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Traditional methods for constructing simulation models can involve severalsteps that require manual pre-processing of large data sets. This process maybe time-consuming and prone to human errors, while also leading to modelsthat are inconvenient to customize for varying simulation scenarios. In thisthesis, we propose an alternate data preparation methodology in emergencyvehicle transport simulation, which aims to eliminate parts of the manualpre-processing. Our research is based on a previous case study using datafrom Sweden’s Southern Healthcare Region. The methodology we propose isinstantiated through a proof-of-concept software module that replacespreviously used static input sets by introducing dynamic runtime calculationsof ambulance travel times. This was done in two steps where we first evaluatedseveral routing engines according to needs extracted from the studied case.Secondly, we implemented and integrated the chosen routing engine into thepreviously mentioned module. Testing of the module showed feasible andconsistent performance, demonstrating the potential usage of our proposedmethodology in emergency vehicle transport simulation.

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During the 20th century, transportation programs were focused on the development of the basic infrastructure for the transportation networks. In the 21st century, the focus has shifted to management and operations of these networks. Transportation network reliability measure plays an important role in judging the performance of the transportation system and in evaluating the impact of new Intelligent Transportation Systems (ITS) deployment. The measurement of transportation network travel time reliability is imperative for providing travelers with accurate route guidance information. It can be applied to generate the shortest path (or alternative paths) connecting the origins and destinations especially under conditions of varying demands and limited capacities. The measurement of transportation network reliability is a complex issue because it involves both the infrastructure and the behavioral responses of the users. Also, this subject is challenging because there is no single agreed-upon reliability measure. This dissertation developed a new method for estimating the effect of travel demand variation and link capacity degradation on the reliability of a roadway network. The method is applied to a hypothetical roadway network and the results show that both travel time reliability and capacity reliability are consistent measures for reliability of the road network, but each may have a different use. The capacity reliability measure is of special interest to transportation network planners and engineers because it addresses the issue of whether the available network capacity relative to the present or forecast demand is sufficient, whereas travel time reliability is especially interesting for network users. The new travel time reliability method is sensitive to the users' perspective since it reflects that an increase in segment travel time should always result in less travel time reliability. And, it is an indicator of the operational consistency of a facility over an extended period of time. This initial theoretical effort and basic research was followed by applying the new method to the I-4 corridor in Orlando, Florida. This dissertation utilized a real life transportation data warehouse to estimate travel time reliability of the I-4 corridor. Four different travel time stochastic models: Weibull, Exponential, Lognormal, and Normal were tested. Lognormal was the best-fit model. Unlike the mechanical equipments, it is unrealistic that any freeway segment can be traversed in zero seconds no matter how fast the vehicles are. So, an adjustment of the developed best-fit statistical model (Lognormal) location parameter was needed to accurately estimate the travel time reliability. The adjusted model can be used to compute and predict travel time reliability of freeway corridors and report this information in real time to the public through traffic management centers. Compared to existing Florida Method and California Buffer Time Method, the new reliability method showed higher sensitivity to geographical locations, which reflects the level of congestion and bottlenecks. The major advantages/benefits of this new method to practitioners and researchers over the existing methods are its ability to estimate travel time reliability as a function of departure time, and that it treats travel time as a continuous variable that captures the variability experienced by individual travelers over an extended period of time. As such, the new method developed in this dissertation could be utilized in transportation planning and freeway operations for estimating the important travel time reliability measure of performance. Then, the segment length impacts on travel time reliability calculations were investigated utilizing the wealth of data available in the I-4 data warehouse. The developed travel time reliability models showed significant evidence of the relationship between the segment length and the results accuracy. The longer the segment, the less accurate were the travel time reliability estimates. Accordingly, long segments (e.g., 25 miles) are more appropriate for planning purposes as a macroscopic performance measure of the freeway corridor. Short segments (e.g., 5 miles) are more appropriate for the evaluation of freeway operations as a microscopic performance measure. Further, this dissertation has explored the impact of relaxing an important assumption in reliability analysis: Link independency. In real life, assuming that link failures on a road network are statistically independent is dubious. The failure of a link in one particular area does not necessarily result in the complete failure of the neighboring link, but may lead to deterioration of its performance. The "Cause-Based Multimode Model" (CBMM) has been used to address link dependency in communication networks. However, the transferability of this model to transportation networks has not been tested and this approach has not been considered before in the calculation of transportation networks' reliability. This dissertation presented the CBMM and applied it to predict transportation networks' travel time reliability that an origin demand can reach a specified destination under multimodal dependency link failure conditions. The new model studied the multi-state system reliability analysis of transportation networks for which one cannot formulate an "all or nothing" type of failure criterion and in which dependent link failures are considered. The results demonstrated that the newly developed method has true potential and can be easily extended to large-scale networks as long as the data is available. More specifically, the analysis of a hypothetical network showed that the dependency assumption is very important to obtain more reasonable travel time reliability estimates of links, paths, and the entire network. The results showed large discrepancy between the dependency and independency analysis scenarios. Realistic scenarios that considered the dependency assumption were on the safe side, this is important for transportation network decision makers. Also, this could aid travelers in making better choices. In contrast, deceptive information caused by the independency assumption could add to the travelers' anxiety associated with the unknown length of delay. This normally reflects negatively on highway agencies and management of taxpayers' resources.

