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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. A preliminary investigation was first conducted at traffic junctions identified to have a greater occurrence of accidents involving elderly pedestrians and motorists. This preliminary investigation looked into the efficacy of infrastructure-oriented solutions in reducing the occurrence of such accidents. It was observed that infrastructure alone was inadequate in ensuring safety of elderly pedestrians. Next, a questionnaire was administered in order to gain information regarding traits, attitudes and behaviours pertinent to traffic safety. Subsequently, structural equation modelling was used to analyse the data via exploratory, confirmatory and path analysis. This was followed by an in-depth discussion which explored the relationship between the latent constructs of traits, attitudes and behaviours, as well as social demographic variables such as age, gender and education level. It was found that poor cognitive ability and poor attitudes towards transport safety were both positively correlated with unsafe behaviour; strong psychosocial beliefs were positively correlated with poor attitudes towards transport safety, but negatively correlated with unsafe behaviour. The study concludes with recommendations to improve traffic outcomes for the elderly., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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. To this end, this study proposes an improved safety potential field, named the Work-Zone Crash Risk Field (WCRF). The WCRF force can be used to measure the crash risk of individual vehicles that enter a work zone considering the influence of multiple features, upon which the overall crash risk of the road segment in a specific time window can be estimated. With the overall crash risk used as a label, the time-window-based traffic data are used to train and validate an eXtreme Gradient Boosting (XGBoost) classifier, and the Shapley Additive Explanations (SHAP) method is integrated with the XGBoost classifier to identify the key risk-contributing traffic features. To assess the proposed approach, a case study is conducted using real-time vehicle trajectory data collected in two work zones along a highway in China. The results demonstrate that the WCRF-based SSM outperforms conventional SSMs in identifying crash risks in work zone transition areas on highways. In addition, we perform lane-based analysis regarding the impact of setting up work zones on highway safety and investigate the heterogeneity in risk-contributing features across different work zones. Several interesting findings from the analysis are reported in this paper. Compared to existing SSMs, the WCRF-based SSM offers a more practical and comprehensive way to describe the crash risk in work zones. The approach using the developed WCRF technique offers improved capabilities in identifying key risk-contributing features, which is expected to facilitate the development of safety management strategies for work zones., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

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. At the mesoscale, computerized tomography (CT) X-ray scanning and MATLAB analysis were employed to investigate the distribution of the aggregate within the ERAM. The results revealed that mixing temperature played a significant role in the diffusion and distribution between the EP and the aged asphalt mastic, thus impacting the mechanical properties of the material. Heating at 180 °C (the recommended mixing temperature of EP) resulted in a wider blending zone between the EP and the aged asphalt mastic compared to heating at 160 °C (the usual mixing temperature of ordinary reclaimed asphalt mixtures). The overall dispersion of the aggregate in the ERAM exhibited greater homogeneity in the vertical direction than in the horizontal direction. Adjusting the gradation of the RAP was found to be effective in reducing horizontal variability in the distribution of the coarse aggregate, fine aggregate, and air voids in the ERAM. Adjusting the RAP gradation further enhanced the vertical homogeneity in the distribution of the fine aggregate, while its impact on the vertical distribution of the coarse aggregate was minimal. Short-term aging led to increased variability in the distribution of the coarse aggregate, fine aggregate, and air voids within the ERAM. However, adjusting the gradation was effective in mitigating the adverse effects of short-term aging on both horizontal and vertical homogeneity in the aggregate distribution.

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. To bridge this knowledge gap, this study investigated a real-world expressway and collected its traffic crash data between 2013 and 2020. Then, according to the time-spatial density ratio ( Pts ), road sections were assigned into three classes corresponding to low, medium, and high risk levels of traffic. Next, different classifiers were compared that were trained using the transformed and resampled feature data to construct a traffic crash risk prediction model. Last, but not least, partial dependence plots (PDPs) were employed to interpret the results and analyze the importance of individual features describing the geometry, pavement, structure, and weather conditions. The results showed that a variety of data balancing algorithms improved the performance of the classifiers, the ensemble classifier superseded the others in terms of the performance metrics, and the combined SMOTEENN and random forest algorithms improved the classification accuracy the most. In the future, the proposed traffic crash risk prediction method will be tested in more road maintenance and design safety assessment scenarios.

