scholarly journals Exploring Travel Time Distribution and Variability Patterns Using Probe Vehicle Data: Case Study in Beijing

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Peng Chen ◽  
Rui Tong ◽  
Guangquan Lu ◽  
Yunpeng Wang
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Goodness Of Fit ◽  
Travel Times ◽  
Unit Distance ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Travel Time Distribution ◽  
Road Type ◽  
Distance Travel

Exploring travel time distribution and variability patterns is essential for reliable route choices and sophisticated traffic management and control. State-of-the-art studies tend to treat different types of roads equally, which fails to provide more detailed analysis of travel time characteristics for each specific road type. In this study, based on a vast amount of probe vehicle data, 200 links inside the Third Ring Road of Beijing, China, were investigated. Four types of roads were covered including urban expressways, auxiliary roads of urban expressways, major roads, and secondary roads. The day-of-week distributions of unit distance travel time were first analyzed. Kolmogorov-Smirnov test, Anderson-Darling test, and chi-squared test were employed to test the goodness-of-fit of different distributions and the results showed lognormal distribution was best-fitted for different time periods and road types compared with normal, gamma, and Weibull distribution. In addition, four reliability measures, that is, unit distance travel time, coefficient of variation, buffer time index, and punctuality rate, were used to explore the day-of-week travel time variability patterns. The results indicated that urban expressways, auxiliary roads of urban expressways, and major roads have regular and distinct morning and afternoon peaks on weekdays. It is noteworthy that in daytime the travel times on auxiliary roads of urban expressways and major roads share similar variability patterns and appear relatively stable and reliable, while urban expressways have most reliable travel times at night. The results of analysis help enable a better understanding of the volatile travel time characteristics of each road type in urban network.

Vehicle Reidentification as Method for Deriving Travel Time and Travel Time Distributions: Investigation

2003 ◽  
Vol 1826 (1) ◽  
pp. 25-30 ◽  
Author(s):  
Carlos Sun ◽  
Glenn Arr ◽  
Ravi P. Ramachandran
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Goodness Of Fit ◽  
Performance Test ◽  
Time Window ◽  
Time Windows ◽  
Travel Times ◽  
Travel Time Distribution ◽  
Vehicle Reidentification ◽  
And Control

Vehicle reidentification was investigated as a method for deriving travel time and travel time distributions with loop and video detectors. Vehicle reidentification is the process of tracking vehicles anonymously from site to site to produce individual vehicle travel times and overall travel time distribution. Travel time and travel time distribution are measures of the performance and reliability of the transportation system and are useful in many transportation applications such as planning, operations, and control. Findings from the investigation included ( a) results from a platoon reidentification algorithm that improved upon a previous indvidual vehicle reidentification algorithm, ( b) sensitivity analysis on the effect of time windows in deriving travel times, and ( c) derivation and goodness of fit of travel time distributions using vehicle reidentification. Arterial data from Southern California were used in testing the algorithm’s performance. Test results showed that the algorithm can reidentify vehicles with an accuracy of greater than 95.9% with 92.4% of total vehicles; can calculate individual travel times with approximately 1% mean error with the most effective time window; and can derive travel time distributions that fit actual distributions at a 99% confidence level.

scholarly journals Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Wenwen Qin ◽  
Meiping Yun
Keyword(s):  
Markov Chain ◽  
Travel Time ◽  
Time Distribution ◽  
State Transition ◽  
Transition Function ◽  
Travel Times ◽  
Travel Time Distribution ◽  
Multimodal Distributions ◽  
State Transition Function ◽  
Link Travel Time

Despite the wide application of Floating Car Data (FCD) in urban link travel time and congestion estimation, the sparsity of observations from a low penetration rate of GPS-equipped floating cars make it difficult to estimate travel time distribution (TTD), especially when the travel times may have multimodal distributions that are associated with the underlying traffic states. In this case, the study develops a Bayesian approach based on particle filter framework for link TTD estimation using real-time and historical travel time observations from FCD. First, link travel times are classified by different traffic states according to the levels of vehicle delays. Then, a state-transition function is represented as a Transition Probability Matrix of the Markov chain between upstream and current links with historical observations. Using the state-transition function, an importance distribution is constructed as the summation of historical link TTDs conditional on states weighted by the current link state probabilities. Further, a sampling strategy is developed to address the sparsity problem of observations by selecting the particles with larger weights in terms of the importance distribution and a Gaussian likelihood function. Finally, the current link TTD can be reconstructed by a generic Markov Chain Monte Carlo algorithm incorporating high weighted particles. The proposed approach is evaluated using real-world FCD. The results indicate that the proposed approach provides good accurate estimations, which are very close to the empirical distributions. In addition, the approach with different percentage of floating cars is tested. The results are encouraging, even when multimodal distributions and very few or no observations exist.

