Number Of Vehicles Research Articles

Analysis of the impact of exhaust gas emissions on diesel fueled vehicles in view from the year of manufacture (Case study on the Mitsubishi L 300 vehicle)

Air pollution is a condition caused by exhaust gas from motor vehicles operating on the highway. To minimize greater air pollution, the government is making preventive efforts by carrying out regular vehicle emission tests which are currently being carried out targeting transport vehicles. This is triggered by the increasing development of the automotive industry so that every year manufacturers produce these vehicles to meet the vehicle market, especially transport vehicle. With the increase in the number of vehicles every year, this is one of the causes of air pollution, so it needs to be controlled. This research aims to determine the effect of vehicle exhaust emissions on the year of vehicle production, in diesel fueled vehicles. The method used is an experiment using two-way Anova analysis on the results of tests carried out on Mitsubhisi L 300 vehicles with a total of 30 test samples, by testing vehicles produced in 2017 - 2022. The results of the research show that older vehicles will produce gas emissions. tall one. This is in accordance with the results of the ANOVA test which accepts the statement that there is at least one vehicle age that has a significant effect on smoke concentration, so it can be concluded that vehicles with an old age, in this case 2017, have the potential to produce high emission gases, therefore preventive efforts need to be taken. to vehicle engines

Traffic Signal Controller System and Vehicle Count-Based Road Congestion Detection

Abstract: Traffic congestion is one of the major modern-day crisis in every big city in the world. Recent study of World Bank has shown that average vehicle speed has been reduced from 21 km to 7 km per hour in the last 10 years in Dhaka. Inter metropolitan area studies suggest that traffic congestion reduces regional competitiveness and redistributes economic activity by slowing growth in county gross output or slowing metropolitan area employment growth. As more and more vehicles are commissioning in an already congested traffic system, there is an urgent need for a whole new traffic control system using advanced technologies to utilize the already existent infrastructures to its full extent. Since building new roads, flyovers, elevated expressway etc. needs extensive planning, huge capital and lots of time; focus should be directed upon availing existing infrastructures more efficiently and diligently. Previously different techniques had been proposed, such as infra-red light sensor, induction loop etc. to acquire traffic date which had their fair share of demerits. In recent years, image processing has shown promising outcomes in acquiring real time traffic information using CCTV footage installed along the traffic light. Different approaches have been proposed to glean traffic data. Some of them count total number of pixels , some of the work calculate number of vehicles .These methods have shown promising results in collecting traffic data.

Study on Location Selection of Urban Two-Level Joint Express Delivery Stations Considering Fair Cost Allocation among Enterprises

The rapid growth of e-commerce has heightened the importance for express delivery companies to ensure timely deliveries. Consequently, it is essential to explore ways to deliver more packages to customers while simultaneously reducing costs through the adoption of a joint distribution mode. This study presents a two-level delivery location selection model within the joint distribution mode, considering factors such as delivery station capacity and the number of transport vehicles, with the objective of minimizing the total cost associated with selecting delivery station locations. The proposed model is addressed using a combination of the k-means algorithm and the improved discrete firefly algorithm. In addition, to facilitate equitable cost allocation among enterprises, the Shapley value method is introduced in this study. A case study based on real data from an urban distribution network in the city of Hebei Province, China, is adopted to perform the experiments. The results of this study indicate that the improved algorithm not only improves solution accuracy but also reduces solution time when compared to both the particle swarm optimization and artificial bee colony methods. Furthermore, the application of the Shapley value method demonstrates the efficacy of a rational allocation of costs.

Evaluation of Urban Quality Improvement Based on the MABAC Method and VIKOR Method: A Case Study of Shandong Province, China

In the current stage of urbanization in China, urban construction has gradually shifted from incremental construction dominated by real estate to stock transformation based on improving urban quality. The evaluation of urban renewal projects is crucial for decision-making and resource allocation. However, there is no consensus on the evaluation indicators and methods. This study evaluates the current status of urban quality using the MABAC method based on numerical indicators. It evaluates the improvement of urban quality using the VIKOR method based on public satisfaction indicators. It is found that the comprehensive weights and subjective weights of the five first-level indicators in the evaluation index system of urban quality improvement in Shandong Province, namely blue–green space improvement, air cleanliness improvement, road traffic improvement, life service improvement, and governance capacity improvement, are quite different. The weight distribution of these indicators needs to be adjusted and optimized, especially for improving living services, as well as secondary indicators such as black and odorous water treatment, urban clean heating, the number of public vehicles per 10,000 people in the city, the 15 min living circle, the renovation of old residential areas, and the construction of urban intelligent management platforms. Based on the comprehensive evaluation results, an attempt is made to propose a more scientific evaluation index system, providing valuable references for urban renewal management.

