Is There a Predisposition towards the Use of New Technologies within the Traffic Field of Emerging Countries? The Case of the Dominican Republic

"Cyclist at 12 o'clock!": a systematic review of in-vehicle advanced driver assistance systems (ADAS) for preventing car-rider crashes

Introduction

While Advanced Driver Assistance Systems (ADAS) have become a prominent topic in road safety research, there has been relatively little discussion about their effectiveness in preventing car collisions involving specific vulnerable road users, such as cyclists. Therefore, the primary objective of this systematic literature review is to analyze the available evidence regarding the effectiveness of in-vehicle ADAS in preventing vehicle collisions with cyclists.

Methods

To achieve this goal, this systematic review analyzed a selection of original research papers that examined the effectiveness of ADAS systems in preventing car-cyclist collisions. The review followed the PRISMA protocol, which led to the extraction of 21 eligible studies from an initial pool of 289 sources indexed in the primary scientific literature databases. Additionally, word community-based content analyses were used to examine the research topics and their links within the current scientific literature on the matter.

Results

Although the current number of studies available is still scarce (most sources focus on car-motorcyclist or car-pedestrian crashes), the overall quality of the available studies has been reasonably good, as determined by the selected evaluation methods. In terms of studies' outcomes, the literature supports the value of in-vehicle ADAS for preventing car-cyclist crashes. However, threatful side effects such as unrealistic expectations of these systems and users' overconfidence or desensitization are also highlighted, as well as the need to increase driver training and road user awareness.

Conclusion

The results of this study suggest that Advanced Driver Assistance Systems have significant potential to contribute to the prevention of driving crashes involving cyclists. However, the literature emphasizes the importance of concurrently enhancing user-related skills in both ADAS use and road-user interaction through educational and training initiatives. Future research should also address emerging issues, such as ADAS-related behavioral ergonomics, and conduct long-term effectiveness assessments of ADAS in preventing car-cycling crashes and their subsequent injuries.

Systematic review registration

PROSPERO, unique identifier CRD42024505492, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=505492.

A model of extended technology acceptance for behavioral intention toward EVs with gender as a moderator

Technology has contributed significantly to the adoption of EVs (EVs) in the era of industry 4. 0. However, consumer intentions for EVs have been elusive, and the pace of adoption has been confined. Therefore, this study aims to investigate the influence of external factors in promoting customer behavioral intention for EVs. The study also investigates the mediating role of perceived ease of use (PEU) and perceived usefulness (PU) between external factors and consumer intentions for EVs. It also examined the moderating role of gender on the study variables. A study approach based on the expanded version of the technology acceptance model (TAM) was utilized to analyse data from 203 customers in China. The model was tested using structural equation model (SEM) and multigroup analysis (MGA) techniques. The results indicated that two external factors have a positive relationship with TAM constructs. The results also indicate that PEU and PU have a serial mediating relationship between external factors and behavioral intention. Finally, the study revealed gender-related differences in TAM for EVs. The study's findings help managers to design successful strategies by knowing the external factors impacting customer EV intentions and gender differences. Finally, this is a ground-breaking research that applies TAM to the automobile sector. As a result, examining both new and current factors and evaluating them in a new setting adds to the body of the literature on the adoption of technology.

Road safety in Nigeria: unravelling the challenges, measures, and strategies for improvement

Stakeholders play a crucial role in ensuring the smooth design and implementation of policies worldwide. This research presents the results of three focus groups carried out with stakeholders from different transport ministries and agencies involved in road safety in Nigeria. It aimed to understand their perception of road safety in Nigeria through discussing the road safety situation, measures, and strategies for improvement. Results showed that poor road design, organisation and implementation of road safety legislation, road user behaviour and inadequate research were impeding the improvement of road safety in Nigeria. Amongst the road safety measures discussed, public education and information campaigns were rated by each stakeholder group as very effective and easy to implement despite this not being supported by the wider literature. However, there were some variations in perception regarding the effectiveness of measures such as traffic control, road design and maintenance. Improving the road safety situation in Nigeria would require more concerted effort and an overall system-based policy reform involving both government and road users. The results of this study will contribute to and provide research evidence-based recommendations needed to design and develop integrated road safety policies and sustainable implementation strategies in Nigeria and other developing countries.

