Shared spaces have been proposed and implemented as a means of supporting safe and sustainable road networks by reducing the prominence of vehicles and defining places for people. However, empirical evidence on their performance remains limited, particularly in the Australian context. This paper presents a before-and-after empirical evaluation of three shared space applications in New South Wales, Australia. The implementations were delivered through the “Streets as Shared Spaces” program by local government authorities with support from the New South Wales Government. Each application was similar in scale but situated in different geographic and land-use contexts, representing lower-intensity shared space interventions within a broader spectrum of shared space design.Crowdsourced vehicle performance data and routine administrative crash statistics informed the evaluation, directly addressing safety concerns highlighted in the existing literature. Changes in speed, acceleration and braking behaviour were initially assessed using descriptive before-and-after comparisons, while near-miss and crash records provided descriptive indicators of safety conditions. The speed data was further examined using a modified difference-in-differences (DiD) framework, with within-site pre-intervention conditions serving as the internal comparator and time-series diagnostics used to assess baseline stability. The results suggest reductions in vehicle speeds across both shared space segments and surrounding links, with largely limited changes in acceleration and braking behaviour, aside from some variation near approach and exit segments to the shared space. Safety indicators show no clear deterioration in safety during the early post-implementation period, although longer-term monitoring is required. The findings are indicative and case specific rather than causal but illustrate how connected vehicle data can support scalable and transferable monitoring of place-based street interventions.

The global atmospheric greenhouse gas (GHG) accumulation trajectory has been subjected to fluctuations in the context of the COVID-19 pandemic. Country-level virus prevalence and geography conditions added complexity to understanding atmospheric GHG accumulation sensitivities in terms of the growth rate. Here, extensive data sets were comprehensively analyzed to capture historical and projected fate of atmospheric GHG concentrations. Although a temporary slowdown was observed during the lockdown, global atmospheric GHG growing rates exhibited a sharp rebound during the early economic recovery after COVID-19, which would threaten climate goals without proactive measures. Despite this consistent global trend, various countries demonstrated differential relative changes in growth rates, representing their specific responses to the pandemic crisis. After systematic consideration of socio-economic and demographic factors and employment of optimal regression models, transportation and industry variables emerged as the strongest predictors for country-specific GHG accumulation sensitivities during lockdown and recovery phases, respectively. Addressing global health and climate change issues would necessitate sustainable government actions and economic decisions in anticipation of future pandemic-related events.

Practitioner engagement methods are essential for understanding diverse perspectives and providing opportunities to develop a unified strategic approach to contentious transport planning issues, such as shared spaces. These are road infrastructure designs that minimize the separation between travel modes and equalize the priority across all the modes. This study investigates the design and implementation of a novel engagement methodology, Lego Serious Play ® (LSP), to discuss the implementation of shared space solutions in practice while also enhancing participant experience. A case study of practitioners in New South Wales, Australia was conducted to identify opportunities, challenges, and the future potential of applying the methodology. Outcomes from the workshops revealed that the LSP methodology enabled participants to articulate their views, debate various ideas, and compromise. Shared spaces emerged as a key strategy for achieving place-based outcomes, particularly through a zone-based implementation approach in which design elements are gradually introduced across a road network to help users adapt to the changing environment. Key aspects and metrics were identified, shaping future guidance for shared spaces design and assessment. The storytelling component of the LSP technique was particularly effective in fostering discussion and achieving consensus. The findings suggest that the LSP methodology holds potential for application in other engineering and design disciplines, offering a novel approach to engagement and collaborative problem-solving.

Travel time reliability is crucial for transport operators as it directly affects traveler behavior and public transport patronage. This study investigates public transport reliability focusing on the underexamined metric "pull-out deadhead" of bus services, which is the travel time from the depot to the first stop of a route. Road traffic congestion affects this journey, thus potentially having an impact on route-level reliability. To investigate the influence of pull-out deadheads, 3 months of bus scheduling and performance data across 23 first stops were analyzed concerning a case study in Brisbane, Australia. The results of the aggregate analysis revealed that there was a positive correlation between deadhead reliability and on-time running reliability, indicating that the more reliable a pull-out deadhead, the more reliable the service customers receive. A disaggregate deadhead time distribution analysis of the first stops was also undertaken to provide potential recommendations to improve deadhead reliability. Strategies that could improve reliability included application of a pre-service bus signal priority, and scheduling greater travel time budgets for trips between the depot and first stop. However, it should be noted that the services analyzed generally had on-time running percentages that exceeded 80%, suggesting a relatively reliable service. Ultimately, the results of this study offer alternative considerations for transport operators to enhance their services and improve the overall travel experience for public transport users. Further research incorporating additional traffic variables and applying deadhead modeling to other case study locations will expand understanding of the impact of deadheads on bus travel time reliability.