Characteristics, source analysis, and health risk of PM2.5 in the urban tunnel environment associated with E10 petrol usage.

The increase in the number of motor vehicles has intensified the impact of traffic sources on air quality. Our aim was to illustrate the characteristics of PM2.5 emissions from vehicles fueled with E10 (a blend of 10% ethanol and 90% gasoline). A 21-day PM2.5 sampling in a fully enclosed urban tunnel and the component analysis were completed, and the characteristics, sources, and health risks of tunnel PM2.5 were studied. Moreover, the PM2.5 pH and its sensitivity were investigated by the thermodynamic model (ISORROPIA-II). In addition, exposure models were used to assess the health risks of different heavy metals in PM2.5 to humans through respiratory pathways. The two-point Cu/Sb ratio (entrance: 4.0 ± 1.4; exit: 4.4 ± 1.7) was close to the diagnostic criteria indicating a significant impact from brake wear. NO3-, NH4+, and SO42- constituted the main components of water-soluble ions in PM2.5 of the tunnel, accounting for 83.0-84.6% of the total concentration of inorganic ions. The organic carbon/elemental carbon ratio of the tunnel was greater than 2, indicating that the contribution of gasoline vehicle exhaust was significant. The average emission factors of PM2.5 in the fleet was 31.4 ± 16.6 mg/(veh·km). The pH value of PM2.5 in a tunnel environment (4.6 ± 0.3) was more acidic than that in an urban environment (4.9 ± 0.6). The main sensitive factors of PM2.5 pH in the urban atmosphere and tunnel environment were total ammonia (sum of gas and aerosol, NH3) and temperature, respectively. The results of the health risk assessment showed that Pb posed a potential carcinogenic risk, while As and Cd presented unacceptable risks for tunnel workers. The non-carcinogenic risk index of heavy metals of PM2.5 in the tunnel environment exceeded the safety threshold.

Security, Privacy, and Decentralized Trust Management in VANETs: A Review of Current Research and Future Directions

Vehicular Ad Hoc Networks (VANETs) are powerful platforms for vehicular data services and applications. The increasing number of vehicles has made the vehicular network diverse, dynamic, and large-scale, making it difficult to meet the 5G network’s demanding requirements. Decentralized systems are interesting and provide attractive services because they are publicly available (transparency), have an append-only ledger (robust integrity protection), remove single points of failure, and enable distributed key management and communication in a peer-to-peer network. Researchers dedicated substantial efforts to advancing vehicle communications, however conventional cryptographic mechanisms are insufficient which enabled us to look at decentralized technologies. Therefore, we revisit decentralized approaches with VANETs. Endpoint devices hold a wallet which may incorporate threshold key management methods like MPC wallets, HD Wallets, or multi-party threshold ECDSA/EdDSA/BLS. We also discuss trust management approaches and demonstrate how decentralization can improve integrity, security, privacy, and resilience to single points of failure. We also conduct a comprehensive review, comparing them with current requirements, and the latest authentication and secure communication architectures, which require the involvement of trusted but non-transparent authorities in certificate issuance/revocation. We highlight the limitations of these schemes from PKI deployment and recommend future research, particularly in the realm of quantum cryptography.

Performance Evaluation of Onboard Processing Capability Reduction in Cooperative Vehicles Using 5G and Artificial Intelligence

