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.

The representative path set between an origin and destination (OD) pair is useful for various transportation areas such as traffic assignment, travel information service, and traffic management. With the development of telecommunication technology, a car navigation system now collects trajectory-type data that can show the routes generated by drivers. Thus, the travel paths of individual travelers are available for the transportation study. Unfortunately, the availability of individual path data cannot guarantee direct application to the transportation model because it does not give us the significant path set between the OD pairs. Every driver in the real world has different starting and finishing points, yet these points are in a same origin and destination zone. In this study, a new methodology to determine the representative path set between an OD pair based on vehicle trajectory data is developed. This new model is tested with simulation data and compares various results according to clusteri...