stranded passengers transferred within the given time window and headway constraint. Herein, we present the optimization model and solution method, and the proposed method is validated. The effectiveness of the proposed control method is evaluated in the Changchun urban transit network. By analyzing stranded passengers at stations under different numbers of vehicles and time periods, the results show that the proposed model can significantly provide routing arrangements to maximize the number of passengers evacuated from stations. The results are useful in the development of emergency evacuation plans to prevent secondary accidents and evacuate stranded passengers during a rail transit line emergency.

Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection

This paper develops a novel method of bridge damage detection using statistical analysis of data from an acceleration-based bridge weigh-in-motion (BWIM) system. Bridge dynamic analysis using a vehicle-bridge interaction model is carried out to obtain bridge accelerations, and the BWIM concept is applied to infer the vehicle axle weights. A large volume of traffic data tends to remain consistent (e.g., most frequent gross vehicle weight (GVW) of 3-axle trucks); therefore, the statistical properties of inferred vehicle weights are used to develop a bridge damage detection technique. Global change of bridge stiffness due to a change in the elastic modulus of concrete is used as a proxy of bridge damage. This approach has the advantage of overcoming the variability in acceleration signals due to the wide variety of source excitations/vehicles—data from a large number of different vehicles can be easily combined in the form of inferred vehicle weight. One year of experimental data from a short-span reinforced concrete bridge in Slovenia is used to assess the effectiveness of the new approach. Although the acceleration-based BWIM system is inaccurate for finding vehicle axle-weights, it is found to be effective in detecting damage using statistical analysis. It is shown through simulation as well as by experimental analysis that a significant change in the statistical properties of the inferred BWIM data results from changes in the bridge condition.

Methodology for determining ecological benefits of advanced tolling systems

Toll plazas are places on the road network where increased emissions of exhaust gases occur due to changes in vehicle driving regime in their corresponding impact areas. Therefore, they provide a great potential in terms of the ability to significantly reduce the emission of pollutants by using advanced technologies. In light of this, this paper aims at getting the most accurate quantification of pollutant emission (CO, CO2, HC and NOx) for the various vehicle categories which use Manuel System (MS), Electronic toll collection (ETC) with mechanical barriers and Multi Lane Free Flow (MLFF) system, for determining ecological benefits that can be achieved using advanced tolling systems. The measurement of the emission of harmful gases was carried out in real field conditions for the five most common classes of passenger cars, light truck and semi-trailer-truck. Vehicle speed, fuel consumption and emission of pollutants were recorded every second, in various driving processes in the impact areas of toll plazas, as well as in numerous scenarios that involve a different number of vehicles in a queue. The obtained results show that the use of the MLFF system, compared to the MS, can achieve a reduction in CO2 in the range of 25%–45% and the reduction in NOx in the range of 32%–98%, depending on the type of vehicle and the considered scenario. The case study of the tolling system in the Republic of Serbia, on a sample of 77,408,112 vehicles, has shown that moving from the existing to an advanced MLFF tolling system allows for annual ecological benefits ranging from 1,349,862 € to 1,491,391 €.

Vision-based vehicle detection and counting system using deep learning in highway scenes

Intelligent vehicle detection and counting are becoming increasingly important in the field of highway management. However, due to the different sizes of vehicles, their detection remains a challenge that directly affects the accuracy of vehicle counts. To address this issue, this paper proposes a vision-based vehicle detection and counting system. A new high definition highway vehicle dataset with a total of 57,290 annotated instances in 11,129 images is published in this study. Compared with the existing public datasets, the proposed dataset contains annotated tiny objects in the image, which provides the complete data foundation for vehicle detection based on deep learning. In the proposed vehicle detection and counting system, the highway road surface in the image is first extracted and divided into a remote area and a proximal area by a newly proposed segmentation method; the method is crucial for improving vehicle detection. Then, the above two areas are placed into the YOLOv3 network to detect the type and location of the vehicle. Finally, the vehicle trajectories are obtained by the ORB algorithm, which can be used to judge the driving direction of the vehicle and obtain the number of different vehicles. Several highway surveillance videos based on different scenes are used to verify the proposed methods. The experimental results verify that using the proposed segmentation method can provide higher detection accuracy, especially for the detection of small vehicle objects. Moreover, the novel strategy described in this article performs notably well in judging driving direction and counting vehicles. This paper has general practical significance for the management and control of highway scenes.

Distributed and Energy-Efficient Mobile Crowdsensing with Charging Stations by Deep Reinforcement Learning

Mobile crowdsensing (MCS) represents a new sensing paradigm that utilizes the smart mobile devices to collect and share data. Traditional MCS systems mainly leverages the people carried smartphones and other wearable devices which are constrained by the limited sensing capability and battery power. With the popularity of unmanned vehicles like unmanned aerial vehicles (UAVs) and driverless cars, they can provide much more reliable, accurate and cost-efficient sensing services due to to their equipped more powerful sensors. In this paper, we propose a distributed control framework for e nergy-efficient and DI stributed VE hicle navigation with cha R ging s T ations, called “e-Divert”. It is a distributed multi-agent deep reinforcement learning (DRL) solution, which uses a convolutional neural network (CNN) to extract useful spatial features as the input to the actor-critic network to produce a real-time action. Also, e-Divert incorporates a distributed prioritized experience replay for better exploration and exploitation, and a long short-term memory (LSTM) enabled N-step temporal sequence modeling module. The solution fully explores the spatiotemporal nature of the considered scenario for better vehicle cooperation and competition between themselves and charging stations, to maximize the energy efficiency, data collection ratio, geographic fairness, and minimize the energy consumption simultaneously. Through extensive simulations, we find an appropriate set of hyperparameters that achieve the best performance, i.e., 5 actors in Ape-X architecture, priority exponent 0.5, and LSTM sequence length 3. Finally, we compare with four baselines including one state-of-the-art approach MADDPG. Results show that our proposed e-Divert significantly improves the energy efficiency, as compared to MADDPG, by 3.62 and 2.36 times on average when varying different numbers of vehicles and charging stations, respectively.

