Assessing the impact of car-following driving style on traffic conflict risk using asymmetric behavior model and explainable machine learning.

To deepen the understanding of the impact of car-following driving style (CFDS) on traffic conflict risk and address the lack of clear CFDS evaluation metrics, this study proposes an improved CFDS metric based on the Asymmetric Behavior (AB) theory. Interpretable machine learning models were utilized for regression analysis to examine the relationship between CFDS and conflict risk. The generalized AB model calculates the difference between vehicle trajectories and the Newell trajectory, constructing the driving style evaluation metric, which quantifies driver aggressiveness in a manner that is both computationally straightforward and easily interpretable.

Attitudes and behaviour of elderly in cognisance of transport safety when navigating pedestrian facilities.

The number of accidents involving elderly pedestrians has been increasing from year to year, in spite of various road safety initiatives having been implemented. In line with Singapore's ageing population, this presents a worrying trend. This study aims to shed light on possible contributing factors via a human factors analysis.

Homogeneity Enhancement of Mixtures Containing Epoxy Polymer and 100% Reclaimed Asphalt Pavement.

The utilization of reclaimed asphalt pavement (RAP) could reduce the cost of pavements containing epoxy polymer (EP) materials. This study was aimed at improving the homogeneity of an EP-reclaimed asphalt mixtures (ERAMs) at both the micro- and meso-scale to provide a reference for an ERAM production process. At the microscale, nanoindentation tests were conducted to characterize the diffusion between the EP and aged asphalt mastic.

Toward safer highway work zones: An empirical analysis of crash risks using improved safety potential field and machine learning techniques.

Due to complex traffic conditions, transition areas in highway work zones are associated with a higher crash risk than other highway areas. Understanding risk-contributing features in transition areas is essential for ensuring traffic safety on highways. However, conventional surrogate safety measures (SSMs) are quite limited in identifying the crash risk in transition areas due to the complex traffic environment.

Comparing Resampling Algorithms and Classifiers for Modeling Traffic Risk Prediction.

Road infrastructure has significant effects on road traffic safety and needs further examination. In terms of traffic crash prediction, recent studies have started to develop deep learning classification algorithms. However, given the uncertainty of traffic crashes, predicting the traffic risk potential of different road sections remains a challenge.

An investigation of self-service technology usage during the COVID-19 pandemic: The changing perceptions of 'self' and technologies.

Self-service technologies (SSTs) are not new to modern consumers, yet the COVID-19 pandemic brings new motivations into SST usage. This study aims to revisit consumers' SST usage under the pandemic context, focusing on consumers' changing perceptions on social interactions (i.e.

Influence of different visual guiding facilities in urban road tunnel on driver's spatial right-of-way perception.

Urban road tunnel construction is becoming ever more prevalent, and traffic safety in tunnel operation is even more important. This study evaluates the influence of different visual guiding facilities on drivers' spatial right-of-way perception by combining quantitative and qualitative methods in order to provide a basis for traffic safety optimization in the tunnel. Simulation scenes were designed for six common types of visual guiding facilities as comprising no facility (baseline), horizontal strips, edge markers, LED arch, vertical stripes, and combination (multiple) facilities.

Young male motorcycle rider perception response times to abrupt- and gradual-onset hazards.

Response time (RT) measures in crash reconstruction are inherently constrained by the need to define a start point (onset). In straight-forward situations where the hazard appears abruptly from behind an obstruction (abrupt onset), hazard onset is typically defined as when the hazard is first visible to the motorist. In contrast, in scenarios where there is no clearly defined point of entry (gradual onset), and the potential hazard gradually transitions to an immediate hazard, the onset point is more ambiguous.

Does COVID-19 Promote Self-Service Usage among Modern Shoppers? An Exploration of Pandemic-Driven Behavioural Changes in Self-Collection Users.

Due to health concerns related to COVID-19, shoppers have learned to minimise social contact by adopting various contactless self-service technologies to fulfil their consumption needs. This study explores shoppers' behavioural changes in relation to self-service, using the special research context of e-commerce self-collection services. By synthesising insights from the health psychology literature, this study proposes an affective-cognitive-social perspective to explain the pandemic-driven behavioural changes of self-collection users.

Panic buying during COVID-19: Survival psychology and needs perspectives in deprived environments.

Panic buying (PB), a typical consumer behaviour induced by crisis, was observed worldwide in the face of COVID-19 pandemic. Drawing on Survival Psychology and Maslow's motivation theories, this study introduced a theoretical model to establish the factors affecting consumers' PB and investigate their interrelationships. An online survey was designed and administered to 508 respondents in Singapore.

Contactless channel for shopping and delivery in the context of social distancing in response to COVID-19 pandemic.

Arising from the global COVID-19 pandemic, social distancing has become the new norm that shapes consumers' shopping and consumption activities. In response, the contactless channel (i.e.

The Determinants of Panic Buying during COVID-19.

