The outbreak of infectious diseases affects people’s lifestyles significantly, as they undertake fewer outdoor activities as a protective measure and to follow government orders of restricted movements. This paper reviewed the scientific literature related to transport and infectious diseases to gain insights into managing such circumstances. The outcomes indicate that the transport sector has a two-fold role during the outbreak of infectious diseases: controlling the spread of infection and assessing the impact of reduced outdoor activities on the transport sector. It was found that local and international travel restrictions, if applied at the initial stages, are effective in controlling the spread of infectious disease; at a later stage, behavioral changes become prominent in limiting the spread. Further, the outbreaks resulted in a significant reduction in mobility, altering traffic patterns with lower peaks and improving traffic safety. The public transport mode share reduced considerably and people preferred cars and active modes. These changes also showed positive impacts on air pollution and water pollution. Further, the air transport and tourism sector were noted to be the hardest hit and will recover slowly. The outcomes from the review will be useful for planners and administrators in managing future emergency conditions better.

Public transport use has drastically declined during the COVID-19 pandemic because the virus spreads through close contact with infected people and contaminated surfaces, especially in closed-environments. Evidence suggests that following the necessary safety guidelines can limit the spread of the virus, however, non-compliance to precautionary measures has been reported widely. Ignoring the precautionary measures may lead to quick transmission of the virus since public transport vehicles generally resemble closed-environments. This study explores people’s intentions to use public transport during the COVID-19 pandemic while adhering to precautionary measures. For this purpose, a total of 1516 useable responses were collected through a questionnaire survey conducted in Lahore, Pakistan. Regression models were developed to model the intentions to use public transport during the pandemic, to adhere to the precautionary measures while using public transport during the pandemic, and to use public transport while suffering from COVID-19 disease. The results of this study will help in understanding the intentions of the public transport users during the COVID-19 pandemic. It will also provide insights for policymakers and public transport operators about further necessary actions to promote safe public transport use during the current and any possible future pandemics. As public transport use declined during the pandemic, policy implications for alternative mobility options such as demand-responsive-transport (DRT) are also presented. Considering the study results, a policy interventions framework is proposed to promote the safe use of various public transport modes, particularly in developing regions.

The high use of gasoline and diesel vehicles results in environmental pollution and loss of natural resources. The use of electric vehicles can be socially beneficial and reduce air pollution. In this research study, the main aims are to identify the potential of Electric Vehicles (EVs) in Pakistan using a Stated Preferences (SP) survey. An SP questionnaire was designed using the theoretical framework of the Theory of Planned Behavior (TPB). This questionnaire consisted of travelers’ personal and travel characteristics and various statements designed to address the variables of TPB such as Attitudes Towards the Electric Vehicles, Subjective Norms (SN), Perceived Behavioral Control (PBC), and Behavioral Intentions (BI). The behavior items were designed in the context of travelers’ willingness to buy and use EVs. Confirmatory Factor Analyses (CFAs) were conducted to validate the designed hypotheses related to the variables of TPB. The cross-analysis revealed that there are variations in responses across different occupations, trip distances, travel modes, and vehicle ownership. The developed structural models showed that the Attitudes and PBC have a positive influence on BI and PBC, which also have a direct impact on travelers’ willingness to use EV. The SN, PBC, and Attitudes are significant predictors of behavioral intentions in the willingness to buy EV and PBC is not a direct predictor of travelers’ willingness to buy an EV. The BI acts as a mediator to explain the indirect effects of Attitudes, SN, and PBC on willingness to purchase and use an EV. In the end, a framework is also proposed for the behavior-related interventions to promote the EVs considering the socio-economic context of Pakistan.

This study aims to identify the travelers’ adoption behavior towards electric vehicles (EVs) using the theoretical background of the Norm Activation Model (NAM) theory. A questionnaire was designed and conducted in Lahore, Pakistan. A total of 402 usable samples were obtained. The collected data were analyzed using factor analysis and Structural Equation Modeling methods. The factor analysis confirmed the hypothesis of the statements designed according to the NAM theory, that is, awareness of consequences (AC), ascription of responsibility (AR), and personal norm (PN). Other factor analyses resulted in the following reliable factors: social and economic values (SEV), personal preferences (PP), willingness to buy (Buy), and willingness to use (Use) of an EV. The results of SEM revealed that the AC, AR, and SEV are significant predictors of PN, whereas the PN and PP are also positive predictors of travelers’ willingness to buy and use. The young travelers (≤30 years), motorcycle users, employees, and trip distance (>10 km) have significant and positive correlations with the PN. The car ownership status of travelers has a positive correlation with the ownership and usage of EVs. Suitable behavioral intervention techniques were derived to promote the ownership and usage of EVs in the context of developing regions.

Information on the trajectories of turning vehicles at signalized intersections can be used in numerous applications, such as movement planning of autonomous vehicles, realistic representation of surrounding vehicle movements in driving simulator and virtual reality applications, and in microscopic simulation tools. However, no proper framework is currently available to realistically model and estimate trajectories of turning vehicles reflecting the intersection geometries, which is critical for the reliability of simulation models. This study explores the applicability of the minimum-jerk principle, which has been initially applied in neuroscience and robotics domains, to model and simulate free-flow trajectories of turning vehicles. The modeling method is validated by comparing model outputs with empirical trajectories collected at several signalized intersections in Nagoya, Japan. The capability of the model in realistically capturing the variations in turning trajectories based on intersection geometry (e.g., intersection angle and turning radius) is also explained. Further, the applicability of the modeling framework at intersections with different geometric features under different speeds and accelerations are also discussed.