Ridesharing

Ridesharing is becoming a popular mode of transportation with profound effects on the industry. Recent algorithms for vehicle-to-customer matching have been developed; yet cross-study evaluations of their performance and applicability to real-world ridesharing are lacking. Evaluation is complicated by the online and real-time nature of the ridesharing problem. In this paper, we develop a simulator for evaluating ridesharing algorithms, and we provide a set of benchmarks to test a wide range of scenarios encountered in the real world. These scenarios include different road networks, different numbers of vehicles, larger scales of customer requests, and others. We apply the benchmarks to several state-of-the-art search and join based ridesharing algorithms to demonstrate the usefulness of the simulator and the benchmarks. We find quickly-computable heuristics outperforming other more complex methods, primarily due to faster computation speed. Our work points the direction for designing and evaluating future ridesharing algorithms.

Multi-depot Two-Echelon Fuel Minimizing Routing Problem with Heterogeneous Fleets: Model and Heuristic

We formulate the two-echelon routing problem considering multiple depots and heterogeneous fleets. Our study (a) presents a Mixed Integer Linear Programming (MILP) formulation with load-dependent fuel minimization objective, (b) uses driving cycles to represent speed variations along a path, (c) allows the vehicles to return to any depot/satellite, and (d) conserves the total number of vehicles at each depot/satellite. We call the problem a Multi-Depot Two-Echelon Fuel Minimizing Routing Problem (MD2E-FMRP). Prior studies assumed there is a fixed number of vehicles available at each satellite/depot, whereas we allow different number of vehicles of each vehicle type at each satellite and depot. Our formulation relaxes several unrealistic assumptions in existing two-echelon formulations and hence has greater practical application. Despite the relaxation of constraints, the running time of our model is comparable to existing formulations. Gurobi optimizer is used to find a better upper bound for up to 56 node instances within a given time limit of 10,000s. We also propose an Adaptive Large Neighborhood Search (ALNS) based heuristic solution technique that outperformed Gurobi in all the tested instances of MD2E-FMRP. We observe an average saving of 13.11% in fuel consumption by minimizing fuel consumed instead of minimizing distance. In general, adapting heterogeneous fleets results in fuel savings and consequently lower emissions compared to using a homogeneous fleet.

Compressed fuzzy logic based multi-criteria AODV routing in VANET environment

Vehicular ad hoc networks (VANETs) are the core of intelligent transportation systems (ITS) to obtain safety, better transportation services, and improved traffic management. Providing more reliable and efficient on demand routing protocol is one of the main challenges in these networks research scope. This paper argues a compressed fuzzy logic based method to enhance Ad hoc on demand distance vector (AODV) routing decision by jointly considering number of relays, distance factor, direction angle, and vehicles speed variance. The proposed scheme is simulated in both freeway and urban scenarios with different number of vehicles using real time interaction between both OMNet++ and SUMO simulators. Simulation results show that the proposed approach can get better performance in terms of packet delivery ratio, throughput, mean delay, and number of sent control packets.

DesCom: Routing Decision using Estimation Time in VDTN

VDTN was proposed as a disrupting network which is established on the paradigm of the delay-tolerant network. VDTN uses vehicular nodes to convey messages as, it permits sparse opportunistic network connectivity, which is considered by the low node density where the vehicular traffic is sporadic, and no end-to-end paths exist between nodes. The message bundle is directed from the sender to the receiver node based on the routing protocol decision. While Routing protocols take decisions based on different metrics like Time to live, Location, Remaining Buffer Size, meeting probability, etc. In this paper, a routing protocol named DesCom is proposed for Vehicular Delay-Tolerant Network under a highly suppressed and sparse environment. DesCom takes the decision based on Message TTL, Transmission rate, and Estimation time. Estimation time is calculated in our previous work. The protocol defines whether to direct the message to the requested node or search the other more suitable node to carry that data bundles. After compiling multiple simulations with different numbers of vehicles and comparing DesCom with other routing protocols it is concluded that DesCom has the least buffer time with low latency along with good delivery probability.

A Priority-Based Multichannel Mac to Support the Non-Safety Applications in SCH Interval at RSU in V2I Communication

Abstract The transmission of safety applications in VANET is clearly the top concern. However, the non-safety applications, which improve the quality of experience, as well as the efficiency of the traffic, also need to be paid more attention. In VANET, the safety applications are transmitted in control channel interval and the non-safety applications can be transmitted only in service channel interval. In this paper, we propose a priority-based multichannel MAC to support the non-safety applications in service channel interval in Vehicle-to-Infrastructure communication with the presence of roadside unit. The novel MAC also allocates service channel according to the priority and divides the vehicles, which have the same required service channel, into four priority groups to enhance the EDCA mechanism. We use the mathematical model and the simulation for evaluating the performance under the influence of velocity with a different number of vehicles in the network.