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Dhaka, one of the fastest-growing megacities in the world, faces severe traffic congestion leading to a loss of 3.2 million business hours per day. While peak-spreading policies hold the promise to reduce the traffic congestion levels, the absence of comprehensive data sources makes it extremely challenging to develop econometric models of departure time choices for Dhaka. This motivates this paper, which develops advanced discrete choice models of departure time choice of car commuters using secondary data sources and quantifies how level-of-service attributes (e.g., travel time), socio-demographic characteristics (e.g., type of job, income, etc.), and situational constraints (e.g., schedule delay) affect their choices. The trip diary data of commuters making home-to-work and work-to-home trips by personal car/ride-hailing services (957 and 934 respectively) have been used in this regard. Given the discrepancy between the stated travel times and those extracted using the Google Directions API, a sub-model is developed first to derive more reliable estimates of travel time throughout the day. A mixed multinomial logit model and a simple multinomial logit model are developed for outbound and return trip, respectively, to capture the heterogeneity associated with different departure time choice of car commuters. Estimation results indicate that the choices are significantly affected by travel time, schedule delay, and socio-demographic factors. The influence of type of job on preferred departure time (PDT) has been estimated using two different distributions of PDT for office employees and self-employed people (Johnson’s SB distribution and truncated normal respectively). The proposed framework could be useful in other developing countries with similar data issues.

This paper explores the impact of COVID-19 on shopping behavior in two neighboring developing economies: Bangladesh and India. While the previous studies investigating the impact of COVID-19 on sho...

The rapid advancement of information and communication technology has brought a revolution in the domain of public transport (PT) planning alongside other areas of transport planning and operations. Of particular significance are the passively generated big data sources (e.g., smart cards, detailed vehicle location data, mobile phone traces, social media) which have started replacing the traditional surveys conducted onboard, at the stops/stations and/or at the household level for gathering insights about the behavior of the PT users. This paper presents a systematic review of the contemporary research papers related to the use of novel data sources in PT planning with particular focus on (1) assessing the usability and potential strengths and weaknesses of different emerging big data sources, (2) identifying the challenges and highlighting research gaps. Reviewed articles were categorized based on qualitative pattern matching (similarities/dissimilarities) and multiple sources of evidence analysis under three categories—use of big data in (1) travel pattern analysis, (2) PT modelling, and (3) PT performance assessment. The review revealed research gaps ranging from methodological and applied research on fusing different forms of big data as well as big data and traditional survey data; further work to validate the models and assumptions; lack of progress on developing more dynamic planning models. Findings of this study could inform transport planners and researchers about the opportunities/challenges big data bring for PT planning. Harnessing the full potential of the big data sources for PT planning can be extremely useful for cities in the developing world, where the PT landscape is changing more rapidly, but traditional forms of data are expensive to collect.

The occurrence of heavy rainfall in the south-eastern hilly region of Bangladesh makes this area highly susceptible to recurrent flash flooding. As the region is the commercial capital of Bangladesh, these flash floods pose a significant threat to the national economy. Predicting this type of flooding is a complex task which requires a detailed understanding of the river basin characteristics. This study evaluated the susceptibility of the region to flash floods emanating from within the Karnaphuli and Sangu river basins. Twenty-two morphometric parameters were used. The occurrence and impact of flash floods within these basins are mainly associated with the volume of runoff, runoff velocity, and the surface infiltration capacity of the various watersheds. Analysis showed that major parts of the basin were susceptible to flash flooding events of a ‘moderate’-to-‘very high’ level of severity. The degree of susceptibility of ten of the watersheds was rated as ‘high’, and one was ‘very high’. The flash flood susceptibility map drawn from the analysis was used at the sub-district level to identify populated areas at risk. More than 80% of the total area of the 16 sub-districts were determined to have a ‘high’-to-‘very-high’-level flood susceptibility. The analysis noted that around 3.4 million people reside in flash flood-prone areas, therefore indicating the potential for loss of life and property. The study identified significant flash flood potential zones within a region of national importance, and exposure of the population to these events. Detailed analysis and display of flash flood susceptibility data at the sub-district level can enable the relevant organizations to improve watershed management practices and, as a consequence, alleviate future flood risk.

