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This paper presents integrated models of probabilistic vehicle routing and scheduling model with time windows and dynamic traffic simulation. The probabilistic model incorporates the uncertainty of travel times. After applying the models to test road network, incorporating the uncertainty of travel times using the models not only allows freight carriers to reduce their total costs, but also improves the environment in terms of decreasing CO₂ emissions. As well, the paper investigated the effects and profitability of co-operative freight transport systems using a simulation model based on forecast vehicle routing and scheduling model. Results showed cooperative freight transport systems can lead to a substantial reduction in total delivery costs and total travel times within the whole road network.

This paper presents models for evaluating effects of implementing TDM schemes on road traffic with the consideration of uncertainty of travel times. The models are composed of drivers' behavioural model for identifying the optimal starting time and route with the designated time for arrival at the destination as well as dynamic traffic simulation model for estimating the change of travel times. A case study for applying the models on a test road network showed that TDM schemes of park and ride systems and road pricing enabled drivers to obtain benefits by improving the reliability of travel times as well as benefits by reducing travel times.

In this paper, a model to estimate travel time reliability is proposed assuming a stochastic user equilibrium. Travel time reliability is defined as the probability that travel time between the origin and destination does not exceed a standard travel time corresponding to each service level. Also it is shown how to calculate travel time reliability in a large-scale network. This model is applied to the road network in the Hanshin area to evaluate new lines in the national road plan from the viewpoint of travel time reliability. Some important links and lines in the future road network are evaluated from the viewpoint of travel time reliability.

In this paper, we discussed relations between drivers' cognition and the dynamics of traffic conditions. When we take into account drivers' cognition, many types of psychological impedance to behavioral change could be accounted for, including cognitive conservatism and habitual decision making. Based on psychological theories with respect to these impedance to behavioral change, we elucidated the process of changes in traffic conditions that might be induced by changes in road-network structure. As a result of the discussion, we concluded that traffic conditions are unlikely change as much as predicted by theories that assume a simple, and sometimes unique, relation between a traffic condition and a road-network structure, e.g., theories that assume user equilibrium.

Travel behaviour depends on the travellers' perception of the trip characteristics. The uncertainty of the journey time is one of the determinants of the choice. Studies of decision behaviour in uncertain conditions show the limited validity of the common assumptions in traffic models. Decision making under uncertainty has to be considered as a contingent process, depending on the objectives and conditions with which the choice is made. Expected utility is one of the many possible criteria used by people to decide. There is a discrepancy between the objective and subjective value of this concept. This is due to the bias with respect to the perception of very likely and very unlikely events. In many cases the expected utility is a less applicable objective, e.g. because people want to arrive before a certain deadline and maximize the probability to achieve that. The fact that probabilities of travel times have to be learned from experiences introduces a dynamic character of choice behaviour.

Most of the reported properties of decision-making under uncertainty still have to be verified for travel behaviour.

Road network performance when a large disaster happens depends on how the road traffic is regulated. To evaluate the performance of the road network, one should consider the traffic regulation in an emergent condition. Authors have proposed the idea of area traffic regulation when a large disaster occurs (Iida, et al., 2000). In the former paper, a bi-level optimisation model to calculate the optimal regulation ratio is proposed. This paper proposes an efficient and fast method to calculate regulation ratios for two-stage road traffic regulation. The simplified model adopts linear programming method instead of bi-level optimisation method, which is quicker and simpler. The simpler algorithm contributes to conduct various kinds of case studies, and helps finding a reliability of the road network considering traffic regulations after a disaster.