Transport poverty meets the digital divide : accessibility and connectivity in rural communities

Peer reviewedPublisher PD

Transit Demand Estimation And Crowding Prediction Based On Real-Time Transit Data

With an increasing number of intelligent analytic techniques and increasing networking capabilities, municipal transit authorities can leverage real-time data to estimate transit volume and predict crowding conditions. We introduce a proactive Transit Demand Estimation and Prediction System (TraDEPS) – an approach that has the potential to prevent crowding and improve transit service, by measuring the transit activity (the number of passengers on the individual modes of public transportation and the demand on a route), and estimating crowding levels at a given time. This system utilizes a combination of real-time data streams from multiple sources, a predictive model and data analytics for transit management. The problem of transit crowding is translated into transit activity prediction, as the latter is a straightforward indicator of the former. This thesis delivers the following contributions: (1) A crowding prediction model. (2) A system supporting the methodology. (3) A feature which displays different crowding level conditions of a route on a web map

A systematic approach for improving predicted arrival time using historical data in absence of schedule reliability

Public transit operations are susceptible to change, both in traffic flow and other conditions that could affect operations such as bridge openings, road floods, and torrential downpours. Traditionally, riders at waiting stops are not informed of the transit vehicles’ status along the route. Although it is normally not needed for daily transit operations, live location information is particularly useful in cases when vehicles are running behind schedule. This thesis introduces a method for gathering and analyzing historical location and telemetry data of public transit vehicles to better determine estimated arrival time for a vehicle on a closed-loop public transit pattern. The research creates a system for sending real-time locations of transit vehicles to riders through a wide array of mediums including web pages, computer programs, graphical information displays in public locations, mobile phone applications, mobile text messaging, and internet feeds. The system incorporates a weighted estimated arrival time for one route in the city, the University of North Carolina Wilmington campus loop shuttle route, which serves as a working demonstration of these concepts. The approach shows improvement over an arrival time estimate using only average speed

LocateMyBus: IoT-Driven Smart Bus Transit

Uncertainty of traffic in cities makes it difficult for metropolitan buses to adhere to predetermined schedules, making it strenuous for commuters to plan travel reliably. The proposed LocateMyBus system leverages Internet of Things(IoT) set-ups at bus stops and buses, and Machine Learning(ML) to assuage this uncertainty by allowing commuters to track live-runningstatus of buses, disseminate tentative and live-status to commuters through Public Announcement(PA) systems at bus-stops and a web-application interface. The schedule prediction module provides a tentative schedule of buses with stop-wise arrival times estimated using ML based on historic and real-time route data. Arrival times of two bus-routes in the Massachusetts Bay Area were collected for a period of four months by periodically querying its real-time General Transit Feed Systems(GTFS). This dataset was used to train and validate the proposed ML methods. The IoT system was modeled on Proteus, and validated with a miniature prototype. LocateMyBus is proposed as a step forward toward minimal intervention algorithmic set-ups to ease the uncertainty associated with bus commute in cities. It enables commuters to track live running status and avail ML-predicted tentative schedules. Furthermore, it eradicates the computation requirements of GPS-based systems, whilst ensuring stop-level tracking granularity. LocateMyBus\u27s ability to log bus arrival times at each stop paves the way to building real-time GTFSs

Exploring Data Driven Models of Transit Travel Time and Delay

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

A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities

Transportation plays a key role in today’s economy. Hence, intelligent transportation systems have attracted a great deal of attention among research communities. There are a few review papers in this area. Most of them focus only on travel time prediction. Furthermore, these papers do not include recent research. To address these shortcomings, this study aims to examine the research on the arrival and travel time prediction on road-based on recently published articles. More specifically, this paper aims to (i) offer an extensive literature review of the field, provide a complete taxonomy of the existing methods, identify key challenges and limitations associated with the techniques; (ii) present various evaluation metrics, influence factors, exploited dataset as well as describe essential concepts based on a detailed analysis of the recent literature sources; (iii) provide significant information to researchers and transportation applications developer. As a result of a rigorous selection process and a comprehensive analysis, the findings provide a holistic picture of open issues and several important observations that can be considered as feasible opportunities for future research directions

Multi-headed self-attention mechanism-based Transformer model for predicting bus travel times across multiple bus routes using heterogeneous datasets

Bus transit is a crucial component of transportation networks, especially in urban areas. Bus agencies must enhance the quality of their real-time bus travel information service to serve their passengers better and attract more travelers. Various models have recently been developed for estimating bus travel times to increase the quality of real-time information service. However, most are concentrated on smaller road networks due to their generally subpar performance in densely populated urban regions on a vast network and failure to produce good results with long-range dependencies. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database and the vehicle probe data. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. This study developed a multi-headed self-attention mechanism-based Univariate Transformer neural network to predict the mean vehicle travel times for different hours of the day for multiple stations across multiple routes. In addition, we developed Multivariate GRU and LSTM neural network models for our research to compare the prediction accuracy and comprehend the robustness of the Transformer model. To validate the Transformer Model's performance more in comparison to the GRU and LSTM models, we employed the Historical Average Model and XGBoost model as benchmark models. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. Only the historical average bus travel time was used as the input parameter for the Transformer model. Other features, including spatial and temporal information, volatility measures (e.g., the standard deviation and variance of travel time), dwell time, expected travel time, jam factors, hours of a day, etc., were captured from our dataset. These parameters were employed to develop the Multivariate GRU and LSTM models. The model's performance was evaluated based on a performance metric called Mean Absolute Percentage Error (MAPE). The results showed that the Transformer model outperformed other models for one-hour ahead prediction having minimum and mean MAPE values of 4.32 percent and 8.29 percent, respectively. We also investigated that the Transformer model performed the best during different traffic conditions (e.g., peak and off-peak hours). Furthermore, we also displayed the model computation time for the prediction; XGBoost was found to be the quickest, with a prediction time of 6.28 seconds, while the Transformer model had a prediction time of 7.42 seconds. The study's findings demonstrate that the Transformer model showed its applicability for real-time travel time prediction and guaranteed the high quality of the predictions produced by the model in the context of a complicated extensive transportation network in high-density urban areas and capturing long-range dependencies.Includes bibliographical references

Tracking Vehicles using the geolocation capabilities of the Celluar Phone: Is is feasible?

In 1996, the U.S. Federal Communications Commission (FCC) made it mandatory for all wireless communications services, such as mobile phones, to be equipped with Automatic Location Identification (ALI) capability. This required that all public safety answering point (PSAP) attendants who answer a 911 call from a cellular phone be able to locate the caller to a specified degree of accuracy. This requirement was the impetus that led to momentous technological activity to provide means to geo-locate wireless phone calls. The interest amongst transportation professionals in using this technology for fleet management applications was supervenient. This thesis investigates the feasibility of tracking vehicles, for example school buses, using the cellular phone geo-location technology. Specifically, the accuracy (or errors) of the RadioCamera technology of TrafficMaster (formerly US Wireless Corporation) will be evaluated and a conclusion on its suitability for vehicular tracking made