Evasive actions to prevent pedestrian collisions in varying space/time contexts in diverse urban and non-urban areas.

This study aims to identify driver-safe evasive actions associated with pedestrian crash risk in diverse urban and non-urban areas. The research focuses on the integration of quantitative methods and granular naturalistic data to examine the impacts of different driving contexts on transportation system performance, safety, and reliability. The data is derived from real-life driving encounters between pedestrians and drivers in various settings, including urban areas (UAs), suburban areas (SUAs), marked crossing areas (MCAs), and unmarked crossing areas (UMCAs). By determining critical thresholds of spatial/temporal proximity-based safety surrogate techniques, vehicle-pedestrian conflicts are clustered through a K-means algorithm into different risk levels based on drivers' evasive actions in different areas. The results of the data analysis indicate that changing lanes is the key evasive action employed by drivers to avoid pedestrian crashes in SUAs and UMCAs, while in UAs and MCAs, drivers rely on soft evasive actions, such as deceleration. Moreover, critical thresholds for several Safety Surrogate Measures (SSMs) reveal similar conflict patterns between SUAs and UMCAs, as well as between UAs and MCAs. Furthermore, this study develops and delivers a pseudo-code algorithm that utilizes the critical thresholds of SSMs to provide tangible guidance on the appropriate evasive actions for drivers in different space/time contexts, aiming to prevent collisions with pedestrians. The developed research methodology as well as the outputs of this study could be potentially useful for the development of a driver support and assistance system in the future.

How do driving behavior and attitudes toward road safety vary between developed and developing countries? Evidence from Iran and the Netherlands.

INTRODUCTION: The rates of road traffic injuries and fatalities in developing countries are significantly higher than in developed countries. This study examines the differences in driving behavior, road safety attitudes, and driving habits between a developed country (the Netherlands) and a developing country (Iran), which bear major differences in terms of crash involvement per population. METHOD: In this context, this study assesses the statistical association of crash involvement with errors, lapses, aggressive driving incidents, and non-compliance with traffic rules, attitudes, and habits. Structural equation modeling was used to evaluate data obtained from 1,440 questionnaires (720 samples for each group). RESULTS: The results revealed that more insecure attitudes toward traffic-regulation observance, negative driving habits, and risky behaviors, such as traffic rule violations act as influential factors of crash involvement. Iranian participants showed a greater likelihood to get involved in violations and driving habits with a higher level of risk. In addition, lower levels of safety attitudes toward traffic-regulation observance were observed. On the other hand, Dutch drivers were more likely to report lapses and errors. Dutch drivers also reported safer behavior in terms of unwillingness to engage in risky behaviors such as violations (speeding and no-overtaking). The structural equation models for crash involvement based on behaviors, attitudes, and driving habits were also evaluated for their accuracy and statistical fit using relevant indicators. PRACTICAL APPLICATIONS: Finally, the findings of the present study point out the need for extensive research in some areas to foster policies that can effectively enhance safer driving.

Who is More Likely (Not) to Make Home-Based Work Trips During the COVID-19 Pandemic? The Case of Scotland.

In this study, we used survey data (n = 6,000) to investigate the work trip patterns of Scottish residents at various points of the COVID-19 pandemic. We focused specifically on the reported patterns of weekly work trips made during the government-enforced lockdown and subsequent phases of restriction easing. This was of particular importance given the widespread changes in work trips prompted by COVID-19, including a significant rise in telecommuting and a reduction in public transport commuting trips. The survey data showed that the vast majority of respondents (∼85%) made no work trips during lockdown, dropping to ∼77% following the easing of some work-related restrictions. Zero-inflated hierarchical ordered probit models were estimated to determine the sociodemographic and behavioral factors affecting the frequency of work trips made during three distinct periods. The model estimation results showed that the socioeconomic characteristics of respondents influenced work trips made throughout the pandemic. In particular, respondents in households whose main income earner was employed in a managerial/professional occupation were significantly more likely to make no work trips at all stages of the pandemic. Those with a health problem or disability were also significantly more likely to make no work trips throughout the pandemic. Other interesting findings concern respondents' gender, as males were more likely to complete frequent work trips than females throughout the pandemic, and differences between densely populated areas and the rest of Scotland, as respondents from a large city (Edinburgh or Glasgow) were significantly more likely to make frequent work trips as restrictions were eased.

The impact of COVID-19 on future public transport use in Scotland.

This paper examines the determinants of changes in future public transport use in Scotland after the COVID-19 pandemic. An online questionnaire was distributed to 994 Scottish residents in order to identify travel habits, attitudes and preferences during the different phases of the COVID-19 outbreak and travel intentions after the pandemic. Quota constraints were enforced for age, gender and household income to ensure the sample was representative of the Scottish population. The respondents indicated that they anticipated they would make less use of buses and trains at the end of the pandemic. Over a third expect to use buses (36%) and trains (34%) less, whilst a quarter expect to drive their cars more. As part of the analysis, a random parameter bivariate probit model with heterogeneity in the means of random parameters was estimated to provide insights into the socio-demographic, behavioural and perceptual factors which might affect future public transport usage. The inclusion of random parameters allows for the potential effects of unobserved heterogeneity within the independent variables to be captured, whilst making allowances for heterogeneity in the means of the random parameters. The model estimation showed that several factors, including pre-lockdown travel choices, perceived risk of COVID-19 infection, household size and region significantly affected intended future use of public transport. In addition, several variables related to age, region, pre-lockdown travel choices and employment status resulted in random parameters. The current paper contributes to our understanding of the potential loss of demand for public transport and the consequences for future equitable and sustainable mobility. Our findings are highly relevant for transport policy when developing measures to strengthen the resilience of the public transport system during and after the pandemic.