Fifth-generation (5G) technology is one of the keys to the Industrial Revolution known as Industry 4.0 as it provides faster connectivity and allows a greater number of devices to be connected simultaneously. In the transport sector, newly produced vehicles are equipped with various sensors and applications to help drivers perform safe maneuvers. However, moving from semiautonomous to fully autonomous vehicles to cooperating systems remains a major challenge. Many researchers have focused on artificial intelligence (AI) techniques and the ability to share information to achieve this cooperative behavior. This information can be made up of different data, which can be obtained from different sensors such as laser imaging detection and ranging (LiDAR), radar, camera, global positioning system (GPS), or data related to the current speed, acceleration, or position. The combination of the different shared data is performed depending on the approach of each navigation algorithm. This data fusion will allow a better understanding of the environment but will overload the network, as the traffic generated will be massive. Therefore, this paper addresses the challenge of achieving this cooperation between vehicles from the point of view of network requirements and computational capacity. In addition, this study contributes to advancing theory into real-world practice by examining the performance of cooperative navigation algorithms in the midst of the migration of computational resources from onboard vehicle equipment to the cloud. In particular, it investigates the transition from a cooperative navigation algorithm based on a decentralized architecture to a semidecentralized one as computationally demanding processes previously performed onboard are performed in the cloud. Additionally, the paper discusses the indispensable role of 5G in fulfilling the escalating demands for high throughput and low latency in these services, particularly as the number of vehicles increases. The results of the tests show that the AI acting alone cannot achieve optimal performance, even using 100% of the computational capacity of the onboard equipment in the vehicle. However, a system that integrates 5G and AI-based joint decisions can achieve better performance, reduce the computational resources consumed in the vehicle, and increase the efficiency of collaborative choices by up to 83.3%.

Artificial Intelligence Based CCTV-Accident Detection System

Urban infrastructure and public safety are severely hampered by traffic congestion and accidents. This research suggests an enhanced traffic light control system that makes use of deep learning methods and IoT integration to address these problems. The system computes vehicle density in real-time and indicates smart light conditions at traffic signals by combining the object identification algorithm of YOLOv3 with Internet of Things sensors. The secret to the system is the use of YOLOv3 for precise vehicle identification and categorization in video feeds recorded by security cameras. The technology can effectively identify cars, collisions, and violent incidents on the road by utilising YOLOv3. In order to enable quick emergency response, the system sends notification alerts via GSM connection to neighbouring police stations and hospitals upon detection. In addition, the suggested system uses Internet of Things devices to calculate the number of vehicles at crossings. Traffic signal timings can be dynamically adjusted thanks to this real-time data on vehicle flow, which improves traffic flow and lessens congestion. IoT integration also makes it possible for traffic signals to have smart light indication, which adjusts signal timings in response to current traffic circumstances to improve efficiency and safety.

Enhancing Urban Traffic Management Through Hybrid Convolutional and Graph Neural Network Integration

Traffic congestion has made city planning and citizen well-being difficult due to fast city growth and the increasing number of vehicles. Traditional traffic management fails to solve urban transportation's ever-changing issues. Traffic prediction and control systems are vital for enhancing Traffic Flow (TF) and minimizing congestion. Smart cities need advanced prediction models to regulate urban TF as traffic management becomes more complex. This paper introduces a hybrid Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN) model for better real-time traffic management. The hybrid model combines CNNs' spatial feature extraction with GNNs' structural and relational data processing to analyze and predict traffic conditions. Traffic camera images are pre-processed to extract spatial characteristics. Traffic network graph construction is used for structural research. The model accurately captures traffic topology and space. The proposed method sequentially processes spatial data with CNNs and integrates them with GNNs. The final hybrid model is trained on one year of traffic data from diverse circumstances and events. The hybrid model is compared to CNN, GNN, and traditional Traffic Prediction Models (TPM) like ARIMA and SVM utilizing MAE, RMSE, and MAPE. The hybrid GNN+CNN model outperforms benchmark models with lower MAE, RMSE, and MAPE across several prediction intervals.

Effects of moving vehicles and running lane on the dynamic response of a long-span floating suspension bridge

ABSTRACT Long-span floating bridges are not as robust as bottom-fixed bridges under moving vehicle loads due to weaker foundation stiffness. Understanding the effect of vehicles on the vehicle-induced dynamic response is essential for structural and operational safety assessments. This paper developed a dynamic analysis framework for floating bridges under moving vehicles, including different running lanes. The effects of moving vehicle speed, weight, running lane, vehicle number, spacing in the fleet, and vehicle fleet arrangement on the dynamic response of the example floating suspension bridge were investigated. The primary research outcomes include the following: 1) The developed framework can efficiently compute the dynamic response of the example floating bridge under moving vehicles. Compared with the bottom-fixed bridge scheme, the floating bridge under the same moving vehicle has larger dynamic responses, especially the lateral vibration response of the girder. 2) The vertical response of the floating bridge is mainly affected by the vehicle weight. The offset distribution of vehicle running lanes exaggerates the lateral vibrations of the bridge, of which the magnitude is similar to the vertical response. 3) Increasing the vehicle spacing in the fleet and implementing two-way opposing fleet traffic can effectively reduce the vehicle-induced lateral vibrations of the long-span floating suspension bridge. The design of the floating bridge can benefit from a better understanding of its dynamic response under moving vehicles.

