Estimating Corridor Travel Time by Using Transit Vehicles as Probes

2003 ◽  
Vol 1855 (1) ◽  
pp. 60-65 ◽  
Author(s):  
Frederick W. Cathey ◽  
Daniel J. Dailey
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Travel Times ◽  
Information Density ◽  
Time Estimates ◽  
Real Time Applications

A corridor approach to travel-time estimates by using transit vehicles as probes is presented. These estimates increase the information density along the corridor, compared with use of only probe information at specified points. Speed estimates are provided that track the significant changes identified in inductance-loop data, but the estimate of the speed appears to be conservative. Comparison of instantaneous travel times, often used for real-time applications, and travel time computed by using a corridor speed surface indicates that the instantaneous travel times have a delay in tracking changes in the corridor and have higher maximum travel time.

scholarly journals The Effect of Route-choice Strategy on Transit Travel Time Estimates

2019 ◽  
Author(s):  
Nate Wessel ◽  
Steven Farber
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Imperfect Information ◽  
Optimal Path ◽  
Shortest Paths ◽  
Route Choice ◽  
Travel Times ◽  
Selection Strategies ◽  
Time Estimates ◽  
Average Travel Time

Estimates of travel time by public transit often rely on the calculation of a shortest-path between two points for a given departure time. Such shortest-paths are time-dependent and not always stable from one moment to the next. Given that actual transit passengers necessarily have imperfect information about the system, their route selection strategies are heuristic and cannot be expected to achieve optimal travel times for all possible departures. Thus an algorithm that returns optimal travel times at all moments will tend to underestimate real travel times all else being equal. While several researchers have noted this issue none have yet measured the extent of the problem. This study observes and measures this effect by contrasting two alternative heuristic routing strategies to a standard shortest-path calculation. The Toronto Transit Commission is used as a case study and we model actual transit operations for the agency over the course of a normal week with archived AVL data transformed into a retrospective GTFS dataset. Travel times are estimated using two alternative route-choice assumptions: 1) habitual selection of the itinerary with the best average travel time and 2) dynamic choice of the next-departing route in a predefined choice set. It is shown that most trips present passengers with a complex choice among competing itineraries and that the choice of itinerary at any given moment of departure may entail substantial travel time risk relative to the optimal outcome. In the context of accessibility modelling, where travel times are typically considered as a distribution, the optimal path method is observed in aggregate to underestimate travel time by about 3-4 minutes at the median and 6-7 minutes at the \nth{90} percentile for a typical trip.

Prediction Model of Bus Arrival Time for Real-Time Applications

2005 ◽  
Vol 1927 (1) ◽  
pp. 195-204 ◽  
Author(s):  
Ranhee Jeong ◽  
Laurence R. Rilett
Keyword(s):  
Prediction Model ◽  
Travel Time ◽  
Real Time ◽  
Arrival Time ◽  
Historical Data ◽  
Transportation Systems ◽  
Test Bed ◽  
Travel Time Prediction ◽  
Ann Models ◽  
Real Time Applications

Advanced traveler information systems (ATIS) are one component of intelligent transportation systems (ITS), and a major component of ATIS is travel time information. Automatic vehicle location (AVL) systems, which are a part of ITS, have been adopted by many transit agencies to track their vehicles and to predict travel time in real time. Because of the complexity involved, there is no universally adopted approach for this latter application, and research is needed in this area. The objectives of the research in this paper are to develop a model to predict bus arrival time using AVL data and apply the model for real-time applications. The test bed was a bus route located in Houston, Texas, and the travel time prediction model considered schedule adherence, traffic congestion, and dwell times. A historical data-based model, regression models, and artificial neural network (ANN) models were used to predict bus arrival time. It was found that ANN models outperformed both the historical data-based model and the regression model in terms of prediction accuracy. It was also found that the ANN models can be used for real-time applications.

Travel-Time Estimates Obtained from Intelligent Transportation Systems and Instrumented Test Vehicles: Statistical Comparison

2002 ◽  
Vol 1804 (1) ◽  
pp. 8-16 ◽  
Author(s):  
William L. Eisele ◽  
Laurence R. Rilett
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Data Sources ◽  
Travel Times ◽  
System Monitoring ◽  
Test Vehicle ◽  
Cellular Telephones ◽  
Mobile Cellular ◽  
Time Estimates

Accurate estimation of travel time is necessary for monitoring the performance of the transportation system. Often, travel times are estimated indirectly by using instantaneous speeds from inductance loop detectors and making a number of assumptions. Although these travel times may be acceptable estimates for uncongested conditions, they may have significant error during congested periods. Travel times also may be obtained directly from intelligent transportation systems (ITS) data sources such as automatic vehicle identification (AVI). In addition, mobile cellular telephones have been touted as a means for obtaining this information automatically. Data sources that collect travel-time estimates directly provide travel-time data for both real-time and off-line transportation system monitoring. Instrumented test vehicle runs are often performed to obtain travel-time estimates for system monitoring and other transportation applications. Distance measuring instruments (DMIs) are a common method of instrumentation for test vehicles. DMI travel-time estimates are compared with AVI travel-time estimates by using a variety of statistical approaches. The results indicate that the travel-time estimates from test vehicles instrumented with DMI are within 1% of travel-time estimates from AVI along the study corridor. These results reflect that DMI is an accurate instrumented test vehicle technology and, more important, AVI data sources can replace traditional system monitoring data collection methods when there is adequate tag penetration and infrastructure. A method for identifying instrumented test vehicle drivers who may require additional data collection training is provided. The described procedures are applicable to any instrumented vehicle technique (e.g., the Global Positioning System) in comparison to any ITS data source that directly estimates travel time (e.g., mobile cellular telephones).

