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 Investigation with Bluetooth Sensors of Bicycle Travel Time Estimation on a Short Corridor

2012 ◽  
Vol 8 (1) ◽  
pp. 303521 ◽  
Author(s):  
Zhenyu Mei ◽  
Dianhai Wang ◽  
Jun Chen
Keyword(s):  
Travel Time ◽  
Information Acquisition ◽  
Sampling Error ◽  
Primary Source ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Time Data ◽  
Spatial Error ◽  
Travel Time Data

Accurate travel time information acquisition is essential to the effective planning and management of bicycle travel conditions. Traditionally, video camera data have been used as the primary source for measuring the quality of bicycle travel time. This paper deals with an investigation of bicycle travel time estimation on a short corridor, using Bluetooth sensors, based on field survey of travel time at one arterial road in Hangzhou. Usually bicycle travel time estimates with Bluetooth sensors contain three types of errors: spatial error, temporal error, and sampling error. To avoid these, we introduced filters to “purify” the time series. A median filtering algorithm is used to eliminate the outlier observations. The filtering scheme has been applied on Genshan East Road and Moganshan Road. Test data are used to measure the quality of bicycle travel time data collected by the Bluetooth sensors, and the results show that the new technology is a promising method for collecting high-quality travel time data that can be used as ground truth for evaluating other sources of travel time and other intelligent transportation system applications.

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.

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

Travel-time errors in the laterally inhomogeneous earth

1971 ◽  
Vol 61 (6) ◽  
pp. 1639-1654 ◽  
Author(s):  
Cinna Lomnitz
Keyword(s):  
Travel Time ◽  
Root Mean Square ◽  
Time Estimation ◽  
Systematic Errors ◽  
Least Square ◽  
Travel Time Estimation ◽  
Mean Square ◽  
Travel Times ◽  
The Earth ◽  
Lateral Inhomogeneity

abstract Travel times from earthquakes or explosions contain both positive and negative systematic errors. Positive skews in travel-time residuals due to epicenter mislocation, and negative skews due to lateral inhomogeneity in the Earth, are analyzed. Methods for travel-time estimation are critically reviewed. Recent travel-time tables, including the J-B tables, are within the range of root-mean-square travel-time fluctuations; the J-B tables are systematically late but cannot be reliably improved by least-square methods. Effects of lateral inhomogeneity at teleseismic distances can be estimated by chronoidal methods independently of standard tables, but the available explosion data are insufficiently well-distributed in azimuth and distance for this purpose.

Estimating Travel Time Summary Statistics of Larger Intervals from Smaller Intervals Without Storing Individual Data

2002 ◽  
Vol 1804 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Dongjoo Park ◽  
Laurence R. Rilett ◽  
Parichart Pattanamekar ◽  
Keechoo Choi
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Transportation Systems ◽  
Identification System ◽  
Time Interval ◽  
Travel Time Estimation ◽  
Time Data ◽  
Probe Vehicles ◽  
Mean And Variance ◽  
Link Travel Time

Historically, real-time intelligent transportation systems data are aggregated into discrete periods, typically of 5 to 10 min duration, and are subsequently used for travel time estimation and forecasting. In a previous study of link and corridor travel time estimation and forecasting by using probe vehicles, it was shown that the optimal aggregation interval size is a function of the traffic condition and the application. It is expected that traffic management centers will continue to collect travel time statistics (e.g., mean and variance) from probe vehicles and archive this data at a minimum time interval. Statistical models are developed for estimating the mean and variance of the link and route or corridor travel time for a larger interval by using only the observed mean travel time and variance for each smaller or basic interval. The proposed models are demonstrated by using travel time data obtained from Houston, Texas, which were collected as part of the automatic vehicle identification system of the Houston TranStar system. It was found that the proposed models for estimating link travel time mean and variance for a larger interval were easy to implement and provided results that had minimal error. The route or corridor travel time mean and variance model had considerable error compared with the link travel time mean and variance models.

scholarly journals Urban Link Travel Time Estimation Based on Low Frequency Probe Vehicle Data

