Explore the vast range of titles available.

Contraction hierarchies are a simple approach for fast routing in road networks. Our algorithm calculates exact shortest paths and handles road networks of whole continents. During a preprocessing step, we exploit the inherent hierarchical structure of road networks by adding shortcut edges. A subsequent modified bidirectional Dijkstra algorithm can then find a shortest path in a fraction of a millisecond, visiting only a few hundred nodes. This small search space makes it suitable to implement it on a mobile device. We present a mobile implementation that also handles changes in the road network, like traffic jams, and that allows instantaneous routing without noticeable delay for the user. Also, an algorithm to calculate large distance tables is currently the fastest if based on contraction hierarchies.

The impact of variable speed limits (VSL) on aggregate traffic flow behaviour on motorways is shown to bear similarities to the impact of ramp metering, in particular, when addressing potentially active bottlenecks. A quantitative model of the VSL impact is proposed that allows for VSL to be incorporated in a macroscopic second-order traffic flow model as an additional control component. The integrated motorway network traffic control problem involving ramp metering and VSL control measures is formulated as a constrained discrete-time optimal control problem and is solved efficiently even for large-scale networks by a suitable feasible direction algorithm. An illustrative example of a hypothetical motorway stretch is investigated under different control scenarios, and it is shown that traffic flow efficiency can be substantially improved when VSL control measures are used, particularly in integration with coordinated ramp metering.

We investigate a hierarchy of eddy-viscosity terms in proper orthogonal decomposition (POD) Galerkin models to account for a large fraction of unresolved fluctuation energy. These Galerkin methods are applied to large eddy simulation (LES) data for a flow around a vehicle-like bluff body called an Ahmed body. This flow has three challenges for any reduced-order model: a high Reynolds number, coherent structures with broadband frequency dynamics, and meta-stable asymmetric base flow states. The Galerkin models are found to be most accurate with modal eddy viscosities as proposed by Rempfer & Fasel (J. Fluid Mech., vol. 260, 1994a, pp. 351–375; J. Fluid Mech. vol. 275, 1994b, pp. 257–283). Robustness of the model solution with respect to initial conditions, eddy-viscosity values and model order is achieved only for state-dependent eddy viscosities as proposed by Noack, Morzyński & Tadmor (Reduced-Order Modelling for Flow Control, CISM Courses and Lectures, vol. 528, 2011). Only the POD system with state-dependent modal eddy viscosities can address all challenges of the flow characteristics. All parameters are analytically derived from the Navier–Stokes-based balance equations with the available data. We arrive at simple general guidelines for robust and accurate POD models which can be expected to hold for a large class of turbulent flows.

Capacity estimation is an important tool for the design and dimensioning of pedestrian facilities. The literature contains different procedures and specifications that show considerable differences with respect to the estimated flow values. Moreover, new experimental data indicate a stepwise growth of capacity with width and thus challenge the validity of the specific flow concept. To resolve these differences, we experimentally studied the unidirectional pedestrian flow through bottlenecks under laboratory conditions. The time development of quantities such as individual velocities, density, and individual time gaps in bottlenecks of different widths is presented. The data show a linear growth of flow with width. The comparison of the results with experimental data from other authors indicates that the basic assumption of the capacity estimation for bottlenecks has to be revised. In contrast to most planning guidelines, our main result is that a jam occurs even if the incoming flow does not overstep the capacity defined by the maximum flow according to the fundamental diagram.

This article analyzes the variation in bike commuting in large American cities, with a focus on assessing the influence of bike paths and lanes, which have been the main approach to increasing cycling in the USA. To examine the role of cycling facilities, we used a newly assembled dataset on the length of bike lanes and paths in 2008 collected directly from 90 of the 100 largest U.S. cities. Pearson’s correlation, bivariate quartile analysis, and two different types of regressions were used to measure the relationship between cycling levels and bikeways, as well as other explanatory and control variables. Ordinary Least Squares and Binary Logit Proportions regressions confirm that cities with a greater supply of bike paths and lanes have significantly higher bike commute rates—even when controlling for land use, climate, socioeconomic factors, gasoline prices, public transport supply, and cycling safety. Standard tests indicate that the models are a good fit, with R 2 ranging between 0.60 and 0.65. Computed coefficients have the expected signs for all variables in the various regression models, but not all are statistically significant. Estimated elasticities indicate that both off-street paths and on-street lanes have a similar positive association with bike commute rates in U.S. cities. Our results are consistent with previous research on the importance of separate cycling facilities and provide additional information about the potentially different role of paths vs. lanes. Our analysis also revealed that cities with safer cycling, lower auto ownership, more students, less sprawl, and higher gasoline prices had more cycling to work. By comparison, annual precipitation, the number of cold and hot days, and public transport supply were not statistically significant predictors of bike commuting in large cities.

