Driver's Perspective Research Articles

Evaluation of driver demand for in-vehicle information: An integrated method combining clustering and multivariate ordered probit model

Introduction: The proper execution of driving tasks requires information support. While new technologies have increased the convenience of information access, they have also increased the risk of driver distraction and information overload. Meeting drivers’ demands and providing them with adequate information are crucial to driving safety. Methods: Based on a sample of 1,060 questionnaires, research on driving information demands is conducted from the perspective of drivers. A principal component analysis and the entropy method are integrated to quantify the driving information demands and preferences of drivers. The K-means classification algorithm is selected to classify the different types of driving information demands, including dynamic traffic information demands (DTIDs), static traffic information demands (STIDs), automotive driving status information demands (ATIDs), and total driving information demands (TDIDs). Fisher's least significant difference (LSD) is used to compare the differences in the numbers of self-reported crashes among different driving information demand levels. A multivariate ordered probit model is established to explore the potential factors that influence the different types of driving information demand levels. Results: The DTID is the driver's most in-demand information type, and accordingly, gender, driving experience, average driving mileage, driving skills, and driving style significantly affect the driving information demand levels. Moreover, the number of self-reported crashes decreased as the DTID, ATID, and TDID levels decreased. Conclusion: Driving information demands are affected by a variety of factors. This study also provides evidence that drivers who have higher driving information demands are more likely to drive more carefully and safely than their counterparts who do not exhibit high driving information demands. Practical implications: The results are indicative of the driver-oriented design of in-vehicle information systems and the development of dynamic information services as a way to avoid negative impacts on driving.

Exploring driver and user perspectives of service quality in sharing economy transport platforms

With the rise of the sharing economy, companies such as Uber have emerged as an alternative to traditional transport methods. Digital sharing economy platforms enable the encounter of two types of ‘users’: peer providers (i.e. drivers) and travellers (commonly referred as users). To acknowledge the needs of both the user and the provider, we must understand their perceptions of service quality (SQ) in this new context. However, existing research about SQ in platform-based transport focuses only on the users’ perspective. Given that peer providers are an essential part of peer-to-peer transport services, this exploratory study aims to capture the perceptions of SQ from the perspective of users and drivers. Relying on service quality theory, two quality assessment scales are designed for the users and the providers, respectively. Based on the data obtained from 646 users and drivers, Structural Equation Modelling (SEM) is used to validate the proposed scales. This study contributes to the literature of quality management in the sharing economy and provides valuable insights for platform managers.

Research on the Design and Evaluation Method of a Tractor Intelligent Control Interface

With the development of technology, future tractors will become more intelligent. More complex interfaces and several control components tend to cause drivers’ cognitive load to be easily overloaded. Therefore, an evaluation method of the control interface should be proposed from the cognitive load perspective. To realise this concept, first, the influence of the design elements of the interface on behaviour time was investigated and a behaviour time model was established. Consequently, the experimental data could be well fitted by the model (R 2 > 0.9), and the behaviour time of the operating interfaces could be effectively predicted. Second, an evaluation index of the interface was proposed based on behaviour time, which could evaluate the interface’s design level. Finally, the validity of the evaluation method was verified. The results provide theoretical guidance for human–machine interfaces for tractors from the perspective of drivers’ cognitive load.

Exploration into the Needs and Requirements of the Remote Driver When Teleoperating the 5G-Enabled Level 4 Automated Vehicle in the Real World-A Case Study of 5G Connected and Automated Logistics.

Connected and automated vehicles have the potential to deliver significant environmental, safety, economic and social benefits. The key advancement for automated vehicles with higher levels of automation (SAE Level 4 and over) is fail-operational. One possible solution for the failsafe mode of automated vehicles is a 5G-enabled teleoperation system controlled by remote drivers. However, knowledge is missing regarding understanding of the human-machine interaction in teleoperation from the perspective of remote drivers. To address this research gap, this study qualitatively investigated the acceptance, attitudes, needs and requirements of remote drivers when teleoperating a 5G-enabled Level 4 automated vehicle (5G L4 AV) in the real world. The results showed that remote drivers are positive towards the 5G L4 AV. They would like to constantly monitor the driving when they are not controlling the vehicle remotely. Improving their field of vision for driving and enhancing the perception of physical motion feedback are the two key supports required by remote drivers in 5G L4 AVs. The knowledge gained in this study provides new insights into facilitating the design and development of safe, effective and user-friendly teleoperation systems in vehicle automation.

