On scaling time dependent shortest path computations for Dynamic Traffic Assignment

Dynamic Traffic Assignment (DTA) models provide a powerful tool to realistically represent the complex interactions between travelers and the transportation infrastructure in large regions, and they have been increasingly adopted by transportation network planners and operators in the last decade. Time dependent shortest path (TDSP) calculations at the core of most DTA methodologies usually require storing and comparing millions of discovered paths. This makes the problem I/O intensive in addition to it inherently being computationally demanding. In this paper we present a use case on scaling up the TDSP calculations within an established existing DTA software framework with distributed computing. Our approach alleviates I/O bottlenecks by using RAM disks and improves a label correcting shortest path algorithm by using priority queues which also leads to better workload balancing among parallel processes. Tests with real-world transportation networks show drastic run time performance improvements, in some cases by a factor of 12x. This suggests that our methodology enables the analysis of much larger networks. Furthermore, the improvements were achieved with relatively minor modifications to the base code, which makes this approach appealing for the enhancement of other existing DTA implementations.