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Xiyang Zhou ◽  
Zhaosheng Yang ◽  
Wei Zhang ◽  
Xiujuan Tian ◽  
Qichun Bing
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Time Window ◽  
Low Frequency ◽  
Time Estimation ◽  
Estimation Accuracy ◽  
Travel Time Estimation ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Link Travel Time

To improve the accuracy and robustness of urban link travel time estimation with limited resources, this research developed a methodology to estimate the urban link travel time using low frequency GPS probe vehicle data. First, focusing on the case without reporting points for the GPS probe vehicle on the target link in the current estimation time window, a virtual report point creation model based on theK-Nearest Neighbour Rule was proposed. Then an improved back propagation neural network model was used to estimate the link travel time. The proposed method was applied to a case study based on an arterial road in Changchun, China: comparisons with the traditional artificial neural network method and the spatiotemporal moving average method revealed that the proposed method offered a higher estimation accuracy and better robustness.

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.

scholarly journals A Study on Travel Time Estimation of Diverging Traffic Stream on Highways Based on Timestamp Data

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Sunghoon Kim ◽  
Hwapyeong Yu ◽  
Hwasoo Yeo
Keyword(s):  
Travel Time ◽  
Polynomial Regression ◽  
Time Estimation ◽  
Superior Performance ◽  
Travel Time Estimation ◽  
Time Data ◽  
Modified Method ◽  
Road Section ◽  
Traffic Stream ◽  
Improved Performance

Travel time is valuable information for both drivers and traffic managers. While properly estimating the travel time of a single road section, an issue arises when multiple traffic streams exist. In highways, this usually occurs at the upstream of diverge bottleneck. The aim of this paper is to provide a new framework for travel time estimation of a diverging traffic stream using timestamp data only. While providing the framework, the main focus of this paper is on performing a few analyses on the stage of travel time data classification in the proposed framework. Three sequential steps with a few statistical approaches are provided in this stage: detection of data divergence, classification of divergent data, and outlier filtering. First, a divergence detection index (DDI) of data has been developed, and the analysis results show that this new index is useful in finding the threshold of determining data divergence. Second, three different methods are tested in terms of properly classifying the divergent data. It is found that our modified method based on the approach used by Korea Expressway Corporation shows superior performance. Third, a polynomial regression-based method is used for outlier filtering, and this shows reasonable performance even at a relatively low market penetration rate (MPR) of probe vehicles. Then, the overall performance of the travel time estimation framework is tested, and this test demonstrates that the proposed framework can show improved performance in distinctively estimating the travel times of two different traffic streams in the same road section.

scholarly journals Investigation of Bicycle Travel Time Estimation Using Bluetooth Sensors for Low Sampling Rates

2014 ◽  
Vol 26 (5) ◽  
pp. 383-391 ◽  
Author(s):  
Zhenyu Mei ◽  
Dianhai Wang ◽  
Jun Chen ◽  
Wei Wang
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Frequency Noise ◽  
Multiple Time ◽  
Travel Times ◽  
Data Filtering ◽  
Valid Data ◽  
Sampling Intervals ◽  
Filtering Procedure

Filtering the data for bicycle travel time using Bluetooth sensors is crucial to the estimation of link travel times on a corridor. The current paper describes an adaptive filtering algorithm for estimating bicycle travel times using Bluetooth data, with consideration of low sampling rates. The data for bicycle travel time using Bluetooth sensors has two characteristics. First, the bicycle flow contains stable and unstable conditions. Second, the collected data have low sampling rates (less than 1%). To avoid erroneous inference, filters are introduced to “purify” multiple time series. The valid data are identified within a dynamically varying validity window with the use of a robust data-filtering procedure. The size of the validity window varies based on the number of preceding sampling intervals without a Bluetooth record. Applications of the proposed algorithm to the dataset from Genshan East Road and Moganshan Road in Hangzhou demonstrate its ability to track typical variations in bicycle travel time efficiently, while suppressing high frequency noise signals.

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