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. the 'self' element) and technologies. The impacts of social distancing, individualistic culture, self-identity as technology users, and innovativeness on consumers' SST usage are explored in the field of smart locker self-collection service. A survey instrument is designed for data collection, and the data are analysed through a hierarchical regression followed by latent class analysis. The findings confirm the contributing effects of the four proposed factors on consumers' SST usage. Further, four distinct SST user segments emerge which are labelled as: technology lovers, social excluders, SST embracers, and indifferent pandemic responders. This study contributes to the SST literature by emphasising the pandemic-induced effects on the consumption environment externally and individuals' self-perceptions internally, both leading to behavioural implications of SST usage., Competing Interests: The authors declare that they have no conflict of interest., (© 2022 Elsevier Ltd. All rights reserved.)

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. An eye tracker was used to obtain eye movement data of subject drivers exposed to various scenes. After each driving test, psychological feeling of the subjects in tunnel driving, and their ratings of alignment and contour guidance of various facilities were obtained by questionnaires. The research results showed that setting locations and facility types have a great impact on drivers' glance location and glance duration. When horizontal stripes, edge markers, LED arch and vertical stripes were set separately, alignment guidance and contour guidance performance were found to vary in inverse proportion. Horizontal stripes and edge markers are the preferred and necessary facilities, which are conducive to ensuring longitudinal and horizontal rights-of-way. LED arch and vertical stripes are also good supplements to spatial right-of-way. The combination of these 4 types of facilities can provide multi-level alignment guidance and contour guidance functions, which is beneficial to traffic safety in the tunnel., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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. In this study, a reasonable hazard onset was proposed for measuring RTs of motorcycle riders to gradual-onset hazards, following which the RTs to abrupt- and gradual-onset hazards were determined and compared. A study on motorcycle rider RTs was conducted in Singapore in which a sample of young male adults (licensed riders with more than one year's effective riding experience, and unlicensed subjects) were equipped with eye tracking glasses and were presented with two pairs of abrupt-/ gradual-onset hazard scenarios on a simple motorcycle simulator. Their RTs were recorded. Initial deviation from the straight path (start of turning manoeuvre) of the intruding hazard vehicle was determined to be a more appropriate onset (start point when measuring RTs) for gradual-onset hazards when compared with referencing the onset against travel lane incursions. Participants generally took longer to respond to gradual-onset hazards than abrupt-onset hazards. Unlicensed subjects tended to underperform relative to licensed riders. The findings from this study contribute to the literature in the relatively novel field of motorcycle RTs and should be of interest to crash reconstructionists as well road safety professionals in designing road operations., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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. The survey instrument is used for online data collection ( n = 500), and a combined (descriptive and quantitative) method is adopted for data analysis. Our results suggest that, although with a relatively weak predictive power, the affective and cognitive appraisals of health risks lead to the reinforced usage of self-collection service. This also applies to the factors of action/coping planning and subjective norm. This study theoretically contributes to the self-service literature and creates managerial implications for retailers and logistics operators.

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. Then, structural equation modelling was implemented to study the survey data. The results indicated that the effects of several constructs namely, normative social influence, observational learning, perceived severity, and perceived scarcity, with mediation by control, impose significant influence on consumers' PB. Analysis of total effects showed that normative social influence has the strongest effect on PB. This is followed by perceived scarcity, control, social trust, observational learning, and perceived severity. Two diverse research paradigms centring on Survival Psychology and Maslow's motivation theories were integrated to offer a logical explanation of the motivation driving PB. By utilising a theory-driven approach, the current study has offered a unique approach to interpreting consumers' PB. Overall, this study enhances current research on consumers' PB, offers new insights into understanding the motivating factors behind consumers' PB, and implicates policies on tackling hoarding situations in the event of crisis such as the COVID-19 pandemic., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Elsevier Ltd. All rights reserved.)

