Conformity Analysis of GTFS Routes and Bus Trajectories

International Journal of Information and Electronics Engineering, 2015

Travel delays and the ever increasing traffic queues negatively affect the everyday lives of commuters. Users of public transit systems, such as the TTC, are left unaware of the length of the delay they might experience on their journey. One of the most important factors in an Intelligent Transportation System (ITS) is the ability to predict the bus's arrival time accurately which not only increases the current customers' satisfaction, but also attracts new customers for the system. The objectives of this paper are (1) to predict the bus travel times using GPS data, and (2) to develop a parametric optimization model to improve bus arrival times based on the speed distribution. The bus travel time optimization model proposed in this research explicitly includes arrival time, dwell times, schedule adherence, and traffic congestion, all critical factors for accurate bus arrival times. In this paper, a test bus route was used in downtown Toronto, Canada. The optimization model performed significantly better than the historical data-based models. The proposed model identified the relationship between travel times and the independent variables, leading to superior results.

Users of mass transit systems such as those of buses and trains normally rely on accurate route maps, stop locations, and service schedules when traveling. If the route map, service schedule, or stop location has errors it can reduce the transit agency’s ridership. In this paper, the problem of deriving transit systems by mining raw GPS data is studied. Specifically, we propose and evaluate novel classification features with spatial and temporal clustering techniques that derive bus stop locations, route geometries, and service schedules from GPS data. Subsequently, manual and expensive field visits to record and annotate the initial or updated route geometries, transit stop locations, or service schedules is no longer required by transit agencies. This facilitates a massive reduction in cost for transit agencies. The effectiveness of the proposed algorithms is validated on the third largest public transit system in the United States.

Journal of Intelligent Transportation Systems, 2009

The rapid progress of information technology (IT) may provide us new insights into understanding traffic phenomena, and could help mitigate traffic problems. One of the key applications of IT to traffic and transport analysis is the identification of the location of moving objects using the global positioning system (GPS) . It is expected that detailed traffic analysis could be carried out using these data. In this paper, we first summarise the various applications of probe data in transport analysis. GPS data is merely a sequence of locations, and further data transformation such as map matching, data reduction, processing, and reporting is needed to use them effectively. We then discuss the application of bus probe data to evaluating travel time variability and the level of service (LOS) of roads. A methodology for evaluating the road network from the viewpoint of travel time stability and reliability using bus probe data is proposed. Travel time distributions of arbitrary routes are estimated by statistically summing up directly observed multiple travel time distributions. Based on the development of methodologies to estimate travel time distributions of arbitrary routes covered by the bus probe survey, this study proposes an approach to evaluate the LOS of road networks based on the concept of travel time reliability.

2017

In Santiago’s public transport system, several users’ complaints are received daily. In 2016, 40% of the 28 thousand complaints received by the corresponding agency (DTPM) corresponded to buses non stopping at a bus stop even though there were passengers waiting for the bus. To avoid this undesirable behavior, a fine is issued to bus operators any time they incur in such a behavior. Nonetheless, from the legal point of view, users’ complaints are not enough to issue the fine. A fine is issued only when enforcement officers observe this behavior in situ. In 2015, a total of 1200 inspections were performed, resulting in 138 fines issued. Compared to the more than 10 thousand complaints received that year (DTPM, 2016), the total number of fines is very low.

Journal of Transportation Engineering, 2014

To improve customer satisfaction and reduce operation costs, transit authorities have been striving to monitor transit service quality and identify the key factors to enhance it. The recent advent of passive data collection technologies, e.g., automated fare collection (AFC) and automated vehicle location (AVL), has shifted a data-poor environment to a data-rich environment and offered opportunities for transit agencies to conduct comprehensive transit system performance measures. However, most AFC and AVL systems are not designed for transit performance measures, implying that additional data processing and visualization procedures are needed to improve both data usability and accessibility. This study attempts to develop a data-driven platform for online transit performance monitoring. The primary data sources come from the AFC and AVL systems in Beijing, where a passenger's boarding stop (origin) and alighting stop (destination) on a flat-rate bus are not recorded. The individual transit rider's origin and destination can be estimated by utilizing a series of data-mining techniques, which are then incorporated into a regional-map platform for transit performance measures. A multilevel framework is proposed to calculate the network-level speed, route-level travel time reliability, stop-level ridership, and headway variance. These statistics are interactively displayed on a map through a simplified transit GIS data model. This platform not only serves as a data-rich visualization platform to monitor transit network performance for planning and operations, it also intends to take advantage of e-science initiative for data-driven transportation research and applications.