The significance of developing shared road infrastructure in cities throughout the world is growing. Driven by the need to improve traffic management in ways that enhance multiple sustainability outcomes, developing the tools needed to test shared space proposals is becoming more sought after by responsible agencies. This paper reviews approaches to simulation modeling focused on representing and assessing shared spaces, culminating in a new approach presented here called the Integrated Pedestrian–Vehicle Model (IPVM)—a novel framework that combines social force models, car-following models and other algorithms from the robotics domain to better describe both mobility and activity within a shared space. The IPVM recognizes that while shared spaces are inherently multimodal, past efforts have tended to use pedestrian models as a starting point. Most consider the interaction of pedestrians with other pedestrians and static road infrastructure. Shared space models are generally microscopic models that integrate a social force model with a variety of car-following models to describe the interaction between vehicles and pedestrians. However, there is little research and few practical methodologies that address the long-range conflict avoidance between vehicles and pedestrians. This aspect is crucial for accurately representing the desire lines and pathways of pedestrians and active transport users in complex environments like shared spaces. The IPVM describes and visualizes shared road infrastructure with an absence of separating infrastructure between users and outputs. It generates metrics that can be used in conjunction with the latest evaluation approaches to gauge the sustainability credentials of shared space road proposals. Enhanced modeling of shared space solutions can lead to more effective implementation, which can potentially reduce the presence of cars, increase public and active transport use and lead to a more sustainable transport system.

The COVID-19 pandemic has entailed profound societal changes at many levels and, in particular, the mobility patterns of communities worldwide. There has been a profound modification in collective travel behaviour, mainly because of the restrictions enforced by governing authorities to reduce the likelihood of infection transmission. Perceptions regarding the severity of the disease and mitigation measures to restrict its spread may have an effect on travel behaviour. This research explores the impact of these perceptions on individuals' travel behaviour by utilising a structural equation modelling approach for different travel modes regarding free-time and leisure mobility. The investigation considers data derived from a global survey performed in nine countries during May 2020, during the first wave of the pandemic. The countries included were Australia, Brazil, China, Ghana, India, Italy, Norway, South Africa, and the United States of America. Results indicate that inhabitants of these countries have various perceptions regarding the effectiveness of travel restrictions for different transport modes. The disease contraction probability is perceived as higher for public transport modes; accordingly, people tend to travel significantly less by train and bus. For some countries, even if the disease restriction policies are considered effective for both private and public transport, survey participants travel less frequently across all travel modes. Active travel or travelling alone is not influenced significantly by an individual's perceptions of the disease. This study examines the correlations between disease perception and travel behaviour for policymaking to revive sustainable travel transports and active travel, which is essential for improving physical and mental health during the pandemic.

The theoretical connections between off-site construction (OSC) and lean have only been marginally addressed in existing literature. This study analyses how distinct OSC strategies (1) affect flow and completion times, and (2) support simplification in construction (by reducing the number of parts and/or steps). Flow, which entails the smooth and reliable transfer of work across trades, is at the heart of lean whereas simplification is one principle of this management philosophy. Industry data based on three residential projects built in Australia using traditional construction or OSC strategies (panels/cassettes, and/or bathroom pods), and 15 hypothetical scenarios were used to develop a simulation exercise. The results show that OSC can substantially reduce schedule durations, particularly when different prefab elements are jointly used, but flow variability [measured by coefficient of variation (COV)] remains unchanged when compared to traditional construction. OSC was found to minimize the number of parts on-site by having building materials and components aggregated off-site and delivered as subassemblies. Yet, the number of steps did not reduce substantially, and the reductions only occurred when panels/cassettes were used. Alternative perspectives explaining the numerical results obtained are also discussed. The main contribution of this paper lies in measuring the impact of distinct OSC strategies from a flow viewpoint as well as in empirically examining their benefits from a simplification viewpoint. The study extends existing knowledge by leveraging industry data to quantify such impacts utilizing a tailored simulation approach. This provides an important addition to the still limited number of investigations measuring and comparing flow under different conditions.