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., shopping online, self-collecting and returning parcels via delivery lockers) is ideally positioned to fulfil consumers' shopping/logistics needs while avoiding all unnecessary social interactions. Thus, this study examines the factors that motivate consumers' migration to the contactless channel by viewing consumers' channel choice as both health-related and shopping behaviours. Anchored on the synthesised insights of protection motivation theory and automation acceptance theory, the conceptual framework and a series of hypotheses are proposed. A survey instrument is used for data collection, and the data are analysed using structural equation modelling. Our findings reveal that perceived channel characteristics such as compatibility and trust directly contribute to the relative value of the contactless channel; these characteristics are also correlated where trust perception reinforces compatibility perception. The channel characteristics are further influenced by consumers' perceived susceptibility of COVID-19; that is, susceptibility perception enhances channel compatibility but decreases consumers' trust in the contactless channel. However, the impacts of susceptibility become insignificant with a low level of severity perception, confirming the stage-based conceptualisation of severity. Furthermore, the severity perception of COVID-19 is found to amplify the positive impacts of susceptibility perception but attenuate its negative impact. Our study promotes a deeper integration between the health and service literature and encourages more interdisciplinary studies in this nexus. Considering the practical context of social distancing, our findings suggest a struggle between compatibility perception and trust concern that shapes consumers' behaviours., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Elsevier B.V. All rights reserved.)

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. As compared with conventional BNs, the Copula-BN developed in this study has the following advantages: the Copula-BN 1. Has a more rational and explainable structure; 2. Is less likely to be over-fitting and can attain more satisfactory prediction performance; and 3. Can handle not only discrete but also continuous features. In terms of technical innovations, Shapley Additive Explanation (SHAP) is used for feature selection, while Gaussian Copula function is employed to build the dependency structure of the Copula-BN. As for applications, the Copula-BNs are used to investigate the causality of risky lane-changing (LC) and car-following (CF) maneuvers, upon which the comparisons are made between the two essential but risky driving maneuvers. In this study, the Copula-BNs are developed based on the Second Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) database. Upon network evaluation, the Copula-BNs for both risky LC and CF maneuvers demonstrate satisfactory structure performance and promising prediction performance. Feature inferences are conducted based on the Copula-BNs to respectively illustrate the causation of the two risky maneuvers. Several interesting findings related to features' contribution are discussed in this paper. To a certain extent, the Copula-BN developed using the data-driven method makes a trade-off between prediction and causality within the 'Big Data'. The comparison between risky LC and CF maneuvers also provides a valuable reference for crash risk evaluation, road safety policy-making, etc. In the future, the achievements of this study could be applied in Advanced Driver-Assistance System (ADAS) and accident diagnosis system to enhance road traffic safety., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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. Next, an analysis of the results was done through structural equation modeling. It showed that the effect of the health belief model dimensions (i.e., perceived susceptibility, perceived severity, outcome expectation, cues to action, and self-efficacy) on panic buying is partially mediated by the consumers' perceived scarcity of products. Furthermore, the effect of perceived scarcity on panic buying is partially mediated by consumers' anticipation of regret. This paper expands on the current theoretical understanding of panic buying behavior, giving insights into the possible measures and solutions that policymakers and relevant stakeholders can uptake to manage panic buying in future a pandemic or health crisis.

This paper reports on the long-term effectiveness of a non-formal co-curricular educational program based on a campus ecogarden at a Hong Kong university in developing pro-sustainability awareness, attitudes and behavior among undergraduate students. This service-based, nature-based experiential learning program, termed the Ecogarden Farmer and Biodiversity Surveyor, has been running at the university since 2015. The program is divided into two consecutive phases: a training phase comprising various learning activities and a successive internship phase consisting of the all-round practical tasks involved in managing the garden. A retrospective evaluation of the program using phenomenographic approach and content analysis was adopted to reveal the diversity of students' learning experience, as the indicators of the success of the program. Of 112 participants from 4 cohorts, 32 completed online questionnaires, and semi-structured interviews were successfully conducted with twelve participants, three from each of the four cohorts. The results indicated that the program's outcomes could be categorized into five themes. Most outcomes fit into the theme 'an increase in knowledge and skill level,' followed by 'rise in environmental awareness,''facilitation of personal growth,' and 'enhancement of career development.' Many structural experiences revealed may suggest the success of the program. The longer the participants had participated in the program, the more in-depth and diversified reflection of the senior participants relating to personal development were mentioned. This study provides critical insights into the validity of retrospective program evaluation for assessing the long-term impact of EfS programs by introducing a cross-sectional study of different cohorts as a serial time-point sampling strategy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Cheang, Ng, Wong, Li and Tsoi.)

