This thesis seeks to provide guidance on the deployment of Bluetooth sensors for travel time measurement in work zone corridors. The investigation focuses on the detection characteristics of Class 1 and Class 2 Bluetooth devices, and how cultivating an understanding of these characteristics together with the effect of the sensor inquiry cycle length can suggest a more precise method of travel time measurement. This thesis also explores the range of detection location around a Bluetooth sensor in order to recommend a minimum corridor separation of Bluetooth sensors, and to ascertain the best method of Bluetooth travel time derivation. Finally, this thesis investigates these principles further through multiple side-fire deployments on the I-285 corridor in Atlanta, Georgia; as well as two deployments capturing several hours of active work zone travel time.

Idrottsparken roundabout in Norrkoping is located in the more dense part of the city.Congestion occurs in peak hours causing queue and extended travel time. This thesis aims to provide alternative model to reduce queue and travel time. Types ofobservation data are flow, length of queue, and travel time that are observed during peakhours in the morning and afternoon. Calibration process is done by minimising root meansquare error of queue, travel time, and combination both of them between observation andcalibrated model. SUMO version 0.14.0 is used to perform the microsimulation. There are two proposed alternatives, namely Scenario 1: the additional lane for right turnfrom East leg to North and from North leg to West and Scenario 2: restriction of heavy goodsvehicles passing Kungsgatan which is located in Northern leg of Idrottsparken roundaboutduring peak hours. For Scenario 1, the results from SUMO will be compared with AIMSUNin terms of queue and travel time. The result of microsimulation shows that parameters that have big influence in the calibrationprocess for SUMO are driver imperfection and driver’s reaction time, while for AIMSUN isdriver’s reaction time and maximum acceleration. From analysis found that the model of thecurrent situation at Idrottsparken can be represented by model simulation which usingcombination between root mean square error of queue and travel time in calibration andvalidation process. Moreover, scenario 2 is the best alternative for SUMO because itproduces the decrease of queue and travel time almost in all legs at morning and afternoonpeak hour without accompanied by increase significant value of them in the other legs. Thecomparison between SUMO and AIMSUN shows that, in general, the AIMSUN has higherchanges value in terms of queue and travel time due to the limited precision in SUMO forroundabout modelling.

This research investigates analytical models that might serve to support decisions in the early stages of designing high volume small parts order picking systems. Because the development of analytical closed-forms is challenging, a common approach is to use simulation models for detailed design performance assessment. However, simulation is not suitable for early stage design purposes; because simulation models are time-consuming (thus expensive) to construct and execute, especially when the number of alternatives to evaluate is large. If available, analytical models are computationally cheaper. They provide faster and more flexible solutions and though usually less detailed, may be adequate to support early stages of design. The challenge is to develop generic analytic models providing useful results for a class of problems. This research focuses on a class of problems in high volume small parts order picking systems with pick-to-buffer technology. This is a new technology, and not yet in widespread use. The novelty in the modeling approach is the distinct separation of item-picking and order assembly operations which permits the development of performance models for both throughput and service level. Essentially the system is modeled as a tandem queue, and the two detailed models for the picking and assembly subsystems are developed based on detailed description of the operations. Solving the model provides estimates for performance measures, such as order cycle time and system throughput, which are essential in design. The approximation method requires estimating the squared coefficient of interdeparture times from the classical GX/G/1 queuing model, and a suitable approximation is derived in this thesis. Computational tests show the model to provide reasonably accurate estimates of system performance, with minimal computational overhead. To support the proposed queuing model, new models are developed for estimating mean and squared coefficient of variation for pick and assembly operation times. These models include the variability of order contents and the picking process, along with the physical layout. Results of the estimation compare very well with that of simulation.

Travelers' value of travel time savings (VTTS) are often used to estimate the benefits of transportation facilities, including managed lanes (MLs). With various eligibility criteria and time of day pricing on the MLs, the VTTS estimation is complicated. This is evident by the underestimation of VTTS on MLs in many of the previous studies. This study investigates stated preference (SP) survey design strategies and differentiating the VTTS for ordinary and some common urgent situations faced by the travelers in an attempt to improve on VTTS estimation on MLs. This study used three different survey design strategies (including a D-efficient design) in an internet based survey of Katy Freeway travelers. It was found that a random attribute level generation strategy, where the VTTS presented in the alternative was adjusted based on the answer to a previous SP question, performs better than the other two designs with respect to VTTS estimation and other survey design efficiency criteria. The analysis to differentiate the VTTS for ordinary and urgent trips was carried out using the state of art in the mixed logit model estimation. It was found that travelers value their travel time savings much more when facing most of these urgent situations rather than ordinary situations. Both peak and off-peak period travelers' VTTS were also found to be significantly greater when on urgent trips. Survey design attribute level ranges were found to significantly affect the VTTS estimation. Further, in order to understand the policy implications of these findings it was demonstrated that classifying all trips as ordinary can significantly underestimate the VTTS benefits offered by the MLs. Additionally, the VTTS of any urgent trips would be greatly underestimated. The study also demonstrated that many of the low and medium income group travelers on urgent trips can have VTTS greater than that of the highest VTTS traveler from the high income group on an ordinary trip. These findings have significant policy implications since the benefits of MLs (and of most transportation investments) are primarily derived from travel time savings. Underestimating the VTTS and hence the benefits for MLs can result in reducing the likelihood of funding such facilities. This study provides an important first step in the proper estimation of these benefits by suggesting modifications to SP surveys to better capture the influence of urgent trips on the value of a ML facility.

