The introduction of Autonomous Vehicles (AVs) will have far-reaching effects on road traffic in cities and on highways.The implementation of Automated Highway Systems (AHS), possibly with a dedicated lane only for AVs, is believed to be a requirement to maximise the benefit from the advantages of AVs. We study the ramifications of an increasing percentage of AVs on the traffic system with and without the introduction of a dedicated AV lane on highways. We conduct an analytical evaluation of a simplified scenario and a macroscopic simulation of the city of Singapore under user equilibrium conditions with a realistic traffic demand. We present findings regarding average travel time, fuel consumption, throughput and road usage. Instead of only considering the highways, we also focus on the effects on the remaining road network. Our results show a reduction of average travel time and fuel consumption as a result of increasing the portion of AVs in the system. We show that the introduction of an AV lane is not beneficial in terms of average commute time. Examining the effects of the AV population only, however, the AV lane provides a considerable reduction of travel time (≈ 25%) at the price of delaying conventional vehicles (≈ 7%). Furthermore a notable shift of travel demand away from the highways towards major and small roads is noticed in early stages of AV penetration of the system. Finally, our findings show that after a certain threshold percentage of AVs the differences between AV and no AV lane scenarios become negligible.

This paper presents an algorithm, called the Backwards Incremental System Optimum Search (BISOS) for achieving system near-optimum traffic assignment by incrementally limiting accessibility of roads for a chosen set of agents. The described algorithm redistributes traffic volumes homogeneously around the city and converges significantly faster than existing methods for system optimum computation in current literature. Furthermore, as previous methods have mainly been developed for theoretical purposes, the solutions provided by them do not contain all the necessary information for a practical implementation such as explicit paths for the commuting population. In contrast, the BISOS algorithm preserves the information about the exact paths of all commuters, throughout the whole process of computing the system optimum assignment. Furthermore, a realistic traffic scenario is simulated using Singapore as a case study by utilizing survey and GPS traffic data. The BISOS routing method needs 15 times less routing computations to get within 1% of the optimal solution for a simulated scenario compared to conventional methods for system optimum computation.

Identification of critical segments in a road network is a crucial task for transportation system planners as it allows for in depth analysis of the robustness of the city's infrastructure. The current techniques require a considerable amount of computation, which does not scale well with the size of the system. With recent advances in machine learning, especially classification techniques, there are methods, which can prove to be more efficient replacements of current approaches. In this paper we propose a neural network (NN) based approach for classification of critical roads under user equilibrium traffic (UE) assignment. We, furthermore, introduce a novel predictor attribute, which captures the contrast between UE and system optimum (SO) assignment on the network. Our results demonstrate that the neural network can achieve considerable identification precision of critical road segments and that the SO related attributes significantly increase the classification power. We, furthermore, demonstrate that the NN approach outperforms the commonly used approach of linear regression (LR) and another popular classification approach from the field of machine learning, namely support vector machines (SVM).

A common approach of parallelising an agent-based road traffic simulation is to partition the road network into subregions and assign computations for each subregion to a logical process (LP). Inter-process communication for synchronisation between the LPs is one of the major factors that affect the performance of parallel agent-based road traffic simulation in a distributed memory environment. Synchronisation overhead, i.e., the number of messages and the communication data volume exchanged between LPs, is heavily dependent on the employed road network partitioning algorithm. In this paper, we propose Neighbour-Restricting Graph-Growing (NRGG), a partitioning algorithm which tries to reduce the required communication between LPs by minimising the number of neighbouring partitions. Based on a road traffic simulation of the city of Singapore, we show that our method not only outperforms graph partitioning methods such as METIS and Buffoon, for the synchronisation protocol used, but also is more resilient than stripe spatial partitioning when partitions are cut more finely.

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