Demographic and social differences in autonomous vehicle technology acceptance in Hungary

Progress in automation has resulted in a growing number of autonomous vehicles (AVs). However, demographic and social differences behind the acceptance of AV technology are an emerging topic in the East-Central European region. These countries (e.g., Poland, Slovakia, Romania) move on a similar technological development path, and the social and economic conditions are alike; thus, Hungary represents this region well. This study contributes to fill this niche. We used quantitative research methods (factor analysis, Kruskal-Wallis H test, Pearson-correlation) to analyze with a snowball (non-probability) sampling. The Hungarian respondents (N=949) selected in the sample were interviewed through a face-to-face and online quantitative questionnaire. The results show that gender and age influence mostly the acceptance; residence and occupation have only partial influence. The propensity to take risks is significantly differentiated in almost all demographic segments. The results facilitate differentiation of users based on their demographic characteristics in AV adoption. Furthermore, the integration of risk propensity into the analysis helps to identify which potential user groups are more likely to overcome any fears of novelty or which clusters are more likely to adopt the current framework of safe transport without driver control. The outcomes are of interest to engineers, manufacturers and policy-makers who can adapt their products, services and taking measures to meet the mobility needs of potential users and introduce effective incentives to increase public acceptance of AVs.

Performance of Modular Multilevel Converter in Electric Vehicles Charging Station

The awareness of air pollution is the main reason for the development of Electric Vehicles. Hence, the many number of Electric vehicle connected to grid produce power quality problem in conversion of AC-DC.  In this paper, to avoid this drawback, the MATLAB/SIMULINK model of Modular Multi Level Converter is implemented in charging station. MMC not only provide bidirectional flow but also improve power quality. The converter works on boost mode and buck mode. In G2V, the boost mode is for charging the battery of electric vehicle and buck mode is discharging the battery.         For, the G2V, the buck mode reduces the voltage to charge the battery with current or voltage constant which is about the state of the charge of the battery. The out of the battery is pure dc with mmc which replace the filter also. It increases the life of the battery. The model of the MMC-based charger for electric vehicle was created in MATLAB/Simulink, and simulation results shows the output power of the MMC and circulation current is controlled.

A comprehensive review of urban vegetation as a Nature-based Solution for sustainable management of particulate matter in ambient air.

Air pollution is one of the most pressing environmental threats worldwide, resulting in several health issues such as cardiovascular and respiratory disorders, as well as premature mortality. The harmful effects of air pollution are particularly concerning in urban areas, where mismanaged anthropogenic activities, such as growth in the global population, increase in the number of vehicles, and industrial activities, have led to an increase in the concentration of pollutants in the ambient air. Among air pollutants, particulate matter is responsible for most adverse impacts. Several techniques have been implemented to reduce particulate matter concentrations in the ambient air. However, despite all the threats and awareness, efforts to improve air quality remain inadequate. In recent years, urban vegetation has emerged as an efficient Nature-based Solution for managing environmental air pollution due to its ability to filter air, thereby reducing the atmospheric concentrations of particulate matter. This review characterizes the various mitigation mechanisms for particulate matter by urban vegetation (deposition, dispersion, and modification) and identifies key areas for further improvements within each mechanism. Through a systematic assessment of existing literature, this review also highlights the existing gaps in the present literature that need to be addressed to maximize the utility of urban vegetation in reducing particulate matter levels. In conclusion, the review emphasizes the urgent need for proper air pollution management through urban vegetation by integrating different fields, multiple stakeholders, and policymakers to support better implementation.

Smart Traffic Surveillance: Identifying Helmet-Less Riders and License Plate Recognition with YOLO and OCR Integration using Deep Learning

