# Browsing by Subject "Travel time"

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Item Emergency response vehicle travel time analysis(2016-05) Gopidi, Nithin Reddy; Dimitrov, Nedialko B.; Hasenbein, JohnShow more Fire departments, ambulance services and police departments often worry if they are providing prompt response times in case of an emergency. To be effective, emergency response vehicle (ERV) have to be on the scene within a certain time of the initial emergency call. Emergency response vehicles are exempt from many traffic regulations like speed limit, crossing red signal and moreover other vehicles are expected to yield for ERV. Hence the response time analysis of ERV is very different from the regular traffic study. Advancements in the field of traffic signal control technology brought into picture new traffic signal control device (TCD). These TCDs automatically detect arrival of an ERV to turn traffic signal green for the fire engine to go through. Unfortunately, high installation costs limit the number of TCDs that can be deployed. The key goal of this article is to identify potential intersections in a traffic system for the installation of a TCDs. In this article we propose a method of using Global Positioning System (GPS) data from ERVs to identify slow spots in the traffic system. we start with a brief overview of different map matching techniques. But, most of the map matching algorithms only try to relate the GPS points to the nearest road segment with the objective of only recreating the original path. These methods doesn't help analyze travel time. So we present Pre, Post map match process along with a customized map matching process including a nodal network of entire routes in Austin. Dynamic sizing for identify candidate points. Segment wise wait time analysis. Determine Busy intersection by frequency weighed time delay. We finally present results of the algorithm on 2 years of ERV GPS data from Austin Fire Department. Determine important intersection by frequency weighed time delay. The result can be used by different ERV to significantly improve response times, while meeting the budget restrictions.Show more Item Operational, supply-side uncertainty in transportation networks: causes, effects, and mitigation strategies(2009-08) Boyles, Stephen David; Waller, S. TravisShow more This dissertation is concerned with travel time uncertainty in transportation networks due to ephemeral phenomena such as incidents or poor weather. Such events play a major role in nonrecurring congestion, which is estimated to comprise between one-third and one-half of all delay on freeways. Although past research has considered many individual aspects of this problem, this dissertation is unique in bringing a comprehensive approach, beginning with study of its causes, moving to discussion of its effects on traveler behavior, and then demonstrating how these models can be applied to mitigate the effects of this uncertainty. In particular, two distinctive effects of uncertainty are incorporated into all aspects of these models: nonlinear traveler behavior, encompassing risk aversion, schedule delay, on-time arrival, and other user objectives that explicitly recognize travel time uncertainty; and information and adaptive routing, where travelers can adjust their routes through the network as they acquire information on its condition. In order to accurately represent uncertain events in a mathematical model, some quantitative description of these events and their impacts must be available. On freeways, a large amount of travel data is collected through intelligent transportation systems (ITS), although coverage is far from universal, and very little data is collected on arterial streets. This dissertation develops a statistical procedure for estimating probability distributions on speed, capacity, and other operational metrics by applying regression to locations where such data is available. On arterials, queueing theory is used to develop novel expressions for expected delay conditional on the signal indication. The effects of this uncertainty are considered next, both at the individual (route choice) and collective (equilibrium) levels. For individuals, the optimal strategy is no longer a path, but an adaptive policy which allows for flexible re-routing as information is acquired. Dynamic programming provides an efficient solution to this problem. Issues related to cycling in optimal policies are examined in some depth. While primarily a technical concern, the presence of cycling can be discomforting and needs to be addressed. When considering collective behavior, the simultaneous choices of many self-optimizing users (who need not share the same behavioral objective) can be expressed as the solution to a variational inequality problem, leading to existence and uniqueness results under certain regularity conditions. An improved policy loading algorithm is also provided for the case of linear traveler behavior. Finally, three network improvement strategies are considered: locating information-providing devices; adaptive congestion pricing; and network design. Each of these demonstrates how the routing and equilibrium models can be applied, using small networks as testbed locations. In particular, the information provision and adaptive congestion pricing strategies are extremely difficult to represent without an adaptive equilibrium model such as the one provided in this dissertation.Show more Item Potential impacts of connected-autonomous vehicles on congestion and safety : a look at Austin, Texas(2017-05-02) Archer, Jackson Longstreet; Zhang, Ming, 1963 April 22-; Jiao, JunfengShow more Data is a central component of Connected-Autonomous Vehicle (CAV) systems: the advantages and potential challenges of both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) CAV data underlie the question of wide scale CAV implementation. This report looks at the potential congestion and safety benefits of a vehicle system highly saturated with CAVs in Austin, Texas. Traffic factors such as capacity, intersection delay, and crash rate are examined with respect to their effect on an urban corridor in Austin. The case study relies almost entirely on collected field data to be used as a comparison against potential CAV advantages. In addition to a presentation of the quantitative benefits of CAVs, an infrastructure placement scheme that maximizes data transmission efficiency is also proposed. The results find that vehicle systems can see large improvements in capacity, intersection delay, and number of crashes, and at a relatively inexpensive cost.Show more Item Travel time reliability assessment techniques for large-scale stochastic transportation networks(2010-05) Ng, Man Wo; Waller, S. Travis; Kockelman, Kara M.; Zhang, Zhanmin; Hasenbein, John J.; Morton, David P.Show more Real-life transportation systems are subject to numerous uncertainties in their operation. Researchers have suggested various reliability measures to characterize their network-level performances. One of these measures is given by travel time reliability, defined as the probability that travel times remain below certain (acceptable) levels. Existing reliability assessment (and optimization) techniques tend to be computationally intensive. In this dissertation we develop computationally efficient alternatives. In particular, we make the following three contributions. In the first contribution, we present a novel reliability assessment methodology when the source of uncertainty is given by road capacities. More specifically, we present a method based on the theory of Fourier transforms to numerically approximate the probability density function of the (system-wide) travel time. The proposed methodology takes advantage of the established computational efficiency of the fast Fourier transform. In the second contribution, we relax the common assumption that probability distributions of the sources of uncertainties are known explicitly. In reality, this distribution may be unavailable (or inaccurate) as we may have no (or insufficient) data to calibrate the distributions. We present a new method to assess travel time reliability that is distribution-free in the sense that the methodology only requires that the first N moments (where N is any positive integer) of the travel time to be known and that the travel times reside in a set of known and bounded intervals. Instead of deriving exact probabilities on travel times exceeding certain thresholds via computationally intensive methods, we develop analytical probability inequalities to quickly obtain upper bounds on the desired probability. Because of the computationally intensive nature of (virtually all) existing reliability assessment techniques, the optimization of the reliability of transportation systems has generally been computationally prohibitive. The third and final contribution of this dissertation is the introduction of a new transportation network design model in which the objective is to minimize the unreliability of travel time. The computational requirements are shown to be much lower due to the assessment techniques developed in this dissertation. Moreover, numerical results suggest that it has the potential to form a computationally efficient proxy for current simulation-based network design models.Show more