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 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.

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.

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.

Minimum Sampling Size of Floating Cars for Urban Link Travel Time Distribution Estimation

2019 ◽  
Vol 2673 (3) ◽  
pp. 24-43 ◽  
Author(s):  
Meiping Yun ◽  
Wenwen Qin
Keyword(s):  
Sample Size ◽  
Travel Time ◽  
Real Time ◽  
Time Distribution ◽  
Ground Truth ◽  
Sample Size Determination ◽  
Minimum Sample Size ◽  
Travel Time Distribution ◽  
Minimum Sample ◽  
Link Travel Time

Despite the wide application of floating car data (FCD) in urban link travel time estimation, limited efforts have been made to determine the minimum sample size of floating cars appropriate to the requirements for travel time distribution (TTD) estimation. This study develops a framework for seeking the required minimum number of travel time observations generated from FCD for urban link TTD estimation. The basic idea is to test how, with a decreasing the number of observations, the similarities between the distribution of estimated travel time from observations and those from the ground-truth vary. These are measured by employing the Hellinger Distance (HD) and Kolmogorov-Smirnov (KS) tests. Finally, the minimum sample size is determined by the HD value, ensuring that corresponding distribution passes the KS test. The proposed method is validated with the sources of FCD and Radio Frequency Identification Data (RFID) collected from an urban arterial in Nanjing, China. The results indicate that: (1) the average travel times derived from FCD give good estimation accuracy for real-time application; (2) the minimum required sample size range changes with the extent of time-varying fluctuations in traffic flows; (3) the minimum sample size determination is sensitive to whether observations are aggregated near each peak in the multistate distribution; (4) sparse and incomplete observations from FCD in most time periods cannot be used to achieve the minimum sample size. Moreover, this would produce a significant deviation from the ground-truth distributions. Finally, FCD is strongly recommended for better TTD estimation incorporating both historical trends and real-time observations.

scholarly journals Visualizations of Travel Time Performance Based on Vehicle Reidentification Data

2017 ◽  
Vol 2646 (1) ◽  
pp. 84-92 ◽  
Author(s):  
Stanley Ernest Young ◽  
Elham Sharifi ◽  
Christopher M. Day ◽  
Darcy M. Bullock
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
Traffic Control ◽  
Cumulative Distribution ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Traffic Patterns ◽  
Before And After ◽  
Vehicle Reidentification ◽  
Arterial Performance

This paper provides a visual reference of the breadth of arterial performance phenomena based on travel time measures obtained from reidentification technology that has proliferated in the past 5 years. These graphical performance measures are revealed through overlay charts and statistical distribution as revealed through cumulative frequency diagrams (CFDs). With overlays of vehicle travel times from multiple days, dominant traffic patterns over a 24-h period are reinforced and reveal the traffic behavior induced primarily by the operation of traffic control at signalized intersections. A cumulative distribution function in the statistical literature provides a method for comparing traffic patterns from various time frames or locations in a compact visual format that provides intuitive feedback on arterial performance. The CFD may be accumulated hourly, by peak periods, or by time periods specific to signal timing plans that are in effect. Combined, overlay charts and CFDs provide visual tools with which to assess the quality and consistency of traffic movement for various periods throughout the day efficiently, without sacrificing detail, which is a typical byproduct of numeric-based performance measures. These methods are particularly effective for comparing before-and-after median travel times, as well as changes in interquartile range, to assess travel time reliability.

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