Currently, there are a number of issues with Indian traffic laws that can be resolved in a variety of ways. Riding a motorbike without a helmet is against the law and has increased the number of accidents in India. The current method mostly uses CCTV records to monitor traffic offences. When a rider is not wearing a helmet, traffic officers must zoom in on the licence plate and look into the frame where the infraction is occurring. However, because there are more and more traffic infractions and motorcycle ridership every day, this will take a lot of labour and time. What if there was a technology that would automatically search for traffic violations such as riding a motorbike without a helmet? hence, would automatically retrieve the number plate number of the bike. This study has been effectively completed by recent research using KNN, DNN-Classifier, etc. However, there are limitations to these works in terms of speed, accuracy, or efficiency in object identification and classification. The goal of this research project is to develop a non- helmet rider detection system that can automatically identify and retrieve a vehicle's number plate number when a driver fails to wear a helmet. Three-level Deep Learning for Object Detection is the key idea at play here. The items found are a human, a motorcycle at level one using YOLO, a helmet at level two using YOLO, Licence plate at the last level using YOLO. Next, OCR is used to obtain the number plate registration number. All of these methods—especially the section where the number plate number is extracted—are subject to predetermined limitations and conditions. Because video is the work's input, speed of execution is essential. We have developed a comprehensive algorithm for both number plate number extraction and helmet detection using the above-mentioned approaches.

A new car following model based on weighted average velocity field

Abstract In real traffic, the motion status of forward moving vehicles play an
important role in influencing the following vehicle’s motion behaviour. Nowadays,
autonomous and electric vehicles have been incorporated with car-following mode
when the control process is more complicated and more meaningful. As a
result, researchers are studying car-following control strategies for vehicles to
attain stability in traffic. Based on this, we propose a new car following model
considering weighted average velocity field to describe the average effect of multivehicle
interaction on the whole road. The weighted average velocity field can
be regarded as the weighted average of velocities of finite number of vehicles
preceding the objective vehicle with more weightage to more preceding vehicle.
The stability condition is derived through stability analysis. Subsequently, the
modified Korteweg-de Vries (mKdV) equation is formulated to unveil traffic
congestion in the form of the kink-antikink density wave. Theoretical findings
indicate that the weighted average velocity field yields larger stability regions
compared to the average velocity field. Numerical simulations were conducted,
and the results obtained demonstrate that the weighted average effect of multivehicle
interactions play a crucial role in suppressing traffic jams faster than a
simple average.

Detection of Vehicle Number Plate and Speed Using Machine Learning

In an era marked by a significant upsurge in vehicular traffic and the resultant increase in road accidents, the need for advanced traffic management systems is paramount. This paper presents an innovative solution, "Detection of vehicle number plate and speed using machine learning" which aims to enhance road safety by addressing the persistent problem of over-speeding. The project leverages state-of-the-art technologies, including machine learning, deep learning, and computer vision, to develop a smart system that can detect speeding vehicles, recognize license plates, and record crucial data related to speed limit violations. The primary motivation for this endeavor is the escalating rate of road accidents, particularly in India, driven by speeding in areas susceptible to accidents. The system employs image segmentation, corner detection algorithms, filtering algorithms, and automatic vehicle plate recognition techniques to achieve its objectives. By using machine learning and deep learning models, the system accurately identifies license plates, segments characters on these plates, and recognizes them. In summary, this project represents a significant advancement in road safety and traffic management. By deploying an intelligent system that can proactively detect over-speeding vehicles and maintain comprehensive records, it strives to create a safer and more responsible driving environment, ultimately working towards the prevention of accidents and saving lives. Key Words: Automatic Number Plate Recognition, Vehicle Speed Detection, Machine Learning, Computer Vision, Traffic Management

THE SIMULATION MODEL ALGORITHM TO ASSESS ACTIONS OF UNMANNED COMBAT AERIAL VEHICLES

The increase of combat capabilities of UCAVs brought them to the main range of means to defeat the enemy in both defensive and offensive operations. Therefore, the relevance of issues related to the justification of rational approaches to combat operations planning with the use of unmanned combat aerial vehicles makes it necessary to develop methods to evaluate such operations. This research direction has an important applied value. The article proposes an approach to create a simulation model to evaluate unmanned aerial vehicles actions. The research subject is the planning process of the unmanned combat aerial vehicles use. The work aim is to justify approaches to create ways to evaluate the actions of unmanned combat aerial vehicles based on the simulation model of the researched process. The research uses the discrete-time simulation method. The proposed simulation model essentially simulates two processes: the UAV flight process and the process of the enemy targets detecting. As a rule, the aircrafts flight in space is described by differential equations of motion. This approach requires the implementation of a continuous flow of model time, which leads to significant computational difficulties when building the model. Taking into account the necessary accuracy in the simulation model being created, it is permissible to proceed to a discrete change of the model time with some step comparable to the analyzed processes. This model links together such parameters as the barrage zone scope, the target detection intensity, the number of unmanned combat aerial vehicles, their weapons, and speed. When these parameters are varied, it becomes possible to establish the necessary dependencies between them. This can serve as the basis to develop rational ways of using unmanned combat aerial vehicles under the given conditions. The proposed simulation model can be used in the decision-making support system for combat planning with the use of unmanned combat aerial vehicles.