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. Thus, this study conducts a synthesised review on past studies of lonely consumers. Based on an inductive analysis of 56 articles, 74 key themes are identified. These key themes are further categorised into five major clusters by way of a co-occurrence network analysis. Respectively, the five clusters address the psychological implications related to the dynamics between nonhuman attachment and consumers' loneliness , the commercial implications related to the paradoxical motivations of affiliation and self-affirmation in product selection and the dual information processing mechanism in response to advertisement appeals , and the social implications related to consumers' well-being in an ageing society and the anthropomorphic companionship in a virtual world . A list of research questions is proposed that concludes the review study., Competing Interests: The authors declare no conflict of interest.

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. To estimate the risk potentials of vehicles in driving, unsupervised data labelling is proposed. Based on extracted risk indicator features, vehicles are clustered into various groups labelled with graded risk levels. Data under-sampling of the safe group is performed to reduce the risk-safe class imbalance. Afterwards, the linkages between behaviour features and corresponding risk levels are built using XGBoost, and key features are identified according to feature importance ranking and recursive elimination. The risk levels of vehicles in driving are predicted based on key features selected. As a case study, NGSIM trajectory data are used in which four risk levels are clustered by Fuzzy C-means, 64 key behaviour features are identified, and an overall accuracy of 89% is achieved for behaviour-based risk prediction. Findings show that this approach is effective and reliable to identify important features for driving assessment, and achieve an accurate prediction of risk levels., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

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. From the vehicles' trajectory dataset, we extract car's candidate features and apply fault tree analysis and k-Means clustering algorithm to determine the LC risk level based on the performance indicator of Crash Potential Index (CPI). Random Forest (RF) classifier is applied to select key features from car's candidate features and predict LC risk level. This study also proposes a method to evaluate the resampling methods to resample the LC risk dataset in terms of fitness performance and prediction performance. The cars' trajectory data collected from the Next Generation Simulation (NGSIM) dataset is used for framework development and verification. The sensitivity analysis of CPI indicates that the following cars in the original lane and target lane are respectively the safest and riskiest cars of the surrounding cars in an LC event. The results of resampling method evaluation show that SMOTETomek, which is less likely to be overfitting and has high prediction performance, is well suited for resampling the LC risk dataset on which RF classifier is trained. The results of key feature selection imply that the individual behaviors of the LC car and its surrounding cars in the original lane, the interactions between the LC car and its surrounding cars, and the interactions between the surrounding cars in the target lane (especially the interaction of the cars' accelerations) are of importance to the LC risk., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

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. This study analysed the safety impact of the conflict between opposing straight-through vehicles and right-turn vehicles at RWA junctions in Singapore. A microscopic simulation model based on Fuzzy Cellular Automata (FCA) was developed. Field surveys were carried out to obtain the inputs for calibrating the fuzzy inference system. Different scenarios were analysed and discussed including different types of junctions with and without RWAs. The proposed model is found to be able to simulate decision-making of individual drivers at RWA or before stop line, such as exiting the queueing area or crossing the stop line when faced with different gaps and velocity of opposing straight-through vehicles., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Accident events are generally unexpected and occur rarely. Pre-accident risk assessment by surrogate indicators is an effective way to identify risk levels and thus boost accident prediction. Herein, the concept of Key Risk Indicator (KRI) is proposed, which assesses risk exposures using hybrid indicators. Seven metrics are shortlisted as the basic indicators in KRI, with evaluation in terms of risk behaviour, risk avoidance, and risk margin. A typical real-world chain-collision accident and its antecedent (pre-crash) road traffic movements are retrieved from surveillance video footage, and a grid remapping method is proposed for data extraction and coordinates transformation. To investigate the feasibility of each indicator in risk assessment, a temporal-spatial case-control is designed. By comparison, Time Integrated Time-to-collision (TIT) performs better in identifying pre-accident risk conditions; while Crash Potential Index (CPI) is helpful in further picking out the severest ones (the near-accident). Based on TIT and CPI, the expressions of KRIs are developed, which enable us to evaluate risk severity with three levels, as well as the likelihood. KRI-based risk assessment also reveals predictive insights about a potential accident, including at-risk vehicles, locations and time. Furthermore, straightforward thresholds are defined flexibly in KRIs, since the impact of different threshold values is found not to be very critical. For better validation, another independent real-world accident sample is examined, and the two results are in close agreement. Hierarchical indicators such as KRIs offer new insights about pre-accident risk exposures, which is helpful for accident assessment and prediction., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

