Performance measurement and operations control in advanced public transit systems

2021 ◽  
Author(s):  
Nader Azizi
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Public Transit ◽  
Monitoring And Control ◽  
Real Time Control ◽  
Transit Systems ◽  
Transit Agencies ◽  
Real Time Information ◽  
And Control ◽  
Time Information

In most major cities, levels of traffic congestion are rising along with their associated problems such as travel delays and pollution. While any increase in public transit rider-ship could reduce the level of traffic congestion and related costs, most transit agencies are not able to expand their existing services because of fiscal• and physical constraints. As a result, a growing interest has been developing recently to maximize the transit system efficiency and productivity using new emerging technologies. Recently, the emergence of new technologies such as automatic vehicle location (AVL) and global positioning systems (GPS) has facilitated the design of computer-based real-time decision support systems for public transits. These technologies could significantly help transit agencies improve their operations monitoring and control. In the context of public transit systems, operations monitoring refers to real-time service performance measure and problems detection, and control refers to implementing real time control actions to remedy those problems. This thesis presents a new approach for operations monitoring and control in public transit systems with real-time information. First, an integrated model that combines both headway-based and schedule-based services is presented. To measure the headway or schedule adherence, the model uses predicted arrival times of vehicles at downstream stops. This feature allows the operational managers to avoid major service interruptions by proactively taking necessary corrective actions. Transit agencies have used and continue to use real-time control strategies to improve quality of their services. These strategies are employed by inspectors at various points along a route to remedy the problems as they occur. Practice shows that it is difficult to apply such strategies effectively without real-time information. In the second part of this thesis, a mathematical model for holding control strategy with real-time information is described. The proposed model aims at minimization of the total passengers waiting time and considers both cases of overcrowded and underutilized services. Due to complexity of the holding problem, several metaheuristics are proposed and tested. Among all intelligent search algorithms, a new version of simulated annealing algorithm is proposed to solve the real-time holding control model.

scholarly journals Performance measurement and operations control in advanced public transit systems

2021 ◽  
Author(s):  
Nader Azizi
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Public Transit ◽  
Monitoring And Control ◽  
Real Time Control ◽  
Transit Systems ◽  
Transit Agencies ◽  
Real Time Information ◽  
And Control ◽  
Time Information

In most major cities, levels of traffic congestion are rising along with their associated problems such as travel delays and pollution. While any increase in public transit rider-ship could reduce the level of traffic congestion and related costs, most transit agencies are not able to expand their existing services because of fiscal• and physical constraints. As a result, a growing interest has been developing recently to maximize the transit system efficiency and productivity using new emerging technologies. Recently, the emergence of new technologies such as automatic vehicle location (AVL) and global positioning systems (GPS) has facilitated the design of computer-based real-time decision support systems for public transits. These technologies could significantly help transit agencies improve their operations monitoring and control. In the context of public transit systems, operations monitoring refers to real-time service performance measure and problems detection, and control refers to implementing real time control actions to remedy those problems. This thesis presents a new approach for operations monitoring and control in public transit systems with real-time information. First, an integrated model that combines both headway-based and schedule-based services is presented. To measure the headway or schedule adherence, the model uses predicted arrival times of vehicles at downstream stops. This feature allows the operational managers to avoid major service interruptions by proactively taking necessary corrective actions. Transit agencies have used and continue to use real-time control strategies to improve quality of their services. These strategies are employed by inspectors at various points along a route to remedy the problems as they occur. Practice shows that it is difficult to apply such strategies effectively without real-time information. In the second part of this thesis, a mathematical model for holding control strategy with real-time information is described. The proposed model aims at minimization of the total passengers waiting time and considers both cases of overcrowded and underutilized services. Due to complexity of the holding problem, several metaheuristics are proposed and tested. Among all intelligent search algorithms, a new version of simulated annealing algorithm is proposed to solve the real-time holding control model.

Sensor based real-time information for monitoring and control of a manufacturing process

2021 ◽  
Author(s):  
Debasish Mishra ◽  
Abhinav Gupta ◽  
Pranav Raj ◽  
Aman Kumar ◽  
Saad Anwer ◽  
...  
Keyword(s):  
Real Time ◽  
Manufacturing Process ◽  
Monitoring And Control ◽  
Real Time Information ◽  
And Control ◽  
Time Information

scholarly journals Understanding the Transit Market: A Persona-Based Approach for Preferences Quantification

2020 ◽  
Vol 12 (9) ◽  
pp. 3863 ◽  
Author(s):  
Gamal Eldeeb ◽  
Moataz Mohamed
Keyword(s):  
Real Time ◽  
Choice Model ◽  
Information Provision ◽  
Travel Behaviour ◽  
Error Component ◽  
Transit Market ◽  
Bus Service ◽  
Transit Agencies ◽  
Real Time Information ◽  
Time Information

The study aims at utilizing a persona-based approach in understanding, and further quantifying, the preferences of the key transit market groups and estimating their willingness to pay (WTP) for service improvements. The study adopted an Error Component (EC) interaction choice model to investigate personas’ preferences in a bus service desired quality choice experiment. Seven personas were developed based on four primary characteristics: travel behaviour, employment status, geographical distribution, and Perceived Behavioural Control (PBC). The study utilized a dataset of 5238 participants elicited from the Hamilton Street Railway Public Engagement Survey, Ontario, Canada. The results show that all personas, albeit significantly different in magnitude, are negatively affected by longer journey times, higher trip fares, longer service headways, while positively affected by reducing the number of transfers per trip and real-time information provision. The WTP estimates show that, in general, potential users are more likely to have higher WTP values compared to current users except for at-stop real-time information provision. Also, there is no consensus within current users nor potential users on the WTP estimates for service improvements. Finally, shared and unique preferences for service attributes among personas were identified to help transit agencies tailor their marketing/improvement plans based on the targeted segments.