Civil engineering, specifically transport engineering, is a continually evolving profession. Recent developments in technology have resulted in more automated and visual problem-solving techniques, involving the use of computer programs and simulation, as practitioners and researchers move away from traditional pen and paper approaches. Accordingly, teaching undergraduate university students the basic principles of transport planning, traffic engineering, and highway design effectively is fundamental to the sustainability of the profession. It also is a challenging and dynamic task for educators because enhanced accessibility to technology has changed the way students understand and learn the material being delivered at tertiary education institutions. This paper presents the development of, and feedback from, the implementation of a series of blended learning initiatives (interactive polling exercises, online quizzes, supplementary learning videos, and authentic real-world design project) within an introductory large class-size transport planning and geometric design subject. The process of developing the blended learning initiatives was documented to clearly highlight the benefits and challenges in the transformation process. In addition, qualitative student feedback and student performance between 2016 and 2018 were reviewed to understand the impacts of the transformation. The initiatives were well received; students valued self-paced learning and the exposure to real-world design exercises. From an educator’s perspective, blending made it feasible to deliver complex content whilst offering tailored learning opportunities across the cohort. Although further comprehensive experiments and statistically oriented research are necessary, this case study adds to a growing body of literature that indicates the potential value of blended learning initiatives, especially in the context of large class-size university subjects.

The concept of flow, a core notion of lean, has been proposed and discussed throughout the construction literature for over three decades but is not yet widely applied and disseminated across industry.This paper sets out to perform an exploration of potential underlying root causes of this problem by examining a number of concepts across varied disciplines: (i) metaphysics and ontological assumptions (already discussed in the construction context), (ii) particle/wave duality (from quantum physics), (iii) coemergence (or non-duality) (from Buddhist philosophy), and (iv) cognitive biases and fallacies (based on the work by Tversky and Kahneman).A set of six preliminary and non-exhaustive hypotheses are formulated seeking to provide insights to the problem at hand, namely, "Why is flow not widely understood and applied in construction practice?".Two experiment designs are proposed to test the last three hypotheses, which are related to the pragmatic aspect of this question, and thus these findings can potentially assist in a more widespread adoption of flow in practice.

Ramp metering (RM) is a traffic management technique that aims at controlling the flow of traffic entering specific roadways tailored for fast-moving traffic containing separate multilane divided carriageways (such as motorways, highways, expressways, freeways, and turnpikes). The objective of RM is to minimize congestion on the main thoroughfare of the roadway. RM algorithms have evolved significantly since the 1960s and will continue to do so into the future. While the functionalities of the algorithms remain valid through time, the applications of the RM strategies are continually being updated. Unlike previous reviews that focused on the RM methodological aspect, this study details the recent literature regarding the implementation of RM strategies. The aim of this paper is to provide a global perspective on existing RM applications and the algorithms used, for future reference for both academics and practitioners. The paper provides an indicative historical context and characteristics for each reported project, as well as an overview of the evaluation of these schemes. Based on the current understanding of RM strategies, the paper discusses challenges and the potential future of RM technology.

ObjectiveThe aim of this study was to determine the correlation between the physiological and behavioral responses of drivers in the context of varying cognitive workloads.BackgroundSafe driving is a complex task that requires numerous cognitive and behavioral functions. Any internal or external factor that impairs a driver's cognition may result in accidents and compromise road safety. Quantification of cognitive load present in driving currently and area of significant research. This paper extends this topic by identifying a physiological factor which is both affected by cognitive load and is easily measurable, heart rate, and relating heart rate with driver behavior. The relationship developed could be used in the creation of wearable smart devices to forecast and prevent future road traffic crashes.Method34 students participated in a dual-task experiment that took place in a driving simulator. Cardiovascular responses (heart rate and heart rate variability) and driving performance (lane position and time headways) of the participants were recorded while driving. To create a variable cognitive workload, mobile phone conversations were created with three levels of mental effort (Neutral, problem-solving, and arousal conversations) across a series of driving trials.ResultsThe results show that cognitive load increases significantly for the problem-solving and arousal tasks, with reductions in time headway (0.58 and 1 second respectively) and greater deviations in lane position (0.19 and 0.28 meters respectively), whilst there was no significant change for the neutral conversation. Furthermore, heart rate and heart rate variability increases significantly in problem-solving and arousal tasks where indices identified nearly double There is also a significant positive correlation between a deterioration of driver behavioral performance (lane position) and indicators of heart rate variability (LF/HF ratio) during problem-solving (R=0.6, P value =0.01) and arousal (R=0.61, P value =0.04) tasks.ConclusionThe level of mental processing is a determining factor in the driver's behavioral performance. As the cognitive load of the conversation increases, the driver's behavioral performance is impaired.

