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.

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.

FINDING THE SHORTEST PATHS ON SURFACES BY FAST GLOBAL APPROXIMATION AND PRECISE LOCAL REFINEMENT

1996 ◽  
Vol 10 (06) ◽  
pp. 643-656 ◽  
Author(s):  
RON KIMMEL ◽  
NAHUM KIRYATI
Keyword(s):  
Shortest Path ◽  
Optimal Path ◽  
Shortest Paths ◽  
Geodesic Curvature ◽  
Geodesic Curve ◽  
Computationally Efficient ◽  
Global Approximation ◽  
Curve Shortening Flow ◽  
Curve Shortening ◽  
Fixed End

Finding the shortest path between points on a surface is a challenging global optimization problem. It is difficult to devise an algorithm that is computationally efficient, locally accurate and guarantees to converge to the globally shortest path. In this paper a two stage coarse-to-fine approach for finding the shortest paths is suggested. In the first stage the algorithm of Ref. 10 that combines a 3D length estimator with graph search is used to rapidly obtain an approximation to the globally shortest path. In the second stage the approximation is refined to become a shorter geodesic curve, i.e., a locally optimal path. This is achieved by using an algorithm that deforms an arbitrary initial curve ending at two given surface points via geodesic curvature shortening flow. The 3D curve shortening flow is transformed into an equivalent 2D one that is implemented using an efficient numerical algorithm for curve evolution with fixed end points, introduced in Ref. 9.

Dynamic Discretization Discovery Algorithms for Time-Dependent Shortest Path Problems

2021 ◽  
Author(s):  
Edward Yuhang He ◽  
Natashia Boland ◽  
George Nemhauser ◽  
Martin Savelsbergh
Keyword(s):  
Lower Bound ◽  
Travel Time ◽  
Shortest Path ◽  
Optimal Path ◽  
Upper Bounds ◽  
Shortest Path Problem ◽  
Time Dependent ◽  
Time Interval ◽  
Shortest Path Problems ◽  
Discovery Algorithms

Finding a shortest path in a network is a fundamental optimization problem. We focus on settings in which the travel time on an arc in the network depends on the time at which traversal of the arc begins. In such settings, reaching the destination as early as possible is not the only objective of interest. Minimizing the duration of the path, that is, the difference between the arrival time at the destination and the departure from the origin, and minimizing the travel time along the path from origin to destination, are also of interest. We introduce dynamic discretization discovery algorithms to efficiently solve such time-dependent shortest path problems with piecewise linear arc travel time functions. The algorithms operate on partially time-expanded networks in which arc costs represent lower bounds on the arc travel time over the subsequent time interval. A shortest path in this partially time-expanded network yields a lower bound on the value of an optimal path. Upper bounds are easily obtained as by-products of the lower bound calculations. The algorithms iteratively refine the discretization by exploiting breakpoints of the arc travel time functions. In addition to time discretization refinement, the algorithms permit time intervals to be eliminated, improving lower and upper bounds, until, in a finite number of iterations, optimality is proved. Computational experiments show that only a small fraction of breakpoints must be explored and that the fraction decreases as the length of the time horizon and the size of the network increases, making the algorithms highly efficient and scalable. Summary of Contribution: New data collection techniques have increased the availability and fidelity of time-dependent travel time information, making the time-dependent variant of the classic shortest path problem an extremely relevant problem in the field of operations research. This paper provides novel algorithms for the time-dependent shortest path problem with both the minimum duration and minimum travel time objectives, which aims to address the computational challenges faced by existing algorithms. A computational study shows that our new algorithm is indeed significantly more efficient than existing approaches.

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 Parking Pricing and Model Split under Uncertainty

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Chengjuan Zhu ◽  
Bin Jia ◽  
Linghui Han ◽  
Ziyou Gao
Keyword(s):  
Travel Time ◽  
Confidence Level ◽  
Time Budget ◽  
Route Choice ◽  
Analytic Solutions ◽  
User Equilibrium ◽  
Travel Times ◽  
Modal Split ◽  
Parking Pricing ◽  
The Impact

In order to investigate different route choice criteria in a competitive highway/park-and-ride (P&R) network with uncertain travel times on the road, a bilevel programming model for solving the problem of determining parking fees and modal split is presented. In the face of travel time uncertainty, travelers plan their trips with a prespecified on-time arrival probability. The impact of three route choice criteria: the mean travel time, the travel time budget, and mean-excess travel time, is compared for parking pricing and modal split. The model at user equilibrium is described as a minimization model. And the analytic solutions are given. Analytic solutions show that both flow and travel time at equilibrium are independent of the price difference of travel expense on money. The main findings from the numerical results are elaborated. While given a confidence level, the flow on the highway changed significantly with the criteria, although the differences of the travel times are small. Travelers can be guided to choose their modes coordinately by improving the quality of the transit service. The optimal parking fees can be affected markedly by the confidence level. Finally, the influence of the log-normal distribution parameters is tested and analyzed.

