scholarly journals A Method for Solving Reliability of Route Travel Time

2020 ◽  
Vol 2020 ◽  
pp. 1-5
Author(s):  
Hongwei Ma ◽  
Gan Yi ◽  
Jianmin Qi ◽  
Zhenjie Zhang
Keyword(s):  
Travel Time ◽  
Edgeworth Expansion ◽  
Historical Data ◽  
Empirical Distribution Function ◽  
Empirical Distribution ◽  
Travel Time Reliability ◽  
Distribution Law ◽  
Travel Time Distribution ◽  
Discretization Algorithm ◽  
Link Travel Time

In order to obtain an effective method for solving route travel time reliability, this paper proposes an effective new method to calculate travel time reliability using an independent link travel time function. Based on several months of historical data, the results show that the Edgeworth expansion can better reflect travel time distribution law. In addition, travel time reliability can be calculated more conveniently by combining an approximate discretization algorithm and an empirical distribution function.

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.

Evaluating Travel Time Reliability Based on Fuzzy Logic

2011 ◽  
Vol 97-98 ◽  
pp. 952-955
Author(s):  
Xiong Fei Zhang ◽  
Rui Min Li ◽  
Min Liu ◽  
Qi Xin Shi
Keyword(s):  
Fuzzy Logic ◽  
Travel Time ◽  
Time Distribution ◽  
Travel Time Reliability ◽  
Statistical Measures ◽  
Travel Time Distribution ◽  
Network Operations ◽  
Deviation Coefficient ◽  
Measure Of Performance ◽  
Stochastic Properties

Travel time reliability, as a measure of performance, is attracting more and more attention because unreliable transportation information hinders travelers’ decision making and creates difficulties for authorities to manage network operations. Since travel time reliability is closely related to the stochastic properties of the day-to-day travel time distribution, several statistical measures have been proposed, including standard deviation, coefficient of variation, buffer index, misery index and so on. Each of these measures is derived from travel time distribution but captures only one or two characteristics of travel time. In this paper, an effort is made to evaluate travel time reliability incorporating as many characteristics of travel time as possible based on fuzzy logic. The basic rules are: (1) the larger the variance is, the more unreliable the travel time is; (2) the larger the travel times of unlucky travelers are, the more unreliable the travel time is; (3) the larger the distribution skews to the left, the more unreliable the travel time is. The proposed methodology has been tested and analyzed with field data.

Travel Time Reliability Measures Accommodating Scheduling Preferences of Travelers

2019 ◽  
Vol 2673 (4) ◽  
pp. 708-721 ◽  
Author(s):  
Mojtaba Rajabi-Bahaabadi ◽  
Afshin Shariat-Mohaymany ◽  
Shu Yang
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Travel Time Reliability ◽  
Time Data ◽  
Reliability Measures ◽  
Time Performance ◽  
Early Arrival ◽  
Travel Time Distribution ◽  
Travel Time Data ◽  
Schedule Adherence

Existing travel time reliability measures fail to accommodate scheduling preferences of travelers and cannot distinguish between the variability associated with early and late arrivals. This study introduces two new travel time reliability measures based on concepts from behavioral economics. The first proposed measure is an indicator of the width of travel time distribution. It considers scheduling preferences of travelers and can distinguish between early arrival and late arrival. The second measure determines the skewness of travel time distribution. To estimate the proposed measures, travel time is modeled by mixture models and closed-form expressions are derived for the expected values of early and late arrivals. In addition, real travel time data from a freeway segment is used to compare the proposed measures with the existing travel time reliability measures. The results suggest that, although there exist significant correlations between travel time reliability measures, travelers’ preferences have considerable effects on the travel time reliability as perceived by them. Furthermore, four measures are developed based on the notions of early and late arrivals to assess the on-time performance (schedule adherence) of transit vehicles at stop level. The results of this study show that the four measures can serve as complementary to the existing on-time performance indices.

scholarly journals Estimation of Bimodal Urban Link Travel Time Distribution and Its Applications in Traffic Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yuxiong Ji ◽  
Shengchuan Jiang ◽  
Yuchuan Du ◽  
H. Michael Zhang
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Urban Traffic ◽  
Traffic Operations ◽  
Time Model ◽  
Urban Streets ◽  
Travel Time Distribution ◽  
Proposed Model ◽  
Estimate Travel Time ◽  
Link Travel Time

Vehicles travelling on urban streets are heavily influenced by traffic signal controls, pedestrian crossings, and conflicting traffic from cross streets, which would result in bimodal travel time distributions, with one mode corresponding to travels without delays and the other travels with delays. A hierarchical Bayesian bimodal travel time model is proposed to capture the interrupted nature of urban traffic flows. The travel time distributions obtained from the proposed model are then considered to analyze traffic operations and estimate travel time distribution in real time. The advantage of the proposed bimodal model is demonstrated using empirical data, and the results are encouraging.

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.