The COVID-19 pandemic has seen an unmatched level of panic buying globally, a type of herd behavior whereby consumers buy an uncommonly huge amount of products because of a perception of scarcity. Drawing on the health belief model, perceived scarcity, and anticipated regret theories, this paper formulated a theoretical model that linked the determinants of panic buying and analyzed their interrelationships. Subsequently, data were collated from 508 consumers through an online survey questionnaire in Singapore that was conducted during the early stage of the pandemic, before the onset of the circuit breaker in April 2020.

A data-driven feature learning approach based on Copula-Bayesian Network and its application in comparative investigation on risky lane-changing and car-following maneuvers.

The era of 'Big Data' provides opportunities for researchers to have deep insights into traffic safety. By taking advantages of 'Big Data', this study proposes a data-driven method to develop a Copula-Bayesian Network (Copula-BN) using a large-scale naturalistic driving dataset with multiple features. The Copula-BN is able to explain the causality of a risky driving maneuver.

Rise of 'Lonely' Consumers in the Post-COVID-19 Era: A Synthesised Review on Psychological, Commercial and Social Implications.

Loneliness is a pervasive problem recognised as a serious social issue, and the prevailing COVID-19 pandemic has exacerbated loneliness to greater prominence and concern. We expect a rise of a massive group of 'lonely' consumers who are deeply entrenched in the social isolation caused by COVID-19. There is an urgent need to revisit the phenomenon of lonely consumers to better prepare academic researchers, public policy makers and commercial managers in the post-COVID-19 era.

A feature learning approach based on XGBoost for driving assessment and risk prediction.

This study designs a framework of feature extraction and selection, to assess vehicle driving and predict risk levels. The framework integrates learning-based feature selection, unsupervised risk rating, and imbalanced data resampling. For each vehicle, about 1300 driving behaviour features are extracted from trajectory data, which produce in-depth and multi-view measures on behaviours.

Key feature selection and risk prediction for lane-changing behaviors based on vehicles' trajectory data.

Risky lane-changing (LC) behavior of vehicles on the road has negative effects on traffic safety. This study presents a research framework for key feature selection and risk prediction of car's LC behavior on the highway based on vehicles' trajectory dataset. To the best of our knowledge, this is the first study that focuses on key feature selection and risk prediction for LC behavior on the highway.

Safety impact of right-turn waiting area at signalised junctions conditioned on driver's decision-making based on fuzzy cellular automata.

Right-turn waiting area (RWA) is a short demarcated queueing area ahead of the stop line that allows the right-turn vehicles at signalised junctions under the permissive filtering signal operation to proceed into the junction-box at the onset of full green signal phase. The RWA layout gives guidance to vehicle placement of turning vehicles which improves safety and mitigates vehicle queue overflow of the right-turn vehicles. RWA enhances the capacity of right-turn lanes while alleviating conflict severity in some cases.

Safety evaluation of driver cognitive failures and driving errors on right-turn filtering movement at signalized road intersections based on Fuzzy Cellular Automata (FCA) model.

This paper proposes a simulation-based approach to estimate safety impact of driver cognitive failures and driving errors. Fuzzy Logic, which involves linguistic terms and uncertainty, is incorporated with Cellular Automata model to simulate decision-making process of right-turn filtering movement at signalized intersections. Simulation experiments are conducted to estimate the relationships between cognitive failures and driving errors with safety performance.

Effects of driver age and experience in abrupt-onset hazards.

Novice drivers and older drivers are found to have the highest crash risk among all drivers and this has motivated many research studies into various aspects of novice and older drivers. Although age-related declines were expected, studies did not find older drivers to respond slower to hazards. This study examined the hazard detection and response latencies of 14 young novice drivers, 14 young experienced drivers, and 12 older experienced drivers, to abrupt-onset hazards.

The effect of road tunnel environment on car following behaviour.

In order to overcome urban space constraints, underground road systems are becoming popular options for cities. Existing literature suggests that accident rates in road tunnels are lower than those in open roads. However, there is a lack of understanding in how the road tunnel environment affects inter-vehicle interactions.

Value-added utilisation of recycled concrete in hot-mix asphalt.

The feasibility of partial substitution of granite aggregate in hot-mix asphalt (HMA) with waste concrete aggregate was investigated. Three hybrid HMA mixes incorporating substitutions of granite fillers/fines with 6%, 45% untreated, and 45% heat-treated concrete were evaluated by the Marshall mix design method; the optimum binder contents were found to be 5.3%, 6.

Driver perception response time during the signal change interval.

The importance of perception response time (PRT) values for traffic signal change interval design, and the need to monitor the design PRT value, are challenges facing transportation professionals. However, current methods used to validate the design PRT value from on-site observational studies have failed to yield convincing proof that the 1 second design value is adequate. A modification to on-site data capture and extraction, using a transitional zone (TZ), is used to overcome this deficiency.