scholarly journals Route Choice of the Shortest Travel Time Based on Floating Car Data

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Jingwei Shen ◽  
Yifang Ban
Keyword(s):  
Travel Time ◽  
Optimal Path ◽  
Route Choice ◽  
Edge Weight ◽  
Map Matching ◽  
Traffic Condition ◽  
Floating Car Data ◽  
Road Map ◽  
Shortest Distance ◽  
Comparison Results

Finding a route with shortest travel time according to the traffic condition can help travelers to make better route choice decisions. In this paper, the shortest travel time based on FCD (floating car data) which is used to assess overall traffic conditions is proposed. To better fit FCD and road map, a new map matching algorithm which fully considers distance factor, direction factor, and accessibility factor is designed to map all GPS (Global Positioning System) points to roads. A mixed graph structure is constructed and a route analysis algorithm of shortest travel time which considers the dynamic edge weight is designed. By comparing with other map matching algorithms, the proposed method has a higher accuracy. The comparison results show that the shortest travel time path is longer than the shortest distance path, but it costs less traveling time. The implementation of the route choice based on the shortest travel time method can be used to guide people’s travel by selecting the space-time dependent optimal path.

scholarly journals Finding Your Way: Shortest Paths on Networks

2021 ◽  
Vol 9 ◽  
Author(s):  
Teresa Rexin ◽  
Mason A. Porter
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Real World ◽  
Major Part ◽  
Shortest Paths ◽  
Travel Distance ◽  
Daily Lives ◽  
Total Cost ◽  
The Real ◽  
The Cost

Traveling to different destinations is a major part of our lives. We visit a variety of locations both during our daily lives and when we are on vacation. How can we find the best way to navigate from one place to another? Perhaps we can test all of the different ways of traveling between two places, but another method is to use mathematics and computation to find a shortest path between them. In this article, we discuss how to construct shortest paths and introduce Dijkstra’s algorithm to minimize the total cost of a path, where the cost may be the travel distance, the travel time, or some other quantity. We also discuss how to use shortest paths in the real world to save time and increase traveling efficiency.

scholarly journals Finding Your Way: Shortest Paths on Networks

2020 ◽  
Author(s):  
Teresa Rexin ◽  
Mason A. Porter
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Real World ◽  
Shortest Paths ◽  
Travel Distance ◽  
Dijkstra’S Algorithm ◽  
Total Cost ◽  
The Real ◽  
Dijkstra's Algorithm ◽  
The Cost

Traveling to different destinations is a big part of our lives. How do we know the best way to navigate from one place to another? Perhaps we could test all of the different ways of traveling between two places, but another method is using mathematics and computation to find a shortest path. We discuss how to find a shortest path and introduce Dijkstra’s algorithm to minimize the total cost of a path, where the cost may be the travel distance or travel time. We also discuss how shortest paths can be used in the real world to save time and increase traveling efficiency.

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).

Customized Pretrip Prediction of Freeway Travel Times for Road Users

2005 ◽  
Vol 1917 (1) ◽  
pp. 28-36 ◽  
Author(s):  
Jacorien A. A. Wouters ◽  
Kin-Fai Chan ◽  
Joost Kolkman ◽  
Rutger W. Kock
Keyword(s):  
Travel Time ◽  
Online Survey ◽  
Travel Times ◽  
Time Data ◽  
Road Users ◽  
Travel Time Data ◽  
Average Travel Time ◽  
Survey Results ◽  
Actual Travel Time ◽  
Future Date

One objective of the Department of Transport (DoT) in the Netherlands is to provide better information to road users about the traffic situation on Dutch freeways. The idea was put forward to use existing historical freeway inductive loop data to predict a customized pretrip travel time for road users. To investigate the feasibility and usefulness of that idea, DoT launched the AIDA project. A prototype database was constructed; it contained almost 2 years of travel time data for all Dutch freeway road sections with inductive loops. A statistical algorithm was designed to compute the average travel time for any freeway journey on any future date and time. An Internet trial application was built to test the database and algorithm. Accuracy of the travel time predictions was evaluated with independent loop data. The usefulness for road users was investigated with an online survey. Results show a good match between the predicted and actual travel times. Only in 10% of analyzed cases did the actual travel time exceed the predicted travel time by more than 5 min. Of 161 respondents, 50% indicated that they found the information useful. Furthermore, 22% indicated that they would consider a different departure time on the basis of AIDA information. Thus the project has shown convincingly that the AIDA concept is not only feasible but also useful to road users. Presently DoT is looking into the uses of the concept for road users and possibly also for traffic operators.

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