Bluetooth technology applications have improved travel time data collection efforts and allowed for collection of large data sets at a low cost per data unit. Mean travel times between pairs of points are available, but the primary value of this technique is the availability of the entire distribution of travel times throughout multiple days and time periods, allowing for a greater understanding of travel time variations and reliability. The use of these data for transportation planning, engineering and operations continues to expand. Previous applications of similar data sources have included travel demand and simulation model validation, work zone traffic patterns, transit ridership and reliability, pedestrian movement patterns, and before-after studies of transportation improvements. This thesis investigates the collection and analysis of Bluetooth-enabled travel time data along a multimodal arterial corridor in San Luis Obispo, California. Five BlueMAC devices collected multimodal travel time data in January and February 2016 along Los Osos Valley Road. These datasets were used to identify and process known sources of error such as occasions where vehicles using the roadway turn off and make an intermediate stop and multiple reads from the same vehicle; quantify travel time performance and reliability along arterial streets; and compare transit, bicycle, and pedestrian facility performance. Additionally, a travel time model was estimated based on segment characteristics and Bluetooth data to estimate average speeds and travel time distributions.

Travel time estimation can be used in strategical distribution of ambulances and ambulance stations. A more accurate travel time estimation can lead to a better distribution of these ambulance sites. External factors such as weather and traffic conditions can affect the travel time from a starting location to a destination. In this work, we investigate how the SOS Alarm dataset of ambulance trips data and the machine learning model Gradient Boosted Decision Trees can be used to estimate travel time, and how these estimationscan be improved by incorporating aforementioned conditions when predicting travel time. Results showed that reasonable performance can be achieved for a subset of data where the precise origin and destination is known compared to a subset where the precise origin is unknown, and that traffic conditions could improve model performance on a subset of data containing trips only for a single route. Including weather represented as individual weather parameters did not, however, lead to enhanced performance.

Today the regional railway system lacks the capacity at the central station in order to meet the demand for both the public and freight transportation. The West link is a railway infrastructure project that is expected to both increase train commuting and also increase the train capacity at the central station in Gothenburg. The purpose of this thesis work is to do an elastic demand analysis between car traffic and the West Link in modal shift to determine how many travelers will change mode. Also traffic simulations will be made to investigate the traffic conditions around the stations when the West link has been implemeted. The simulation results showed that nowadays there are congestions on the major highways and some of the smaller low capacity roads. In the future, congestions will become more significant if no countermeasures are implemented like the West link. In the elastic demand analysis the results were overall around 47% – 51% on a demand of between 350 000 – 400 000 trips. As a conclusion it cannot be said whether the West link will ease the pressure on car traffic or not. The reasons are that the population will increase about 100 000 inhabitants until 2030 and the West link is assumed by the region to have this amount of travelers each day. This would result in the same traffic pressure as today with congestions in certain parts. If the number of West link users will be around 200 000, as the results of this thesis shows, then the West link will ease the pressure on car traffic.

Since the Swedish health care system is mostly organized at a regional level and the population density across the Swedish regions are variating, the supply of health care differs among the regions. Population density and the centralization of care is also affecting the distribution of health care facilities since most opportunities for care will be found where population density is high, such as larger cities. In northern Sweden where the larger parts of the area are sparsely populated and the main cities are mostly located along the coast, accessibility to health care for the rural population and their urban counterparts are therefore very different. The level of accessibility to health care can be more crucial in some situations than in other, one of these is childbirth. Since the labour process can develop quickly, the need to get the right care at the right time can be vital for both mother and child.  In the past years there have been changes to the maternity hospital distribution in northern Sweden. This is due to the closure of Sollefteå maternity hospital and the temporal closing of Lycksele maternity hospital. The aim of this thesis is to analyse the change in accessibility between 2013 and 2019 as a result of these shutdowns. This is done using service area analysis through the ArcGIS network analyst extension. The result show that the closures have had large impact on the spatial accessibility, increasing travel times to maternity hospitals for the inland population. Those who are mostly affected are people who already had poor accessibility, these are also places who have a small population. The result also shows that around 90 % of the women could reach their closest maternity hospital within one hour in 2013 compared to around 80 % in 2019. Around 40 % of the women are at higher risk of childbirth complications since they exceed 20 minutes travel time, this was the same value in 2013 as for 2019. As conclusion, closures have decreased accessibility for women in the inland of northern Sweden but the largest part of the population remain having good accessibility to their closest maternity hospital.

This paper develops and applies a practical method to estimate the benefits of improved reliability of road networks. We present a general methodology to estimate the scheduling costs due to travel time variability for car travel. In contrast to existing practical methods, we explicitly consider the effect of travel time variability on departure time choices. We focus on situations when only mean delays are known, which is typically the case when standard transport models are used. We first show how travel time variability can be predicted from mean delays. We then estimate the scheduling costs of travellers, taking into account their optimal departure time choice given the estimated travel time variability. We illustrate the methodology for air passengers traveling by car to Amsterdam Schiphol Airport. We find that on average planned improvements in network reliability only lead to a small reduction in access costs per trip in absolute terms, mainly because most air passengers drive to the airport outside peak hours, when travel time variability tends to be low. However, in relative terms the reduction in access costs due to the improvements in network reliability is substantial. In our case we find that for every 1 Euro reduction in travel time costs, there is an additional cost reduction of 0.7 Euro due to lower travel time variability, and hence lower scheduling costs. Ignoring the benefits from improved reliability may therefore lead to a severe underestimation of the total benefits of infrastructure improvements.