Although nonmotorized transport (NMT) offers economic, environmental, and health benefits to individuals and communities, understanding nonmotorized travel behavior is a challenging task due to complex interactions of a wide range of factors. While behavioral models offer a conceptual framework to understand human behavior, their use in the study of travel behavior in developing countries is still in its infancy. This study uses three behavioral models—the theory of planned behavior, the theory of triadic influence, and the ecological model of health behavior—to identify potential factors influencing intentions and behavior toward the use of NMT by middle-income working adults, inhabiting the Chittagong City Corporation (CCC) area of Bangladesh. A total of 720 middle-income working adults (aged between 18 and 65 years) were randomly selected and interviewed at major commercial and retail business areas of the CCC. Multiple linear and binary logistic models were developed to quantify the extent of the influence of different factors on nonmotorized mode choice behavior. Results indicated that personal factors (proximal) such as attitude, subjective norm, and behavioral control influence respondents’ intentions and motivation in choosing NMT. However, the current use of NMT was less controlled by intention, while factors associated with the social, cultural, and built environment had (distal) significant influence. The findings of this study could assist urban planners in adopting structural and nonstructural measures to promote NMT use.

Many young adults are susceptible to obesity issues and the increased health risks associated with a lack of physical activity. Those who are prone to gaining weight include university students. An active transport system (walking and cycling), in combination with well-funded public transport, are essential components of a sustainable urban transport network, offering many benefits to the health of the individual, as well as the environment, economy, and society as a whole. The spatial association between active mobility (i.e. the physical activity of a human being for locomotion) of young adults and the environment, however, is poorly understood. This study presents a GIS-based model to determine association of various environmental (natural and built environment) factors with locational accessibility of active and public transport trips taken by university students. A GIS-based ensemble of Frequency Ratio (FR) and the Analytical Hierarchy Process (AHP) model was established. We analysed the characteristics of locations accessed by university students in relation to eight environmental factors including slope, elevation, land use, population density, travel time, building density, intersection density, and public transport service area. The model was applied to the Grenoble metropolitan region of France, an area well-known for policies which promote active transport. The results indicated that intersection density and land use are strongly associated with active and public transport accessibility, with weights of 0.17 and 0.16, respectively. The presence of infrastructure to support active travel, and regulation to limit vehicular speed, also improved accessibility. Approximately 50% of the area of the Grenoble metropolitan region was defined as accessible and suitable (‘moderate’ to ‘very high’ degree) for active mobility. The results of this study could allow city planners to monitor the existing status of active and public transport facilities, and identify areas that require additional work to improve accessibility.

Two and three layers Josephson junction based pentagonal and hexagonal patched superconducting antennas have been thoroughly simulated. In the present work, the resonant frequency decreased with an added gold layer compared with only PEC (Perfect Electric Conductor) based Josephson junction. The VSWR (Voltage standing wave ratio) values are observed simultaneously with variable gain (in dB) for the transmission matching purpose. Compared to a conventional micro strip patch antenna, these simulation designs consist of two and three layers of Josephson junction under different patch sizes with varying radiation boundary. Furthermore, we exhibited that while using only PEC and glass for the junctions, the $60\ \mu \mathrm{m}$ patch size for pentagonal and hexagonal mesas provide the best resonant frequency and VSWR values, with the most positive gain total compared to other patch sizes.

The promotion of active transport (a type of sustainable transportation) such as walking is a form of response against environmental pollution engendering from transport sector. Pedestrian level of service (PLOS) is a measurement tool to evaluate the degree of pedestrian accommodation on roadway to provide a comfortable and safe walking environment. The roadway characteristics-based model to measure PLOS has been widely applied since this approach is conceived as being transferable to different contexts. We present a comprehensive framework to measure the influence of pedestrian facilities on perceived PLOS qualitatively and quantitatively. We modeled triangular relationships among pedestrian facilities, perceived roadway conditions (accessibility, safety, comfort, and attractiveness), and perceived PLOS to identify pedestrian facilities, related to footpath, carriageway, and transit, influencing perceived PLOS. We developed these models for a case study of Chittagong Metropolitan Area in Bangladesh. Poor condition of pedestrian facilities in the region resulted in PLOS B as the highest tier of perceived PLOS. Findings of this study showed that accessibility and attractiveness influenced the perceived PLOS for footpath, carriageway, and transit, whereas safety is an important roadway condition for carriageway and transit facilities. We further measured the influence of 22 selected parameters of pedestrian facilities on roadway conditions and perceived PLOS. We concluded that achieving a better perceived PLOS is dependent on the availability, maintenance, and planning of different pedestrian facilities, as improper placement and poor condition of such facilities increased the probability that a lower level PLOS will be perceived.