Rethinking Transit Time Reliability by Integrating Automated Vehicle Location Data, Passenger Patterns, and Web Tools

2017 ◽  
Vol 18 (4) ◽  
pp. 756-766 ◽  
Author(s):  
Benedetto Barabino ◽  
Cristian Lai ◽  
Carlino Casari ◽  
Roberto Demontis ◽  
Sara Mozzoni
Keyword(s):  
Transit Time ◽  
Automated Vehicle ◽  
Location Data ◽  
Web Tools ◽  
Vehicle Location

Use of Automated Vehicle Location Data for Route- and Segment-Level Analyses of Bus Route Reliability and Speed

2017 ◽  
Vol 2649 (1) ◽  
pp. 9-19 ◽  
Author(s):  
Wen Xun Hu ◽  
Amer Shalaby
Keyword(s):  
Evaluation Framework ◽  
Transit Signal Priority ◽  
Automated Vehicle ◽  
Location Data ◽  
Vehicle Location ◽  
Beneficial Impact ◽  
Level Model ◽  
Intersection Density ◽  
Transit Reliability ◽  
Transit Routes

Reliability and speed are arguably the most important indicators of surface transit performance for both operators and passengers. They can be influenced by a variety of factors, including service characteristics of bus routes, physical infrastructure, signal settings, traffic conditions and ridership patterns. These factors have often been analyzed individually for their impact on transit reliability or speed. Studies considering more than one factor tend to use one or two transit routes to explore their effects. The study that is the subject of this paper proposed an evaluation framework to guide the selection of an appropriate reliability measure. Regression analysis was applied subsequently to determine the factors that exhibit a statistically significant relationship with transit reliability and speed at both the route and segment levels. Automated vehicle location data of a bus route sample that is representative of the entire bus network in the City of Toronto, Ontario, Canada were used. Features significantly associated with reliability and speed were compared. The results showed that lower transit reliability and speed are significantly associated with the increase in service distance, signalized intersection density, stop density, volume of boarding and alighting passengers, and traffic volume. By segregating bus route segments on the basis of the presence of transit signal priority, the results of the segment-level model demonstrated the beneficial impact of transit signal priority on improving transit reliability.

Evaluating the Performance of Coordinated Signal Timing: Comparison of Common Data Types with Automated Vehicle Location Data

2018 ◽  
Vol 2672 (18) ◽  
pp. 128-142 ◽  
Author(s):  
Stephen M. Remias ◽  
Christopher M. Day ◽  
Jonathan M. Waddell ◽  
Jenna N. Kirsch ◽  
Ted Trepanier
Keyword(s):  
Performance Metrics ◽  
Essential Element ◽  
Transportation Systems ◽  
Signal Timing ◽  
Trajectory Data ◽  
Data Types ◽  
Automated Vehicle ◽  
Signal Coordination ◽  
Location Data ◽  
Vehicle Location

Performance measures are essential for managing transportation systems, including signalized corridors. Coordination is an essential element of signal timing, enabling reliable progression of traffic along corridors. Improved progression leads to less user delay, which leads to user cost savings and lower vehicle emissions. This paper presents a comparative study of signal coordination assessment using four different technologies. These technologies include detector-based high-resolution controller data, Bluetooth/Wi-Fi sensors, segment-based probe vehicle data, and automated vehicle location data consisting of GPS-based vehicle trajectories, representing the data anticipated from emerging connected vehicle technologies. The data were compiled for a 4.2-mi corridor in Holland, Michigan. The results show that all of the data sources were able to identify, at some level, where coordination issues existed. Detector-based controller data and GPS-based vehicle trajectory data were capable of showing greater detail, and could be used to make offset adjustments. The paper concludes by demonstrating the identification of signal coordination issues with the use of visual performance metrics incorporating automated vehicle location (AVL) trajectory data.

Crowding cost estimation with large scale smart card and vehicle location data

2017 ◽  
Vol 95 ◽  
pp. 105-125 ◽  
Author(s):  
Daniel Hörcher ◽  
Daniel J. Graham ◽  
Richard J. Anderson
Keyword(s):  
Smart Card ◽  
Cost Estimation ◽  
Large Scale ◽  
Location Data ◽  
Vehicle Location

Location-Based Internationalization and Localization With Mobile Computing

2022 ◽  
pp. 39-58
Author(s):  
Arpit Kumar Sharma ◽  
Arvind Dhaka ◽  
Amita Nandal ◽  
Akshat Sinha ◽  
Deepika Choudhary
Keyword(s):  
Mobile Computing ◽  
Native Language ◽  
Android Application ◽  
Location Data ◽  
Web Tools ◽  
Dead End ◽  
Static Data ◽  
Android System ◽  
Multiple Languages

The Android system operates on many smartphones in many locales. Websites and web tools have their own requirements in day-to-day life. To reach the maximum users, the app and website should handle all the resources such as text strings, functions, layouts, graphics, and any other static data that the app/website needs. It requires internationalization and localization of the website and app to support multiple languages. The basic idea of this chapter is to present an approach for localizing the Android application according to the location data that the app received from the device, but many users do not allow the “access location” feature so this approach will be a dead end in this case. The authors have proposed some other techniques to achieve this feature of localization and internationalization by implementing the “choose language” service so that the app can itself optimize its content and translate it into the user's native language.

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