Mengungkap Makna Pendapatan dalam Perspektif Sopir Bus di Masa Pandemi Covid-19

Purpose: This study aims to understand in depth the meaning of income according to the perspective of city transportation bus drivers at the Paciran terminal during the COVID-19 pandemic. In this study, the meaning of income is formed by the environment of the city transport bus drivers.Method: This study uses the approach with the interpretive paradigm of Husserl's transcendental phenomenology. The phenomenological method emphasizes the meaning that arises from each individual's experience and awareness. Results: The results showed that the informants had different meanings of income. Income is interpreted as a form of material used to meet family needs and future savings. Income is a non-material form, namely the income of inner satisfaction by feeling happy because it helps passengers who need transportation, and income is a spiritual form by applying "flexible" fares to passengers who need transportation for humanitarian purposes, It is believed that someone will get more sustenance than what is given to others because sustenance has already been arranged.Implications: This study reveals the meaning of income from the perspective of bus drivers. The approach through different professions but close to the community provides expansion and development of accounting theory from various existing perspectives, including the internalization of local wisdom values in it.Novelty: This study has a novelty in Hurssel's phenomenological approach in the context of the meaning of income from the perspective of bus drivers which has a meaning that is contrary to the concept of accounting theory.

Disentangling Determinants of Ride-Hailing Services among Malaysian Drivers

Ride-hailing has emerged as one of the progressive sharing economy platforms. As a digital platform, both riders and drivers are critical to achieving sustainable ride-hailing transactions. Previous studies have gained little insight into ride-hailing services from drivers’ perspectives. This study investigates the salient factors that determine the usage of ride-hailing services among drivers in Malaysia by extending the technology acceptance model (TAM), introducing governmental regulations, and integrating perceived risk and trust into the model. We collected data from a total of 495 ride-hailing drivers across Malaysia. Our results suggest that a driver’s intention to use ride-hailing services is determined by perceived ease of use, perceived usefulness, and governmental regulations, which lead to actual usage. However, unexpectedly enough, the results signify that perceived risk does not affect the intention to use ride-hailing unless there is trust among the drivers. Overall, this paper draws attention to the substantial contrast in its results from the majority of prior TAM literature and has thoroughly improved the exploratory power of TAM by introducing new variables into the model, particularly from the perspective of ride-hailing drivers. This study is expected to bring theoretical and practical contributions to improve the country’s ride-hailing industry.

The epidemiology of road traffic accidents and associated factors among drivers in Dilla Town, Southern Ethiopia.

Data on the magnitude of road traffic accidents (RTAs) were mostly obtained through police records and hospital registration data. However, insufficient data reporting masked the gravity of the problem, and little attention was paid to the magnitude and correlation of road traffic accidents from the driver's perspective. Therefore, this study aimed to assess the prevalence of RTA and related factors among drivers. A community-based cross-sectional study involving 316 drivers was conducted in Southern Ethiopia. The participants were chosen using a systematic random sample technique, and the data were obtained using an interview-administered structured questionnaire. To analyze the data, SPSS software (version 20) was employed. In addition to descriptive statistics, binary logistic regression analysis was also employed to find factors connected to traffic accidents. RTA factors were considered statistically significant if they had a P-value of 0.05 or below in the multivariate analysis. The RTA among drivers was 126 (39.9%) (95% confidence interval (CI): 34.2-45.6%) in the previous year. The following factors were associated with RTA: vehicle maintenance (AOR = 0.11, 95% CI: 0.09, 0.96), media utilization (AOR = 0.38, 95% CI: 0.18, 0.65), participation in driving-related training (AOR = 0.73, 95% CI: 0.28, 0.91), punishment for prior traffic violations (AOR = 0.56, 95% CI: 0.47, 0.83), and risky driving behavior (AOR = 7.89, 95% CI: 3.22, 12.38). Two-fifths of the drivers were involved in a traffic accident. Risky driving behaviors, vehicle maintenance, media usage, attending driving-related training in the previous 2 years, and prior experience with traffic police punishment or warning were all strongly linked to road traffic accidents. In light of these statistics, the Federal Ministry of Transport of Ethiopia and other stakeholders should support making it mandatory for drivers to check their vehicles' safety, provide them with safety training, raise awareness about vehicle maintenance and risky driving behaviors, and enforce strict penalties for traffic violations.

