The deployment of autonomous buses (AB) is expected to have consequences for service design facilitated by its cost function structure. We study the impacts of AB deployment in line-based public transport (PT) systems. In particular, we examine the transition phase where AB is sequentially deployed, involving the selection of lines for which AB will be introduced. To this end, we develop a modeling framework using a dynamic public transportation assignment and operations simulation model that captures users' adaptive path choices. An analytical model is used to determine the initial solutions in terms of service frequency and vehicle capacity for the simulation framework. Due to their different cost function structures, the deployment of AB may be accompanied by changes in the service frequency and vehicle capacity settings and consequently also on passenger flow distribution across the network. Both the simultaneous and the sequential deployment of AB on multiple lines are investigated. Deployment solutions are assessed in terms of the both total operator and user cost. The decision variables are vehicle capacity per line, service frequency per line and vehicle technology per line - i.e. either manually driven or fully automated buses. The framework is applied to a case study in Kista, Stockholm. The study shows that AB service have the potential to attract passengers through improved service provision. A sensitivity analysis is carried out concerning the effects of different cost parameters and demand levels on the deployment of AB in fixed line operations.

The maturing of autonomous driving technology in recent years has led to several pilot projects and the initial integration of autonomous pods and buses into the public transport (PT) system. An emerging field of interest is the design of public transport networks operating autonomous buses and the potential to attract higher levels of travel demand. In this work a multi-objective optimization and multi-agent simulation framework is developed to study potential changes in the network design and frequency settings compared to conventional PT systems when autonomous vehicles (AV) systems are deployed on fixed-route networks. During the optimization process multiple deployment scenarios (network configurations and service frequency) are evaluated and optimized considering the operator cost, user cost and infrastructure preparation costs of the system. User-focused network design and operator-focused network design are studied for a real-world urban area in Sweden. The results provide insights into the network design and level of service implications brought about by the deployment of autonomous bus (AB) when those are integrated in route-based PT systems. We show that the deployment of autonomous buses result with a network design that increases service ridership. In the context of our case study this increase is likely to primarily substitute walking.

Recent developments in modular transport vehicles allow deploying multi-purpose vehicles which can alternately transport different kinds of flows. In this study, we propose a novel variant of the pickup and delivery problem, the multi-purpose pickup and delivery problem, where multi-purpose vehicles are assigned to serve a multi-commodity flow. We solve a series of use case scenarios using an exact optimization algorithm and an adaptive large neighborhood search algorithm. We compare the performance of a multi-purpose vehicle fleet to a mixed single-use vehicle fleet. Our findings suggest that total costs can be reduced by an average of 13% when multi-purpose vehicles are deployed, while at the same time reducing the total vehicle trip duration and total distance travelled by an average of 33% and 16%, respectively. The size of the fleet can be reduced by an average of 35%. The results can be used by practitioners and policymakers to decide on whether the combination of passenger and freight demand flows with multi-purpose vehicles in a given system will yield benefits compared to existing fleet configurations.

The continuous increase in urban deliveries and the ongoing urbanization of large cities require the development of efficient and sustainable transportation solutions. This study investigates the impact of modular vehicle concepts and the consolidation of different demand types in the route planning on the efficiency of the urban freight and passenger transportation system. Modularity is achieved by connecting multiple vehicles together to form a platoon. The consolidation of different demand types is realized by simultaneously consider passenger and freight demand in the optimization algorithm. The considered vehicles are specific for each demand type by can be connected freely, hence it is possible to transport different demand types in the same platoon. The cost terms in the problem formulation are comprised of travel time costs, travel distance costs, fleet size costs, and cost considering unserved requests. The modular vehicle operations are modeled in a novel pickup and delivery problem which is solved using CPLEX and Adaptive Large Neighborhood Search (ALNS). In an extensive scenario study, the potentials of the new modular vehicle type are explored for different spatial and temporal demand distributions. A parameter study on vehicle capacity, vehicle range and cost saving assumptions is performed to study their influence on the efficiency. The experiments carried out indicate a general cost savings of 48% due to modularity and an additional 9% due to consolidation. The reduction mainly stems from reduced operating costs and reduced trip duration, while the same number of requests can be served in all cases. Empty vehicle kilometers are reduced by more than 60% by consolidation and modularity. The proposed model and optimization framework can be used by companies and policy makers to identify required fleet sizes, optimal vehicle routes and cost savings due to different types of operation and vehicle technology.