Service Vehicles Research Articles

Harmonising liability: exploring carrier responsibilities in transportation contracts and their application to autonomous vehicle services under the UAE Law

ABSTRACT This article introduces the challenges presented in establishing governance of autonomous vehicles, dealing with questions of liability, where domestic law needs to cohere with international transport conventions, Islamic jurisprudence, Supreme Court jurisprudence, and technical safety standards. The primary objective of the article is to assess the applicability of established legal norms governing carrier liability in traditional road transport contracts in the United Arab Emirates. These legal tenets, rooted in Islamic jurisprudence and drawn from historical sources such as the ‘Al-Ahkam Al-Adliya Journal,’ constitute the foundation of civil law in numerous Arab countries, including the Arab Gulf States, Egypt, Jordan, Syria, and others. Melding legal and technical perspectives, this study provides legal insights for judges, arbitrators, and legal practitioners, and offers solutions to the liability challenges that may arise when autonomous vehicles begin operating on UAE streets or any other Arab country.

Investigating Mode Choice Behavior for the Introduction of Underground Shared Autonomous Vehicle Service: Case Study in the city of Seoul

Investigating Mode Choice Behavior for the Introduction of Underground Shared Autonomous Vehicle Service: Case Study in the city of Seoul

Multi-objective task offloading for highly dynamic heterogeneous Vehicular Edge Computing: An efficient reinforcement learning approach

Vehicular Edge Computing (VEC) provides a flexible distributed computing paradigm for offloading computations to the vehicular network, which can effectively solve the problem of limited vehicle computing resources and meet the on-vehicle computing requests of users. However, the conflict of interest between vehicle users and service providers leads to the need to consider a variety of conflict optimization goals for computing offloading, and the dynamic nature of vehicle networks, such as vehicle mobility and time-varying network conditions, make the offloading effectiveness of vehicle computing requests and the adaptability to complex VEC scenarios challenging. To address these challenges, this paper proposes a multi-objective optimization model suitable for computational offloading of dynamic heterogeneous VEC networks. By formulating the dynamic multi-objective computational offloading problem as a multi-objective Markov Decision Process (MOMDP), this paper designs a novel multi-objective reinforcement learning algorithm EMOTO, which aims to minimize the average task execution delay and average vehicle energy consumption, and maximize the revenue of service providers. In this paper, a preference priority sampling module is proposed, and a model-augmented environment estimator is introduced to learn the environmental model for multi-objective optimization, so as to solve the problem that the agent is difficult to learn steadily caused by the highly dynamic change of VEC environment, thus to effectively realize the joint optimization of multiple objectives and improve the decision-making accuracy and efficiency of the algorithm. Experiments show that EMOTO has superior performance on multiple optimization objectives compared with advanced multi-objective reinforcement learning algorithms. In addition, the algorithm shows robustness when applied to different environmental settings and better adapting to highly dynamic environments, and balancing the conflict of interest between vehicle users and service providers.

Online optimal scheduling for battery swapping charging systems with partial delivery

Battery swapping–charging system (BSCS) is a promising operating paradigm to provide centering charging and battery swapping service for electric vehicles in transportation electrification. Facing real-time information on battery demand under the limited transporting trucks, flexible online optimization of battery delivery and transportation routing is essential for meeting practical requirements. This paper investigates the real-time scheduling problem in BSCS, considering the battery partial delivery, energy demand, delivery deadline, and vehicle routing. Considering the non-deterministic polynomia hardness of battery transportation, the offline BSCS is a time-consuming task and is unsuitable for the online setting. A Lagrangian relaxation-based Benders decomposition is proposed for parallel and real-time implementation, improving the scheduling efficiency. To tackle future information such as battery demands and delivery deadlines, by introducing the dummy copy, the offline algorithm is embedded within a rolling horizon framework to solve in real-time repeatedly. Finally, case studies using real road maps in Shanghai and Belgium have verified the validity of the proposed online framework and confirmed the necessity of considering partial delivery in enhancing the operation flexibility of BSCS. The computational efficiency of the proposed algorithm is studied under different scales of the road network, and the profit from partial delivery and online implementation are highlighted.

