Air pollution caused by vehicle emissions has raised serious public health concerns. Vehicle emissions generally depend on many factors, such as the nature of the vehicle, driving style, traffic conditions, emission control technologies, and operational conditions. Concerns about the certification cycles used by various regulatory authorities are growing due to the difference in emission during certification procedure and Real Driving Emissions (RDE). Under laboratory conditions, certification tests are performed in a ‘chassis dynamometer’ for light-duty vehicles (LDVs) and an ‘engine dynamometer’ for heavy-duty vehicles (HDVs). As a result, the test drive cycles used to measure the automotive emissions do not correctly reflect the vehicle’s real-world driving pattern. Consequently, the RDE regulation is being phased in to reduce the disparity between type approval and vehicle’s real-world emissions. According to this review, different variables such as traffic signals, driving dynamics, congestions, altitude, ambient temperature, and so on have a major influence on actual driving pollution. Aside from that, cold-start and hot-start have been shown to have an effect on on-road pollution. Contrary to common opinion, new technology such as start-stop systems boost automotive emissions rather than decreasing them owing to unfavourable conditions from the point of view of exhaust emissions and exhaust after-treatment systems. In addition, the driving dynamics are not represented in the current laboratory-based test procedures. As a result, it is critical to establish an on-road testing protocol to obtain a true representation of vehicular emissions and reduce emissions to a standard level. The incorporation of RDE clauses into certification procedures would have a positive impact on global air quality.

Despite much having been published about the effects of the built environment (BE) on urban travel in the developed world, few articles have so far been published based on studies using a megacity in a developing country. The paper addresses the existing gaps in research by conducting a study in Dhaka, one of the densest urban areas globally. An integrated framework based on the structural equation model and discrete choice model is used to examine how individual commute mode choice behavior is influenced by the BE, as mediated by car ownership and commute distance. Three BE features—population density, street connectivity, and job-to-household ratio—have a direct and total positive association with non-motorized transport use. Although being close to bus stops does not directly affect people’s choice to take non-motorized transport, it does promote non-motorized travel in an indirect way by decreasing car ownership and commute distance. Population density, job-to-household ratio, proximity to the nearest central business district, and bus stop proximity all have a positive direct and total impact on transit use, although larger employment densities directly support automobile use over transit. Understanding how the BE affects commute distance, car ownership, and mode choice is a useful reference for the development of practical measures to reduce demand for automobiles.

The outbreak of the COVID-19 pandemic is an unprecedented shock throughout the world which leads to generate a massive social, human, and economic crisis. However, there is a lack of research on geographic modeling of COVID-19 as well as identification of contributory factors affecting the COVID-19 in the context of developing countries. To fulfill the gap, this study aimed to identify the potential factors affecting the COVID-19 incidence rates at the district-level in Bangladesh using spatial regression model (SRM). Therefore, data related to 32 demographic, economic, weather, built environment, health, and facilities related factors were collected and analyzed to explain the spatial variability of this disease incidence. Three global (Ordinary least squares (OLS), spatial lag model (SLM) and spatial error model (SEM)) and one local (geographically weighted regression (GWR)) SRMs were developed in this study. The results of the models showed that four factors significantly affected the COVID-19 incidence rates in Bangladesh. Those four factors are urban population percentage, monthly consumption, number of health workers, and distance from the capital. Among the four developed models, the GWR model performed the best in explaining the variation of COVID-19 incidence rates across Bangladesh with a R ² value of 78.6%. Findings from this research offer a better insight into the COVID-19 situation and would help to develop policies aimed to prevent the future epidemic crisis.

This study explores the modality and trip chaining patterns of individuals in Dhaka, Bangladesh. We use household-level trip data for a day collected from randomly selected respondents of the Dhaka Metropolitan Development Plan area. We found that walking and rickshaw are the dominant modes of travel. The majority of individuals are unimodal and mostly depend on either walking or on rickshaws. Individuals generally walk for the first and last-mile connections to public transit. Cars are used more for non-home-based business trips. Personalized modes such as cars, cycles, and motorcycles are present at a higher proportion in the super complex trip chain than other types of chains.

The structure of the industrial sectors of a region is a driving factor to explain the growth of a region’s economy. In Bangladesh, spatial variation in economic growth exists among different regions. A primary factor in this variation is the economic structure of the region, which can be characterized by basic and non-basic sectors. Economic base theory helps to understand how data can be used to study the economic development process and evaluate alternating development strategies. The aim of the study, thus, is to identify the basic and non-basic sectors using economic base theory and to explore the short-run regional economic structure in selected districts. Data of selected districts from economic census, 2003, and 2013 has been analyzed for thirteen sectors. The comparative analysis of the selected districts reveals that all of the districts except Bandarban are becoming economically weak during the timeframe. In contrast, Bhola district is in economic malaise relative to the other districts. The lagging behind the Transport, Storage, and Communication sector triggers to the disadvantaged economic condition for all the districts during the timeframe. The findings of this study would act as a reference for regional planners to allocate resources in different economic sectors of these districts. JBIP, Vol. 13, 2020 (Printed in June 2021), pp. 17-28