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. Simulation results show Different types of cognitive failures are found to have varied relationship with driving errors and safety performance. For right-turn filtering movement, cognitive failures are more likely to result in driving errors with denser conflicting traffic stream. Moreover, different driving errors are found to have different safety impacts. The study serves to provide a novel approach to linguistically assess cognitions and replicate decision-making procedures of the individual driver. Compare to crash analysis, the proposed FCA model allows quantitative estimation of particular cognitive failures, and the impact of cognitions on driving errors and safety performance., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

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. Older drivers were found to take longer times before fixating on an abrupt-onset road hazard but appeared to have insignificantly faster reaction times after the initial fixation. Hence, the overall response latency did not suggest any age effects. Older drivers also scanned the roadway less as compared to their younger counterparts. No effects of experience were found. The findings provided insight on age-related declines in hazard detection whose effects have been masked by other components of hazard response., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Objective: This study applies a simulation technique to evaluate the hypothesis that red light cameras (RLCs) exert important effects on accident risks. Conflict occurrences are generated by simulation and compared at intersections with and without RLCs to assess the impact of RLCs on several conflict types under various traffic conditions., Method: Conflict occurrences are generated through simulating vehicular interactions based on an improved cellular automata (CA) model. The CA model is calibrated and validated against field observations at approaches with and without RLCs. Simulation experiments are conducted for RLC and non-RLC intersections with different geometric layouts and traffic demands to generate conflict occurrences that are analyzed to evaluate the hypothesis that RLCs exert important effects on road safety., Results: The comparison of simulated conflict occurrences show favorable safety impacts of RLCs on crossing conflicts and unfavorable impacts for rear-end conflicts during red/amber phases. Corroborative results are found from broad analysis of accident occurrence., Conclusions: RLCs are found to have a mixed effect on accident risk at signalized intersections: crossing collisions are reduced, whereas rear-end collisions may increase. The specially developed CA model is found to be a feasible safety assessment tool.

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. In this study, car following data are obtained from traffic video footages of open and tunnel expressways in Singapore. A total of 15,325 car following headways (with car as the follower) are analysed and significant factors affecting headways are found to be speed, and lane. Significant effect of leading vehicle type is only found for tunnel expressway. Headways are generally longer in the tunnel environment. Assessment of collision time measures and safety margins also reveal safer car following behaviour and lower rear-end collision risks in the tunnel expressway. The results are discussed from a behavioural perspective. Overall, the findings show that road tunnels are superior in terms of safety but at reduced traffic capacity ., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

At intersection, vehicles coming from different directions conflict with each other. Improper geometric design and signal settings at signalized intersection will increase occurrence of conflicts between road users and results in a reduction of the safety level. This study established a cellular automata (CA) model to simulate vehicular interactions involving right-turn vehicles (as similar to left-turn vehicles in US). Through various simulation scenarios for four case cross-intersections, the relationships between conflict occurrences involving right-turn vehicles with traffic volume and right-turn movement control strategies are analyzed. Impacts of traffic volume, permissive right-turn compared to red-amber-green (RAG) arrow, shared straight-through and right-turn lane as well as signal setting are estimated from simulation results. The simulation model is found to be able to provide reasonable assessment of conflicts through comparison of existed simulation approach and observed accidents. Through the proposed approach, prediction models for occurrences and severity of vehicle conflicts can be developed for various geometric layouts and traffic control strategies., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Gap acceptance of violating pedestrians was studied at seven stretches of signalised pedestrian crossings in Singapore. The provision of the traffic light signals provide a 'safer' crossing option to these pedestrians, as compared to uncontrolled crossings and mid-block arterial roads. However, there are still people choosing to cross at the riskier period (Red Man phase). The paper discusses about the size of traffic gaps rejected and accepted by pedestrians and the behaviour of riskier pedestrians (those adapting partial gap). The likelihood of pedestrians accepting gaps between vehicular traffic as a combination of different influencing independent variables such as traffic, environmental and personal factors was studied and modelled using logistic regression., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