This abridged paper provides an overview of an alternative way of approaching strategic transport planning utilizing pervasive crowd sourced data within an automated methodology citing the Rapidex model as an example. The presented model addresses road conditions due to the reason of data availability by TomTom and Google. Additional data is not as universally available yet. The approach specifically prioritizes speed over detail which meets the functional requirements in data-poor regions which need rapid high-level input to their preliminary planning. The paper begins with an exploration of the issue of ‘data poverty’ with an examination of the limitations it causes on affected regions as well as global implications because of environmental impact. Then, an overview is provided of the concept of ‘automated transport planning’ as a potential methodological solution to the noted issues which is somewhat analogous to the broader approach of automated planning in AI (Ghallab et al., 2004). Finally, future work and concluding remarks are provided to the reader.

Background: In a short time, the COVID-19 pandemic turned into a global emergency. The fear of becoming infected and the lockdown measures have drastically changed people?s daily routine. The aim of this study is to establish the psychological impact that the COVID-19 pandemic is entailing, particularly with regards to levels of stress, anxiety and depression, and to the risks of developing Post-Traumatic Stress Disorder (PTSD). Methods: The study, carried out with a sample of 1612 subjects distributed in seven countries (Australia, China, Ecuador, Iran, Italy, Norway and the United States), allowed us to collect information about the psychological impact of COVID-19. Results: The findings of this study show that the levels of stress, depression and anxiety, as well as the risks of PTSD, are higher than average in over half of the considered sample. The severity of these disorders significantly depends on gender, type of outdoor activities, characteristics of their homes, eventual presence of infected ac-quaintances, time dedicated to looking for related information (in the news and social networks), type of source information and, in part, to the level of education and income. Conclusions: We conclude that COVID-19 has a very strong psychological impact on the global population. This appears to be linked to the coping strategies adopted, level of mindful awareness, socio-demographic variables, people?s habits and the way individuals use means of communication and information.

Autonomous vehicles (AVs) are being extensively tested on public roads in several states in the USA, such as California, Florida, Nevada, and Texas. AV utilization is expected to increase into the future, given rapid advancement and development in sensing and navigation technologies. This will eventually lead to a decline in human driving. AVs are generally believed to mitigate crash frequency, although the repercussion of AVs on crash severity is ambiguous. For the data-driven and transparent deployment of AVs in California, the California Department of Motor Vehicles (CA DMV) commissioned AV manufacturers to draft and publish reports on disengagements and crashes. This study performed a comprehensive assessment of CA DMV data from 2014 to 2019 from a safety standpoint, and some trends were discerned. The results show that decrement in automated disengagements does not necessarily imply an improvement in AV technology. Contributing factors to the crash severity of an AV are not clearly defined. To further understand crash severity in AVs, the features and issues with data are identified and discussed using different machine learning techniques. The CA DMV accident report data were utilized to develop a variety of crash AV severity models focusing on the injury for all crash typologies. Performance metrics were discussed, and the bagging classifier model exhibited the best performance among different candidate models. Additionally, the study identified potential issues with the CA DMV data reporting protocol, which is imperative to share with the research community. Recommendations are provided to enhance the existing reports and append new domains.

As largely documented in the literature, the stark restrictions enforced worldwide in 2020 to curb the COVID-19 pandemic also curtailed the production of air pollutants to some extent. This study investigates the perception of the air pollution as assessed by individuals located in ten countries: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the USA. The perceptions towards air quality were evaluated by employing an online survey administered in May 2020. Participants (N = 9394) in the ten countries expressed their opinions according to a Likert-scale response. A reduction in pollutant concentration was clearly perceived, albeit to a different extent, by all populations. The survey participants located in India and Italy perceived the largest drop in the air pollution concentration; conversely, the smallest variation was perceived among Chinese and Norwegian respondents. Among all the demographic indicators considered, only gender proved to be statistically significant.