Analysis of pedestrian accident injury-severities at road junctions and crossings using an advanced random parameter modelling framework: The case of Scotland.

This paper investigates the determinants of injury severities in pedestrian-motor vehicle accidents at signalised and unsignalised junctions, and at physically-controlled and human-controlled crossings in Scotland. The accident data were drawn from the official police crash report database of the UK spanning a period between 2010 and 2018. Correlated random parameter ordered probit models with heterogeneity in the means were developed in order to account for the multi-layered impact of unobserved heterogeneity on statistical estimation. The model estimation results showed that the severities of accident injuries are affected by roadway, location, weather, vehicle, and driver characteristics as well as temporal attributes (including time and day of the accident). Factors such as the urban context, lighting and weather conditions and road surface conditions were found to result in correlated random parameters, thus capturing the intricate, yet interactive effects of unobserved heterogeneity, and particularly the unobserved behavioural response of road users to different traffic control types at junctions and crossings. Vehicle type, driver's gender and day-of-the-week were observed to influence the random parameters' distributions. Empirically, the results showcase variations in the determinants of injury severities at signalised and unsignalised junctions, and at physically-controlled and human-controlled crossings. Even though most of these variations were related to the magnitude of impact of the determinants, differences in the directional effects on injury severities were also identified, mainly for factors related to weather conditions, hazard presence on the road, and temporal characteristics of the accidents.

Survey data on public perceptions towards flying cars and flying taxi services.

An online survey was conducted to evaluate public perceptions towards an emerging transportation technology, namely the flying car, which is expected to join the existing traffic fleet within the following decades. Responses from 692 survey participants were collected. Approximately 84% of the participants were from the United States, and the remaining 16% were from the rest of the world. The data resulting from the survey include several aspects of public perceptions towards flying cars, as for example: willingness to use and pay for flying cars; willingness to use and pay for flying taxi services; perceptions towards potential benefits and concerns arising from the future use of flying cars; perceptions towards considering residence relocation; and perceptions towards potential security measures to improve operational safety of flying cars. In addition, information relating to several dimensions of driving and travel behaviours and habits, and socio-demographic information of the participants were also collected. The dataset can be used as a baseline to design future surveys on Advanced Air Mobility (AAM) and flying cars, and to compare consumer perceptions across different regions and during different time periods.

Trips for outdoor exercise at different stages of the COVID-19 pandemic in Scotland.

INTRODUCTION: The COVID-19 pandemic has had exceptional effects on travel behaviour in the UK. This paper focuses specifically on the outdoor exercise trips of Scottish residents at several distinct points of the COVID-19 pandemic. Given the negative health consequences of limited exercise, this study aims to determine the sociodemographic and behavioural factors affecting frequency of outdoor exercise trips. METHODS: Using recent public survey data (n=6000), random parameters ordered probit models (with allowances for heterogeneity in the means of random parameters) are estimated for three points during the pandemic: the most stringent lockdown, modest restriction easing and further easing of restrictions. RESULTS: The survey data show frequent outdoor exercise in the early stages of the pandemic, with ∼46% making six or more weekly trips during lockdown, reducing to ∼39% during the first phase of restriction easing, and further to ∼34% during the following phase of easing. The model estimations show that common factors, dominated by socioeconomic and demographic variables, influenced the frequency of outdoor exercise trips across most survey groups. The modelling framework also allowed insights into the impact of unobserved characteristics within several independent variables; for example, the lockdown exercise trip rates of those with a health problem or disability, and those over 65, were both found to be dependent on personal vehicle access. CONCLUSIONS: The findings suggest that those with a health problem or disability, those who live in households' where the main income earner is employed in a semi-skilled/unskilled manual occupation or is unemployed and ethnic minority groups (i.e., any mixed, Asian, or Black background) were significantly more likely to complete no weekly outdoor exercise trips throughout the pandemic. As a result, we suggest that these groups are at higher risk of the negative health consequences associated with limited physical activity. Policy implications are discussed in terms of mitigating this effect, as well as reducing transport inequity related to vehicle access.

Extending the Heston model to forecast motor vehicle collision rates.

