New dynamic travel demand modeling methods in advanced data collecting environments.

摘要

Estimating and forecasting travel demand have been a popular study topic among transportation researchers; however the research needs to pursue new direction with the advent of data from the potential availability of newer types of data previously not envisaged. In this dissertation, the author reviews previous studies on this topic and develops approaches for two aspects of travel demand analysis in the transportation network: A newer OD estimation method and a household activity-based demand modeling framework.; First, a trip-based dynamic OD estimation model is developed. Several previous studies on OD trip table estimation focused on a static problem and many recent dynamic OD estimation methods also have not sufficiently proved their practical applicability. In order to overcome the shortcomings, this dissertation introduces supplementary information (i.e., vehicle trajectory data) to a dynamic OD estimation model.; However, the trip-based approach has certain well-known shortcomings. OD estimation results can not give satisfactory solutions for forecasting purposes, and the estimated OD table only contains materialized trips, which implies that no latent travel demand is included in the table. Therefore, the estimated OD table does not have sufficient information for identifying the real travel demand pattern and it is not so useful for transportation planning works.; Contrarily, a standard four-step model has a better capability for explaining a travel demand pattern. However, when we load the OD trip table calculated by the four-step model, we might see some discrepancies between simulated traffic patterns and the ground truth. The discrepancies can come from various factors such as insufficient network capacities and unexplained influencing factors. When the discrepancy is caused by insufficient network capacities, then it can be solved by an iterative adjusting procedure. Using the ground truth such as link traffic counts, it might be updated correctly. However, if the discrepancies come from incapability of the four-step model, then we should look for a new approach. The capability of the four-step model already has been criticized continuously by numerous activity researchers because a trip-based approach does not correctly consider the real motivation of travel.; To overcome these drawbacks, the second item of fucud in the dissertation is in developing a dynamic agent-based household activity and travel demand simulation model framework named DYNAHAP. The framework calculates a demand pattern in terms of activity chains generated by synthetic families. A traffic simulator then executes the activity chains, and finally an aggregated dynamic traffic pattern is generated.; In order to calibrate DYNAHAP, huge activity data should be gathered. Such tasks had been regarded very difficult or even nearly impossible before, but with the development of data collecting technologies, currently we have several ways for collecting the activity chains of individuals. Like vehicle trajectory data, sample activity chains collected from personal communication devices such as PDA (Personal digital assistant) could be used for DYNAHAP calibration. Some numerical test results also will be given for proving the performance of the developed models. In last chapter, some important issues for future study are also discussed.