Are Small- and Medium-Sized Community Paratransit Riders Ready to Adopt Real-Time Information (RTI) Technology?

2018 ◽  
Vol 2672 (51) ◽  
pp. 56-67
Author(s):  
Jeffrey J. LaMondia ◽  
Travis Gajkowski ◽  
Veronica Ramirez
Keyword(s):  
Real Time ◽  
Performance Measure ◽  
Infrastructure Investment ◽  
Factors Affecting ◽  
Transit Agencies ◽  
Older Populations ◽  
Real Time Information ◽  
Time Information ◽  
Estimation Parameters

This research seeks to understand the factors affecting who, in small and medium-sized communities, would adopt real-time information (RTI) technology when using paratransit services. It further provides a planning performance measure that can identify areas which would benefit most from the introduction of such RTI technology. These goals are addressed by modeling paratransit riders’ likelihood of adopting RTI technology as well as forecasting communities’ likely adoption using on-board passenger survey data collected in two small- and medium-sized communities in Alabama and Georgia. Overall, the estimation parameters highlight that future generations of paratransit patrons who are familiar with general technology and use paratransit for non-routine trips would be most interested in and gain the most benefit from this RTI technology. The performance measure is also most effective in long-term transportation planning, applied to future older populations (currently 45–55 years old) who are more comfortable using technology. Transit agencies will be able to use this information to determine whether RTI would be appropriate for their communities as well as the populations that should be targeted for this introduction prior to undertaking a large study or infrastructure investment.

Measuring risk of missing transfers in public transit systems using high-resolution schedule and real-time bus location data

Urban Studies ◽  
2020 ◽  
pp. 004209802091932 ◽  
Author(s):  
Luyu Liu ◽  
Harvey J Miller
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Public Transit ◽  
Transit Systems ◽  
Location Data ◽  
Empirical Probability ◽  
Vehicle Location ◽  
Two Measures ◽  
Transit Agencies ◽  
Public Transit Agency

The emergence of urban Big Data creates new opportunities for a deeper understanding of transportation within cities, revealing patterns and dynamics that were previously hidden. Public transit agencies are collecting and publishing high-resolution schedule and real-time vehicle location data to help users schedule trips and navigate the system. We can use these data to generate new insights into public transit delays, a major source of user dissatisfaction. Leveraging open General Transit Feed Specification (GTFS) and administrative Automatic Passenger Counter (APC) data, we develop two measures to assess the risk of missing bus route transfers and the consequent time penalties due to delays. Risk of Missing Transfers (RoMT) measures the empirical probability of missed transfers, and Average Total Time Penalty (ATTP) shows overall time loss compared to the schedule. We apply these measures to data from the Central Ohio Transit Authority (COTA), a public transit agency serving the Columbus, Ohio, USA metropolitan area. We aggregate, visualise and analyse these measures at different spatial and temporal resolutions, revealing patterns that demonstrate the heterogeneous impacts of bus delays. We also simulate the impacts of dedicated bus lanes reducing missing risk and time penalties. Results demonstrate the effectiveness of measures based on high-resolution schedule and real-time vehicle location data to assess the impacts of delays and to guide planning and decision making that can improve on-time performance.

scholarly journals A Robust Optimization Approach to Public Transit Mobile Real-time Information

2018 ◽  
Vol 30 (5) ◽  
pp. 501-512 ◽  
Author(s):  
Weimin Ma ◽  
Nannan Lin ◽  
Xiaoxuan Chen ◽  
Wenfen Zhang
Keyword(s):  
Robust Optimization ◽  
Real Time ◽  
Public Transit ◽  
Linear Optimization ◽  
Optimization Approach ◽  
Arrival Times ◽  
Information Optimization ◽  
Almost All ◽  
Real Time Information ◽  
Time Information

In the past few years, numerous mobile applications have made it possible for public transit passengers to find routes and learn about the expected arrival times of their transit vehicles. Previous studies show that provision of accurate real-time bus information is vital to passengers for reducing their anxieties and wait times at bus stops. Inadequate and/or inaccurate real-time information not only confuses passengers but also reinforces the bad image of public transit. However, almost all methods of real-time information optimization are aimed at predicting bus arrival or travel times. In order to make up for the lack of information accuracy, this paper proposes a new approach to optimize mobile real-time information for each transit route based on robust linear optimization. An error estimation is added to current bus arrival time information as a new element of mobile bus applications. The proof process of the robust optimization model is also presented in this paper. In the end, the model is tested on two comparable bus routes in Shanghai. The real-time information for these two routes was obtained from Shanghai Bus, a mobile application used in  Shanghai City. The test results reflect the validity, disadvantages, and risk costs of the model.

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