S-LDM: Server local dynamic map for 5G-based centralized enhanced collective perception

The automotive field is undergoing significant technological advances, which includes making the next generation of autonomous vehicles smarter, greener and safer through vehicular networks, which are often referred to as Vehicle-to-Everything (V2X) communications. Together with V2X, centralized maneuver management services for autonomous vehicles are increasingly gaining importance, as, thanks to their complete view over the road, they can optimally manage even the most complex maneuvers targeting L4 driving and beyond. These services face the challenge of strictly requiring a high reliability and low latency, which are tackled with the deployment at orchestrated Multi-Access Edge Computing (MEC) platforms. In order to properly manage safety-critical maneuvers, these services need to receive a large amount of data from vehicles, even though the useful subset of data is often related to a specific context on the road (e.g., to specific road users or geographical areas). Decoding and post-processing a large amount of raw messages, which are then for the most part filtered, increases the load on safety-critical services, which should instead focus on meeting the deadlines for the actual control and management operations. On this basis, we present an innovative open-source, 5G & MEC enabled service, called Server Local Dynamic Map (S-LDM). The S-LDM is a service that collects information about vehicles and other non-connected road objects using standard-compliant messages. Its primary purpose is to create a centralized dynamic map of the road that can be shared efficiently with other services managing L4 automation, when needed. By doing so, the S-LDM enables these services to widely and precisely understand the current situation of sections of the road, offloading them from the need of quickly processing a large number of messages. After a detailed description of the service architecture, we validate it through extensive laboratory and pilot trials, involving the MEC platforms and production 5G networks of three major European network operations and two Stellantis vehicles equipped with V2X On-Board Units (OBUs). We show how it can efficiently handle high update rates and process each messages in less than few tenths of microseconds. We also provide a complete scalability analysis with details on deployment options, providing insights on where new instances should be created in practical 5G-based V2X scenarios.

An Inventory Control of Wuling Car Spare Parts Using a Risk Analysis Appoarch

PT XWZ is a service company engaged in the automotive sector, which serves the stages of unit sales to service car vehicles (after sales service) for the Wuling car brand. In improving the vehicle service or maintenance process, it is necessary to have an optimal availability of spare parts. From the inventory data in the warehouse, the majority of spare parts stored in the warehouse are classified as slow moving parts. By conducting an ABC and FMEA approach, it is expected to be able to increase the reliability of maintenance needs so as to increase inventory according to the criticality value of spare parts. In this research, 19 spare parts items were found to be classified as critical or high risk items.

The Effect of Service Quality on Customer Satisfaction at Apeng Auto Service Workshop Using the Potential Gain in Customer Value Method

Bengkel Apeng Auto Service operates as a provider of motor vehicle service, encompassing not only repairs but also offering oil changes and car washing services. Situated at Jl. Imam Bonjol no.7, Geluran, Taman District, Sidoarjo Regency, this establishment is equipped to address various mechanical and body-related issues in automobiles. Furthermore, it provides both new and used spare parts. This study employs a customer-centric potential gain approach to pinpoint attributes contributing to decreased customer satisfaction. Additionally, there exists one attribute with the lowest PGCV index, indicating satisfaction aligns with customer expectations; however, this doesn't rule out the potential for improvement, as evidenced by a PGCV index value of less than 10.5. The first attribute, E2 - Mechanic's Attention to Customer Preferences, holds a PGCV index of 10.46, while the second attribute, RE3 - Information about Product Sales to Customers, has a PGCV index of 10.27. It is advisable for Bengkel Apeng Auto to periodically assess their service quality to identify potentially dissatisfactory aspects from customers' perspectives. This practice enables the implementation of corrective measures to enhance customer satisfaction levels.

