4,522 research outputs found
Dynamic User Equilibrium (DUE)
The quantitative analysis of road network traffic performed through static
assignment models yields the transport demand-supply equilibrium under
the assumption of within-day stationarity. This implies that the relevant
variables of the system (i.e. user flows, travel times, costs) are assumed to
be constant over time within the reference period. Although static
assignment models satisfactorily reproduce congestion effects on traffic flow
and cost patterns, they do not allow to represent the variation over time of
the demand flows (i.e. around the rush hour) and of the network
performances (i.e. in presence of time varying tolls, lane usage, signal plans,
link usage permission); most importantly, they cannot reproduce some
important dynamic phenomena, such as the formation and dispersion of
vehicle queues due to the temporary over-saturation of road sections, and
the spillback, that is queues propagation towards upstream roads
Distance Oracles for Time-Dependent Networks
We present the first approximate distance oracle for sparse directed networks
with time-dependent arc-travel-times determined by continuous, piecewise
linear, positive functions possessing the FIFO property.
Our approach precomputes approximate distance summaries from
selected landmark vertices to all other vertices in the network. Our oracle
uses subquadratic space and time preprocessing, and provides two sublinear-time
query algorithms that deliver constant and approximate
shortest-travel-times, respectively, for arbitrary origin-destination pairs in
the network, for any constant . Our oracle is based only on
the sparsity of the network, along with two quite natural assumptions about
travel-time functions which allow the smooth transition towards asymmetric and
time-dependent distance metrics.Comment: A preliminary version appeared as Technical Report ECOMPASS-TR-025 of
EU funded research project eCOMPASS (http://www.ecompass-project.eu/). An
extended abstract also appeared in the 41st International Colloquium on
Automata, Languages, and Programming (ICALP 2014, track-A
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Time-dependent stochastic shortest path(s) algorithms for a scheduled transportation network
Following on from our work concerning travellersâ preferences in public transportation networks (Wu and Hartley, 2004), we introduce the concept of stochasticity to our algorithms. Stochasticity greatly increases the complexity of the route finding problem, so greater algorithmic efficiency becomes imperative. Public transportation networks (buses, trains) have two important features: edges can only be traversed at certain points in time and the weights of these edges change in a day and have an uncertainty associated with them. These features determine that a public transportation network is a stochastic and time-dependent network. Finding multiple shortest paths in a both stochastic and time-dependent network is currently regarded as the most difficult task in the route finding problems (Loui, 1983). This paper discusses the use of k-shortest-paths (KSP) algorithms to find optimal route(s) through a network in which the edge weights are defined by probability distributions. A comprehensive review of shortest path(s) algorithms with probabilistic graphs was conducted
Shortest-path and minimumdelay algorithms in networks with time-dependent edge-length
We consider in this paper the shortest-path problem in networks in which the delay (or weight) of the edges changes with time according to arbitrary functions. We present algorithms for finding the shortest-path and minimum-delay under various waiting constraints and investigate the properties of the derived path. We show that if departure time from the source node is unrestricted then a shortest path can be found that is simple and achieves a delay as short as the most unrestricted path. In the case of restricted transit, it is shown that there exist cases where the minimum delay is finite but the path that achieves it is infinite
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