Monitoring and Predicting Freeway Travel Time Reliability

2005 ◽  
Vol 1917 (1) ◽  
pp. 54-62 ◽  
Author(s):  
J. W. C. van Lint ◽  
H. J. van Zuylen
Keyword(s):  
Travel Time ◽  
Discrete Choice Models ◽  
Time Of Day ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Explanatory Variables ◽  
Travel Time Distribution ◽  
Mean And Variance ◽  
Context Specific

Generally, the day-to-day variability of route travel times on, for example, freeway corridors is considered closely related to the reliability of a road network. The more that travel times on route r are dispersed in a particular time-of-day (TOD) and day-of-week (DOW) period, the more unreliable travel times on route r are conceived to be. In the literature, many different aspects of the day-to-day travel time distribution have been proposed as indicators of reliability. Mean and variance do not provide much insight because those metrics tend to obscure important aspects of the distribution under specific circumstances. It is argued that both skew and width of this distribution are relevant indicators for unreliability; therefore, two reliability metrics are proposed. These metrics are based on three characteristic percentiles: the 10th, 50th, and 90th percentile for a given route and TOD-DOW period. High values of either metric indicate high travel time unreliability. However, the weight of each metric on travel time reliability may be application- or context-specific. The practical value of these particular metrics is that they can be used to construct so-called reliability maps, which not only visualize the unreliability of travel times for a given DOW-TOD period but also help identify DOW-TOD periods in which congestion will likely set in (or dissolve). That means identification of the uncertainty of start, end, and, hence, length of morning and afternoon peak hours. Combined with a long-term travel time prediction model, the metrics can be used to predict travel time (un)reliability. Finally, the metrics may be used in discrete choice models as explanatory variables for driver uncertainty.

Research on Link Travel Time Reliability Model Based on Massive Expressway Toll Data

2014 ◽  
Vol 505-506 ◽  
pp. 719-726 ◽  
Author(s):  
Tao Wen ◽  
Chang Cheng Li ◽  
Chun Jiang Che ◽  
Lian De Zhong ◽  
Xin Xin
Keyword(s):  
Normal Distribution ◽  
Travel Time ◽  
Estimation Method ◽  
Model Parameters ◽  
Reliability Model ◽  
Travel Time Reliability ◽  
Time Data ◽  
Moment Estimation ◽  
Travel Time Data ◽  
Link Travel Time

Massive expressway toll data contained lots of valuable information. However, the skills of mining and analyzing toll data were limited currently. This study explored the modeling method of road network travel time reliability based on massive toll data. Firstly, this study obtained travel time data sample of each link at different months, and analyzed travel time statistical properties preliminarily. Secondly, this study used normal distribution, gamma distribution and Weibull distribution to fit travel time data sample, and different statistical indicators were involved to measure the fitting effect. Fitting results showed that normal distribution for link travel time was more rational and acceptable than the others. Thus, this study established link travel time reliability model, and proposed moment estimation method of calibrating the model parameters. In practical application, the reliability model can be used to judge traffic operating posture for expressway management department, and also can be used to forecast travel time information, to provide valuable reference on decision-making for drivers travel plan or route choice.

scholarly journals Validation of the Highway Capacity Manual Urban Street Travel Time Reliability Methodology using Empirical Data

2019 ◽  
Vol 2673 (4) ◽  
pp. 415-426 ◽  
Author(s):  
Ernest O. A. Tufuor ◽  
Laurence R. Rilett
Keyword(s):  
Travel Time ◽  
Empirical Data ◽  
Empirical Distribution ◽  
Extended Period ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Urban Street ◽  
The Mean

The 6th edition of the Highway Capacity Manual (HCM-6) includes the concept of travel time reliability (TTR), which attempts to determine the distribution of average trip travel times over an extended period. TTR is an inherent part of travelers’ route choice decisions and is used by traffic managers to better quantify operations rather than simply using average travel times. The focus of this paper is on the HCM-6 urban street TTR methodology contained in Chapter 17. The approach uses historical data (e.g., weather and volume fluctuations) and simple empirical data (e.g., 1-day volume count) to provide the user with average travel time and reliability predictions for a traffic facility over an extended period (e.g., a year). To the best of the authors’ knowledge, there is no existing literature on validating the HCM-6 methodology with empirical data. The goals of this paper were to validate the HCM-6 urban street reliability methodology by comparing the empirical Bluetooth (BT) travel time distributions with the estimated HCM-6 distribution, and to propose potential HCM-6 augmentation strategies. Archived BT data from a 0.5-mi urban arterial in Lincoln, Nebraska was used for comparison. It was found that there were statistically significant differences, but minimal practical differences, between the mean of the predicted HCM-6 travel time distribution and the mean of the empirical distribution. However, the HCM-6 distribution had a lower variance than the empirical distribution. Not surprisingly, the HCM-6 model underestimated the TTR metrics (buffer index and the planning time index) by approximately 62% and 9%, respectively.

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