Environmental Implications of the Soil-to-Groundwater Migration of Heavy Metals in Mining Area Hotspots

Groundwater contamination was studied at several hotspot sites in the Majdanpek copper mining area (Serbia). These sites include a milling facility, a metallurgical wastewater treatment plant, a heavy vehicle service area, and a waste disposal site. In addition to Cu, high concentrations of As and heavy metals (Cd and Pb) were detected in groundwater and soil at the same sampling points. Mining operations and heavy vehicle transport activities have been identified as the main sources of pollution. The migration of metals from soil to groundwater, expressed as a concentration ratio, were the highest for Co and the lowest for Mn. The environmental implications of groundwater pollution were studied using the heavy metal pollution index (HPI), Nemerov pollution index (NPI), hazard index (HI), and incremental lifetime cancer risk (ILCR). HPI and NPI show the high potential of groundwater to have adverse environmental effects. HPI ranges in the following descending order of metals: Cd > Pb > As > Mn > Ni > Cr > Hg > Cu > Zn. NPI exceeds the threshold of 0.7 in 66.7% of the samples. Potential human exposure to the studied groundwater may cause severe health problems in adults, with HI ranging from 0.61 to 5.45 and ILCR from 1.72 × 10−4 to 1.27 × 10−3. Children were more susceptible to non-carcinogenic risk than adults, with HI ranging from 0.95 to 8.27. However, the results indicated that children were less prone to carcinogenic risks, with ILCR ranging from 5.35 × 10−5 to 3.98 × 10−4. Arsenic is the most contributing element to both risks. This research imposes the need for enhanced groundwater monitoring at hotspots in the mining area and the adoption of remediation plans and measures.

Kepuasan dan Kepercayaan Pelanggan sebagai Faktor Penentu Loyalitas pada Layanan Transportasi Online Grab di Kota Medan

This study aims to examine the role of customer satisfaction and trust as determinants of loyalty in Grab online transportation services in Medan City. The research method used is a qualitative approach with a survey method. Data collection was carried out through the distribution of online questionnaires to 60 respondents selected using purposive sampling technique. The data were analyzed descriptively using a Likert scale. The results showed that customer satisfaction, which includes service quality, driving experience, driver professionalism, vehicle cleanliness, and complaint handling, was rated quite well by the respondents. Customer trust in Grab as a safe, transparent, and reliable transportation choice has also proven to be an important factor in building loyalty. However, customer loyalty to Grab is not very strong, given the intense competition in the online transportation industry. The implication is that Grab and similar companies need to continue improving service quality, ensuring customer safety and comfort, responding to complaints effectively, and implementing effective marketing strategies to retain customers. This research provides valuable insights for online transportation service providers in their efforts to enhance customer satisfaction, trust, and loyalty amidst dynamic market competition.

The Urgency Of The Mobile Sharia Service Program at BPD Kaltimtara Syariah as Supporting IKN Development

The market opportunity for sharia banks is still very large, which is a challenge for sharia banks to attract and convert Muslim people into customers. Kutai Kartanegara Regency is one of the districts of East Kalimantan province which has an area of 27,263.10 km² and has a large potential market for sharia customers. This type of research is called field research, and the research approach in this research is a descriptive qualitative approach. This research was carried out at Bank Kaltim Syariah Tenggarong branch and other that have implemented the Sharia Mobile Service program as well as customers who use and do not use sharia product services in several border sub-districts in Kutai Kartanegara district. Data collection techniques through observation, interviews and documentation as well as data analysis techniques through qualitative descriptive analysis in this research, namely, explaining the urgency of community needs-based sharia mobile services. The sharia mobile service program already exists at the Kaltimtara Bank Balikpapan and Samarinda branches, but website do not match those carried out in the field. Bank Kaltimtara Syariah North Penajam Paser sub-branch and Tenggarong Kutai Kartanegara Branch need to have sharia mobile service vehicles for both of them. This has been reviewed from 5 conditions, namely the economic condition of the community, socio-cultural demographics/psychography of the community, politics , government, and law, technology, and competitive factors. The results went to the people of Kutai Kartanegara Regency,the community wants sharia-based services and takes into account the pick-up and drop-off services available in the Sharia Mobile Service program as well as financing that is profitable for customers.