Bangkok is widely known for its severe traffic congestion. The Thai government advocates the concept of jobs and housing balance (JHB) as a strategy for reducing traffic congestion in Metropolitan Bangkok. The basic idea is to decentralize the jobs to the neighboring provinces so that the commuters would live closer to their workplaces and thereby alleviate traffic congestion. The main purpose of this research is to examine empirically the effectiveness of JHB in reducing the severity of traffic congestion in the Bangkok Metropolitan Region. For this purpose, three data sets derived from the Bangkok Metropolitan Region Extended City Model (BMR-ECM) were obtained from the Office of the Commission for the Management of Land Traffic and the National Statistical Office of Thailand. Travel time index (TTI) was developed to measure congestion. In addition to JHB, a number of land use variables were included in the analysis. They are population density, school density, and job accessibility index. Multiple regression models of TTI as functions of JHB and other variables were estimated at two geographic scales: subsector and traffic analysis zone (TAZ). The study finds JHB is significant in influencing congestion levels in the Bangkok Metropolitan Region. Other influential factors include the population density, school density, and job accessibility. All of these factors are found to be statistically significant in explaining the variation of traffic congestion at the traffic analysis zone level, but not at the subsector level, however.

Research in the field of travel time measurement using Bluetooth technology has been an area of great interest in recent years as transportation professionals strive to increase the cost-effectiveness, accuracy, anonymity, and safety of travel time data collection methods. Commonly used travel time data collection methods include the use of inductive loops, video cameras, and probe vehicles. However, Bluetooth, a globally accepted wireless technology, serves as the medium being utilized by more and more transportation consultants, public agencies, and academics in the collection of travel time data. This study seeks to develop a methodology for measuring travel times on arterial roads using Bluetooth technology. A literature review of general travel time methods and Bluetooth travel time methods was conducted to provide the context for a Bluetooth field deployment development and implementation. The study presents the deployment plan and data analysis of a case study conducted on Spring Street in Atlanta, Georgia. Variable heights, Bluetooth to Bluetooth interference, and detection of Bluetooth devices in probe vehicles are investigated and recommendations are suggested for future Bluetooth travel time studies.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references. Available also from Dissertation Abstracts.

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. / text

Travel time is one of the most important inputs in many Intelligent Transport Systems (ITS). As a result, this information needs to be accurate and dynamic in both spatial and temporal dimensions. For the estimation of travel time, data from fixed sensors such as Inductive Loop Detectors (ILD) and cameras have been widely used since the 1960 s. However, data from fixed sensors may not be sufficiently reliable to estimate travel time due to a combination of limited coverage and low quality data resulting from the high cost of implementing and operating these systems. Such issues are particularly critical in the context of Less Developed Countries, where traffic levels and associated problems are increasing even more rapidly than in Europe and North America, and where there are no pre-existing traffic monitoring systems in place. As a consequence, recent developments have focused on utilising moving sensors (i.e. probe vehicles and/or people equipped with GPS: for instance, navigation and route guidance devices, mobile phones and smartphones) to provide accurate speed, positioning and timing data to estimate travel time. However, data from GPS also have errors, especially for positioning fixes in urban areas. Therefore, map-matching techniques are generally applied to match raw positioning data onto the correct road segments so as to reliably estimate link travel time. This is challenging because most current map-matching methods are suitable for high frequency GPS positioning data (e.g. data with 1 second interval) and may not be appropriate for low frequency data (e.g. data with 30 or 60 second intervals). Yet, many moving sensors only retain low frequency data so as to reduce the cost of data storage and transmission. The accuracy of travel time estimation using data from moving sensors also depends on a range of other factors, for instance vehicle fleet sample size (i.e. proportion of vehicles equipped with GPS); coverage of links (i.e. proportion of links on which GPS-equipped vehicles travel); GPS data sampling frequency (e.g. 3, 6, 30, 60 seconds) and time window length (e.g. 5, 10 and 15 minutes). Existing methods of estimating travel time from GPS data are not capable of simultaneously taking into account the issues related to uncertainties associated with GPS and spatial road network data; low sampling frequency; low density vehicle coverage on some roads on the network; time window length; and vehicle fleet sample size. Accordingly this research is based on the development and application of a methodology which uses GPS data to reliably estimate travel time in real-time while considering the factors including vehicle fleet sample size, data sampling frequency and time window length in the estimation process. Specifically, the purpose of this thesis was to first determine the accurate location of a vehicle travelling on a road link by applying a map-matching algorithm at a range of sampling frequencies to reduce the potential errors associated with GPS and digital road maps, for example where vehicles are sometimes assigned to the wrong road links. Secondly, four different methods have been developed to estimate link travel time based on map-matched GPS positions and speed data from low frequency data sets in three time windows lengths (i.e. 5, 10 and 15 minutes). These are based on vehicle speeds, speed limits, link distances and average speeds; initially only within the given link but subsequently in the adjacent links too. More specifically, the final method draws on weighted link travel times associated with the given and adjacent links in both spatial and temporal dimensions to estimate link travel time for the given link. GPS data from Interstate I-880 (California, USA) for a total of 73 vehicles over 6 hours were obtained from the UC-Berkeley s Mobile Century Project. The original GPS dataset which was broadcast on a 3 second sampling frequency has been extracted at different sampling frequencies such as 6, 30, 60 and 120 seconds so as to evaluate the performance of each travel time estimation method at low sampling frequencies. The results were then validated against reference travel time data collected from 4,126 vehicles by high resolution video cameras, and these indicate that factors such as vehicle sample size, data sampling frequency, vehicle coverage on the links and time window length all influence the accuracy of link travel time estimation.

"In many cities around the world traffic congestion has been increasing faster than can be dealt with by new road construction. To resolve this problem traffic management devices and technology such as ramp meters are increasingly being utilized."--leaf 1. / Masters of Information Technology