Using Bus Travel Time Data to Estimate Travel Times on Urban Corridors

2004 ◽  
Vol 1870 (1) ◽  
pp. 18-25 ◽  
Author(s):  
Partha Chakroborty ◽  
Shinya Kikuchi
Keyword(s):  
Travel Time ◽  
Travel Times ◽  
Time Data ◽  
Travel Time Data ◽  
Bus Travel Time

Seismic wave travel times from nuclear explosions

1958 ◽  
Vol 48 (4) ◽  
pp. 377-398
Author(s):  
Dean S. Carder ◽  
Leslie F. Bailey
Keyword(s):  
Travel Time ◽  
Travel Times ◽  
Strong Variation ◽  
Time Data ◽  
Owens Valley ◽  
Central California ◽  
Nuclear Explosions ◽  
Travel Time Data ◽  
The Pacific ◽  
Wave Travel

Abstract A large number of seismograph records from nuclear explosions in the Nevada and Pacific Island proving grounds have been collected and analyzed. The Nevada explosions were well recorded to distances of 6°.5 (450 mi.) and weakly recorded as far as 17°.5, and under favorable circumstances as far as 34°. The Pacific explosions had world-wide recording except that regional data were necessarily meager. The Nevada data confirm that the crustal thickness in the area is about 35 km., with associations of 6.1 km/sec. speeds in the crust and 8.0 to 8.2 km/sec. speeds beneath it. They indicate that there is no uniform layering in the crust, and that if higher-speed media do exist, they are not consistent; also, that the crust between the proving grounds and central California shows a thickening probably as high as 70 or 75 km., and that this thickened portion may extend beneath the Owens Valley. The data also point to a discontinuity at postulated depths of 160 to 185 km. Pacific travel times out to 14° are from 4 to 8 sec. earlier than similar continental data partly because of a thinner crust, 17 km. or less, under the atolls and partly because speeds in the top of the mantle are more nearly 8.15 km/sec. than 8.0 km/sec. More distant points, at 17°.5 and 18°.5, indicate slower travel times—about 8.1 km/sec. A fairly sharp discontinuity at 19° in the travel-time data is indicated. Travel times from Pacific sources to North America follow closely Jeffreys-Bullen 1948 and Gutenberg 1953 travel-time curves for surface foci except they are about 2 sec. earlier on the continent, and Arctic and Pacific basin data are about 2 sec. still earlier. The core reflection PcP shows a strong variation in amplitude with slight changes in distance at two points where sufficient data were available.

Meaningful Modeling of Section Bus Running Times by Time Varying Mixture Distributions of Fixed Components

2020 ◽  
Vol 2674 (8) ◽  
pp. 626-637
Author(s):  
Beda Büchel ◽  
Francesco Corman
Keyword(s):  
Travel Time ◽  
Explanatory Power ◽  
Time Varying ◽  
Time Data ◽  
Running Time ◽  
Novel Approach ◽  
Travel Time Data ◽  
Conventional Models ◽  
Bus Travel Time ◽  
Transit Reliability

Understanding the variability of bus travel time is a key issue in the optimization of schedules, transit reliability, route choice analysis, and transit simulation. The statistical modeling of bus travel time data is of increasing importance given the increasing availability of data. In this paper, we introduce a novel approach to modeling the day-to-day variability of urban bus running times on a section level. First, the explanatory power of conventionally used distributions is examined, based on likelihood and effect size. We show that a mixture model is a powerful tool to increase fitting performance, but the applied components need to be justified. To overcome this issue, we propose a novel model consisting of two individual characteristic distributions representing either off-peak or peak hour dynamics. The observed running time distribution at every hour of the day can be described as a combination (mixture) of the two dynamics. The proposed time varying model uses a small set of parameters, which are physically interpretable and capable of accurately describing running time distributions. With our modeling approach, we reduce the complexity of mixture models and increase the explanatory power and fit compared with conventional models.