We present an alternative approach to the forecasting of motor vehicle collision rates. We adopt an oft-used tool in mathematical finance, the Heston Stochastic Volatility model, to forecast the short-term and long-term evolution of motor vehicle collision rates. We incorporate a number of extensions to the Heston model to make it fit for modelling motor vehicle collision rates. We incorporate the temporally-unstable and non-deterministic nature of collision rate fluctuations, and introduce a parameter to account for periods of accelerated safety. We also adjust estimates to account for the seasonality of collision patterns. Using these parameters, we perform a short-term forecast of collision rates and explore a number of plausible scenarios using long-term forecasts. The short-term forecast shows a close affinity with realised rates (over 95% accuracy), and outperforms forecasting models currently used in road safety research (Vasicek, SARIMA, SARIMA-GARCH). The long-term scenarios suggest that modest targets to reduce collision rates (1.83% annually) and targets to reduce the fluctuations of month-to-month collision rates (by half) could have significant benefits for road safety. The median forecast in this scenario suggests a 50% fall in collision rates, with 75% of simulations suggesting that an effective change in collision rates is observed before 2044. The main benefit the model provides is eschewing the necessity for setting unreasonable safety targets that are often missed. Instead, the model presents the effects that modest and achievable targets can have on road safety over the long run, while incorporating random variability. Examining the parameters that underlie expected collision rates will aid policymakers in determining the effectiveness of implemented policies.

A Novel Method for Estimating Emissions Reductions Caused by the Restriction of Mobility: The Case of the COVID-19 Pandemic.

The COVID-19 pandemic is the single largest event in contemporary history in terms of the global restriction of mobility, with the majority of the world population experiencing various forms of "lockdown". This phenomenon incurred increased amounts of teleworking and time spent at home, fewer trips to shops, closure of retail outlets selling non-essential goods, and the near disappearance of leisure and recreational activities. This paper presents a novel method for an economy-wide estimate of the emissions reductions caused by the restriction of movement. Using a global multiregional macro-economic model complemented by Google Community Mobility Reports (CMRs) and national transport data, we cover 129 individual countries and quantify direct and indirect global emissions reductions of greenhouse gases (GHG; 1173 Mt), PM2.5 (0.23 Mt), SO2 (1.57 Mt), and NOx (3.69 Mt). A statistically significant correlation is observed between cross-country emission reductions and the stringency of mobility restriction policies. Due to the aggregated nature of the CMRs, we develop different scenarios linked to consumption, work, and lifestyle aspects. Global reductions are on the order of 1-3% (GHG), 1-2% (PM2.5), 0.5-2.8% (SO2), and 3-4% (NOx). Our results can help support crucial decision making in the post-COVID world, with quantified information about how direct and indirect consequences of mobility changes benefit the environment.

Do High Visibility Enforcement programs affect aggressive driving behavior? An empirical analysis using Naturalistic Driving Study data.

This paper investigates the effect of High Visibility Enforcement (HVE) programs on different types of aggressive driving behavior, namely, speeding, tailgating, unsafe lane changes and 'other' aggressive driving behavior types (occurrence of not-yielding right-of-way and red light or stop signs violations). For this purpose, the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data are used, which include forward-facing videos and time series information with regard to trips conducted at or near the locations of HVE implementation. To capture the intensity and duration of speeding and tailgating, scaled metrics are developed. These metrics can capture varying levels of aggressive driving behavior enabling, thus, a direct comparison of the various behavioral aspects over time and among different drivers. To identify the effect of HVE and other trip, driver, vehicle or environmental factors on speeding and tailgating, while accounting for possible interrelationship among the behavior-specific scaled metrics, Seeming Unrelated Regression Equation (SURE) models were developed. To analyze the likelihood of occurrence of unsafe lane changes and 'other' aggressive driving behavior types, a grouped random parameters ordered probit model with heterogeneity in means and a correlated grouped random parameters binary logit model were estimated, respectively. The results showed that drivers' awareness of HVE implementation has the potential to decrease aggressive driving behavior patterns, especially unsafe lane changes and 'other' aggressive driving behaviors.

Analysis of stationary and dynamic factors affecting highway accident occurrence: A dynamic correlated grouped random parameters binary logit approach.

Traditional accident analysis typically explores non-time-varying (stationary) factors that affect accident occurrence on roadway segments. However, the impact of time-varying (dynamic) factors is not thoroughly investigated. This paper seeks to simultaneously identify pre-crash stationary and dynamic factors of accident occurrence, while accounting for unobserved heterogeneity. Using highly disaggregate information for the potential dynamic factors, and aggregate data for the traditional stationary elements, a dynamic binary random parameters (mixed) logit framework is employed. With this approach, the dynamic nature of weather-related, and driving- and pavement-condition information is jointly investigated with traditional roadway geometric and traffic characteristics. To additionally account for the combined effect of the dynamic and stationary factors on the accident occurrence, the developed random parameters logit framework allows for possible correlations among the random parameters. The analysis is based on crash and non-crash observations between 2011 and 2013, drawn from urban and rural highway segments in the state of Washington. The findings show that the proposed methodological framework can account for both stationary and dynamic factors affecting accident occurrence probabilities, for panel effects, for unobserved heterogeneity through the use of random parameters, and for possible correlation among the latter. The comparative evaluation among the correlated grouped random parameters, the uncorrelated random parameters logit models, and their fixed parameters logit counterpart, demonstrate the potential of the random parameters modeling, in general, and the benefits of the correlated grouped random parameters approach, specifically, in terms of statistical fit and explanatory power.