Consumer acceptance of autonomous delivery robots for last-mile delivery: Technological and health perspectives

The unprecedented outbreak of the novel coronavirus has led to a great shift toward online retailing and accelerated the need for contactless delivery. This study investigates how technological and health belief factors influence consumer acceptance of autonomous delivery robots (ADRs). Anchored in four behavioral theories [i.e., technology acceptance model, health belief model, perceived value (VAL) theory and trust theory], a synthesized model is developed. A total of 500 valid responses were collected through an online questionnaire in Singapore, and structural equation modeling was conducted to examine the responses. The results revealed that perceived ease of use (EOU), perceived usefulness (UFN), perceived susceptibility (SUS), perceived severity (SEV), self-efficacy (SEL) and cues to action (CUE) have a positive and significant influence on consumers' perceptions of the value of ADRs. The total effect analysis also showed that perceived VAL strongly affects consumer acceptance of ADRs. Academically, this study introduces both technological and health belief factors to explain consumer acceptance of ADRs. It also provides recommendations for policymakers and autonomous delivery robot developers on policy formulation, public communication, product design and infrastructure development.

Insights into the relationship between usability and willingness to use a robot in the future workplaces: Studying the mediating role of trust and the moderating roles of age and STARA

Background and aim

Human–robot collaboration is the key component of the fourth industrial revolution concept. Workers’ willingness to collaborate with industrial robots is a basic requirement for an efficient and effective interaction. The roles of human-robot trust and technology affinity as mediators in the relationship between robot usability and worker willingness were analyzed in this study. As other critical variables, the mediator roles of Age and STARA were also calculated.

Materials and methods

This study included 400 workers from a car company who interacted with industrial robots in their daily work activities. After examining the questionnaires’ validity and reliability, the main variables were determined to be willingness to use robots and robot usability. AMOS software also considered human-robot trust and worker technology affinity as mediators. The bootstrapping method was used to evaluate indirect relationships. A set of goodness-of-fit indices were presented to determine the adequacy of the goodness of fit between the proposed model and the data.

Results

Based on model fit indices, an overall satisfactory model fit was obtained for the direct/indirect relationship between robot usability and worker willingness to use it (with mediating role of human-robot trust). Workers’ age and fear of Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA) were identified as moderators in the relationship between usability and willingness.

Conclusion

Attention to the robot usability and the role of workers’ trust in robots appears to be required to ensure workers’ willingness to use robots and the success of human-robot collaboration in future workplaces. As the workers age and their fear of robots grows, usability can play a larger role in increasing their willingness to put robots to work.

Environmentally friendly, but behaviorally complex? A systematic review of e-scooter riders' psychosocial risk features

INTRODUCTION

E-scooters have made a place for themselves on urban roads as an affordable, easy-to-use and environmentally friendly method of transportation. However, and partly because of their road behaviors and safety outcomes, e-scooter users have started to represent a focus of attention for transport planners and policymakers.

AIM

The present systematic review aims to target and analyze the existing studies investigating the psychosocial characteristics of e-scooter riders, focusing on their behavioral and risk-related features.

METHODS

For this systematic review, the PRISMA methodology was used, which allows for the selection of suitable papers based on the study topic, in accordance with a set of pre-defined criteria and a search algorithm. A total of 417 indexed articles were filtered, resulting in only 32 eligible original articles directly addressing the issue. WOS, Scopus, NCBI, Google Scholar, and APA databases were used to create and test search techniques.

RESULTS

At the literature level, most of the existing studies are distributed in a few regions of the globe. At the user's level, results show how e-scooters are most commonly used by young, highly educated, urban-dwelling males, usually for short trips. In regard to road behavior, individuals with the lowest degrees of risk perception remain more prone to engaging in risky road behaviors likely to increase their crash involvement. This might be worsened by the lack of normative e-scooter regulations (and their enforcement) in many countries, plus the marked absence of road training processes. As common limitations, it can be mentioned that 87.5% of these studies used self-report methods, while 59.4% had local coverage.

CONCLUSIONS

The findings of this systematic review endorse the growing need to develop and enforce traffic laws and training processes for e-scooter users. In addition, road safety education and training programs are highlighted by existing studies as potentially pertinent alternatives to increase risk perception, and reduce risky behaviors, road conflicts and crash likelihood among e-scooter riders.