In a survey of 1,402 current and potential cyclists in Metro Vancouver, 73 motivators and deterrents of cycling were evaluated. The top motivators, consistent among regular, frequent, occasional and potential cyclists, were: routes away from traffic noise and pollution; routes with beautiful scenery; and paths separated from traffic. In factor analysis, the 73 survey items were grouped into 15 factors. The following factors had the most influence on likelihood of cycling: safety; ease of cycling; weather conditions; route conditions; and interactions with motor vehicles. These results indicate the importance of the location and design of bicycle routes to promote cycling.

The Vehicle Routing Problem (VRP) was introduced 50 years ago by Dantzig and Ramser under the title \"The Truck Dispatching Problem.\" The study of the VRP has given rise to major developments in the fields of exact algorithms and heuristics. In particular, highly sophisticated exact mathematical programming decomposition algorithms and powerful metaheuristics for the VRP have been put forward in recent years. The purpose of this article is to provide a brief account of this development.

It is expected that electric vehicles (EVs) will soon represent a large share of the demand for electricity. Several research works have extolled the advantages of these devices as flexible demands, not only to charge their batteries when it is cheaper to do so, but also to provide services in the form of vehicle-to-grid (V2G) power injections to the system. These services, however, could reduce the useful life of the battery and thus introduce a cost that needs to be taken into account when scheduling the charging of these vehicles. This study presents a scheduling algorithm for EVs under a real time pricing scheme with uncertainty. The objective function explicitly takes into account the cost of battery degradation not only when used to provide services to the system but also in terms of the EV utilisation for motion. The results show that the scheduling of the V2G services is sensitive to the electricity prices uncertainty and to the degradation costs derived from the energy arbitrage. Also, the optimal energy state of charge of the batteries is highly dependent on whether the cost of battery degradation is taken into account or not.

Astochastic, mixed-integer program (MIP) involving joint chance constraints is developed that generates least-cost vehicle redistribution plans for shared-vehicle systems such that a proportion of all near-term demand scenarios are met. The model aims to correct short-term demand asymmetry in shared-vehicle systems, where flow from one station to another is seldom equal to the flow in the opposing direction. The model accounts for demand stochasticity and generates partial redistribution plans in circumstances when demand outstrips supply. This stochastic MIP has a nonconvex feasible region. A novel divide-and-conquer algorithm for generating p -efficient points, used to transform the problem into a set of disjunctive, convex MIPs and handle dual-bounded chance constraints, is proposed. Assuming independence of random demand across stations, a faster cone-generation method is also presented. In a real-world application for a system in Singapore, the potential of redistribution as a fleet management strategy and the value of accounting for inherent stochasticities are demonstrated.

The vehicle delay tolerant networks (DTNs) make opportunistic communications by utilizing the mobility of vehicles, where the node makes delay-tolerant based “carry and forward” mechanism to deliver the packets. The routing schemes for vehicle networks are challenging for varied network environment. Most of the existing DTN routing including routing for vehicular DTNs mainly focus on metrics such as delay, hop count and bandwidth, etc. A new focus in green communications is with the goal of saving energy by optimizing network performance and ultimately protecting the natural climate. The energy–efficient communication schemes designed for vehicular networks are imminent because of the pollution, energy consumption and heat dissipation. In this paper, we present a directional routing and scheduling scheme (DRSS) for green vehicle DTNs by using Nash Q-learning approach that can optimize the energy efficiency with the considerations of congestion, buffer and delay. Our scheme solves the routing and scheduling problem as a learning process by geographic routing and flow control toward the optimal direction. To speed up the learning process, our scheme uses a hybrid method with forwarding and replication according to traffic pattern. The DRSS algorithm explores the possible strategies, and then exploits the knowledge obtained to adapt its strategy and achieve the desired overall objective when considering the stochastic non-cooperative game in on-line multi-commodity routing situations. The simulation results of a vehicular DTN with predetermined mobility model show DRSS achieves good energy efficiency with learning ability, which can guarantee the delivery ratio within the delay bound.