Cost-aware task offloading in vehicular edge computing: A Stackelberg game approach

With the popularity of vehicular communication systems and mobile edge vehicle networking, intelligent transportation applications arise in Internet of Vehicles (IoVs), which are latency-sensitive, computation-intensive, and requiring sufficient computing and communication resources. To satisfy the requirements of these applications, computation offloading emerges as a new paradigm to utilize idle resources on vehicles to cooperatively complete tasks. However, there exist several obstacles for realizing successful task offloading among vehicles. For one thing, extra cost such as communication overhead and energy consumption occurs when a task is offloaded on a service vehicle, it is unlikely to expect the service vehicle will contribute its resources without any reward. For another, since there are many vehicles around, both user vehicles and service vehicles are trying to strike a balance between cost and profit, through matching the perfect service/user vehicles and settled with optimal offloading plan that is beneficial to all parties. To solve these issues, this work focuses on the design of effective incentive mechanisms to stimulate vehicles with idle resources to actively participate in the offloading process. A fuzzy logic-based dynamic pricing strategy is proposed to accurately evaluate the cost of a vehicle for processing the task, which provides insightful guidance for finding the optimal offloading decision. Meanwhile, the competitive and cooperation relations among vehicles are thoroughly investigated and modeled as a two-stage Stackelberg game. Particularly, this work emphasizes the social attributes of vehicles and their effect on the offloading decision making process, multiple key properties such as the willingness of UV to undertake the task locally, the reputation of UV and the satisfaction of SV for the allocated task proportion, are carefully integrated in the design of the optimization problem. A distributed algorithm with applicable complexity is proposed to solve the problem and to find the optimal task offloading strategy. Extensive simulations are conducted on real-world scenarios and results show that the proposed mechanism achieves significant performance advantages in terms of vehicles' utilities, cost, completion delay under varied network and channel environment, which justifies the effectiveness and efficiency of this work.

Dynamic task offloading and service caching based on game theory in vehicular edge computing networks

Vehicular edge computing (VEC) enables task offloading from vehicle to edge servers deployed on Road Side Units (RSUs) and to vehicle with idle resources, which is considered as a promising vehicle network architecture. However, efficient task offloading and task resource cache management in the VEC network is challenging. In this work, we formulate a joint task offloading and service caching problem (JTOSCP) for VEC with edge-vehicle cooperation, aiming to minimize the total task delay-energy value and cost value of all vehicles while guaranteeing task processing delay tolerance. The formulated JTOSCP is proven as NP-hard problem. Due to the complexity of JTOSCP, we propose a cooperative task service caching and task offloading algorithm named CO-MATCH. In this algorithm, we design the dynamic programming-based service caching (DPSC) Algorithm to incentivize edge services and volunteer vehicles to cache, which takes into account both service caching preferences and social similarity. The Many-to-One Matching Game (MOMG) Algorithm is proposed to stimulate edge device games and achieve the optimal task offloading. Then, the CO-MATCH method is proven to be stable. Simulation results demonstrate that our proposed approach can significantly improve the quality of service of user vehicles compared to the other methods.

WEB BASED EV CHARGING STATION FINDER AND SLOT BOOKING

One of the primary challenges is the inadequate availability of EV charging stations, particularly in certain regions or areas with high population density. This scarcity makes it inconvenient for electric vehicle owners to find accessible charging points, leading to range anxiety and reluctance in adopting electric vehicles. This project introduces an innovative solution for optimizing on-road vehicle services and electric vehicle (EV) charging station slot booking systems. Through this integrated platform, users can seamlessly access a range of on-road services, including vehicle maintenance, repair, and emergency assistance, while also reserving slots for EV charging stations. Leveraging advanced technologies, such as real-time GPS tracking and automated scheduling algorithms, the platform facilitates efficient dispatching of service providers to users’ locations, ensuring timely assistance and minimizing downtime for vehicles. Key Words: Electric vehicle charging locator, EV charging point finder, Charging station locator app, Electric car charging station map, EV charger locator service, Electric vehicle charging spot finder, Charging station finder tool, EV charging station search engine.

Semiquantitative Risk Evaluation Reveals Drivers of African Swine Fever Virus Transmission in Smallholder Pig Farms and Gaps in Biosecurity, Tanzania.