Could Technology and Intelligent Transport Systems Help Improve Mobility in an Emerging Country? Challenges, Opportunities, Gaps and Other Evidence from the Caribbean

Apart from constituting a topic of high relevance for transport planners and policymakers, support technologies for traffic have the potential to bring significant benefits to mobility. In addition, there are groups of “high potential” users, such as young adults, who constitute an essential part of the current market. Notwithstanding, and especially in low and middle-income countries (LMICs), their knowledge and acceptance remain understudied. This study aimed to assess the appraisal of intelligent transport systems (ITS) and other technological developments applicable to mobility among Dominican young adults. Methods: In this study, we used the data gathered from 1414 Dominicans aged between 18 and 40, responding to the National Survey on Mobility in 2018 and 2019. Results: Overall, and although there is a relatively high acceptance, attributed value, and attitudinal predisposition towards both intelligent transportation systems and various support technologies applicable to mobility, the actual usage rates remain considerably low, and this is probably exacerbated by the low and middle-income status of the country. Conclusions: The findings of this study suggest the need to strengthen information and communication flows over emerging mobility-related technologies and develop further awareness of the potential benefits of technological developments for everyday transport dynamics.

Safety Verification of Driving Resource Occupancy Rules Based on Functional Language

Autonomous driving is a safety-critical system, and the occupancy of its environmental resources affects the safety of autonomous driving. In view of the lack of safety verification of environmental resource occupation rules in autonomous driving, this paper proposes a verification method of automatic driving model based on functional language through CSPM. Firstly, the modeling and verification framework of an autopilot model based on CSPM is given. Secondly, the process algebra definition of CSPM is given. Thirdly, the typical single loop environment model in automatic driving is abstracted, and the mapping method from automatic driving model to CSP is described in detail for the automatic driving environment and the typical collision, overtaking, lane change and other scenes involved. Finally, the autopilot model of the single loop is mapped to CSPM, and the application effect of this method is discussed by using FDR tool. Experiments show that this method can verify the safety of autonomous driving resources, thereby improving the reliability of the autonomous driving model.

Short-Term Prediction of Bike-Sharing Demand Using Multi-Source Data: A Spatial-Temporal Graph Attentional LSTM Approach

As a convenient, economical, and eco-friendly travel mode, bike-sharing greatly improved urban mobility. However, it is often very difficult to achieve a balanced utilization of shared bikes due to the asymmetric spatio-temporal user demand distribution and the insufficient numbers of shared bikes, docks, or parking areas. If we can predict the short-run bike-sharing demand, it will help operating agencies rebalance bike-sharing systems in a timely and efficient way. Compared to the statistical methods, deep learning methods can automatically learn the relationship between the inputs and outputs, requiring less assumptions and achieving higher accuracy. This study proposes a Spatial-Temporal Graph Attentional Long Short-Term Memory (STGA-LSTM) neural network framework to predict short-run bike-sharing demand at a station level using multi-source data sets. These data sets include historical bike-sharing trip data, historical weather data, users’ personal information, and land-use data. The proposed model can extract spatio-temporal information of bike-sharing systems and predict the short-term bike-sharing rental and return demand. We use a Graph Convolutional Network (GCN) to mine spatial information and adopt a Long Short-Term Memory (LSTM) network to mine temporal information. The attention mechanism is focused on both temporal and spatial dimensions to enhance the ability of learning temporal information in LSTM and spatial information in GCN. Results indicate that the proposed model is the most accurate compared with several baseline models, the attention mechanism can help improve the model performance, and models that include exogenous variables perform better than the models that only consider historical trip data. The proposed short-term prediction model can be used to help bike-sharing users better choose routes and to help operators implement dynamic redistribution strategies.

What Kind of Travellers Are Using Carsharing in Beijing? A Study Based on Selective Ensemble Learning

As a new urban travel mode, carsharing is significantly different from private cars, buses and other travel modes. Therefore, clarifying the typical characteristics of carsharing, such as individual users’ attributes, travel environment and travel behaviour, is conducive to accurately grasping the development of carsharing. In this study, a selective ensemble learning model is established to analyse typical travel characteristics of carsharing. Firstly, personal characteristics, environmental characteristics and behavioural characteristics were obtained through integrating order data, global positioning system data and station information. Then, based on a consolidated view of carsharing, different types of carsharing travel characteristics were distinguished using selective ensemble learning. Lastly, all kinds of carsharing travel are described in detail. It was identified through this research that carsharing travel can be divided into four kinds: long distance for leisure and entertainment, medium and short distances for business and commuting, a mixed category of medium and short distances for business and residence, and a mixed category of long distance for business and residence. This study can provide a theoretical reference and practical basis for precise planning and design and the scientific operation of carsharing.

