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Distribution of Value of Time and Ways to Model Value of Time in Long-Range Planning Models
As managed lanes (ML) become more integrated in regional urban networks with existing general purpose (GP) lanes, the distribution of travelers’ value of time (VOT) is becoming more important for transportation planning agencies to quantify in order to accurately predict future travel patterns. Since travelers’ VOT varies depending on a multitude of factors, this study investigates ways that we can determine the VOT distribution of a region from existing travel data as well as effective ways that we can model VOT using traffic assignment algorithms. In networks with available link volumes and toll data on segments where travelers have the option of choosing to stay on the GP lanes or entering a ML facility, a VOT distribution can be inferred assuming that travelers who enter the ML choose to do so based on a certain “threshold” VOT. When modeling these VOT distributions, errors are observed in the traffic assignment results when both the continuous nature of VOT distributions are discretized, and when varying toll values are assumed to be constant. Specifically in the context of TransCAD software, link travel time errors appear to be much less significant than flow errors when tested on a nine node network. Additional experimentation on larger regional networks is needed to verify the significance of these errors and their impact on predicted travel patterns.Civil, Architectural, and Environmental Engineerin
An integrated method for short-term prediction of road traffic conditions for intelligent transportation systems applications
The paper deals with the short-term prediction of road traffic conditions within Intelligent Transportation Systems applications. First, the problem of traffic modeling and the potential of different traffic monitoring technologies are discussed. Then, an integrated method for short-term traffic prediction is presented, which integrates an Artificial Neural Network predictor that forecasts future states in standard conditions, an anomaly detection module that exploits floating car data to individuate possible occurrences of anomalous traffic conditions, and a macroscopic traffic model that predicts speeds and queue progressions in case of anomalies. Results of offline applications on a primary Italian motorway are presented
Improving Livability Using Green and Active Modes: A Traffic Stress Level Analysis of Transit, Bicycle, and Pedestrian Access and Mobility
Understanding the relative attractiveness of alternatives to driving is vitally important toward lowering driving rates and, by extension, vehicle miles traveled (VMT), traffic congestion, greenhouse gas (GHG) emissions, etc. The relative effectiveness of automobile alternatives (i.e., buses, bicycling, and walking) depends on how well streets are designed to work for these respective modes in terms of safety, comfort and cost, which can sometimes pit their relative effectiveness against each other. In this report, the level of traffic stress (LTS) criteria previously developed by two of the authors was used to determine how the streets functioned for these auto alternative modes. The quality and extent of the transit service area was measured using a total travel time metric over the LTS network. The model developed in this study was applied to two transit routes in Oakland, California, and Denver, Colorado
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
The Spatial Variability of Vehicle Densities as Determinant of Urban Network Capacity
Due to the complexity of the traffic flow dynamics in urban road networks,
most quantitative descriptions of city traffic so far are based on computer
simulations. This contribution pursues a macroscopic (fluid-dynamic) simulation
approach, which facilitates a simple simulation of congestion spreading in
cities. First, we show that a quantization of the macroscopic turning flows
into units of single vehicles is necessary to obtain realistic fluctuations in
the traffic variables, and how this can be implemented in a fluid-dynamic
model. Then, we propose a new method to simulate destination flows without the
requirement of individual route assignments. Combining both methods allows us
to study a variety of different simulation scenarios. These reveal fundamental
relationships between the average flow, the average density, and the
variability of the vehicle densities. Considering the inhomogeneity of traffic
as an independent variable can eliminate the scattering of congested flow
measurements. The variability also turns out to be a key variable of urban
traffic performance. Our results can be explained through the number of full
links of the road network, and approximated by a simple analytical formula
Optimization of route choice, speeds and stops in time-varying networks for fuel-efficient truck journeys
A method is presented for the real-time optimal control of the journey of a truck, travelling between a pair of pick-up/drop-off locations in a time-varying traffic network, in order to reduce fuel consumption. The method, when applied during the journey, encapsulates the choice of route, choice of speeds on the links, and choice of stop locations/durations; when applied pre-trip, it additionally incorporates choice of departure time. The problem is formulated by using a modified form of space-time extended network, in such a way that a shortest path in this network corresponds to an optimal choice of not only route, stops and (when relevant) departure time, but also of speeds. A series of simple illustrative examples are presented to illustrate the formulation. Finally, the method is applied to a realistic-size case study
Modeling the power consumption of a Wifibot and studying the role of communication cost in operation time
Mobile robots are becoming part of our every day living at home, work or
entertainment. Due to their limited power capabilities, the development of new
energy consumption models can lead to energy conservation and energy efficient
designs. In this paper, we carry out a number of experiments and we focus on
the motors power consumption of a specific robot called Wifibot. Based on the
experimentation results, we build models for different speed and acceleration
levels. We compare the motors power consumption to other robot running modes.
We, also, create a simple robot network scenario and we investigate whether
forwarding data through a closer node could lead to longer operation times. We
assess the effect energy capacity, traveling distance and data rate on the
operation time
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