African swine fever (ASF) has remained persistent in Tanzania since the early 2000s. Between 2020 and 2021, pig farms in twelve districts in Tanzania were infected with ASF, and ≥4,804 pigs reportedly died directly due to the disease with disruption to livelihoods. We conducted semiquantitative field investigations and rapid risk assessment (RRA) to understand the risk factors and drivers of ASF virus (ASFV) amplification and transmission in smallholder pig farms, and determine the gaps in biosecurity through hazard profiling, focus group discussions and expert opinion. Outbreaks were connected by road and aligned along the pig product value chain and reported in the northern, central, and southern parts of Tanzania. The patterns of outbreaks and impacts differed among districts, but cases of ASF appeared to be self-limiting following significant mortality of pigs in farms. Movement of infected pigs, movement of contaminated pig products, and fomites associated with service providers, vehicles, and equipment, as well as the inadvertent risks associated with movements of animal health practitioners, visitors, and scavengers were the riskiest pathways to introduce ASFV into smallholder pig farms. Identified drivers and facilitators of risk of ASFV infection in smallholder pig farms were traders in whole pigs, middlemen, pig farmers, transporters, unauthorized animal health service providers, and traders in pork. All identified pig groups were susceptible to ASFV, particularly shared adult boars, pregnant and lactating sows, and other adult females. The risk of ASF for smallholder pig farms in Tanzania remains very high based on a systematic risk classification. The majority of the farms had poor biosecurity and no single farm implemented all identified biosecurity measures. Risky practices and breaches of biosecurity in the pig value chain in Tanzania are profit driven and are extremely difficult to change. Behavioural change communication must target identified drivers of infections, attitudes, and practices.

Vehicle Maintenance System using Python

Owners of vehicles can avoid unforeseen car problems by maintaining their vehicles regularly. Early detection of vehicle concerns is crucial to preventing them from developing into serious difficulties. Owners of vehicles frequently inquire about service at auto shops. Vehicle technicians examine crucial parts of the vehicle and fix problems to make sure they are in good condition and won't break down suddenly. The study was carried out by the researchers to evaluate the everyday activities and transactions carried out in auto repair facilities. The researchers gathered preliminary data and discovered that the majority of auto repair companies or garages still handle their daily transactions manually. Customer records are still entered manually, as are transactions with clients, tracking of vehicle repairs as they are made, updates on vehicle services, and billing. The functioning of the garage and the response time to client inquiries will be slowed by the ongoing usage of manual procedures. The researchers have identified a need to modernize how auto repair shops work. Customers and car garages will both profit from the system's installation. Utilizing the technology to run the company will help car garages expand and improve operational efficiency. Customers may do business for car service easily and comfortably

Optimizing Last-Mile Delivery: A Multi-Criteria Approach with Automated Smart Lockers, Capillary Distribution and Crowdshipping

Background: This publication presents a review, multiple criteria optimization models, and a practical example pertaining to the integration of automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. This publication addresses challenges in logistics and transportation, aiming to enhance efficiency, reduce costs and improve customer satisfaction. This study integrates automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. Methods: A review of the existing literature synthesizes key concepts, such as facility location problems, vehicle routing problems and the mathematical programming approach, to optimize supply chain operations. Conceptual optimization models are formulated to solve the complex decision-making process involved in last-mile delivery, considering multiple objectives, including cost minimization, delivery time optimization, service level minimization, capacity optimization, vehicle minimization and resource utilization. Results: The multiple criteria approaches combine the vehicle routing problem and facility location problem, demonstrating the practical applicability of the proposed methodology in a real-world case study within a logistics company. Conclusions: The execution of multi-criteria models optimizes automated smart locker deployment, capillary distribution design, crowdshipping and last-mile delivery strategies, showcasing its effectiveness in the logistics sector.

Stochastic home care transportation with dynamically prioritized patients: An integrated facility location, fleet sizing, and routing approach

We study a home health care (HHC) problem that is characterized by prioritized patients and uncertain demands. In practice, HHC supply chain networks often struggle to meet high demand due to a shortage of service vehicles. Additionally, disruptions caused by natural calamities and pandemics (e.g., COVID-19) further compound these challenges, necessitating the consideration of real-life characteristics such as patient priorities, infrastructure locations, and transportation of medical supplies with uncertain demands. To formulate the problem, we propose a multi-depot and multi-period chance-constrained optimization model with precedence constraints, assuming that the demand quantities for medical supplies are random variables. Since patients’ medical conditions vary in severity, the priority of each patient is translated into a time-dependent potential healthcare cost that changes dynamically over the planning horizon. The solution to the proposed model determines the optimal locations for the base Mobile Health Facilities (MHFs) and the fleet size of HHC vehicles, and generates scheduling and routing plans to visit patients within specified time windows. We propose a unique three-phase solution approach, integrated with stochastic simulation, to address the problem. We then assess the robustness of the proposed model based on a realistic case of HHC service provision in Hong Kong and explore the optimal values for two model parameters, namely the Vehicle Threshold Index and the MHF Threshold Index. The performance evaluation tests show that the proposed solution method is efficient and effective for solving real-world problems.