Microstructural and textural evolution of double stage cold rolled non-grain oriented electrical steel

Non-grain oriented electrical steels (NGOES) are typically used in electric applications with rotating magnetic fields and thus essential for the production of electric motors. The increasing number of electric vehicles results in a growing demand for electrical steel sheets used in the stacked laminations of rotor and stator components. E-mobility applications require the best possible magnetic properties for optimum performance and efficiency, especially at high frequencies. In addition, increased mechanical strength is necessary to withstand the centrifugal forces resulting from high revolution speeds. Chemical composition, microstructure and crystallographic texture strongly influence the magnetic properties, whereas the mechanical properties are mainly affected by the chemical composition and grain size. Optimizing the texture is a possible method for improving the magnetic properties without significantly compromising the mechanical strength. In this work a special processing route for the production of 3.25% Si NGOES sample material has been studied, using laboratory cold rolling and annealing facilities. Compared to the classical processing route of single stage cold rolling and annealing, this alternative method involves two cold rolling steps with an intermediate annealing treatment between the first and second rolling sequence and a final annealing treatment after the second rolling step. The samples were produced with different intermediate thicknesses, changing the amount of cold rolling deformation of each sample variation. The degree of deformation affects the recrystallization and grain growth behavior and thus the resulting microstructure and texture. The microstructural and textural evolution was investigated in the hot rolled, intermediate annealed and final annealed state using electron backscatter diffraction (EBSD). Finished samples were additionally examined using magnetic testing and tensile testing. The results indicate a correlation between the intermediate thickness and the final grain size of the steel samples. Furthermore, the highly grain size dependent mechanical strength and magnetic losses are influenced. EBSD-data were used to interpret the texture and to calculate a parameter describing the magnetic quality of the texture. The results show a strong improvement of magnetically favorable texture components along with increasing intermediate thickness, enabling significantly better polarization values in rolling direction and transversal direction. However, anisotropy of the magnetic properties increases as well due to the textural changes.

Iterated variable neighborhood search for integrated scheduling of additive manufacturing and multi-trip vehicle routing problem

This study addresses a novel problem structure for integrated scheduling of additive manufacturing and delivery processes. In the manufacturing process, we consider batch production of parts, simulating Selective Laser Melting technology in parallel additive manufacturing machines with heterogeneous specifications. A batch production, referred to as a build, must consist of parts with homogenous material. During the production run, sequence-dependent material setup times occur in between builds. In the delivery process, parts are delivered in batches using a limited number of homogenous capacitated vehicles. Each vehicle might perform multi-trips. Thus, the route for each trip and departure schedule among trips must be decided. In this regard, batch delivery is independent to batch production. The problem objective function is to minimize the total completion time of all parts. For optimally solving the problem, a mixed-integer linear programming model is devised. To solve larger problems sizes under a lower computation time, an iterated variable neighborhood search algorithm with embedded rule-based heuristics is proposed. The computational experiment denotes that the proposed algorithm with route minimization rule effectively solves small-scale problems, while capacity usage maximization rule performs the best in large-scale problems and outperforms other existing metaheuristics. Further result analysis is also performed to provide managerial insights.

Relief supply prepositioning strategies via option contract reserve fleet vehicles

In preparing for disasters, relief supplies can be prepositioned as tangible forms in warehouses or as intangible forms, such as production capacity. However, the prepositioning of relief supplies in the absence of a sufficient number of vehicles may delay the delivery time and reduce relief operation performance. Budget pressure may prevent local disaster relief authorities from maintaining a large fleet; thus, they may opt for fleet vehicle reserves through an option contract as an alternative. We propose a model for coordinating prepositioned tangible and intangible relief supplies, considering fleet vehicle reserves through an option contract. The objective function includes the reduction of deprivation cost, which considers the suffering of beneficiaries. We obtain the optimal quantity of the prepositioned tangible and intangible supplies and the fleet vehicles. Furthermore, we propose benchmark relief supply prepositioning models without fleet vehicle reserves and compare the performance of the three models. We prove that reserving fleet vehicles can reduce social costs, alleviate the suffering of beneficiaries and increase the profit of the supplier. We examine the applicability of the proposed model through numerical examples.