Travel Time Reliability Model Research Articles

Calibrating the Highway Capacity Manual Arterial Travel Time Reliability Model

AbstractThe latest edition of the Highway Capacity Manual (HCM-6) includes, for the first time, a methodology for estimating and predicting the average travel time distribution (TTD) of urban stree...

Analysis of Component Errors in the Highway Capacity Manual Travel Time Reliability Estimations for Urban Streets

The Highway Capacity Manual 6th edition (HCM6) includes a new methodology to estimate and predict the distribution of average travel times (TTD) for urban streets. The TTD can then be used to estimate travel time reliability (TTR) metrics. Previous research on a 0.5-mi testbed showed statistically significant differences between the HCM6 estimated TTD and the corresponding empirical TTD. The difference in average travel time was 4 s that, while statistically significant, is not important from a practical perspective. More importantly, the TTD variance was underestimated by 70%. In other words, the HCM6 results reflected a more reliable testbed than field measurement. This paper expands the analysis on a longer testbed. It identifies the sources and magnitude of travel time variability that contribute to the HCM6 error. Understanding the potential sources of error, and their quantitative values, are the first steps in improving the HCM6 model to better reflect actual conditions. Empirical Bluetooth travel times were collected on a 1.16-mi testbed in Lincoln, Nebraska. The HCM6 methodology was used to model the testbed, and the estimated TTD by source of travel time variability was compared statistically to the corresponding empirical TTD. It was found that the HCM6 underestimated the TTD variability on the longer testbed by 67%. The demand component, missing variable(s), or both, which were not explicitly considered in the HCM6, were found to be the main source of the error in the HCM6 TTD. A focus on the demand estimators as the first step in improving the HCM6 TTR model was recommended.

Assessment of Flight Block Time Reliability Under Different Delay Time Windows: A Case Study

In transportation field, reliability is the probability of reaching the destination from the starting point within the expected time. While the concept of reliability, including travel time reliability model and its evaluation indicators have been extensively studied in ground transportation, they are relatively very scarce in air transportation. Flight block time reliability not only exert a strong influence on passenger behavior but also greatly affect aerodromes, airlines and air traffic management, thus affecting the entire air transportation. This paper proposes a methodology for evaluating the flight block time under different delay time windows. We applied the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method to set windows which reflects different delays. We proposed a probabilistic model of flight block time based on a series system. A case study is performed to compare flight block time, flight air time, flight taxi-in time, and flight taxi-out time at different time reliability metrics with different delays. In particular, we found the best fit for each flight time segment of three delay time windows among nine well-known distribution functions to calculate the flight block time reliability indexes. Based on our analysis, we find that the reliability of delay time window 3 from 22:20- 23:00 to be relatively less compared to the other delay time windows. The results from the reliability estimation demonstrate that the model is efficient in estimating the reliability of flight block time in different delay time windows.

Comparative Analysis of Parameter Evaluation Methods in Expressway Travel Time Reliability

Different parameter results are produced using various methods with the same sample, which follows the lognormal distribution. Therefore, massive historical data of an expressway toll are collected to fit the lognormal distribution of the travel times of cars and trucks. Maximum likelihood estimation is compared with least squares estimation based on five indexes:the sum of squared errors (SSE), the SSE of the cumulative distribution function, the coefficient of determination, the relative error of the skewness coefficient, and the relative error of the kurtosis coefficient. Experimental results show that the maximum likelihood estimator is more efficient than the least squares estimator in fitting the cumulative probability distribution. The unit distance travel time reliability model for the section is established, and the parameter estimation method and the appropriate threshold are provided. Finally, the proposed model is validated using the Shaanxi expressway network in a case study. The results show that travel time reliability is affected by travel distance and departure time. In addition, departure time more significantly affects the travel delay of cars than that of trucks.

Distribution route multi-objective optimisation of dangerous goods considering the time reliability

In view of the increasingly prominent security issues of dangerous goods transportation, a distribution route optimisation model of dangerous goods is proposed. Firstly, the travel time reliability model and transportation risk model are established respectively. Besides, the multi-objective optimisation model of dangerous goods route is built based on the travel time reliability. Secondly, the genetic algorithm is designed to solve the optimisation model. Finally, a numerical case is used to verify the feasibility and effectiveness of the model and algorithm. Study result shows that the distribution route optimisation model of dangerous goods has an important theoretical value and practical significance of reducing the accident risk, increasing the time reliability and ensuring dangerous goods transportation safety.

Modeling Travel Time Reliability of Road Network Considering Connected Vehicle Guidance Characteristics Indexes

Travel time reliability (TTR) is one of the important indexes for effectively evaluating the performance of road network, and TTR can effectively be improved using the real-time traffic guidance information. Compared with traditional traffic guidance, connected vehicle (CV) guidance can provide travelers with more timely and accurate travel information, which can further improve the travel efficiency of road network. Five CV characteristics indexes are selected as explanatory variables including the Congestion Level (CL), Penetration Rate (PR), Compliance Rate (CR), release Delay Time (DT), and Following Rate (FR). Based on the five explanatory variables, a TTR model is proposed using the multilogistic regression method, and the prediction accuracy and the impact of characteristics indexes on TTR are analyzed using a CV guidance scenario. The simulation results indicate that 80% of the RMSE is concentrated within the interval of 0 to 0.0412. The correlation analysis of characteristics indexes shows that the influence of CL, PR, CR, and DT on the TTR is significant. PR and CR have a positive effect on TTR, and the average improvement rate is about 77.03% and 73.20% with the increase of PR and CR, respectively, while CL and DT have a negative effect on TTR, and TTR decreases by 31.21% with the increase of DT from 0 to 180 s.

