The increasing volumes of road transportation contribute to congestion on road, which leads to delays and other negative impacts on the reliability of transportation. Moreover, transportation is one of the main contributors to the growth of carbon dioxide equivalent emissions, where the impact of road transportation is significant. Therefore, governmental organizations and private commercial companies are looking for greener transportation solutions to eliminate the negative externalities of road transportation. In this paper, we present a novel solution framework to support the operational-level decisions for intermodal transportation networks using a combination of an optimization model and simulation. The simulation model includes stochastic elements in form of uncertain travel times, whereas the optimization model represents a deterministic and linear multi-commodity service network design formulation. The intermodal transportation plan can be optimized according to different objectives, including costs, time and CO2e emissions. The proposed approach is successfully implemented to real-life scenarios where differences in transportation plans for alternative objectives are presented. The solutions for transportation networks with up to 250 services and 20 orders show that the approach is capable of delivering reliable solutions and identifying possible disruptions and alternatives for adapting the unreliable transportation plans.

Two-echelon vehicle routing problems which contain synchronization between vehicles can be deeply impacted by time uncertainty, because one vehicle's delay can propagate to other vehicles. In this paper, we evaluate the deterministic solution of such a problem based on simulated travel time scenarios. The information obtained by simulation is incorporated in the optimization procedure iteratively. Computational results show that the degree of synchronization in an instance is directly correlated with the potential improvements by reoptimization. We present findings on the number of travel time scenarios required to obtain a representative picture of the stochastic solutions. In addition, we demonstrate that time dependent travel times can be aggregated on a city-wide level and linearized as a function of free flow times without major loss of reliability.

Travel demand forecasting is essential for many decisions, such as infrastructure investments and policy measures. Traditionally travel demand modelling has considered trip frequency, mode, destination and route choice. This thesis considers two other choice dimensions, hypothesised to have implications for travel demand forecasting. The first part investigates how the increased possibilities to overcome space that ICT (information and communication technology) provides, can be integrated in travel demand forecasting models. We find that possibilities of modelling substitution effects are limited, irrespective of data source and modelling approach. Telecommuting explains, however, a very small part of variation in work trip frequency. It is therefore not urgent to include effects from telecommuting in travel demand forecasting. The results indicate that telecommuting is a privilege for certain groups of employees, and we therefore expect that negative attitudes from management, job suitability and lack of equipment are important obstacles. We find also that company benefits can be obtained from telecommuting. No evidences that telecommuting gives rise to urban sprawl is, however, found. Hence, there is ground for promoting telecommuting from a societal, individual and company perspective. The second part develops a departure time choice model in a mixed logit framework. This model explains how travellers trade-off travel time, travel time variability, monetary and scheduling costs, when choosing departure time. We explicitly account for correlation in unobserved heterogeneity over repeated SP choices, which was fundamental for accurate estimation of the substitution pattern. Temporal constraints at destination are found to mainly restrict late arrival. Constraints at origin mainly restrict early departure. Sensitivity to travel time uncertainty depends on trip type and intended arrival time. Given appropriate input data and a calibrated dynamic assignment model, the model can be applied to forecast peak-spreading effects in congested networks. Combined stated preference (SP) and revealed preference (RP) data is used, which has provided an opportunity to compare observed and stated behaviour. Such analysis has previously not been carried out and indicates that there are systematic differences in RP and SP data. / QC 20100825

Travel demand forecasting is essential for many decisions, such as infrastructure investments and policy measures. Traditionally travel demand modelling has considered trip frequency, mode, destination and route choice. This thesis considers two other choice dimensions, hypothesised to have implications for travel demand forecasting. The first part investigates how the increased possibilities to overcome space that ICT (information and communication technology) provides, can be integrated in travel demand forecasting models. We find that possibilities of modelling substitution effects are limited, irrespective of data source and modelling approach. Telecommuting explains, however, a very small part of variation in work trip frequency. It is therefore not urgent to include effects from telecommuting in travel demand forecasting. The results indicate that telecommuting is a privilege for certain groups of employees, and we therefore expect that negative attitudes from management, job suitability and lack of equipment are important obstacles. We find also that company benefits can be obtained from telecommuting. No evidences that telecommuting gives rise to urban sprawl is, however, found. Hence, there is ground for promoting telecommuting from a societal, individual and company perspective. The second part develops a departure time choice model in a mixed logit framework. This model explains how travellers trade-off travel time, travel time variability, monetary and scheduling costs, when choosing departure time. We explicitly account for correlation in unobserved heterogeneity over repeated SP choices, which was fundamental for accurate estimation of the substitution pattern. Temporal constraints at destination are found to mainly restrict late arrival. Constraints at origin mainly restrict early departure. Sensitivity to travel time uncertainty depends on trip type and intended arrival time. Given appropriate input data and a calibrated dynamic assignment model, the model can be applied to forecast peak-spreading effects in congested networks. Combined stated preference (SP) and revealed preference (RP) data is used, which has provided an opportunity to compare observed and stated behaviour. Such analysis has previously not been carried out and indicates that there are systematic differences in RP and SP data. / QC 20100825