2016 17th IEEE International Conference on Mobile Data Management (MDM), 2016

The main input data for a traffic management system are trajectories generated by active GPS devices installed in vehicles. In this case, such trajectories can be understood as mobile traffic sensors. A vehicle raw trajectory may be considered as a continuous data stream generated by a GPS device, installed in the vehicle, that continuously transmit its position. In some cities, such as the City of Rio de Janeiro, each bus circulating in the city has to be equipped with such device. Using this fact, the City Hall of Rio de Janeiro offers a public Web service that publishes, at about every minute, the current GPS position of all buses. However, this public data service offers only the instantaneous data, in other words, there is no historical data available. For this reason, it is necessary to develop another service that periodically queries this data and stores its entries for future processing and for useful applications, such as traffic analysis and planning. The objective of this paper is to present the BusesInRio tool that implements this functionality and provides access to bus trajectory data since June, 2014. The current database has approximately 2 billion GPS samples.

Sensors

Quality is an essential aspect of public transport. In the case of regular public passenger transport by road, punctuality and regularity are criteria used to assess quality of service. Calculating metrics related to these criteria continuously over time and comprehensively across the entire transport network requires the handling of large amounts of data. This article describes a system for continuously and comprehensively monitoring punctuality and regularity. The system uses location data acquired continuously in the vehicles and automatically transferred for analysis. These data are processed intelligently by elements that are commonly used by transport operators: GPS-based tracking system, onboard computer and wireless networks for mobile data communications. The system was tested on a transport company, for which we measured the punctuality of one of the routes that it operates; the results are presented in this article.

Intelligent Computer Communication and …, 2010

Bus schedules submitted by public transportation companies often lack accuracy. Generally this relates to traffic being inconsistent over most parts of the road network. We set on finding a model for predicting these speed variations over GPS monitored bus routes, by using association rules, and then apply it on the local bus network of Cluj-Napoca, Romania.

Urban Transport XVII, 2011

A more efficient public transit system means better accessibility and higher opportunities for better economy. To enhance the operation or to provide a better transit service, it is essential to draw a clear picture of the baseline conditions to identify the pros and cons of the existing service. Developing a quantitative spatial database of the service characteristics is deemed necessary for detailed analyses of existing services. Spatial database may help in understanding the geographical extent of the services and identifying the areas that may need special considerations to enhance the services. In this paper, a logical approach is presented to geo-code the spatial data of the public bus services; namely, the stoppage and routes maps (layers). The used geo-data data to develop the bus stops and routes' layers have significant noises. The adopted methodology to filter such noise in data and to develop the spatial database is presented in details. Analyses and conclusions were presented using the presented GIS spatial data techniques.

IETE Journal of Research, 2017

Due to urbanization and increase in traffic density, provision of efficient public transport facility is a major component of planning and development. The spatio-temporal data of vehicles' movement have mainly led to mobility pattern analysis, which eventually reveal new information about the efficiency of transport service. This research focuses on extracting mobility patterns of bus service for students in Lahore City. Trajectory data collected through GPS survey have been processed by using MYSQL spatial database applications and HTML to identify areas in entire route of bus service causing delay. Three different types of delay have been calculated, namely stop delay, simple path delay, and path repeated delay. The entire analysis results reveal that mainly the delay is caused at start segments of planned route or the last segment of stop route. The major cause of delay is bus movement on tertiary roads through congested commercial areas.