Search

Pedestrian distraction is a major causal factor reported in pedestrian fatalities worldwide. Even though many observational studies and laboratory-based research have been conducted to examine the influence of pedestrian distraction on road safety, there is little understanding of the determinants of pedestrian beliefs that influence distracted walking behaviour. The present study examines the associations of pedestrian beliefs related to engagement in mobile phone distraction with psychological factors such as mobile phone addiction, boredom proneness, and mindfulness. Five hundred and fifty-one participants completed a questionnaire about their distraction beliefs (behavioural, normative, and control), mobile phone addiction, boredom proneness, and mindfulness. A Structural Equation Model (SEM) was developed to investigate the influence of mobile phone addiction, boredom proneness, and mindfulness on the three types of beliefs. Mobile phone addiction was significantly associated with behavioural beliefs (factor loading = 0.38) and control beliefs (factor loading = 0.23) but not with normative beliefs. Further, significant associations of boredom proneness were observed with all three types of beliefs: behavioural (factor loading = 0.15), normative (factor loading = 0.13), and control (factor loading = 0.15). Mindfulness showed significant relationships with normative beliefs (factor loading = 0.13) and control beliefs (factor loading = 0.11) but not with behavioural beliefs. This study is the first attempt to investigate the predictors of pedestrian distraction beliefs in the Indian context. The findings can assist the policymakers in understanding the pedestrian psychology behind their distracted walking behaviour.

The crash risk of drivers increases significantly while driving under the influence of alcohol. However, the drivers' intention to drink and drive has not been explored yet in the context of a developing country like India. The present study applied an extended version of the theory of planned behaviour (TPB) to investigate the psychosocial predictors of drunk driving intentions of Indian drivers. 252 drivers participated in a self-reported survey designed for the study. Apart from the standard TPB components (attitudes, subjective norms, and perceived behavioural control), the survey also captured the extension measures such as risk perceptions, moral norms, traffic fatalism, sensation-seeking, conformity tendency, past engagement in drunk driving, crash history and driver demographics. The standard TPB model was successful in explaining 68.6% of the variance in the intention to drink and drive. The attitudes displayed the strongest influence on the intention (β = 0.696, p < 0.001), followed by perceived behavioural control (β = 0.180, p < 0.001); whereas subjective norms showed a moderate significance in influencing the intention (β = 0.062, p < 0.1). Among the extension variables, past behaviour showed the strongest influence on the intention to drink and drive (β = 0.123, p < 0.05), followed by sensation-seeking (β = 0.080, p < 0.05) and traffic fatalism (β = 0.072, p < 0.1). The extended TPB model explained 72% of the variance in the intention to drink and drive. The study findings can assist in the development of strategies/interventions to reduce drunk driving incidences.An earlier version of the paper was presented at the TRB 101st Annual Meeting, Washington, D. C., 2022.

Research on distracted driving due to phone use has increased substantially over the past decades, however, very little is explored about commercial vehicle drivers (e.g., truck drivers) in this aspect. This study focused on examining the prevalence of phone use habits and the associated crash risk for data collected from 490 Indian truck drivers. The data on demographic details, driving history, phone use habits (in everyday life and during driving), history of receiving any penalty for phone use and incidences of crash occurrence, was collected through face-to-face interviews with the drivers. Binary logistic models were used to identify the factors affecting phone use habits during driving and the associated crash risk. Further, the incidences of receiving a penalty for the phone use were examined through cross-tabulation and chi-square statistics. The results showed that 55% of the drivers used a phone during driving, mainly for talking purpose. The model revealed that education, vehicle size, vehicle ownership and everyday life phone use habits were the significant factors associated with phone use while driving. Regarding the history of penalty receiving incidences, 41% of the drivers who used a phone during driving had received the penalty, and 52% of these penalty-receiving drivers were penalized repetitively. The model results for crash risk showed that the frequent phone users were 29 times more likely to be involved in a crash due to phone use compared to the non-frequent users. The results suggest a double level (legislative and company level) prohibition policy for phone use during driving for the truck drivers and also to enforce strict and effective legislative ban especially on the truck routes.

The outbreak of COVID-19 pandemic has resulted in change in both commute and personal travel patterns. Though, in India, lockdown was implemented from 25th March 2020, due to self-awareness and pandemic risk perception, change in commuter behavior was observed from the beginning of March 2020. The period from 15th to 24th March 2020 is considered as the transition phase of COVID-19 outbreak in India (i.e., between no lockdown and lockdown period). This study attempts to use a decision tree approach to investigate the modal preference of 1542 commuters in association with socio-economic and travel characteristics, and safety perceptions with respect to public and private modes during transition to lockdown due to COVID-19 in India. About 41% of commuters stopped traveling during the transition to lockdown phase, 51.3% were using the same mode of transport and 5.3% of commuters shifted from public to private mode. The study findings reported different interactions of factors influencing the decision to use public or private modes of transport for daily commuting during pandemic situations like COVID-19. Interestingly, safety perceptions (associated with personal health) of commuters did not play a significant role in their mode choice behavior during the transition phase. Though people perceived public transportation as unsafe over personal vehicle use, the actual commute patterns did not validate this due to a possible reason that commuters do not have enough alternative modes. Given the uncertainties in the decision making of the commuters regarding their travel behavior due to physical distancing, the insights from this study are important to policymakers and local transport authorities to understand the change in travel patterns.

Traffic noise is one of the major sources of noise pollution in metropolitan regions causing various health hazards (e.g., long-term sleep disturbance, increase in blood pressure, physical tension, etc.). In this research, noise prediction models, which can measure the noise level experienced by the commuters while driving or traveling by motorized vehicles in the Mumbai Metropolitan Region, India, were developed. These models were developed by conducting a comprehensive study of various factors (e.g., vehicle speed, traffic volume and road characteristics, etc.) affecting the levels of concentration of noise. A widespread data collection was done by conducting road trips of total length of 403.80 km via different modes of transport, such as air-conditioned (A/C) car, non A/C car, bus and intermediate public transport (i.e., traditional 3-wheeler autos). Multiple regression analyses were performed to develop a functional relation between equivalent noise levels experienced by passengers while traveling (which was considered as a dependent variable) and explanatory variables such as traffic characteristics, vehicle class, vehicle speed, various other location characteristics, etc. Noise levels are generally higher in the vicinity of intersections and signalized junctions. Independent data sets (for each mode of transport) were used to validate the developed models. It was noted that maximum differences between observed and estimated values from the model were within the range of ±7.8% of the observed value.