Travel time estimation in urban networks using limited probes data

2011 ◽  
Vol 38 (3) ◽  
pp. 305-318 ◽  
Author(s):  
Mohamed El Esawey ◽  
Tarek Sayed
Keyword(s):  
Simulation Model ◽  
Travel Time ◽  
Network Performance ◽  
Time Estimation ◽  
Performance Measure ◽  
Estimation Accuracy ◽  
Travel Time Estimation ◽  
Travel Times ◽  
Time Data ◽  
Travel Time Data

Travel time is a simple and robust network performance measure that is well understood by the public. However, travel time data collection can be costly especially if the analysis area is large. This research proposes a solution to the problem of limited network sensor coverage caused by insufficient sample size of probe vehicles or inadequate numbers of fixed sensors. Within a homogeneous road network, nearby links of similar character are exposed to comparable traffic conditions, and therefore, their travel times are likely to be positively correlated. This correlation can be useful in developing travel time relationships between nearby links so that if data becomes available on a subset of these links, travel times of their neighbours can be estimated. A methodology is proposed to estimate link travel times using available data from neighbouring links. To test the proposed methodology, a case study was undertaken using a VISSIM micro-simulation model of downtown Vancouver. The simulation model was calibrated and validated using field traffic volumes and travel time data. Neighbour links travel time estimation accuracy was assessed using different error measurements and the results were satisfactory. Overall, the results of this research demonstrate the feasibility of using neighbour links data as an additional source of information to estimate travel time, especially in case of limited coverage.

scholarly journals Shear-wave travel times and amplitudes for two well-constrained earthquakes

1978 ◽  
Vol 68 (4) ◽  
pp. 973-985
Author(s):  
Robert S. Hart ◽  
Rhett Butler
Keyword(s):  
Travel Time ◽  
Shear Wave ◽  
Correlation Technique ◽  
Travel Times ◽  
Time Data ◽  
Data Set ◽  
A Value ◽  
Travel Time Data ◽  
Distance Dependence ◽  
Wave Travel

abstract The wave-form correlation technique (Hart, 1975) for determining precise teleseismic shear-wave travel times is extended to two large earthquakes with well-constrained source mechanisms, the 1968 Borrego Mountain, California earthquake and the 1973 Hawaii earthquake. A total of 87 SH travel times in the distance range of 30° to 92° were obtained through analysis of WWSSN and Canadian Network seismograms from these two events. Major features of the travel-time data include a trend toward faster travel times at a distance of about 40° (previously noted by Ibrahim and Nuttli, 1967; Hart, 1975); another somewhat less pronounced trend toward faster times at about 75°; a +6 sec base line shift, with respect to the Jeffreys-Bullen Table, for the Borrego Mountain data; and large azimuthally-dependent scatter for the Hawaiian data, probably reflecting dramatic lateral variations in the near-source region. When azimuthal variations in the Hawaii data are removed, the travel times from both events show very low scatter. The correlations were also used to investigate SH amplitudes for possible distance dependence in the data and variations in tβ*. The Borrego Mountain data show very low scatter and no discernible distance dependence. All of the data are compatible with a value of tβ* = 5.2 ± 0.5. The amplitudes from the Hawaii earthquake show the same azimuthal variations found in the travel-time data. When those effects are removed, the Hawaii data satisfies a value of tβ* equal to 4.0 ± 0.5 and, as with the other data set, no distance dependence is apparent.

scholarly journals Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

2020 ◽  
pp. 1-1
Author(s):  
Luong H. Vu ◽  
Benjamin N. Passow ◽  
Daniel Paluszczyszyn ◽  
Lipika Deka ◽  
Eric Goodyer
Keyword(s):  
Travel Time ◽  
Urban Areas ◽  
Travel Times ◽  
Time Data ◽  
Travel Time Data

scholarly journals Calibrating Microsimulation Parameters for Vehicular Travel Time

2021 ◽  
Vol 9 (5) ◽  
pp. 565-569
Keyword(s):  
Travel Time ◽  
Simulation Models ◽  
Congestion Management ◽  
Traffic Network ◽  
Travel Times ◽  
Time Data ◽  
Urban Road ◽  
Speed Distribution ◽  
Travel Time Data ◽  
Calibration Parameters

