This paper presents issues and challenges associated with development of a comprehensive mode choice model for Dhaka, the capital of Bangladesh and the 11th largest city in the world. As in most other developing countries, reliable level-of-service data are not available for the wide variety of motorized and nonmotorized modes used by travelers in Bangladesh. In addition, the 12 million inhabitants of Dhaka have wide differences in affordability of and accessibility to various modes. These differences result in substantial heterogeneity in inhabitants' choice sets. These choice sets are also unobserved in the data and not easily inferable from the limited information of the network. This paper identifies key limitations of the available data and proposes methods to overcome those limitations. A probabilistic choice set of modes from a small-scale stated preference survey has been used to account for the absence of actual choice set data. The systematic and stochastic errors in the network-derived time data are explicitly accounted for in the model structure. The improvements from the proposed approaches are demonstrated by prediction tests using holdout samples. The proposed approaches have immense potential to improve travel mode choice models for other cities of Bangladesh, as well as cities in other developing countries, which very often face similar data issues.

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In recent years, time engagement behaviors of two generations, namely Baby Boomers and Millennials have sparked much interest because these generations constitute the bulk of the American population today and they also exhibit “atypical” activity–travel patterns compared with other generations. The objective of the current research is to conduct a systematic study of the time engagement behaviors of five American generations: the GI Generation (birth year: 1901–1924), the Silent Generation (birth year: 1925–1943), Baby Boomers (birth year: 1944–1964), Generation X (birth year: 1965–1981), and Millennials (birth year: 1982–2000). Particularly, the study aims at isolating heterogeneity in behaviors associated with structural changes in the society from those associated with inherent generational characteristics. Using data from four waves (1965, 1985, 2005, and 2012) of the American Heritage and Time Use Study, the analysis explores the time engagement behaviors while accounting for the age, period, and cohort effects in addition to different socioeconomic and demographic variables. The analysis reveals that Millennials have generally delayed participation in life-changing events such as marriage and workforce entry, and have exhibited prolonged student status compared with previous generations. Millennials show lower participation in work and higher participation in discretionary activities compared with individuals of the same age group from previous generations. On the other hand, Baby Boomers clearly exhibited increased travel engagement compared with the previous generations at different stages of their lives.

IntroductionConcerns have been raised about the potential for risk compensation in the context of mask mandates for mitigating the spread of COVID-19. However, the debate about the presence or absence of risk compensation for universal mandatory mask-wearing rules—especially in the context of COVID-19—is not settled yet.MethodsMobility is used as a proxy for risky behaviour before and after the mask mandates. Two sets of regressions are estimated to decipher (any) risk-compensating effect of mask mandate in Bangladesh. These include: (1) intervention regression analysis of daily activities at six types of locations, using pre-mask-mandate and post-mandate data; and (2) multiple regression analysis of daily new COVID-19 cases on daily mobility (lagged) to establish mobility as a valid proxy.Results(1) Statistically, mobility increased at all five non-residential locations, while home stays decreased after the mask mandate was issued; (2) daily mobility had a statistically significant association on daily new cases (with around 10 days of lag). Both significances were calculated at 95% confidence level.ConclusionCommunity mobility had increased (and stay at home decreased) after the mandatory mask-wearing rule, and given mobility is associated with increases in new COVID-19 cases, there is evidence of risk compensation effect of the mask mandate—at least partially—in Bangladesh.

Transportation Network Companies (TNCs) have been steadily increasing the share of total trips in metropolitan areas across the world. Micro-modeling TNC operation is essential for large-scale transportation systems simulation. In this study, an agent-based approach for analyzing supply and demand aspects of ride-sourcing operation is done using POLARIS, a high-performance simulation tool. On the demand side, a mode-choice model for the agent and a vehicle-ownership model that informs this choice are developed. On the supply side, TNC vehicle-assignment strategies, pick-up and drop-off operations, and vehicle repositioning are modeled with congestion feedback, an outcome of the mesoscopic traffic simulation. Two case studies of Bloomington and Chicago in Illinois are used to study the framework’s computational speed for large-scale operations and the effect of TNC fleets on a region’s congestion patterns. Simulation results show that a zone-based vehicle-assignment strategy scales better than relying on matching closest vehicles to requests. For large regions like Chicago, large fleets are seen to be detrimental to congestion, especially in a future in which more travelers will use TNCs. From an operational point of view, an efficient relocation strategy is critical for large regions with concentrated demand, but not regulating repositioning can worsen empty travel and, consequently, congestion. The TNC simulation framework developed in this study is of special interest to cities and regions, since it can be used to model both demand and supply aspects for large regions at scale, and in reasonably low computational time.