Comparison of Door-to-Door Transit Travel Time Estimation Using Schedules, Real-Time Vehicle Arrivals, and Smartcard Inference Methods

2021 ◽  
pp. 036119812110237
Author(s):  
Ting Li ◽  
Patrick Meredith-Karam ◽  
Hui Kong ◽  
Anson Stewart ◽  
John P. Attanucci ◽  
...  
Keyword(s):  
Travel Time ◽  
Optimal Path ◽  
Time Estimation ◽  
Future Research ◽  
Travel Time Estimation ◽  
Travel Times ◽  
Transit Systems ◽  
Time Estimates ◽  
Bus Service ◽  
Transportation Research

Estimating passengers’ door-to-door travel time, for journeys that combine walking and public transit, can be complex for large networks with many available path alternatives. Additional complications arise in tap-on only transit systems, where passengers alightings are not recorded. For one such system, the Chicago Transit Authority, this study compares three methods for estimating door-to-door travel time: assuming optimal path choice given scheduled service, as represented in the General Transit Feed Specification (GTFS); assuming optimal path choice given actually operated bus service, as recorded by automatic vehicle location systems; and using inferred path choices based on automated fare collection smartcard records, as processed with an origin-destination-interchange (ODX) inference algorithm. As expected, ODX-derived travel times are found to be longer than those derived from GTFS, indicating that purely schedule-based travel times underestimate the travel times that are actually available and experienced, which can be attributed primarily to suboptimal passenger route choice. These discrepancies additionally manifest in significant spatial variations, raising concerns about potential biases in travel time estimates that do not account for reliability. The findings bring about a more comprehensive understanding of the interactions between transit reliability and passenger behavior in transportation research. Furthermore, these discrepancies suggest areas of future research into the implications of systematic and behavioral assumptions implied by using conventional schedule-based travel time estimates.

scholarly journals Data Processing Techniques for Real-Time Traveler Information: Use of Dedicated Short-Range Communications Probes on Suburban Arterial

2020 ◽  
Vol 14 (1) ◽  
pp. 99-108
Author(s):  
Jinhwan Jang
Keyword(s):  
Data Processing ◽  
Travel Time ◽  
Real Time ◽  
Sequence Data ◽  
Time Lag ◽  
Ground Truth ◽  
Prediction Errors ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Ground Truth Data

Background: As wireless communication technologies evolve, probe-based travel-time collection systems are becoming popular around the globe. However, two problems generally arise in probe-based systems: one is the outlier and the other is time lag. To resolve the problems, methods for outlier removal and travel-time prediction need to be applied. Methods: In this study, data processing methods for addressing the two issues are proposed. After investigating the characteristic of the travel times on the test section, the modified z-score was suggested for censoring outliers contained in probe travel times. To mitigate the time-lag phenomenon, a recurrent neural network, a class of deep learning where temporal sequence data are normally treated, was applied to predict travel times. Results: As a result of evaluation with ground-truth data obtained through test-car runs, the proposed methods showed enhanced performances with prediction errors lower than 13% on average compared to current practices. Conclusion: The suggested methods can make drivers to better arrange their trip schedules with real-time travel-time information with improved accuracy.

scholarly journals Highway travel time information system based on cumulative count curves and new tracking technologies

2016 ◽  
Author(s):  
Margarita Martínez-Díaz ◽  
Francesc Soriguera Martí ◽  
Ignacio Pérez Pérez
Keyword(s):  
Travel Time ◽  
Real Time ◽  
New Technologies ◽  
Time Estimation ◽  
Travel Times ◽  
Important Indicator ◽  
Loop Detectors ◽  
Real Time Estimation ◽  
Cumulative Count ◽  
Time Information

Travel time is probably the most important indicator of the level of service of a highway, and it is also the most appreciated information for its users. Administrations and private companies make increasing efforts to improve its real time estimation. The appearance of new technologies makes the precise measurement of travel times easier than never before. However, direct measurements of travel time are, by nature, outdated in real time, and lack of the desired forecasting capabilities. This paper introduces a new methodology to improve the real time estimation of travel times by using the equipment usually present in most highways, i.e., loop detectors, in combination with Automatic Vehicle Identification or Tracking Technologies. One of the most important features of the method is the usage of cumulative counts at detectors as an input, avoiding the drawbacks of common spot-speed methodologies. Cumulative count curves have great potential for freeway travel time information systems, as they provide spatial measurements and thus allow the calculation of instantaneous travel times. In addition, they exhibit predictive capabilities. Nevertheless, they have not been used extensively mainly because of the error introduced by the accumulation of the detector drift. The proposed methodology solves this problem by correcting the deviations using direct travel time measurements. The method results highly beneficial for its accuracy as well as for its low implementation cost.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3209 

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