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.

Transit travel time and operating speed influence service attractiveness, operating cost, system efficiency and sustainability. The Tri-County Metropolitan Transportation District of Oregon (TriMet) provides public transportation service in the tri-county Portland metropolitan area. TriMet was one of the first transit agencies to implement a Bus Dispatch System (BDS) as a part of its overall service control and management system. TriMet has had the foresight to fully archive the BDS automatic vehicle location and automatic passenger count data for all bus trips at the stop level since 1997. More recently, the BDS system was upgraded to provide stop-level data plus 5-second resolution bus positions between stops. Rather than relying on prediction tools to determine bus trajectories (including stops and delays) between stops, the higher resolution data presents actual bus positions along each trip. Bus travel speeds and intersection signal/queuing delays may be determined using this newer information. This thesis examines the potential applications of higher resolution transit operations data for a bus route in Portland, Oregon, TriMet Route 14. BDS and 5-second resolution data from all trips during the month of October 2014 are used to determine the impacts and evaluate candidate trip time models. Comparisons are drawn between models and some conclusions are drawn regarding the utility of the higher resolution transit data. In previous research inter-stop models were developed based on the use of average or maximum speed between stops. We know that this does not represent realistic conditions of stopping at a signal/crosswalk or traffic congestion along the link. A new inter-stop trip time model is developed using the 5-second resolution data to determine the number of signals encountered by the bus along the route. The variability in inter-stop time is likely due to the effect of the delay superimposed by signals encountered. This newly developed model resulted in statistically significant results. This type of information is important to transit agencies looking to improve bus running times and reliability. These results, the benefits of archiving higher resolution data to understand bus movement between stops, and future research opportunities are also discussed.