Decoupling and scenario analysis of economy-emissions pattern in China's 30 provinces.

The tension between reducing CO2 emissions and economic growth has become increasingly prominent in recent years, while China is vigorously promoting ecological civilization to achieve sustainable development. However, the factors influencing China's current economic emission nexus at the regional and provincial levels and the sustainability of the strong decoupling state remain unclear. We analyze the decoupling of emissions at the national and provincial levels of the Chinese economy from the perspective of historical patterns and current drivers from 1997 to 2019. Also, we developed three scenarios (i.e., pessimistic, median, and optimistic scenarios) to analyze the impact of decoupling relationship changes. We find that China's national decoupling relationship has eased since 1997, but it has not yet reached the ideal state, with provinces mainly exhibiting weak decoupling. The EKC hypothesis is tested for the whole country and 30 provinces and finds that 15 provinces have two turning points, 13 provinces have one turning point, and the others have no turning point. Based on the scenario analysis, the total emissions in the pessimistic scenario (S1) without any improvement of decoupling would increase by 73.97% compared to the level of 2019. However, the total emissions in the optimistic scenario (S3), in which all provinces obtained strong decoupling, are almost half of the level of 2019. This is mainly from the reduction of emissions in the western less developed regions (e.g., Shanxi, Inner Mongolia, and Xinjiang) and developed coastal regions (e.g., Jiangsu and Shandong). On the basis of the results of factor analysis, we put forward policy recommendations for expanding electrification, optimizing industrial structure, and promoting technological innovation.

Knowledge Sharing in the Supply Chain Networks: A Perspective of Supply Chain Complexity Drivers

Background: Knowledge transfer has been an important aspect in supply chain systems; however, there are many factors that affect the impact of transfer effectiveness. In previous studies, less attention has been given to the process of knowledge transfer and therefore, this paper highlights the significance of the process of knowledge transfer in supply chain processes. Enhanced supply chain networks have better performance when knowledge sharing is present, and it is important to identify and eliminate any supply chain complexity to improve the supply chain processes. Methods: A systematic review on the literature has been conducted to critically identify and evaluate the factors of supply chain complexity which impact knowledge transfer. Findings: The findings highlight the key benefits of effective knowledge transfer in supply chain systems by identifying risks associated with supply chain networks. Conclusion: The balanced power of facilitating knowledge transfer in supply chain processes helps in supply chain partnerships and reduces the supply chain complexity. This paper has both theoretical and practical contributions as it adds to the literature by identifying the factors of supply chain complexity drivers which are impacting the knowledge transfer.

Discovering Implicit Working Pace of Online Ride-Hailing Drivers: An Exploratory Study

Due to convenience and flexibility, online ride-hailing has become increasingly more prevalent across the world. However, many violations and road crashes involving online ride-hailing were related to the unhealthy working pace of drivers, especially inadequate rest. This paper enriches the literature by providing a first look into the latent break patterns of online ride-hailing drivers based on a one-month order record dataset. A data mining and knowledge discovery process is presented for extracting and analyzing characteristics of online ride-hailing drivers’ work and rest based on GPS trajectory data, as follows: 1) logical judging to identify non-work order-gaps; 2) dynamic topic modeling to discover latent break patterns; and 3) integrating the topic modeling results with feature analysis results of order-gaps to summarize the time-dependent characteristics of online ride-hailing drivers’ special working pace. The case study results show that the latent break patterns extracted from two cities’ online ride-hailing order records are significantly different in the strength and cycles of the topics, which is greatly related to the travel supply-demand conditions and urban characters. Furthermore, the proposed analytical framework can help mobile transportation platform companies to better understand online ride-hailing markets from the perspective of drivers and to adjust their marketing strategies in real time.