Perceived safety and trust in SAE Level 2 partially automated cars: Results from an online questionnaire

The present online study surveyed drivers of SAE Level 2 partially automated cars on automation use and attitudes towards automation. Respondents reported high levels of trust in their partially automated cars to maintain speed and distance to the car ahead (M = 4.41), and to feel safe most of the time (M = 4.22) on a scale from 1 to 5. Respondents indicated to always know when the car is in partially automated driving mode (M= 4.42), and to monitor the performance of their car most of the time (M = 4.34). A low rating was obtained for engaging in other activities while driving the partially automated car (M= 2.27). Partial automation did, however, increase reported engagement in secondary tasks that are already performed during manual driving (i.e., the proportion of respondents reporting to observe the landscape, use the phone for texting, navigation, music selection and calls, and eat during partially automated driving was higher in comparison to manual driving). Unsafe behaviour was rare with 1% of respondents indicating to rarely monitor the road, and another 1% to sleep during partially automated driving. Structural equation modeling revealed a strong, positive relationship between perceived safety and trust (β = 0.69, p = 0.001). Performance expectancy had the strongest effects on automation use, followed by driver engagement, trust, and non-driving related task engagement. Perceived safety interacted with automation use through trust. We recommend future research to evaluate the development of perceived safety and trust in time, and revisit the influence of driver engagement and non-driving related task engagement, which emerged as new constructs related to trust in partial automation.

Analysis of the Impact of Countdown Signal Timers on Driving Behavior and Road Safety

This article describes the safety and effectiveness issues related to signal countdown timers (SCT). These devices are used in many countries around the world. The impact of these devices on road safety and the effectiveness of traffic lights is presented. During a literature review, more than 18 aspects of device use were recognized. The research involved measurements carried out at three intersections in Płock (Poland). The initial and final period of the green signal for vehicles was analyzed. Headways, incidences of vehicles passing through after the end of the green signal, and red-light violations were examined. Additionally, a fuel consumption analysis and a case study of a road crash in Szczecin (Poland) are presented. Problems related to signal countdown timers working during traffic light failure are described. The research shows different influences of signal countdown timers at various intersections. It was observed that SCTs increase the number of red-light violations and during the red-amber signal. On the other hand, the number of entries during the amber signal with SCTs is lower. A literature review also indicated that the use of SCT causes a reduction of start-up time (positive impact) and increases vehicle speed (negative impact). The article concludes that SCTs do not always fulfill their role in improving road safety and control efficiency. Conclusions can address various stakeholders, including drivers, road authorities, and traffic engineers.

Citizens’ Perceptions in Relation to Transport Systems and Infrastructures: A Nationwide Study in the Dominican Republic

One of the challenges currently faced by emerging countries is to get their citizens to decide to use sustainable transport for their regular trips, in order to reduce the current vehicular pollution rates. The objective of this descriptive research is to examine the perceptions of Dominicans regarding the state of the country’s transport systems and road infrastructure. For this purpose, a nationwide survey procedure was performed. This cross-sectional research used the data retrieved from a sample of 1260 citizens aged over 18, proportional in gender, age, habitat, and province of the Dominican Republic. The results showed how Dominicans believe that, compared to other road features, pedestrian roads and public transport vehicles remain in a very poor condition. Further, citizens report to be more interested about the improvement of road infrastructures than in the implementation of any other set of measures performed to promote sustainable road mobility, including those related with alternative transport means. Finally, this study claims for the need of fostering educational, communicative and participative actions and measures aimed at increasing the value given to sustainable transportation, and the relevance of integrate potential structural and vehicular improvements with those related to human behavior in mobility.

Autonomous Vehicles and Vulnerable Road-Users-Important Considerations and Requirements Based on Crash Data from Two Countries

(1) Background: Passenger vehicles equipped with advanced driver-assistance system (ADAS) functionalities are becoming more prevalent within vehicle fleets. However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries and represent scenarios in which future connected and autonomous vehicles may be challenged in terms of safe manoeuvring. (3) Road intersections are currently very common locations for vulnerable road-user accidents; traffic flows and road-user behaviours at intersections can be unpredictable, with many vehicles behaving inconsistently (e.g., red-light running and failure to stop or give way), and many vulnerable road-users taking unforeseen risks. (4) Conclusions: The challenges of unpredictable vulnerable road-user behaviour at intersections (including road-users violating traffic or safe-crossing signals, or taking other risks) combined with the lack of knowledge of CAV responses to intersection rules, could be problematic. This could be further compounded by changes to nonverbal communication that currently exist between road-users, which could become more challenging once CAVs become more widespread.