Travel time is one of the simplest yet the most important parameter for transportation facility users as well as transportation engineers. Travel time data is valuable for widerange of transportation analysis including congestion management, transportation planning and passenger’sdecision making.Traffic simulation models are now becoming necessary tools to understand the behavior of traffic and reduce vehicular travel times, but it is very important to calibrate these models first. Thisstudy attempts to determines the values of those parameters,using microsimulation,that significantly affect the travel time. These parameters arethenused for calibrating the traffic simulation model that results in realistic travel time.Study was conducted on an urban road andfield data was collected during weekdays for peak hours. The traffic network was modelled usingVISSIM®.The calibration parameters were desired speed distribution, number of lanes,average standstill distance and minimum headway. After calibrating the model, the travel times collected from field data and those by simulations for different modes of transportation were in close agreement.

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.

scholarly journals Evaluation of Outlier Filtering Algorithms for Accurate Travel Time Measurement Incorporating Lane-Splitting Situations

2021 ◽  
Vol 13 (24) ◽  
pp. 13851
Author(s):  
Obada Asqool ◽  
Suhana Koting ◽  
Ahmad Saifizul
Keyword(s):  
Travel Time ◽  
Media Access Control ◽  
Travel Times ◽  
Media Access ◽  
Time Data ◽  
Passenger Cars ◽  
Filtering Algorithms ◽  
Travel Time Data ◽  
The Media ◽  
Two Stages

Malaysia has a high percentage of motorcycles. Due to lane-splitting, travel times of motorcycles are less than passenger cars at congestion. Because of this, collecting travel times using the media access control (MAC) address is not straightforward. Many outlier filtering algorithms for travel time datasets have not been evaluated for their capability to filter lane-splitting observations. This study aims to identify the best travel time filtering algorithms for the data containing lane-splitting observations and how to use the best algorithm. Two stages were adopted to achieve the objective of the study. The first stage validates the performance of the previous algorithms, and the second stage checks the sensitivity of the algorithm parameters for different days. The analysis uses the travel time data for three routes in Kuala Lumpur collected by Wi-Fi detectors in May 2018. The results show that the Jang algorithm has the best performance for two of the three routes, and the TransGuide algorithm is the best algorithm for one route. However, the parameters of Jang and TransGuide algorithms are sensitive for different days, and the parameters require daily calibration to obtain acceptable results. Using proper calibration of the algorithm parameters, the Jang and TransGuide algorithms produced the most accurate filtered travel time datasets compared to other algorithms

Statistical Analysis of Mean Route Travel Times on Seven Primary Arterials in Chicago Metropolitan Region

1998 ◽  
Vol 1625 (1) ◽  
pp. 109-117
Author(s):  
Stephen C. Laffey ◽  
Louie Nan Liu ◽  
Ed J. Christopher
Keyword(s):  
Regression Analysis ◽  
Travel Time ◽  
Sampling Strategy ◽  
Metropolitan Region ◽  
Travel Times ◽  
Time Data ◽  
Data Set ◽  
Travel Time Data ◽  
Significant Difference ◽  
Single Data

Presented is a statistical approach for examining multiple years of travel time data to determine if any trends can be quantified reliably by using only 4 years of observed data. Empirical data for 80 routes covering more than 4025 km were collected in the summers of 1994, 1995, 1996, and 1997 and compiled into a database for public release in the fall of 1997. From this database, seven routes were selected then sampled over all 4 years. These seven routes are primary arterials of regional significance in northeastern Illinois. A multiple regression analysis was performed on the seven arterials to determine if there was a statistically significant difference in the observed mean route travel times over the 4-year period. The regression analysis indicated that only one of seven routes experienced a statistically significant change in directional route travel time between 1994 and 1997. Because only one of seven routes was significantly different, the analyst may merge multiple years of data for an individual route into a single data set to build a more robust database. Also, quantifiable change in travel time may be difficult to perceive year by year. This has significant implications for the design of a sampling strategy that needs to measure performance on approximately 28 980 directional route km of roadways. It would be better to sample fewer routes more intensely on a regular interval than to sample many routes lightly every year.

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