Quantifying weather impacts on traffic operations for implementation into a travel time reliability model

Travel time reliability allows agencies to evaluate the performance of a facility beyond the peak hour, and to consider operations over a longer period of time considering non-recurring events, such as incidents and adverse weather. The Florida Department of Transportation (FDOT) has incorporated travel time reliability as a primary mobility performance measure. This paper details the method developed for addressing the effects of weather on travel time, as well as its implementation on travel time reliability estimation for state-wide reporting in Florida. To account for Florida-specific conditions, weather impacts focus only on rain intensity. The methodology incorporates different rainfall characteristics of regions across Florida and calibrates the rainfall intensity distribution models on a zip code basis for each hour. The probability of clear weather, light rain, and heavy rain is determined for each hour and then applied to the respective freeway or arterial section based on its zip code.

Travel Distance on Each Grade Road of Urban City Based on Reliability

In order to reflect influence of travel time reliability on route choosing, considering traffic accidents’ influence under random state, we analyzed travel distance distribution regularity of each grade road, through traffic assignment. Travel time reliability model was produced and modified, considering the randomness of accidents, delay time, and capacity. The maximum preponderant travel range of each grade road was defined, and stochastic user equilibrium assignment was adopted to get travel turnover and distance, based on the corrected model. And then regulation of distance distribution was analyzed. Conclusion shows that reasonable travel distance of local way, distribution way, and primary way is 1 km, 2.28 km, and 3.54 km, respectively.

A travel time reliability model of urban expressways with varying levels of service

Urban expressways usually experience several levels of service (LOS) because of the stop-and-go traffic flow caused by congestion. Moreover, multiple shock waves generate at different LOS interfaces. The dynamic of shock waves strongly influences the travel time reliability (TTR) of urban expressways. This study proposes a path TTR model that considers the dynamic of shock waves by using probability-based method to characterize the TTR of urban expressways with shock waves. Two model parameters are estimated, namely distribution of travel time (TT) per unit distance and travel distances in different LOS segments. Generalized extreme value distribution and generalized Pareto distribution are derived as distributions of TT per unit distance for six different LOS. Distribution parameters are estimated by using historical floating car data. Travel distances in different LOS segments are calculated based on shock wave theory. The range of TT along the path, which can help drivers arrange their trips, can be obtained from the TTR model. Finally, comparison is made among the proposed TTR model, generalized Pareto contrast model, which does not consider different LOS or existence of shock waves, and normal contrast model, which assumes TT per unit distance as normal distribution without considering shock wave. Results show that the proposed model achieves higher prediction accuracy and reduces the prediction range of TT. The conclusions can be further extended to TT prediction and assessment of measures to improve reliability of TT in a network.

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

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.

Multistate Travel Time Reliability Models with Skewed Component Distributions

The multistate travel time reliability model has demonstrated superior performance, a close relationship with underlying traffic conditions, and ease of interpretation for travel time reliability reporting. This study advances the multistate model by using skewed component distributions—for example, the gamma and lognormal distributions—to accommodate nonsymmetrically distributed travel times, which are commonly observed in congested states. Six alternative models, the single-state normal, gamma, and lognormal distributions and their multistate versions, were fitted to field-collected data. The performance of the models was compared with Akaike's information criterion. The results indicate that the multistate lognormal model consistently out performs alternative models, especially during peak hours. The improved fitting of the lognormal model is mainly reflected in the mode and tail portion of the data distribution. During off-peak hours, the single-state model could provide a compatible but parsimonious alternative to multistate models. The impact of using the multistate lognormal model is demonstrated on travel time reliability reporting by using the travel time buffer index. The study concludes that the multistate lognormal model is the optimal alternative model for modeling travel time under moderate to heavy traffic conditions along freeways.

Rail track infrastructure ownership: investment and operational issues

As privatisation of railway systems reach the political agendas in a number of countries, the separation of track infrastructure from train operations is seen as providing the vehicle which will improve profitability within the rail industry. This paper deals with three main issues related to such separation within a freight railway focus, namely: investment appraisal; track standards and maintenance; and train operating performance. The conflicts of interest between the owners of track and their client operators are discussed in detail. Costs related to track capacity and congestion need to be taken into account, given that additional trains are likely to lead to increased risk of delays to existing services. The paper discusses the use of a travel time reliability model to estimate the additional costs imposed on the system through the introduction of specific train services. It is concluded that investment in individual elements of railway infrastructure must be integrated with the overall financial and customer service strategies of both operators and owners. As an alternative to current practices, a hybrid model of track ownership is put forward here. Under such a model, a joint-venture company with equity from the main OplayersO would be owner of track. This would allow some of the benefits of vertical integration to be retained, whilst providing fair access to new operators.

Development and Usage of Travel Time Reliability Model for Urban Road Network under Ice and Snowfall Conditions

This paper discusses the travel time reliability of road network under ice and snowfall conditions. With the introduction of correction function for the influence of ice and snowfall conditions on free travel time and capacity, the function of travel time was established. According to the limitation of the current travel time reliability, the new definition was defined on the basis of quantifying the relationship between LOS (Level of Service) and travel time reliability. The breakthrough of the traditional idea that the route travel time reliability model was set by general series system was made by considering the route as a whole unit; instead of using a paralleling system; another breakthrough was made to calculate the weighted average travel time reliability of OD (Original Destination) pair. On the basis of OD pair travel time reliability, the road network reliability model was set up. A partial road network was taken as an example to validate the effectiveness and practicality of the evaluation methodology.