A strategic perspective of eco-innovation drivers: Evidence from Canadian SMEs

The purpose of this article is to investigate the effect of the technology factors and the breadth knowledge search on eco-innovation. The analysis is based on a sample of 634 manufacturing firms in Canada. Eco-innovation literature has largely been developed and operationalized through symmetrical analyses. Such methods necessarily limit investigation of synergies between eco-innovation drivers. The study empirically analyses the data set using structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The SEM findings indicate that knowledge search breadth mediates the relationship between the R&D activities and eco-innovation. The fsQCA results confirm synergies among eco-innovation drivers. Overall, three pathways of eco-innovation drivers are sufficient conditions for explaining high eco-innovation performance.

Time-Domain Characteristics of Residential Parking and SEM-BL Integration Model of Parking Method Choice Behaviour

Correct understanding of the residential parking characteristics and parking behavioral regularities is important to the planning of parking facilities, the selection of parking equipment, and the formulation of parking management measures in residential areas. There are few in-depth reports on the time domain characteristics of the residential parking and the parking choice behavior of developers and drivers. Besides, the influence of developers’ potential demands and drivers’ mental latent variables on the results of parking choice has been ignored. To address the gap, in this paper, we propose the concept of latent variables for parking choice based on the analysis of the time domain characteristics of residential parking in different cities and areas. We construct the SEM-BL integrated model for developers and drivers that considers the combined effect of both manifest and latent variables for the actual residential situation and analyze the factors influencing the choice of stereoscopic parking facilities from the perspective of developers and drivers in residential areas. The results show that the type of residential area, floor space, and plot ratio have a critical impact on parking choice. Residential drivers are most concerned about factors such as walking distance after parking, access convenience, pick-up time, pedestrian access, and environment, while developers choose a stereoscopic parking garage with more concern for economy, ignoring the consideration of drivers for walking distance after parking and convenience of accessing the car. Therefore, the mismatch between the needs of both sides is one of the important factors that make it difficult to solve the problem of parking in residential areas. Based on these results, we put forward a proposal for residential parking planning and equipment selection that takes into account the common needs of both developers and drivers.

Flexibilization and precarization of working conditions and labor relations in the perspective of app-based drivers

PurposeThe challenges of the growth of the sharing economy are becoming more and more noticeable and urgent, especially concerning labor relations (e.g. uberization). The purpose of this paper is to understand what app-based drivers think of working conditions and labor relations.Design/methodology/approachThe research was carried out in three stages: bibliographical and documental research, and two empirical research, a quantitative one with the application of a questionnaire in a sample of 54 respondents and another qualitative one using an interview script with ten drivers. For data analysis, the abductive method and the content analysis technique were used.FindingsThe results reveal they have an exhausting labor routine, by checking that they work more hours per week than those who have a formal job. They are driven mainly by the extra income and flexibility that digital platforms of the sector of shared private transportation can offer, although the costs intrinsic to the activity often affect their revenues significantly.Research limitations/implicationsThe number of answers from women was very small, which hinders the analysis of the potential specificities of women app-based drivers. Future studies could focus on this public for a more precise analysis, to bring the discussion on gender to the working context of app-based drivers.Practical implicationsThe authors’ intention with the research reports was to make them relevant, leading to effective policies concerning working conditions and labor relations in the sharing economy, and to stimulate other surveys to understand the activity of an app-based driver of shared private transportation.Originality/valueThe authors’ research and this article contribute to the discussion on new work relationships, motivations and (dis)satisfaction with the activity, from the perspective of app-based drivers.

