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Advancements in Global Positioning System (GPS) technology now make GPS data collection for transportation studies and other transportation applications a reality. Base map for the application can be obtained by importing the road centerline map into GIS software like AutoCAD Map, or Arc/Info or MapixTM. However, such kinds of Road Centerline maps are not available for all places. Therefore, it may be necessary to collect the data using GPS units. This thesis details the use of GPS technology to produce route maps that can be used to predict arrival time of a bus. This application is particularly useful in rural areas, since the bus headway in a rural area is generally larger than that in an urban area. The information is normally communicated through various interfaces such as internet, cable TV, etc., based on the GPS bus location data. The objective of this thesis is to develop an operational procedure to obtain the digitized route map of any desired interval or link length and to examine the accuracy of the digitized map. The operational procedure involved data collection, data processing, algorithm development and coding to produce the digitized route maps. An algorithm was developed produce the digitized route map from the base map of the route, coded in MATLAB, and can be used to digitize the base map into any desired interval of distance. The accuracy comparison is made to determine the consistency between the digitized route map and the base map. / Master of Science

Urban areas are currently facing challenges in terms of traffic congestion due to city expansion and population increase. In some cases, physical solutions are limited. For example, in certain areas it is not possible to expand roads or build a new bridge. Therefore, making public transpiration (PT) affordable, more attractive and intelligent could be a potential solution for these challenges. Accuracy in bus running time and bus arrival time is a key component of making PT attractive to ridership. In this thesis, a dynamic model based on Kalman filter (KF) has been developed to predict bus running time and dwell time while taking into account real-time road incidents. The model uses historical data collected by Automatic Vehicle Location system (AVL) and Automatic Passenger Counters (APC) system. To predict the bus travel time, the model has two components of running time prediction (long and short distance prediction) and dwell time prediction. When the bus closes its doors before leaving a bus stop, the model predicts the travel time to all downstream bus stops. This is long distance prediction. The model will then update the prediction between the bus’s current position and the upcoming bus stop based on real-time data from AVL. This is short distance prediction. Also, the model predicts the dwell time at each coming bus stop. As a result, the model reduces the difference between the predicted arrival time and the actual arrival time and provides a better understanding for the transit network which allows lead to have a good traffic management.

There has been a significant interest in the estimation of time and effort in fixing defects among both software practitioners and researchers over the past two decades. However, most of the focus has been on prediction of time and effort in resolving bugs, or other low level tasks, without much regard to predicting time needed to complete high-level requirements, a critical step in release planning. In this thesis, we describe a mixed-method empirical study on three large IBM projects in which we developed and evaluated a process of training a predictive model constituting a set of 29 features in nine categories in order to predict if whether or not a requirement will be completed within its planned iteration. We conducted feature engineering through iterative interviews with IBM software practitioners as well as analysis of large development and project management repositories of these three projects. Using machine learning techniques, we were able to make predictions on requirement completion time at four different stages of requirement lifetime. Using our industrial partner’s interest in high precision over recall, we then adopted a cost sensitive learning method and maximized precision of predictions (ranging from 0.8 to 0.97) while maintaining an acceptable recall. We also ranked the features based on their relative importance to the optimized predictive model. We show that although satisfying predictions can be made at early stages, even on the first day of requirement creation, performance of predictions improves over time by taking advantage of requirements’ progress data. Furthermore, feature importance ranking results show that although importance of features are highly dependent on project and prediction stage, there are certain features (e.g. requirement creator, time remained to the end of iteration, time since last requirement summary change and number of times requirement has been replanned for a new iteration) that emerge as important across most projects and stages, implying future worthwhile research directions for both researchers and practitioners. / Graduate

The artificial neural network (ANN) approach has been recognized as a capable technique to model the highly complex and nonlinear problem of travel time prediction. In addition to the nonlinearity, a traffic system is also temporally and spatially dynamic. Addressing the temporal-spatial relationships of a traffic system in the context of neural networks, however, has not received much attention. Furthermore, many of the past studies have not fully explored the inclusion of incident information into the ANN model development, despite that incident might be a major source of prediction degradations. Additionally, directly deriving corridor travel times in a one-step manner raises some intractable problems, such as pairing input-target data, which have not yet been adequately discussed. In this study, the corridor travel time prediction problem has been divided into two stages with the first stage on prediction of the segment travel time and the second stage on corridor travel time aggregation methodologies of the predicted segmental results. To address the dynamic nature of traffic system that are often under the influence of incidents, time delay neural network (TDNN), state-space neural network (SSNN), and an extended state-space neural network (ExtSSNN) that incorporates incident inputs are evaluated for travel time prediction along with a traditional back propagation neural network (BP) and compared with baseline methods based on historical data. In the first stage, the empirical results show that the SSNN and ExtSSNN, which are both trained with Bayesian regulated Levenberg Marquardt algorithm, outperform other models. It is also concluded that the incident information is redundant to the travel time prediction problem with speed and volume data as inputs. In the second stage, the evaluations on the applications of the SSNN model to predict snapshot travel times and experienced travel times are made. The outcomes of these evaluations are satisfactory and the method is found to be practically significant in that it (1) explicitly reconstructs the temporalspatial traffic dynamics in the model, (2) is extendable to arbitrary O-D pairs without complete retraining of the model, and (3) can be used to predict both traveler experiences and system overall conditions.