Improving Interstate Freeway Travel Time Reliability Analysis by Clustering Travel Time Distributions

2021 ◽  
pp. 036119812110120
Author(s):  
Xiaoxiao Zhang ◽  
Mo Zhao ◽  
Justice Appiah ◽  
Michael D. Fontaine
Keyword(s):  
Travel Time ◽  
Cumulative Distribution ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Clustering Methods ◽  
Time Data ◽  
Dissimilarity Matrix ◽  
Model Based ◽  
Probe Vehicle ◽  
Vehicle Data

Travel time reliability quantifies variability in travel times and has become a critical aspect for evaluating transportation network performance. The empirical travel time cumulative distribution function (CDF) has been used as a tool to preserve inherent information on the variability and distribution of travel times. With advances in data collection technology, probe vehicle data has been frequently used to measure highway system performance. One challenge with using CDFs when handling large amounts of probe vehicle data is deciding how many different CDFs are necessary to fully characterize experienced travel times. This paper explores statistical methods for clustering CDFs of travel times at segment level into an optimal number of homogeneous clusters that retain all relevant distributional information. Two clustering methods were tested, one based on classic hierarchical clustering and the other used model-based functional data clustering, to find out their performance on clustering distributions using travel time data from Interstate 64 in Virginia. Freeway segments and those within interchange areas were clustered separately. To find the proper data format as clustering input, both scaled and original travel times were considered. In addition, a non-data-driven method based on geometric features was included for comparison. The results showed that for freeway segments, clustering using travel times and the Anderson–Darling dissimilarity matrix and Ward’s linkage had the best performance. For interchange segments, model-based clustering provided the best clusters. By clustering segments into homogenous groups, the results of this study could improve the efficiency of further travel time reliability modeling.

scholarly journals Expansion and contraction of the flowing stream network changes hillslope flowpath lengths and the shape of the travel time distribution

2019 ◽  
Author(s):  
H. J. Ilja van Meerveld ◽  
James W. Kirchner ◽  
Marc J. P. Vis ◽  
Rick S. Assendelft ◽  
Jan Seibert
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Network Dynamics ◽  
Subsurface Flow ◽  
Travel Times ◽  
Stream Networks ◽  
Stream Network ◽  
Transport Distance ◽  
Travel Time Distribution ◽  
Flowing Stream

Abstract. Flowing stream networks dynamically extend and retract, both seasonally and in response to precipitation events. These network dynamics can dramatically alter the drainage density, and thus the length of subsurface flow pathways to flowing streams. We mapped flowing stream networks in a small Swiss headwater catchment during different wetness conditions and estimated their effects on the distribution of travel times to the catchment outlet. For each point in the catchment, we determined the subsurface transport distance to the flowing stream based on the surface topography, and the surface transport distance along the flowing stream to the outlet. We combined the distributions of these travel distances with assumed surface and subsurface flow velocities to estimate the distribution of travel times to the outlet. These calculations show that the extension and retraction of the stream network can substantially change the mean travel time and the shape of the travel time distribution. During wet conditions with a fully extended flowing stream network, the travel time distribution was strongly skewed to short travel times, but as the network retracted during dry conditions, the distribution of the travel times became more uniform. Stream network dynamics are widely ignored in catchment models, but our results show that they need to be taken into account when modeling solute transport and interpreting travel time distributions.

scholarly journals Expansion and contraction of the flowing stream network alter hillslope flowpath lengths and the shape of the travel time distribution

2019 ◽  
Vol 23 (11) ◽  
pp. 4825-4834 ◽  
Author(s):  
H. J. Ilja van Meerveld ◽  
James W. Kirchner ◽  
Marc J. P. Vis ◽  
Rick S. Assendelft ◽  
Jan Seibert
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Network Dynamics ◽  
Subsurface Flow ◽  
Travel Times ◽  
Stream Networks ◽  
Stream Network ◽  
Transport Distance ◽  
Travel Time Distribution ◽  
Flowing Stream

Abstract. Flowing stream networks dynamically extend and retract, both seasonally and in response to precipitation events. These network dynamics can dramatically alter the drainage density and thus the length of subsurface flow pathways to flowing streams. We mapped flowing stream networks in a small Swiss headwater catchment during different wetness conditions and estimated their effects on the distribution of travel times to the catchment outlet. For each point in the catchment, we determined the subsurface transport distance to the flowing stream based on the surface topography and determined the surface transport distance along the flowing stream to the outlet. We combined the distributions of these travel distances with assumed surface and subsurface flow velocities to estimate the distribution of travel times to the outlet. These calculations show that the extension and retraction of the stream network can substantially change the mean travel time and the shape of the travel time distribution. During wet conditions with a fully extended flowing stream network, the travel time distribution was strongly skewed to short travel times, but as the network retracted during dry conditions, the distribution of the travel times became more uniform. Stream network dynamics are widely ignored in catchment models, but our results show that they need to be taken into account when modeling solute transport and interpreting travel time distributions.