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%, 6.5% and 7.0% of grade Pen 60/70 bitumen, respectively. All three hybrid mixes satisfied the Marshall criteria of the Singapore Land Transport Authority (LTA) W3B wearing course specification. The hybrid mix with 6% concrete fillers gave comparable resilient modulus and creep resistance as the conventional W3B mix, while hybrid mixes with higher concrete substitutions achieved better performance. X-ray diffraction (XRD) showed the distinct presence of free lime in the heat-treated concrete, while the scanning electron microscope (SEM) provided an in-depth perspective of the concrete grains in the HMA matrix. The results suggest feasible use of waste concrete as partial aggregate substitution in HMA.

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. The TZ allows the systematic identification and exclusion of those drivers who have time to delay their responses. Their inclusion in past studies inflated PRT values. Their exclusion in this study provides a more accurate value.

This paper reports a before-and-after study which evaluated the impacts of installing and operating red light cameras at two "T" and one "X" signalized intersections on driver stopping propensity upon the onset of amber. Rather than using video cameras, a special purpose data logger working in conjunction with loop sensors was used to gather traffic parameters, vehicle stopping/crossing movements, and signal phases. Logistic modeling was employed to model the revealed stopping/crossing decisions of non-platoon vehicle drivers in response to the onset of amber with traffic, situational and behavioral variables, including their interactions. The results indicate that the variable Cam_Inst that gathered the impacts of red light camera (RLC) installation on driver decision-making at signalized intersection was significant at 5% level only for the camera approach model of the cross-intersection. The significance of Cam_Inst was undermined for the camera approaches at the T-intersections by the inclusion of a significant two-order variable defining the interaction of Cam_Inst with distance from the stop-line. One may, thus, infer that RLC has a fixed-quantum effect at the cross-intersection, but an accentuated effect with distance at the two T-intersections. Lastly, the effects of a RLC at an intersection on the stopping decision at the non-camera approaches were minimal.

Problem: This paper evaluates the stopping propensity of drivers at matured in-service red light camera (RLC) signalized T-intersections after a number of years of camera operation., Method: A special-purpose data logger working in conjunction with loop sensors was used to gather traffic parameters, vehicle stopping/crossing movements, and signal phases at camera (treatment) and noncamera (comparison) approaches. Logistic modeling was employed to determine the stopping/crossing decisions of civilian vehicle drivers in response to the onset of yellow (amber) with traffic and the associated situational and behavioral variables, including their interactions., Summary: The propensity to stop at camera approaches was about 17 times more than at noncamera approaches. This finding strongly affirmed the positive effect of RLC in encouraging drivers to stop. The likelihood to stop was about 6.5 times higher for an approach that faces an opposing right-turn conflict immediately after the onset of red. Higher stopping propensity was also more prevalent during weekdays and in periods of low traffic volumes., Impact on Industry: The findings provided an objective measure on the effectiveness of RLC that would be useful to evaluate intersections for camera installation as well as on the implementation and operational aspects of RLC program.

The common and convenient assumption in accident count analysis, that accidents are Poisson-distributed, is reexamined. Two statistical tests, for evaluating the assumption are described and compared. It is shown that a test based upon a combinatorial analysis is much more accurate than the alternative chi-square test when accident counts are expected to be small. The more accurate test is used to reinterpret data on accident count variability, the results indicating that the Poisson distribution is appropriate for the analysis of accidents at individual sites.

A study involving unobtrusive observation of drivers at horizontal curves before and after realignment is described. The speeds and path radii adopted by drivers in the curves before and after realignment are compared, as are the levels of side friction demanded by each driver while negotiating the curves before and after realignment. The results reveal substantial variations between drivers (with respect to speed, path radius, and side friction demand) and between the path and curve radii. While vehicle speeds increased markedly, the side friction demand was reduced for all curves except one. It is concluded that the margin of safety was increased for all curves, and this is supported by the accident data.