Wiener Linien’s tram network is one of the largest and busiest of its kind worldwide. Maintaining and improving the Level of Service (LoS) is one of the major tasks of the operations division. To direct these improvements efficiently to lines and sections of lines in need, tram line performance needs to be assessed. In this master thesis a Python-based model is developed to assess tram line performance using ideal operational constraints. Furthermore, the model is capable of computing the energy consumption for this optimal case. The tool computes Undisturbed Optimal Travel Times (UOTTs) which serve as a benchmark for tram line performance. Therefor it builds on track alignment data (curves and radii, gradients and switches), speed restriction data and a set of optimal parameters (no traffic, constant acceleration, etc.). Furthermore, train resistance, curve speeds and vehicle type are taken into consideration. These parameters are investigated and selected based on literature studies, interviews with employees of Wiener Linien, as well as field tests. Finally, the model results are compared to real world data for evaluation. A discussion of the results regarding further applications and improvements is performed. / Spårvagnsnätet i Wiener Linien är ett av de största och mest trafikerade i sitt slag i världen. Att underhålla och förbättra servicenivån är en av de viktigaste uppgifterna för driftsavdelningen. För att dessa förbättringar ska kunna riktas effektivt till de linjer som mest behöver det måste spårvagnarnas prestanda bedömas. I detta exjobb utvecklas en Python-baserad modell för att bedöma spårvagnslinjens prestanda med hjälp av ideala trafikparametrar. Dessutom kan modellen beräkna energiförbrukningen för dessa optimala scenarier.Verktyget beräknar Ostörda Optimala Restider (UOTTs) som blir referenser för spårvagnslinjernas prestanda. Metoden bygger på spårutformning (kurvor och radier, lutningar och växlar), hastighetsbegränsningar, och en uppsättning optimala parametrar (ingen vägtrafik, konstant acceleration osv.). Dessutom tas hänsyn till rullmotstånd, hastighet i kurvor och fordonstyp. Dessa parametrar undersöks och väljs ut genom litteraturstudier, intervjuer med anställda vid Wiener Linien samt fälttester.Modellresultaten jämförs med uppmätta restidsdata för utvärdering av den dagliga driften. En diskussion av ytterligare tillämpningar och förbättringar förs.

Congestion is a world-wide problem in transportation. One major reason is random interruptions. The traffic network is inherently stochastic, and strong dependencies exist among traffic quantities, e.g., travel time, traffic speed, link volume. Information in stochastic networks can help with adaptive routing in terms of minimizing expected travel time or disutility. Routing in such networks is different from that in deterministic networks or when stochastic dependencies are not taken into account. This dissertation addresses the optimal routing problems, including the optimal a priori path problem and the optimal adaptive routing problem with different information scenarios, in stochastic and time-dependent networks with explicit consideration of the correlations between link travel time random variables. There are a number of studies in the literature addressing the optimal routing problems, but most of them ignore the correlations between link travel times. The consideration of the correlations makes the problem studied in this dissertation difficult, both conceptually and computationally. The optimal path finding problem in such networks is different from that in stochastic and time-dependent networks with no consideration of the correlations. This dissertation firstly provides an empirical study of the correlations between random link travel times and also verifies the importance of the consideration of the spatial and temporal correlations in estimating trip travel time and its reliability. It then shows that Bellman's principle of optimality or non-dominance is not valid due to the time-dependency and the correlations. A new property termed purity is introduced and an exact label-correcting algorithm is designed to solve the problem. With the fast advance of telecommunication technologies, real-time traffic information will soon become an integral part of travelers' route choice decision making. The study of optimal adaptive routing problems is thus timely and of great value. This dissertation studies the problems with a wide variety of information scenarios, including delayed global information, real-time local information, pre-trip global information, no online information, and trajectory information. It is shown that, for the first four partial information scenarios, Bellman's principle of optimality does not hold. A heuristic algorithm is developed and employed based on a set of necessary conditions for optimality. The same algorithm is showed to be exact for the perfect online information scenario. For optimal adaptive routing problem with trajectory information, this dissertation proves that, if the routing policy is defined in a similar way to other four information scenarios, i.e., the trajectory information is included in the state variable, Bellman's principle of optimality is valid. However, this definition results in a prohibitively large number of the states and the computation can hardly be carried out. The dissertation provides a recursive definition for the trajectory-adaptive routing policy, for which the information is not included in the state variable. In this way, the number of states is small, but Bellman's principle of optimality or non-dominance is invalid for a similar reason as in the optimal path problem. Again purity is introduced to the trajectory-adaptive routing policy and an exact algorithm is designed based on the concept of decreasing order of time.

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.

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