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

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.

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.