Impact of Road Environment on Drivers’ Preference to Merging Location Selection in Freeway Work Zone Merging Areas

In view of the compulsory merging behavior and complex driving environment in freeway work zones, the factors influencing drivers’ merging behavior need to be focused on the given road environment. Realizing the need to mitigate the impact of such a challenging scenario, this study aims to explore the impact of road environment on drivers’ merging location selection in freeway work zone merging areas. The survey data for modelling were collected through questionnaires survey based on the stated preference (SP) method. The logistics regression model was utilized to extract the significant factors influencing merging location selection. The results of fitting effect analysis show that the developed logistics regression models provide a good fit for the survey data. The road conditions and speed limit strategies are the significant factors affecting the drivers’ preference to merging location selection in upstream transition area. The road conditions, traffic environment conditions, speed conditions, and speed limit strategies are the prominent influencing factors to the latter part of advance warning area. It is a comprehensive analysis to consider the influence of road environment on merging location selection from the perspective of drivers, which is expected to support the merging control strategy and avoid the occurrence of traffic crash in work zones.

Quantifying the evolutionary mechanism of COVID-19 impact on international construction multi-projects: a risk driver perspective

Purpose The purpose of this paper is to quantify the evolutionary mechanism of coronavirus disease 2019 (COVID-19) impact on international construction multi-projects.Design/methodology/approach From three concepts of complexity, stressor and detractor risks, a multi-project simulation model under COVID-19 outbreak is proposed to study the characteristics of contingency with three peaks of ordered and disordered states. Specially, COVID-19 brings forth mitigation fee, epidemic prevention fee, holdup fee and schedule delay fee of multi-projects. By integrating parametric model, Monte Carlo and chaos theory, a comparative analysis of its contingency with or without COVID-19 is conducted. Summarizing the simulated results, their total contingencies at certain risk tolerance are obtained at two status of static at one-time point and dynamic over time. Meanwhile, some major risks including detractors, complexities and stressors are screened out for mitigation, especially for epidemic prevention and control. Eventually, the real case is illustrated to demonstrate its validity.Findings It provides a quantitative analysis framework for the impact of epidemic, a once-in-a-century black swan event with a long tail, on construction multi-projects.Practical implications It conduct an effective model to quantify impacts of COVID-19 on international construction multi-projects for implementing effective counter-measures, which lay foundation for claims among different stakeholders.Originality/value The term of detractor risk is applied to describe COVID-19 and quantify its impact upon international construction multi-projects. Further, a hybrid model by integrating parametric model and Monte Carlo in type I/II model is proposed to simulate their contingencies at disordered states. Finally, the simulated outcomes of these models are used to guide effective risk control to meet the requirements by the client.

Survey Based Road Safety Analysis to Understand the Perspective of the General Public

The number of road traffic deaths continues to climb, reaching 1.35 million deaths globally in 2016 and road traffic injury is now the leading cause of death for children and young adults aged 5–29 years (WHO, 2018). Road safety is officially recognized as a global safety issue and it is also included in the Sustainable Development Goals. This is a questionnaire based analysis, which aims to understand the outlook of the general public regarding the current road safety system and to understand the level of awareness regarding the same. It also aims to compare the perspective of drivers and non-drivers towards traffic rules. Majority of the participants were from the age group of 15-30 years as they are most directly affected by road accidents. Our objective is to understand the shortcomings in the current road safety system through the perspective of the general public, which can then help in implementing a more efficient system in order to decrease the number of road accidents. Inadequate traffic system also significantly affects the economic growth of the region. According to the World Road Statistics, 2018, India ranks 1st in the number of road accident deaths hence there is an urgent need to understand the root cause for such a high number of accidents and to find solutions for this issue. Our results show that there is a need to implement stricter rules for road safety and this perspective of the general public is not affected by the status of the individual (driver, non-driver). It also suggests the need for increasing the level of licence tests conducted. Increasing awareness at an early stage among the people regarding the seriousness of following traffic rules can help prevent road accidents at the baseline level

