The user equilibrium concept in traffic assignment is based on fundamental assumptions: perfect information, rationality, and homogeneity. This study examines changes in the traffic pattern and the network behavior when these assumptions are relaxed. In order to relax these assumptions, we employ a day-to-day evolution approach and develop agent-based simulation models that include drivers’ learning model, preference model and preference sensitivity. Using the developed models, we investigate how each assumption affects network traffic patterns. The test results show that the assumption of perfect information is most influencing on the traffic assignment results. In addition, the prior information greatly affects on the route choice when drivers do not have perfect knowledge on network travel time.

In this study, a time-dependent agent-based taxi simulation model was developed. Modeling of taxi system is a complex task because it is dependent on the spatiotemporal pattern of passenger demand. However, the role of taxi as a public transport cannot be under-estimated in the urban area. Not only as a paratransit for the elder and disabled but also as a public transport for complementing the mass transit systems such as subways and arterial buses, a taxi conveys huge number of passengers every day. In this context, a practical modeling method for a taxi system was developed based on equilibrium philosophy and an agent-based simulation framework. In this study, the authors developed a simulation framework for the evaluation of taxi service for enhancing the quality of urban taxi transportation system. The developed model was tested with various passenger travel patterns, and a meaningful policy-related issue for improving the service performance of a taxi system was found for dealing with an asymmetric demand pattern.

DRT service, designed to be flexible in time and space, follows the contemporary trend of on-demand transit provision. However, this type of service often suffers from low profitability due to small demand and/or high operation costs. DRT service is a local business in nature. The existing research primarily focuses on DRT service for regions with low transit demand, but it does not take into account service operation for other types of regions. This study aims to fill in this gap and identify the sources of benefit from DRT operations in varied types of regions. To this end, the analysis compares the DRT operation performance between overpopulated heartland and underpopulated hinterland regions; in each region, the benefit is identified through the difference in key performance indices between the simulated DRT and actual bus operation. The data on the road network and bus operation in Daegu, Korea, in 2021 are used for the DRT simulation. The results show that the heartland DRT benefits more from the reduced vehicle kilometers, while the hinterland DRT gains mostly from the reduced waiting time. Given that both DRT types outperform existing bus services, it is revealed that the heartland DRT is more reliable than the hinterland DRT due to the nature of regional characteristics.

In this paper we discuss a dynamic origin–destination (OD) estimation problem that has been used for identifying time-dependent travel demand on a road network. Even though a dynamic OD table is an indispensable data input for executing a dynamic traffic assignment, it is difficult to construct using the conventional OD construction method such as the four-step model. For this reason, a direct estimation method based on field traffic data such as link traffic counts has been used. However, the method does not account for a logical relationship between a travel demand pattern and socioeconomic attributes. In addition, the OD estimation method cannot guarantee the reliability of estimated results since the OD estimation problem has a property named the ‘underdetermined problem.’ In order to overcome such a problem, the method developed in this paper makes use of vehicle trajectory samples with link traffic counts. The new method is applied to numerical examples and shows promising capability for identifying a temporal and spatial travel demand pattern.

The objective of this study is to develop a non UE (User Equilibrium)-based traffic assignment model which is able to be applied to a real size network. Over the last few decades, the UE principle has been used as a standard route choice principle. However, it also has received considerable criticism since it is difficult to fully describe the route choice behavior of real drivers. The real drivers select a route based on their cumulated experiences, and their perceived travel times are also not deterministic values, but probabilistic values which reflect the various uncertainties existing in the network. If a driver frequently uses a specific route, then the driver has better information on that route than other routes. As a result, a marginal superiority of other paths might not make the driver quit using the frequently selected path. Accordingly, a network traffic pattern is not so sensitive to the change of travel environment and drivers tend to select routes stably and habitually. In addition, the cumulated information in drivers’ minds and the travel experiences are also important to develop traffic operation and management schemes. However, a UE-based traffic assignment model cannot take into account the previous travel experiences and network uncertainties. Therefore, the route choice in the UE-based model might be over-sensitive to the change of travel environment in the network or occasionally forecasted incorrectly because of the absence of consideration for previous travel experiences and perception limit. In order to overcome these problems, the authors developed an agent-based traffic assignment model while making use of “reference point” theory in the field of psychology and “bounded rationality” used in decision making theory. In the developed model, travel time is modeled not as a deterministic value, but as the concept of probability density function in order to reflect the network uncertainties with which the real drivers face in the real world. In addition, the density function is linearly simplified in order to improve applicability to the real-size transportation network problems.

In this study, we examine Braess’ paradox in the context of a Stochastic Transportation Network Design Problem (NDP). In conventional NDPs, major variables such as link travel time and traffic flows are considered to be deterministic. However, in the real world, due to variations in traffic patterns, travelers experience different travel times. To account for this variation, in this study Monte Carlo simulation is employed to incorporate the probabilistic nature of travel demand. A new objective function for the NDP is formulated based on the probabilistic definition of link flows, and the design results are compared with the results obtained from conventional formulations. This new model is used to analyze Braess’ paradox, a well-known counter intuitive network phenomenon. Our results indicate that Braess’ paradox is more likely to be observed in our model, as there are more uncertainties in travel demand.