Bus Signal Priority (BSP), which has been deployed in many cities around the world, is a traffic signal enhancement strategy that facilitates efficient movement of buses through signalized intersections. Most BSP systems do not work well in transit networks with nearside bus stop because of the uncertainty in dwell time. Unfortunately, most bus stops on arterial roadways are of this type in the U.S. This dissertation showed that dwell time at nearside bus stops could be modeled using weighted least squares regression. More importantly, the prediction intervals associated with the estimate dwell time were calculated. These prediction intervals were subsequently used in the improved BSP algorithm that attempted to reduce the negative effects of nearside bus stops on BSP operations. The improved BSP algorithm was tested on urban arterial section of Bellaire Boulevard in Houston, Texas. VISSIM, a micro simulation model was used to evaluate the performance of the BSP operations. Prior to evaluating the algorithm, the parameters of the micro simulation model were calibrated using an automated Genetic Algorithm based methodology in order to make the model accurately represent the traffic conditions observed in the field. It was shown that the improved BSP algorithm significantly improved the bus operations in terms of bus delay. In addition, it was found that the delay to other vehicles on the network was not statistically different from other BSP algorithms currently being deployed. It is hypothesized that the new approach would be particularly useful in North America where there are many transit systems that utilize nearside bus stops in their networks.

Public transportation operates in an environment which, due to its nature of numerous possibly influencing factors, is highly stochastic. This makes predictions of arrival times difficult, yet it’s important to be accurate in order to adhere to travelers expectations. In this study, the focus is on quantifying uncertainty around travel-time predictions as a means to improve the reliability of predictions in the context of public transportation. This is done by comparing Prediction Interval Coverage Probability (PICP) and Normalized Mean Prediction Interval Length (NMPIL). Three models, with two transformations of the response variable, were evaluated on real travel data from Skånetrafiken. The focus of the study was on examining a specific urban bus route, namely line 5 in Malmö, Sweden. The results indicated that a transformation based on the firstDifference achieved a better performance overall, but the results on a stopwise basis varied along the route. In terms of models, the uncertainty quantification revealed that Quantile Regression could be more appropriate at capturing data intervals which provide better coverage but at a shorter interval length, thus being more precise in its predictions. This is likely relatable to the robustness of the model and it being able to deal with extreme observations. A comparison with the current prediction model, which is agnostic in this study, revealed that the proposed point estimates from the Gaussian Process model based on the  firstDifference transformation outperformed the agnostic model on several stops. As such, further research is proposed as there is means for improvement in the current implementation.

City-wide travel time prediction in real-time is an important enabler for efficient use of the road network. It can be used in traveler information to enable more efficient routing of individual vehicles as well as decision support for traffic management applications such as directed information campaigns or incident management. 3D speed maps have been shown to be a promising methodology for revealing day-to-day regularities of city-level travel times and possibly also for short-term prediction. In this paper, we aim to further evaluate and benchmark the use of 3D speed maps for short-term travel time prediction and to enable scenario-based evaluation of traffic management actions we also evaluate the framework for traffic flow prediction. The 3D speed map methodology is adapted to short-term prediction and benchmarked against historical mean as well as against Probabilistic Principal Component Analysis (PPCA). The benchmarking and analysis are made using one year of travel time and traffic flow data for the city of Stockholm, Sweden. The result of the case study shows very promising results of the 3D speed map methodology for short-term prediction of both travel times and traffic flows. The modified version of the 3D speed map prediction outperforms the historical mean prediction as well as the PPCA method. Further work includes an extended evaluation of the method for different conditions in terms of underlying sensor infrastructure, preprocessing and spatio-temporal aggregation as well as benchmarking against other prediction methods. / <p>QC 20190531</p>

It is widely agreed that estimates of freeway segment travel times are more highly valued by motorists than other forms of traveller information. The provision of real-time estimates of travel times is becoming relatively common in many of the large urban centres in the US and overseas. Presently, most traveler information systems are operating based on estimated travel time rather than predicted travel time. However, traveler information systems are most beneficial when they are built upon predicted traffic information (e.g. predicted travel time). A number of researchers have proposed different models to predict travel time. One of these techniques is based on traffic flow theory and the concept of shockwaves. Most of the past efforts at identifying shockwaves have been focused on performing shockwave analysis based on fixed sensors such as loop detectors which are commonly used in many jurisdictions. However, latest advances in wireless communications have provided an opportunity to obtain vehicle trajectory data that potentially could be used to derive traffic conditions over a wide spatial area. This research proposes a new methodology to detect and analyze shockwaves based on vehicle trajectory data and will use this information to predict travel time for freeway sections. The main idea behind this methodology is that average speed on a section of roadway is constant unless a shockwave is created due to change in flow rate or density of traffic. In the proposed methodology first the road section is discretized into a number of smaller road segments and the average speed of each segment is calculated based on the available information obtained from probe vehicles during the current time interval. If a new shockwave is detected, the average speed of the road segment is adjusted to account for the change in the traffic conditions. In order to detect shockwaves, first, a two phase piecewise linear regression is used to find the points at which a vehicle has changed its speed. Then, the points that correspond to the intersection of shockwaves and trajectories of probe vehicles are identified using a data filtering procedure and a linear clustering algorithm is employed to group different shockwaves. Finally, a linear regression model is applied to find propagation speed and spatial and temporal extent of each shockwave. The performance of this methodology was tested using one simulated signalized intersection, trajectories obtained from video processing of a section of freeway in California, and trajectories obtained from two freeway sections in Ontario. The results of this thesis show that the proposed methodology is able to detect shockwaves and predict travel time even with a small sample of vehicles. These results show that traffic data acquisition systems which are based on anonymously tracking of vehicles are a viable substitution to the tradition traffic data collection systems especially in relatively rural areas.