Despite research that has been conducted elsewhere, little is known, to-date, about land cover dynamics and their impacts on land surface temperature (LST) in fast growing mega cities of developing countries. Landsat satellite images of 1989, 1999, and 2009 of Dhaka Metropolitan (DMP) area were used for analysis. This study first identified patterns of land cover changes between the periods and investigated their impacts on LST; second, applied artificial neural network to simulate land cover changes for 2019 and 2029; and finally, estimated their impacts on LST in respective periods. Simulation results show that if the current trend continues, 56% and 87% of the DMP area will likely to experience temperatures in the range of greater than or equal to 30 °C in 2019 and 2029, respectively. The findings possess a major challenge for urban planners working in similar contexts. However, the technique presented in this paper would help them to quantify the impacts of different scenarios (e.g., vegetation loss to accommodate urban growth) on LST and consequently to devise appropriate policy measures.

Disaster management teams could learn much from the Middle East Respiratory Syndrome (MERS) outbreak in South Korea in 2015. The virus outbreak provoked public fear, which resulted in a mass reduction in transit use in the Seoul Metropolitan Area (SMA). Its effect differed among socioeconomic groups and geographical areas. A typical way to analyze this is to associate individual reactions to the disaster with socioeconomic characteristics. However, a more structured approach, which considers behavioral characteristics resulting from the societal position of an individual, would identify the basic reason for such associations. The study hypothesized that the degree of fixity that individuals have in their daily life may elucidate these associations. When fear is prevalent, people having the flexibility to change their lifestyle will make more changes in daily activities and travels. The study examined the influence of public fear of a pandemic disease on travel behavior and the effect of life fixity on individual response to the fear. To this end, smart card data of transit use and changes in travel behavior during the MERS period were examined. The study found that fear was powerful and influenced travel behavior differently depending on life fixity levels and regional characteristics.

With skip-stop rail transit operation, transit agencies can reduce their operating costs and fleet size and passengers can experience reduced in-transit travel times without extra track and technological improvement. However, since skip-stop operation does not serve all stations, passengers for certain origins-destinations could experience increased access time, waiting time, total travel time, and/or transfer. Only when the stopping and skipping stations are carefully coordinated can skipstop service benefit passengers and transit agencies. This research developed a mathematical model using a Genetic Algorithm that coordinated the stopping and skipping stations for skip-stop rail operation. Using the flexibility of a Genetic Algorithm, this model included many realistic conditions, such as different access modes, different stopping scenarios, different collision constraints, and different objective functions. Passengers were put into three types and nine groups depending on their origin-destination pairs and the station and transferchoices. Four types of collision constraints were developed depending on the skipstop strategy. For this research, Seoul Metro system Line No. 4 was used as an example. With skipstop operation, total travel time became about 17–20 percent shorter than with original all-stop operation, depending on the stopping constraints. In-vehicle travel time became about 20–26 percent shorter due to skipping stations, although waiting, transfer, and additional access times increased by 24–38 percent.

This research reaffirms a sustainable transport investment should go forward in a way that enforces consistent economic growth while maintaining environmental benefit. Society’s economic input-output relations reinvest the time saved by the transport system, of which benefit appeared as the income level’s rise. This research empirically proves that the recursive profit is possible when the infrastructure aligns accessibility enhancement with industries’ productivities. Specifically, we found that the interactions between industries and accessibilities make synergy when the industries maintain appropriate productivity. Under the same context, the measure for the socially vulnerable should start at capacity building even given transport, based on which we can build economically equitable accessibility. This research takes environmental sustainability granted as long as the incumbent Korean infrastructure investment principle exists without empirical analysis. A comparative analysis using structural equation models proved the existence of the synergy due to the accessibility-industry interactions. Besides, we captured the quantities of the synergy through groups of partial cross-sectional correlations. Authors conclude that government authority needs to pursue the best synergy between transport plan and economic development strategy, and prepare a capacity building for the socially vulnerable furthermore.

The major goal of this study is to help create more walking- and biking-friendly Transit-Oriented Development (TOD) policies by finding determinants that influence walking and biking to transit stations. Based on the station user survey conducted in Mountain View, California, two mode choice models are estimated for commuter rail users’ trips to the station. To find statistically significant factors that influence the probabilities of choosing walking over driving and also choosing biking over driving, two binominal logit analyses are performed with 40 travel, socio-economic, and built environment variables. The model I (walk vs. auto) analyzes 277 walkers and auto users living within 1.5 miles (2.4 km) of the station, while the model II (bike vs. auto) analyzes 280 bikers and auto users living within 2 miles (3.2 km) of the station. Both models share three predictors: trip distance, car availability, proximity to auto-friendly streets. This research also finds that trip purpose, race (Asian), intersection density influence transit users’ decision to walk over driving, while race (white) and gender affect their decision to bike over driving to the station.