Garage Wala: Transforming India\'s Automobile Service Landscape Through Digital Innovation

Abstract: This study explores the transformative potential of Garage Wala, a burgeoning entrepreneurial venture in the automobile service industry, designed to bridge the gap between vehicle owners and service providers in India. Garage Wala's innovative business model, predicated on the seamless integration of digital technology with traditional car repair services, aims to offer unprecedented convenience, quality, and reliability to vehicle owners. This research paper delves into the genesis of Garage Wala, examining its strategic response to the burgeoning demand for accessible, trustworthy, and efficient automobile maintenance services in an era marked by rapid urbanization and the proliferation of vehicle ownership. Through qualitative and quantitative analyses, the paper assesses Garage Wala's market positioning, operational strategies, and the sustainability of its business model in the face of evolving consumer preferences and intense competition. Drawing on the latest industry data, the study highlights the critical role of digital platforms in enhancing service accessibility, fostering consumer trust, and streamlining operations within the automobile service sector. Furthermore, it evaluates the challenges and opportunities presented by the Indian market's unique characteristics, including regulatory hurdles, the informal economy's prevalence, and the increasing emphasis on environmental sustainability. The findings underscore the potential of platforms like Garage Wala to redefine automotive service paradigms, contributing to a more organized, customer-centric, and sustainable industry landscape in India. This research not only provides valuable insights for academics and practitioners but also offers strategic recommendations for similar start-ups navigating the complexities of the Indian market.

Proactive real-time scheduling method for apron service vehicles based on mixed strategies

Due to the uncertainty of flight arrival and departure times, the scheduling of apron vehicles in airports has always faced significant challenges. This paper considers the real-time scheduling problem of apron vehicles under dynamic requests, establishing a new parking rule to allow apron vehicles to dynamically adjust parking areas. In order to maximize service level and minimize operational costs, this paper proposes a proactive real-time scheduling method for apron vehicles based on mixed strategies. Through strategies such as flexible waiting and dynamic relocation, this paper responds proactively to future requests. The proposed strategies are validated through implementation in large and medium-sized scale cases at Beijing Capital International Airport and Nanjing Lukou International Airport in China, respectively. Numerical results indicate that the proposed scheduling method is effective and applicable. The flexible waiting strategy significantly reduces vehicle travel distance, while the dynamic relocation strategy is significant in shortening request response times.

Acceptance model of new energy vehicles based on PLS-SEM model

The current energy crisis is worsening worldwide, and in China, urban expansion and per capita vehicle ownership have created a growing energy imbalance and increased pressure to reduce carbon emissions.The popularization of new energy vehicles (NEVs) can provide a step forward to solving energy shortage problems, environmental pollution, and global warming. In 2022, the average penetration rate, which is ratio of new energy vehicle sales to vehicle sales, is just 19.1 %. This paper analysed the reasons for the differences in the penetration rates of new energy vehicles in China's 269 prefecture-level cities, using a Geo Detector approach, and the results showed that the level of economic development, the average annual temperature difference, the density of charging piles, the charging price and the number of population all had significant effects(q＞0.12) on the penetration rate. Based on the above studies, a questionnaire was used to investigate the public's acceptance of new energy vehicles in Xinjiang Uygur Autonomous Region, and a PLS-SEM regression analysis was conducted. The results showed that men, young people and people with a certain level of basic education were 5 % more likely to accept new energy vehicles.Unlike previous studies, perceived cost had no significant correlation with the acceptance of new energy vehicles. Perceived risk had a significant negative correlation with the acceptance of new energy vehicles,the path coefficient is −0.1.The acceptance of new energy vehicles was significantly and positively correlated with vehicle quality and service, the public's understanding of new energy vehicles, and subjective norms, their average path coefficients are above 0.1. We argues that the government should maintain a certain level of promotion of new energy vehicles and accelerate the construction of charging piles, based on the aforementioned results.