45 research outputs found
Investigating Freeway Speed-Flow Relationships for Traffic Assignment Applications
Developments in high resolution traffic sensors over the past decades are providing a wealth of empirical speed-flow data. Travel demand models use speed-flow relationships to assign traffic flows to network links. However, speed-flow relationships have not been revalidated against new detailed traffic sensor data. Therefore, it is necessary to revisit speed-flow relationships based on actual measured conditions on network links rather than assuming constant speed-flow relationships over entire highway network systems. Speed-flow relationships have been particularly difficult to calibrate and estimate when traffic volumes approach capacity, i.e. when the v/c ratio approaches one. This thesis empirically evaluates the speed-flow relationships for v/c \u3c 1 using field data. For congested conditions (v/c \u3e 1) a theoretical approach is taken. A new methodology to determine the distribution of the activation of bottlenecks, bottleneck duration, and bottleneck deactivation is proposed. This thesis is a new contribution to understand the stochastic nature of freeway capacity as well as bottleneck duration, activation, and deactivation. Unlike previous research efforts, this thesis studies speed-flow relationships at the lane level and later presents a method to estimate speed-flow relationships at the link level
Urban Network Gridlock: Theory, Characteristics, and Dynamics
AbstractThis study explores the limiting properties of network-wide traffic flow relations under heavily congested conditions in a large-scale complex urban street network; these limiting conditions are emulated in the context of dynamic traffic assignment (DTA) experiments on an actual large network. The primary objectives are to characterize gridlock and understand its dynamics. This study addresses a gap in the literature with regard to the existence of exit flow and recovery period. The one- dimensional theoretical Network Fundamental Diagram (NFD) only represents steady-state behavior and holds only when the inputs change slowly in time and traffic is distributed homogenously in space. Also, it does not describe the hysteretic behavior of the network traffic when a gridlock forms or when network recovers. Thus, a model is proposed to reproduce hysteresis and gridlock when homogeneity and steady-state conditions do not hold. It is conjectured that the network average flow can be approximated as a non-linear function of network average density and variation in link densities. The proposed model is calibrated for the Chicago Central Business District (CBD) network. We also show that complex urban networks with multiple route choices, similar to the idealized network tested previously in the literature, tend to jam at a range of densities that are smaller than the theoretical average network jam density. Also it is demonstrated that networks tend to gridlock in many different ways with different configurations. This study examines how mobility of urban street networks could be improved by managing vehicle accumulation and re-distributing network traffic via strategies such as demand management and disseminating real-time traveler information (adaptive driving). This study thus defines and explores some key characteristics and dynamics of urban street network gridlocks including gridlock formation, propagation, recovery, size, etc
Traffic Assignment Problem for Pedestrian Networks
The estimation of pedestrian traffic in urban areas is often performed with
computationally intensive microscopic models that usually suffer from
scalability issues in large-scale walking networks. In this study, we present a
new macroscopic user equilibrium traffic assignment problem (UE-pTAP) framework
for pedestrian networks while taking into account fundamental microscopic
properties such as self-organization in bidirectional streams and stochastic
walking travel times. We propose four different types of pedestrian
volume-delay functions (pVDFs), calibrate them with empirical data, and discuss
their implications on the existence and uniqueness of the assignment solution.
We demonstrate the applicability of the developed UE-pTAP framework in a small
network as well as a larger scale network of Sydney footpaths
Multimodal urban mobility and multilayer transport networks
Transportation networks, from bicycle paths to buses and railways, are the
backbone of urban mobility. In large metropolitan areas, the integration of
different transport modes has become crucial to guarantee the fast and
sustainable flow of people. Using a network science approach, multimodal
transport systems can be described as multilayer networks, where the networks
associated to different transport modes are not considered in isolation, but as
a set of interconnected layers. Despite the importance of multimodality in
modern cities, a unified view of the topic is currently missing. Here, we
provide a comprehensive overview of the emerging research areas of multilayer
transport networks and multimodal urban mobility, focusing on contributions
from the interdisciplinary fields of complex systems, urban data science, and
science of cities. First, we present an introduction to the mathematical
framework of multilayer networks. We apply it to survey models of multimodal
infrastructures, as well as measures used for quantifying multimodality, and
related empirical findings. We review modelling approaches and observational
evidence in multimodal mobility and public transport system dynamics, focusing
on integrated real-world mobility patterns, where individuals navigate urban
systems using different transport modes. We then provide a survey of freely
available datasets on multimodal infrastructure and mobility, and a list of
open source tools for their analyses. Finally, we conclude with an outlook on
open research questions and promising directions for future research.Comment: 31 pages, 4 figure
Calibration of traffic flow models under adverse weather and application in mesoscopic network simulation
The weather-sensitive traffic estimation and prediction system (TrEPS) aims for accurate estimation and prediction of the traffic states under inclement weather conditions. Successful application of weather-sensitive TrEPS requires detailed calibration of weather effects on the traffic flow model. In this study, systematic procedures for the entire calibration process were developed, from data collection through model parameter estimation to model validation. After the development of the procedures, a dual-regime modified Greenshields model and weather adjustment factors were calibrated for four metropolitan areas across the United States (Irvine, California; Chicago, Illinois; Salt Lake City, Utah; and Baltimore, Maryland) by using freeway loop detector traffic data and weather data from automated surface-observing systems stations. Observations showed that visibility and precipitation (rain-snow) intensity have significant impacts on the value of some parameters of the traffic flow models, such as free-flow speed and maximum flow rate, while these impacts can be included in weather adjustment factors. The calibrated models were used as input in a weather-integrated simulation system for dynamic traffic assignment. The results show that the calibrated models are capable of capturing the weather effects on traffic flow more realistically than TrEPS without weather integration