Analyzing Travel Time Reliability from Sparse Probe Vehicle Data: A Case Study on the Effects of Spatial and Temporal Aggregation

2021 ◽  
pp. 036119812110315
Author(s):  
Markus Steinmaßl ◽  
Stefan Kranzinger ◽  
Karl Rehrl
Keyword(s):  
Travel Time ◽  
Time Of Day ◽  
Temporal Aggregation ◽  
Travel Time Reliability ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Depth Analysis ◽  
Data Source ◽  
Travel Time Index

Travel time reliability (TTR) indices have gained considerable attention for evaluating the quality of traffic infrastructure. Whereas TTR measures have been widely explored using data from stationary sensors with high penetration rates, there is a lack of research on calculating TTR from mobile sensors such as probe vehicle data (PVD) which is characterized by low penetration rates. PVD is a relevant data source for analyzing non-highway routes, as they are often not sufficiently covered by stationary sensors. The paper presents a methodology for analyzing TTR on (sub-)urban and rural routes with sparse PVD as the only data source that could be used by road authorities or traffic planners. Especially in the case of sparse data, spatial and temporal aggregations could have great impact, which are investigated on two levels: first, the width of time of day (TOD) intervals and second, the length of road segments. The spatial and temporal aggregation effects on travel time index (TTI) as prominent TTR measure are analyzed within an exemplary case study including three different routes. TTI patterns are calculated from data of one year grouped by different days-of-week (DOW) groups and the TOD. The case study shows that using well-chosen temporal and spatial aggregations, even with sparse PVD, an in-depth analysis of traffic patterns is possible.

Intensities and travel times of seismic rays in a sphere with a linear velocity function

1977 ◽  
Vol 67 (1) ◽  
pp. 33-42
Author(s):  
Mark E. Odegard ◽  
Gerard J. Fryer
Keyword(s):  
Velocity Profile ◽  
Travel Time ◽  
Upper Mantle ◽  
Intensity Ratio ◽  
Computation Time ◽  
Spherical Body ◽  
Travel Times ◽  
Velocity Function ◽  
Intensity Ratios ◽  
Distance Travel

Abstract Equations are presented which permit the calculation of distances, travel times and intensity ratios of seismic rays propagating through a spherical body with concentric layers having velocities which vary linearly with radius. In addition, a method is described which removes the infinite singularities in amplitude generated by second-order discontinuities in the velocity profile. Numerical calculations involving a reasonable upper mantle model show that the standard deviations of the errors for distance, travel time and intensity ratio are 0.0046°, 0.057 sec, and 0.04 dB, respectively. Computation time is short.

Analytical approach unifying the two-regime scaling for bedload particle motions

2021 ◽  
Author(s):  
Zi Wu ◽  
Arvind Singh ◽  
Efi Foufoula-Georgiou ◽  
Michele Guala ◽  
Xudong Fu ◽  
...  
Keyword(s):  
Travel Time ◽  
Velocity Variation ◽  
Travel Times ◽  
Scaling Exponents ◽  
Time Scaling ◽  
Dispersion Regime ◽  
The Mean ◽  
Bedload Sediment ◽  
Distance Travel ◽  
First Time

<p>Bedload particle hops are defined as successive motions of a particle from start to stop, characterizing one of the most fundamental processes describing bedload sediment transport in rivers. Although two transport regimes have been recently identified for short- and long-hops, respectively <strong>(Wu et al., <em>Water Resour Res</em>, 2020)</strong>, there still lacks a theory explaining how the mean hop distance-travel time scaling may extend to cover the phenomenology of bedload particle motions. Here we propose a velocity-variation based formulation, and for the first time, we obtain analytical solution for the mean hop distance-travel time relation valid for the entire range of travel times, which agrees well with the measured data <strong>(Wu et al., <em>J Fluid Mech</em>, 2021)</strong>. Regarding travel times, we identify three distinct regimes in terms of different scaling exponents: respectively as ~1.5 for an initial regime and ~5/3 for a transition regime, which define the short-hops; and 1 for the so-called Taylor dispersion regime defining long-hops. The corresponding probability density function of the hop distance is also analytically obtained and experimentally verified. </p>

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