This dissertation provides modifications and extensions to the Household Activity Pattern Problem (HAPP) to help move existing formulations from a laboratory prototype toward a more useable activity-based demand modeling product. Previous research on HAPP has been based on a pickup and delivery problem with time window constraints (PDPTW), which does not lend itself easily to application that is compatible with an activity-based forecasting model. Meanwhile, other research on activity-based modeling lacks of the integration of household decisions regarding time-of-day arrival, activity duration and traffic congestion effects on travel. We borrow concepts from economic research and consider that each household member tries to obtain maximum utility by choosing arrival time of activities, choosing activity duration while minimizing travel times and travel costs throughout the course of the day.

The main purpose of this paper is to examine potential environmental benefits of green driving strategies with NGSim data on Interstate-80 near Berkeley, California. We calculate vehicles emissions before and after applying green driving strategies with the VT-Micro emission model. For each vehicle, its trajectory before applying green driving strategies is observed and given in the dataset. We assume that, with the help of green driving strategies, the vehicle could drive at a constant speed over the whole road section with the same travel distance and time as before. After examining impacts of speed-acceleration adjustment on calculated emissions and fuel consumptions, we choose 5127 out of 9951 cars and estimate potential savings in HC, CO, NOx, CO2, and fuel consumptions. With a new model of the relationships between emissions/fuel consumptions and average speeds, we can fit the data with R-squares close to or greater than 0.9 and find that green driving strategies are most effective for traffic flows with average speeds around 50 km/h and potential savings can be from 20% to 60% for different pollutants. In the future, we will continue our studies with more realistic information on vehicle types and other emission models.

Transportation system produces a large percentage of local pollutants including hydrocarbons (HC), carbon monoxide (CO), carbon dioxide from switching to alternative fuels, one measure would be to apply information and communication technologies to help us drive more smoothly so as to decrease pollutants emissions. This paper studies potential benefits of two green driving strategies based on inter-vehicle communication (IVC). Here green driving strategies are similar to intelligent speed adaptation , but we assume that an IVC-equipped vehicle is able to receive detailed trajectory information from other such vehicles with the help of IVC. For the purpose of evaluation, we integrate Newell’s car-following model and VT-Micro to establish a simulation platform. Market penetration rates of IVC-equipped vehicles and delivery delays of messages are two prominent features of IVC systems. We simulate stop-and-go traffic to calculate potential reductions in air pollutant emissions and fuel consumption under different market penetration rates and delivery delays. Results show that significant savings under frequent stop-and-go traffic conditions may be obtained with our strategies (HC: -88.3%, CO: -95.8%, NOx: -91.5%, CO2: -36.3%, Fuel Consumption: -71.3%) for the same travel time and almost the same overall travel distance. It is also shown that relatively large savings can be achieved even for a market penetration rate as low as 1% and communication delays larger than 2 minutes. In the future we will investigate environmental benefits of green driving strategies for more traffic scenarios and realistic communication scenarios.