Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) systems can be powerful tools for transit agencies to archive large, detailed quantities of transit operations data. Managing data quality is an important first step for exploiting these rich datasets. This thesis presents an automated quality assurance (QA) methodology that identifies unreliable archived AVL/APC data. The approach is based on expected travel and passenger activity patterns derived from the data. It is assumed that standard passenger balancing and schedule matching algorithms are applied to the raw AVL/APC data along with any existing automatic validation programs. The proposed QA methodology is intended to provide transit agencies with a supplementary tool to manage data quality that complements, but does not replace, conventional processing routines (that can be vendor-specific and less transparent). The proposed QA methodology endeavours to flag invalid data as “suspect” and valid data as “non-suspect”. There are three stages: i) the first stage screens data that demonstrate a violation of physical constraints; ii) the second stage looks for data that represent outliers; and iii) the third stage evaluates whether the outlier data can be accounted for with valid or invalid pattern. Stop-level tests are mathematically defined for each stage; however data is filtered at the trip-level. Data that do not violate any physical constraints and do not represent any outliers are considered valid trip data. Outlier trips that may be accounted for with a valid outlier pattern are also considered valid. The remaining trip data is considered suspect. The methodology is applied to a sample set of AVL/APC data from Grand River Transit in the Region of Waterloo, Ontario, Canada. The sample data consist of 4-month’s data from September to December of 2008; it is comprised of 612,000 stop-level records representing 25,012 trips. The results show 14% of the trip-level data is flagged as suspect for the sample dataset. The output is further dissected by: reviewing which tests most contribute to the set of suspect trips; confirming the pattern assumptions for the valid outlier cases; and comparing the sample data by various traits before and after the QA methodology is applied. The latter task is meant to recognize characteristics that may contribute to higher or lower quality data. Analysis shows that the largest portion of suspect trips, for this sample set, suggests the need for improved passenger balancing algorithms or greater accuracy of the APC equipment. The assumptions for valid outlier case patterns were confirmed to be reasonable. It was found that poor schedule data contributes to poorer quality in AVL-APC data. An examination of data distribution by vehicle showed that usage and the portion of suspect data varied substantially between vehicles. This information can be useful in the development of maintenance plans and sampling plans (when combined with information of data distribution by route). A sensitivity analysis was conducted along with an impact analysis on downstream data uses. The model was found to be sensitive to three of the ten user-defined parameters. The impact of the QA procedure on network-level measures of performance (MOPs) was not found to be significant, however the impact was shown to be more substantial for route-specific MOPs.

Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) systems can be powerful tools for transit agencies to archive large, detailed quantities of transit operations data. Managing data quality is an important first step for exploiting these rich datasets. This thesis presents an automated quality assurance (QA) methodology that identifies unreliable archived AVL/APC data. The approach is based on expected travel and passenger activity patterns derived from the data. It is assumed that standard passenger balancing and schedule matching algorithms are applied to the raw AVL/APC data along with any existing automatic validation programs. The proposed QA methodology is intended to provide transit agencies with a supplementary tool to manage data quality that complements, but does not replace, conventional processing routines (that can be vendor-specific and less transparent). The proposed QA methodology endeavours to flag invalid data as “suspect” and valid data as “non-suspect”. There are three stages: i) the first stage screens data that demonstrate a violation of physical constraints; ii) the second stage looks for data that represent outliers; and iii) the third stage evaluates whether the outlier data can be accounted for with valid or invalid pattern. Stop-level tests are mathematically defined for each stage; however data is filtered at the trip-level. Data that do not violate any physical constraints and do not represent any outliers are considered valid trip data. Outlier trips that may be accounted for with a valid outlier pattern are also considered valid. The remaining trip data is considered suspect. The methodology is applied to a sample set of AVL/APC data from Grand River Transit in the Region of Waterloo, Ontario, Canada. The sample data consist of 4-month’s data from September to December of 2008; it is comprised of 612,000 stop-level records representing 25,012 trips. The results show 14% of the trip-level data is flagged as suspect for the sample dataset. The output is further dissected by: reviewing which tests most contribute to the set of suspect trips; confirming the pattern assumptions for the valid outlier cases; and comparing the sample data by various traits before and after the QA methodology is applied. The latter task is meant to recognize characteristics that may contribute to higher or lower quality data. Analysis shows that the largest portion of suspect trips, for this sample set, suggests the need for improved passenger balancing algorithms or greater accuracy of the APC equipment. The assumptions for valid outlier case patterns were confirmed to be reasonable. It was found that poor schedule data contributes to poorer quality in AVL-APC data. An examination of data distribution by vehicle showed that usage and the portion of suspect data varied substantially between vehicles. This information can be useful in the development of maintenance plans and sampling plans (when combined with information of data distribution by route). A sensitivity analysis was conducted along with an impact analysis on downstream data uses. The model was found to be sensitive to three of the ten user-defined parameters. The impact of the QA procedure on network-level measures of performance (MOPs) was not found to be significant, however the impact was shown to be more substantial for route-specific MOPs.

Information on transit ridership and operations is a necessary condition as far as efficient management is considered. Transit managements on the acquisition of such a data base can confirm predictions about scheduling, receive warnings about potential dangers and plan future operations on a much broader and precise base. Data from passenger counts provide essential information to marketing and scheduling personnel by identifying peak load-points and the such. Using manual collection methods for such data is expensive and prone to human errors. Automatic Passenger Counting (APC) systems are viewed as an improved and economical technique for data collection. Such systems monitor the progress of a particular vehicle — its position, number of passengers getting on and off, times and distances between stops — and make this data available for processing. These are state of the art systems, mostly microprocessor based and often embracing a modular structure. The Red Pine system is such a system with different dedicated modules for each bank of tasks. Multitasking software is seen to be an powerful tool for such systems and simplify the architecture of the system hardware. A CHMOS hardware design, suited for multitasking softwares is provided. Interfacing software for the Red Pine system has been developed and is explained. Debugging testing and simulation of the Red Pine hardware is detailed. Modifications have been recorded and improvements suggested. / Master of Science

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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.