The restrictive measures implemented in response to the COVID-19 pandemic have triggered sudden massive changes to travel behaviors of people all around the world. This study examines the individual mobility patterns for all transport modes (walk, bicycle, motorcycle, car driven alone, car driven in company, bus, subway, tram, train, airplane) before and during the restrictions adopted in ten countries on six continents: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the United States. This cross-country study also aims at understanding the predictors of protective behaviors related to the transport sector and COVID-19. Findings hinge upon an online survey conducted in May 2020 (N = 9,394). The empirical results quantify tremendous disruptions for both commuting and non-commuting travels, highlighting substantial reductions in the frequency of all types of trips and use of all modes. In terms of potential virus spread, airplanes and buses are perceived to be the riskiest transport modes, while avoidance of public transport is consistently found across the countries. According to the Protection Motivation Theory, the study sheds new light on the fact that two indicators, namely income inequality, expressed as Gini index, and the reported number of deaths due to COVID-19 per 100,000 inhabitants, aggravate respondents’ perceptions. This research indicates that socio-economic inequality and morbidity are not only related to actual health risks, as well documented in the relevant literature, but also to the perceived risks. These findings document the global impact of the COVID-19 crisis as well as provide guidance for transportation practitioners in developing future strategies.

Connected and Automated Vehicle (CAV) technology, although in the development stage, is quickly expanding throughout the vehicle market. However, full market penetration will most likely require considerable planning as key stakeholders, manufacturers, consumers and governing agencies work together to determine optimal deployment strategies. Specifically, road safety is a critical challenge to the widespread deployment and adoption of this disruptive technology. During the transition period fleets will be composed of a combination of CAVs and conventional vehicles, and therefore it is imperative to investigate the repercussions of CAVs on traffic safety at different penetration rates. Since crash severity and frequency in conjunction reflect traffic safety, this study attempts to investigate the effect of CAVs on both crash severity and frequency through a microsimulation modelling exercise. VISSM microsimulation platform is used to simulate a case study of the M1 Geelong Ring Road network (Princes Freeway) in Victoria, Australia. Network performance is evaluated using performance metrics (Total System Travel Time, Delay) and kinematic variables (Speed, acceleration, jerk rate). Surrogate safety measures (time to collision, post encroachment time, etc.) are examined to inspect the safety in the network. The results indicate that the introduction of CAVs does not achieve the expected decrease in crash severity and rates involving manual vehicles, despite the improvement in network performance, given the demand and the set of parameters used in our operational CAV algorithm are intact. Additionally, the study identifies that the safety benefits of CAVs are not proportional to CAV penetration, and full-scale benefits of CAVs can only be achieved at 100 % CAV penetration. Further, considering network efficiency as a performance metric and total crash rate involving conventional vehicles as a safety metric, a Pareto frontier is extracted, for varying CAV operational behaviour. The results presented in this study provide insights into the impacts of CAVs on traffic safety valuable for insurance companies and other industry participants, enabling safety-related services and more enterprising business models.

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A current issue within the driver distraction community centres around different findings regarding the impact of mobile phone conversation on driving found in driving simulators versus instrumented vehicles employed in real-world naturalistic driving studies (NDSs). This paper compares and contrasts the two types of studies and aims to provide reasons for the differences in findings that have been documented. A comprehensive review of literature and consultations with human factors experts highlighted that simulator studies tend to show degradation in driving performance, suggestive of increased crash risk as a result of mobile phone conversation. Whilst NDSs, at times, present data suggesting that mobile phone conversation distraction actually reduces crash risk. This study identifies that these differences may be attributed to behavioural hypotheses associated with driver self-regulation, arousal from cognitive loading, task displacement and gaze concentration - all of which need to be explicitly tested in future driving studies. Metric estimation and application was also revealed to be polarising results and the subsequent assessment of the crash risk. A common metric applied in this domain is the 'Odds Ratio', particularly prevalent in NDSs. This study presents a detailed investigation into the assumptions and application of the Odds Ratio which revealed the potential for over- and under-estimation of the metric depending on the core data and sampling assumptions. Furthermore, this research presents a comparative analysis of select driving simulator studies and an NDS considering only driving behaviour data as a means to consistently compare the findings of both methodologies. The findings from this investigation implores the need for greater consistency in the application of analysis methods and metrics across both simulator and NDSs. Improvements can yield a more robust platform to systematically compare and interpret data across both approaches, ultimately leading to enhanced planning and safety regarding mobile phone use while driving.