Potential of Low-Frequency Automated Vehicle Location Data for Monitoring and Control of Bus Performance

2013 ◽  
Vol 2351 (1) ◽  
pp. 54-64 ◽  
Author(s):  
Yingxiang Yang ◽  
David Gerstle ◽  
Peter Widhalm ◽  
Dietmar Bauer ◽  
Marta Gonzalez
Keyword(s):  
Low Frequency ◽  
Monitoring And Control ◽  
Automated Vehicle ◽  
Location Data ◽  
Vehicle Location ◽  
And Control

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

scholarly journals Measuring temporal and spatial travel efficiency for transit route system using low-frequency bus automatic vehicle location data

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880212 ◽  
Author(s):  
Fengping Yang ◽  
Liqun Peng ◽  
Chenhao Wang ◽  
Yuelong Bai
Keyword(s):  
Travel Time ◽  
Low Frequency ◽  
Travel Speed ◽  
Automatic Vehicle Location ◽  
Current Time ◽  
Minimum Requirement ◽  
Location Data ◽  
Vehicle Location ◽  
Transit Route ◽  
Temporal And Spatial

Although the bus probe data have been widely adopted for examining the transit route efficiency, this application cannot guarantee the accuracy in special temporal and spatial segments due to the inadequate probe samples. This study evaluates the feasibility of automatic vehicle location data as probes for the bus route travel time evaluation. Our techniques explore the minimum requirement of transit automatic vehicle location data to recover the bus trajectories in various spatial–temporal dimensions along the scheduled transit routes. First, a three-dimensional tensor is established to infer the uncovered link traveling information in current time slots and the last short-term period. Then, a general form is proposed to calculate the local mean travel speed and the average link travel time in each separated time slot of day. Finally, a case study has been conducted using field transit automatic vehicle location data running on a bus route corridor in Edmonton, Canada. The results demonstrate the effectiveness and efficiency of low-frequency bus automatic vehicle location data as probes for transit route efficiency measurement by comparing with baseline approaches. This work also supports the feasibility of using automatic vehicle location–equipped buses as customized buses for choosing alternate path based on evaluating the current transit efficiency on all routes.

Simulating a Regularly Sampled: Bus Location System

2000 ◽  
Vol 1733 (1) ◽  
pp. 105-114
Author(s):  
Sarosh I. Khan ◽  
Brian Hoeschen
Keyword(s):  
Traffic Management ◽  
Location System ◽  
Location Data ◽  
Data Space ◽  
Vehicle Location ◽  
Street Traffic ◽  
Transit Agencies ◽  
Link Travel Time ◽  
And Control

Between 1967 and 1997, transit agencies in 20 cities installed automatic vehicle location (AVL) systems to improve safety, efficiency, and quality of service. The bus AVL system typically provides a means of tracking individual buses for fleet management. More recently, the AVL data have also been used to develop algorithms to predict bus arrival time, estimate link travel time, and detect incidents. Other traffic management and control applications are also being explored. Therefore, the availability of a simulation capable of mimicking a bus location system that reports vehicle location at regular, prespecified intervals is becoming increasingly important. CORSIM, an integrated freeway and surface street traffic simulation model, also simulates buses on prespecified routes and stations for given dwell time distributions and frequency of service. However, very little has been reported about CORSIM’s bus simulation module. CORSIM’s bus route simulation module and its drawbacks are examined, and the results of an effort to compare data collected from the Denver Regional Transportation District bus AVL system and the microsimulator for a test network are presented. Bus location data from the field and the simulation were collected at regular intervals under both recurring congestion and nonrecurring congestion conditions. Linear referencing in a geographic information system was used to extract bus location data for the test network from the AVL system, and external programs were written to collect the same data from the microsimulator. Based on the bus location data, space mean speeds were estimated and compared to evaluate the performance of the model. The results are encouraging. However, several obstacles remain and they are discussed in detail.

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.

Computers for data, control and simulation

1988 ◽  
Vol 46 ◽  
pp. 920-921
Author(s):  
David C. Joy
Keyword(s):  
Personal Computers ◽  
Monitoring And Control ◽  
Data Logging ◽  
Data Logger ◽  
Operational Conditions ◽  
Data Control ◽  
The Many ◽  
And Control

Personal computers (PCs) are a powerful resource in the EM Laboratory, both as a means of automating the monitoring and control of microscopes, and as a tool for quantifying the interpretation of data. Not only is a PC more versatile than a piece of dedicated data logging equipment, but it is also substantially cheaper. In this tutorial the practical principles of using a PC for these types of activities will be discussed.The PC can form the basis of a system to measure, display, record and store the many parameters which characterize the operational conditions of the EM. In this mode it is operating as a data logger. The necessary first step is to find a suitable source from which to measure each of the items of interest. It is usually possible to do this without having to make permanent corrections or modifications to the EM.

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