Memory-based variable neighborhood search for green vehicle routing problem with passing-by drivers: a comprehensive perspective

A business delivery model with professional vehicles as well as occasional passing-by vehicles is investigated in this paper. The drivers deliver parcels from the distribution center to customers and the passing-by driver can get a certain amount of compensation in return. To give a satisfactory solution from the perspective of platform owner, customers, professional drivers, occasional drivers, and authority, a multi-layer comprehensive model is proposed. To effectively solve the proposed model, we introduce an improved variable neighborhood search (VNS) with a memory-based restart mechanism. The new algorithm is evaluated on instances derived from Solomon’s benchmark and real-life beer delivery instances. Taguchi experiment is used to tune parameters in the proposed VNS, followed by component analysis and real-life experiments. Experimental results indicate that the proposed strategies are effective and the new delivery model in this paper has some advantages over traditional and single-delivery ones from the comprehensive perspectives of stakeholders in the crowdsourcing logistics system.

Survey Based Road Safety Analysis to Understand the Perspective of the General Public

The number of road traffic deaths continues to climb, reaching 1.35 million deaths globally in 2016 and road traffic injury is now the leading cause of death for children and young adults aged 5–29 years (WHO, 2018). Road safety is officially recognized as a global safety issue and it is also included in the Sustainable Development Goals. This is a questionnaire based analysis, which aims to understand the outlook of the general public regarding the current road safety system and to understand the level of awareness regarding the same. It also aims to compare the perspective of drivers and non-drivers towards traffic rules. Majority of the participants were from the age group of 15-30 years as they are most directly affected by road accidents. Our objective is to understand the shortcomings in the current road safety system through the perspective of the general public, which can then help in implementing a more efficient system in order to decrease the number of road accidents. Inadequate traffic system also significantly affects the economic growth of the region. According to the World Road Statistics, 2018, India ranks 1st in the number of road accident deaths hence there is an urgent need to understand the root cause for such a high number of accidents and to find solutions for this issue. Our results show that there is a need to implement stricter rules for road safety and this perspective of the general public is not affected by the status of the individual (driver, non-driver). It also suggests the need for increasing the level of licence tests conducted. Increasing awareness at an early stage among the people regarding the seriousness of following traffic rules can help prevent road accidents at the baseline level.

Modeling Pedestrian Situated Intent in Dynamic Driving Scenes from the Driver's Perspective

Modeling Pedestrian Situated Intent in Dynamic Driving Scenes from the Driver's Perspective

Monitoring of urban freight flows distribution considering the human factor

The intensive growth of urban freight transport leads to an increase in the amount of transport links, which causes problems such as traffic congestion, an imbalance between the transport services demand and the actual capacity of roads, and the negative impact on the environment. This leads to advanced requirements for urban industrial infrastructures. To optimise freight flows, it becomes necessary to determine the patterns of how drivers choose their routes between the origin and destination points. Drivers participate directly in the transportation process. From the driver's perspective, decision-making about the current route is predicated on the technological parameters of the transportation process (speed, time, etc.), the traffic load of the urban roads, and traffic safety, but also on the level of influence of urban freight flows on the environment. The environmental impact of transportation processes is directly related to the quality of life of city dwellers and increasing of the city sustainability level. This research paper aims to determine the patterns of urban freight flows generation while considering the human factor. The choice of route parameters is significantly affected by individual characteristics of the drivers, which are dependent on the properties of the central nervous system. These properties are grouped by type of temperament. Traffic parameters on different routes have various effects on drivers of dissimilar temperaments. Having estimated the influence of each parameter on the drivers, it is possible to identify preferences of choosing a driving route from among many alternatives. The results of the survey made it possible to determine the parameters of the routes chosen by the drivers moving between the points of departure and arrival of goods, considering the age and temperament of the drivers. The results of the research study described the models for changing the share of origin-destination trips of freight transport, which is performed by drivers of different temperaments along each of the possible routes. This information can be used in modelling of freight transport flows within a city. It could significantly influence the quality of life in the cities and make cities more compliant with social expectations.