The increasing volumes of road transportation contribute to congestion on road, which leads to delays and other negative impacts on the reliability of transportation. Moreover, transportation is one of the main contributors to the growth of carbon dioxide equivalent emissions, where the impact of road transportation is significant. Therefore, governmental organizations and private commercial companies are looking for greener transportation solutions to eliminate the negative externalities of road transportation. In this paper, we present a novel solution framework to support the operational-level decisions for intermodal transportation networks using a combination of an optimization model and simulation. The simulation model includes stochastic elements in form of uncertain travel times, whereas the optimization model represents a deterministic and linear multi-commodity service network design formulation. The intermodal transportation plan can be optimized according to different objectives, including costs, time and CO2e emissions. The proposed approach is successfully implemented to real-life scenarios where differences in transportation plans for alternative objectives are presented. The solutions for transportation networks with up to 250 services and 20 orders show that the approach is capable of delivering reliable solutions and identifying possible disruptions and alternatives for adapting the unreliable transportation plans.

In recent years, traffic congestion in transportation networks has grown rapidly and has become an acute problem. The impetus for studying this problem has been further strengthened due to the fast growing field of Intelligent Vehicle Highway Systems (IVHS). Therefore, it is critical to investigate and understand its nature and address questions of the type: how are traffic patterns formed? and how can traffic congestion be alleviated? Understanding drivers' travel times is key behind this problem. In this paper, we present macroscopic models for determining analytical forms for travel times. We take a fluid dynamics approach by noticing that traffic macroscopically behaves like a fluid. Our contributions in this work are the following: (i) We propose two second-order non-separable macroscopic models for analytically estimating travel time functions: the Polynomial Travel Time (PTT) Model and the Exponential Travel Time (ETT) Model. These models generalize the models proposed by Kachani and Perakis as they incorporate second-order effects such as reaction of drivers to upstream and downstream congestion as well as second-order link interaction effects. (ii) Based on piecewise linear and piecewise quadratic approximations of the departure flow rates, we propose different classes of travel time functions for the first-order separable PTT and ETT models, and present the relationship between these functions. (iii) We show how the analysis of the first-order separable PTT Model extends to the second-order model with non-separable velocity functions for acyclic networks. (iv) Finally, we analyze the second-order separable ETT model where the queue propagation term - corresponding to the reaction of drivers to upstream congestion or decongestion - is not neglected. We are able to reduce the analysis to a Burgers equation and then to the more tractable heat equation. / Singapore-MIT Alliance (SMA)

The calculation of path travel times is an essential component for the dynamic traffic assignment and equilibrium problems. This paper presents a simulation method for calculating actual path travel times for the traffic network with dynamic demands. The method is based on a path-based macroscopic simulation model of network traffic dynamics. There is no need to explicitly model intersection delays in this method. Discontinuity in the travel time caused by traffic light control can be captured by this method. It's flexible in terms that the model is not limited to a specific velocity-density relationship. Some numerical results for signalized and unsignalized networks are reported. / Singapore-MIT Alliance (SMA)

Thesis (Master)--Concordia University (Canada), 2000. / Advisers: Maria Elektorowicz, John Zacharias. Includes bibliographical references.

Accurate travel time data are necessary to monitor and evaluate traffic conditions effectively. In the past 20 years, the hours per year lost by the average driver have increased by 300% in the 85 largest U.S. cities, which translates into lost productivity and increased costs. State department of transportation (DOT) agencies and other government organizations need accurate travel time and speed information to better combat this congestion faced by motorists. In the past, ground truth travel time information was typically collected with probe vehicles using the “floating car” method. However, new methods using data collected from global positioning systems by private companies such as INRIX®, Navteq®, and TomTom® have emerged that allow travel time data to be obtained more cheaply and quickly. The Urban Mobility Report (UMR) has turned to these companies, specifically INRIX®, for calculating congestion indices across the United States. This is done by analyzing average speeds and reference speeds supplied by INRIX. The UMR analysis relies on INRIX-supplied reference speeds to calculate delay, which produces artificially high delay on many suburban arterials. Currently, these reference speeds are determined by taking the 85th percentile of weekly speeds (typically overnight hours [10PM to 6AM]). There is a need to refine the reference speeds on arterials in order to account for signal operations, particularly during the daytime hours, so that the UMR more accurately reflects arterial congestion across the nation. Using Bluetooth and INRIX speed data, this thesis develops a new reference speed methodology that accurately reflects arterial delay during daytime hours. This study found that a 60% daytime free-flow reference speed best represents arterial congestion. Using Highway Capacity Manual (HCM) guidelines, this thesis also explores the use of Bluetooth data for arterial and intersection level of service (LOS) analysis under both HCM 2000 and HCM 2010 methodologies. Through analysis, it was found that Bluetooth data capture more of the high and low LOS values compared to the HCM methodology based on segment speed calculations. These high and low LOS values, as well as the rapidly changing LOS between 15-minute intervals, could be attributed to an insufficient sample size.